`_"""
-
- def __init__(self, **kwargs):
- super(HybridTaskCascade, self).__init__(**kwargs)
-
- @property
- def with_semantic(self):
- """bool: whether the detector has a semantic head"""
- return self.roi_head.with_semantic
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py
deleted file mode 100644
index 584219419d256e7743fd4d5120c56bcfa8f2a9f9..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import List, Optional
-
-import torch
-from PIL import Image
-
-
-class AbstractMultimodalPipeline(ABC):
- @staticmethod
- @abstractmethod
- def name() -> str:
- 'name of the pipeline, should be same as in --multimodal-pipeline'
- pass
-
- @staticmethod
- @abstractmethod
- def image_start() -> Optional[str]:
- 'return image start string, string representation of image start token, or None if not applicable'
- pass
-
- @staticmethod
- @abstractmethod
- def image_end() -> Optional[str]:
- 'return image end string, string representation of image end token, or None if not applicable'
- pass
-
- @staticmethod
- @abstractmethod
- def placeholder_token_id() -> int:
- 'return placeholder token id'
- pass
-
- @staticmethod
- @abstractmethod
- def num_image_embeds() -> int:
- 'return the number of embeds used by a single image (for example: 256 for LLaVA)'
- pass
-
- @abstractmethod
- def embed_images(self, images: List[Image.Image]) -> torch.Tensor:
- 'forward the images through vision pipeline, and return their embeddings'
- pass
-
- @staticmethod
- @abstractmethod
- def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor:
- 'embed tokens, the exact function varies by LLM, for LLaMA it is `shared.model.model.embed_tokens`'
- pass
-
- @staticmethod
- @abstractmethod
- def placeholder_embeddings() -> torch.Tensor:
- 'get placeholder embeddings if there are multiple images, and `add_all_images_to_prompt` is False'
- pass
-
- def _get_device(self, setting_name: str, params: dict):
- if params[setting_name] is None:
- return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- return torch.device(params[setting_name])
-
- def _get_dtype(self, setting_name: str, params: dict):
- return torch.float32 if int(params[setting_name]) == 32 else torch.float16
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/midas/base_model.py b/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/midas/base_model.py
deleted file mode 100644
index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/midas/base_model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
- def load(self, path):
- """Load model from file.
-
- Args:
- path (str): file path
- """
- parameters = torch.load(path, map_location=torch.device('cpu'))
-
- if "optimizer" in parameters:
- parameters = parameters["model"]
-
- self.load_state_dict(parameters)
diff --git a/spaces/Artrajz/vits-simple-api/vits/bert/__init__.py b/spaces/Artrajz/vits-simple-api/vits/bert/__init__.py
deleted file mode 100644
index 6f70b7372c156d3969a2f87328737f63ed4bef71..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/vits/bert/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-""" from https://github.com/PlayVoice/vits_chinese """
-import os
-
-import config
-from utils.download import download_and_verify
-from .ProsodyModel import TTSProsody
-
-URLS = [
- "https://huggingface.co/spaces/maxmax20160403/vits_chinese/resolve/main/bert/prosody_model.pt",
-]
-TARGET_PATH = os.path.join(config.ABS_PATH, "vits/bert/prosody_model.pt")
-EXPECTED_MD5 = None
-
-if not os.path.exists(TARGET_PATH):
- success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5)
\ No newline at end of file
diff --git a/spaces/Audio-AGI/WavJourney/scripts/start_services.sh b/spaces/Audio-AGI/WavJourney/scripts/start_services.sh
deleted file mode 100644
index 2347f327760ad9442f14eff8bb28924021dcfaba..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/scripts/start_services.sh
+++ /dev/null
@@ -1 +0,0 @@
-nohup conda run --live-stream -n WavJourney python services.py > services_logs/service.out 2>&1 &
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/demo.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/demo.py
deleted file mode 100644
index 5213faf4d859bb109a03bcd2721a02d63d2f89ce..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/demo.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import argparse
-import glob
-import multiprocessing as mp
-import os
-import time
-import cv2
-import tqdm
-
-from detectron2.config import get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.utils.logger import setup_logger
-
-from predictor import VisualizationDemo
-from centernet.config import add_centernet_config
-# constants
-WINDOW_NAME = "CenterNet2 detections"
-
-from detectron2.utils.video_visualizer import VideoVisualizer
-from detectron2.utils.visualizer import ColorMode, Visualizer
-from detectron2.data import MetadataCatalog
-
-def setup_cfg(args):
- # load config from file and command-line arguments
- cfg = get_cfg()
- add_centernet_config(cfg)
- cfg.merge_from_file(args.config_file)
- cfg.merge_from_list(args.opts)
- # Set score_threshold for builtin models
- cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
- cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
- if cfg.MODEL.META_ARCHITECTURE in ['ProposalNetwork', 'CenterNetDetector']:
- cfg.MODEL.CENTERNET.INFERENCE_TH = args.confidence_threshold
- cfg.MODEL.CENTERNET.NMS_TH = cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST
- cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
- cfg.freeze()
- return cfg
-
-
-def get_parser():
- parser = argparse.ArgumentParser(description="Detectron2 demo for builtin models")
- parser.add_argument(
- "--config-file",
- default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml",
- metavar="FILE",
- help="path to config file",
- )
- parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.")
- parser.add_argument("--video-input", help="Path to video file.")
- parser.add_argument("--input", nargs="+", help="A list of space separated input images")
- parser.add_argument(
- "--output",
- help="A file or directory to save output visualizations. "
- "If not given, will show output in an OpenCV window.",
- )
-
- parser.add_argument(
- "--confidence-threshold",
- type=float,
- default=0.3,
- help="Minimum score for instance predictions to be shown",
- )
- parser.add_argument(
- "--opts",
- help="Modify config options using the command-line 'KEY VALUE' pairs",
- default=[],
- nargs=argparse.REMAINDER,
- )
- return parser
-
-
-if __name__ == "__main__":
- mp.set_start_method("spawn", force=True)
- args = get_parser().parse_args()
- logger = setup_logger()
- logger.info("Arguments: " + str(args))
-
- cfg = setup_cfg(args)
-
- demo = VisualizationDemo(cfg)
- output_file = None
- if args.input:
- if len(args.input) == 1:
- args.input = glob.glob(os.path.expanduser(args.input[0]))
- files = os.listdir(args.input[0])
- args.input = [args.input[0] + x for x in files]
- assert args.input, "The input path(s) was not found"
- visualizer = VideoVisualizer(
- MetadataCatalog.get(
- cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
- ),
- instance_mode=ColorMode.IMAGE)
- for path in tqdm.tqdm(args.input, disable=not args.output):
- # use PIL, to be consistent with evaluation
- img = read_image(path, format="BGR")
- start_time = time.time()
- predictions, visualized_output = demo.run_on_image(
- img, visualizer=visualizer)
- if 'instances' in predictions:
- logger.info(
- "{}: detected {} instances in {:.2f}s".format(
- path, len(predictions["instances"]), time.time() - start_time
- )
- )
- else:
- logger.info(
- "{}: detected {} instances in {:.2f}s".format(
- path, len(predictions["proposals"]), time.time() - start_time
- )
- )
-
- if args.output:
- if os.path.isdir(args.output):
- assert os.path.isdir(args.output), args.output
- out_filename = os.path.join(args.output, os.path.basename(path))
- visualized_output.save(out_filename)
- else:
- # assert len(args.input) == 1, "Please specify a directory with args.output"
- # out_filename = args.output
- if output_file is None:
- width = visualized_output.get_image().shape[1]
- height = visualized_output.get_image().shape[0]
- frames_per_second = 15
- output_file = cv2.VideoWriter(
- filename=args.output,
- # some installation of opencv may not support x264 (due to its license),
- # you can try other format (e.g. MPEG)
- fourcc=cv2.VideoWriter_fourcc(*"x264"),
- fps=float(frames_per_second),
- frameSize=(width, height),
- isColor=True,
- )
- output_file.write(visualized_output.get_image()[:, :, ::-1])
- else:
- # cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
- cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
- if cv2.waitKey(1 ) == 27:
- break # esc to quit
- elif args.webcam:
- assert args.input is None, "Cannot have both --input and --webcam!"
- cam = cv2.VideoCapture(0)
- for vis in tqdm.tqdm(demo.run_on_video(cam)):
- cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
- cv2.imshow(WINDOW_NAME, vis)
- if cv2.waitKey(1) == 27:
- break # esc to quit
- cv2.destroyAllWindows()
- elif args.video_input:
- video = cv2.VideoCapture(args.video_input)
- width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
- height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
- frames_per_second = 15 # video.get(cv2.CAP_PROP_FPS)
- num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
- basename = os.path.basename(args.video_input)
-
- if args.output:
- if os.path.isdir(args.output):
- output_fname = os.path.join(args.output, basename)
- output_fname = os.path.splitext(output_fname)[0] + ".mkv"
- else:
- output_fname = args.output
- # assert not os.path.isfile(output_fname), output_fname
- output_file = cv2.VideoWriter(
- filename=output_fname,
- # some installation of opencv may not support x264 (due to its license),
- # you can try other format (e.g. MPEG)
- fourcc=cv2.VideoWriter_fourcc(*"x264"),
- fps=float(frames_per_second),
- frameSize=(width, height),
- isColor=True,
- )
- assert os.path.isfile(args.video_input)
- for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames):
- if args.output:
- output_file.write(vis_frame)
-
- cv2.namedWindow(basename, cv2.WINDOW_NORMAL)
- cv2.imshow(basename, vis_frame)
- if cv2.waitKey(1) == 27:
- break # esc to quit
- video.release()
- if args.output:
- output_file.release()
- else:
- cv2.destroyAllWindows()
diff --git a/spaces/Benson/text-generation/Examples/3d Solitario Descargar Gratis.md b/spaces/Benson/text-generation/Examples/3d Solitario Descargar Gratis.md
deleted file mode 100644
index 46a27afa92bf8434e7707dbfd23454d7469db281..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/3d Solitario Descargar Gratis.md
+++ /dev/null
@@ -1,61 +0,0 @@
-Descargar gratis 3D Solitaire: La guía definitiva
-Si eres un fan de los juegos de cartas clásicos, es posible que hayas oído hablar de 3D Solitaire. Es una versión moderna del juego tradicional de solitario, también conocido como Klondike o Patience, con impresionantes gráficos en 3D, ajustes personalizables y un juego adictivo. Pero ¿qué es 3D Solitaire exactamente, y cómo se puede descargar y jugar de forma gratuita? En esta guía, responderemos estas preguntas y más, para que puedas disfrutar de este increíble juego en tu dispositivo. ¡Empecemos!
- ¿Qué es el Solitario 3D?
-3D Solitaire es un tipo de juego de solitario que utiliza gráficos tridimensionales para crear una experiencia realista e inmersiva. A diferencia de las cartas planas y aburridas del juego de solitario estándar, las tarjetas 3D Solitaire tienen una percepción de profundidad y una perspectiva de tamaño real, lo que hace que parezcan estar volando en tu pantalla. También puede personalizar el fondo y el respaldo de la cubierta con sus propias imágenes, o elegir entre una variedad de opciones preestablecidas. Jugar 3D Solitaire es como jugar con cartas reales, pero mejor.
-3d solitario descargar gratis Download ► https://bltlly.com/2v6MSJ
Historia y evolución del Solitario
-Solitaire es uno de los juegos de cartas más antiguos y populares del mundo. Se originó en Europa a finales del siglo XVIII, y se jugó originalmente con una sola baraja de cartas. El objetivo era organizar las cartas en cuatro montones, uno para cada palo, en orden ascendente de As a Rey. El juego también era conocido como Paciencia, porque requería mucha concentración y habilidad.
-Con el tiempo, Solitaire evolucionó en diferentes variaciones, como Spider Solitaire, FreeCell solitaire, Mahjong y más. Algunas de estas variaciones añadieron más mazos, más reglas o más desafíos al juego. Sin embargo, el principio básico seguía siendo el mismo: ordenar las tarjetas de cierta manera.
-
- Las características y beneficios de 3D Solitaire
-3D Solitaire es una de las versiones más avanzadas e innovadoras de Solitaire jamás creadas. Ofrece el juego tradicional de Solitario con un historial completo de deshacer y rehacer, animaciones automáticas y configuraciones personalizadas. Usted puede elegir jugar con puntuación normal o Vegas, ofreciendo una experiencia de juego de cartas auténtica.
-Pero lo que diferencia a 3D Solitaire de otros juegos de Solitaire son sus impresionantes efectos visuales. El juego cuenta con gráficos 3D inmersivos que te hacen sentir como si estuvieras jugando con cartas reales. También puede personalizar el fondo y el respaldo de la cubierta con sus propias imágenes o elegir entre una variedad de opciones preestablecidas. También puede ajustar la velocidad, el desenfoque del movimiento, las sombras, los reflejos de color, la reproducción automática y las opciones de finalización para adaptarse a sus preferencias.
-No solo es visualmente impresionante 3D Solitaire, sino que también desafía tus habilidades lógicas con divertidos rompecabezas de diferentes niveles de dificultad. Puede utilizar indirectas interactivas que le muestran todos los movimientos posibles en la secuencia de lo más probable para resolver el juego menos probable. También puede utilizar soluciones que le muestran los movimientos exactos para resolver el juego, o dejar que el juego se juega automáticamente. También puedes realizar un seguimiento de tu progreso y logros con estadísticas y tablas de clasificación, y comparar tus puntuaciones con otros jugadores de todo el mundo.
- ¿Cómo descargar y jugar 3D Solitaire gratis?
-Ahora que sabes lo que es 3D Solitaire y por qué es impresionante, es posible que se pregunte cómo conseguirlo en su dispositivo. La buena noticia es que 3D Solitaire está disponible de forma gratuita en varias plataformas y fuentes, para que pueda disfrutarlo en cualquier momento y en cualquier lugar. Estas son algunas de las mejores opciones para descargar y jugar 3D Solitaire gratis:
-
- Las mejores plataformas y fuentes para 3D Solitaire
-Dependiendo de su dispositivo y preferencia, puede elegir entre diferentes plataformas y fuentes para 3D Solitaire. Estos son algunos de los más populares:
-
-Este es uno de los mejores juegos de solitario en 3D para dispositivos Android. Tiene más de 10 millones de descargas y una calificación de 4.5 estrellas en Google Play Store. Ofrece más de 100 juegos diferentes de solitario, incluyendo Klondike, Spider, FreeCell, Pyramid, TriPeaks, Golf y más. También puede personalizar las tarjetas, fondos, animaciones, sonidos y ajustes a su gusto. Puedes descargarlo gratis desde Google Play Store o desde el sitio web oficial. También puede actualizar a la versión premium por $2.99 para eliminar anuncios y desbloquear más características.
- Solitario 3D por GrassGames
-Este es otro gran juego de solitario en 3D que funciona en Windows, Mac, Linux, iOS y dispositivos Android. Tiene más de 1 millón de descargas y una calificación de 4.6 estrellas en App Store. Ofrece más de 250 juegos diferentes de solitario, incluyendo Klondike, Spider, FreeCell, Pyramid, TriPeaks, Golf y más. También puede personalizar las tarjetas, fondos, animaciones, sonidos y ajustes a su gusto. Puedes descargarlo gratis desde App Store, Google Play Store o desde el sitio web oficial. También puede actualizar a la versión premium por $4.99 para eliminar anuncios y desbloquear más características.
- Los consejos y trucos para jugar 3D Solitaire como un profesional
-Ya sea que seas nuevo en Solitario o un jugador experimentado, siempre puedes mejorar tus habilidades y estrategias con algunos consejos y trucos. Estos son algunos de los mejores para jugar 3D Solitaire como un profesional:
- Cómo personalizar la configuración y las preferencias
-Una de las mejores cosas de 3D Solitaire es que puedes adaptarlo a tu gusto. Puede cambiar el tamaño de la tarjeta, el estilo, el color, la fuente y el respaldo con sus propias imágenes o elegir entre una variedad de opciones preestablecidas. También puede cambiar la imagen de fondo o color con sus propias imágenes o elegir entre una variedad de opciones preestablecidas. También puede ajustar la velocidad, el desenfoque del movimiento, las sombras, los reflejos de color, la reproducción automática y las opciones de finalización para adaptarse a sus preferencias.
-
- Cómo usar sugerencias y soluciones
-Si estás atascado o necesitas alguna orientación, puedes usar las sugerencias y las funciones de soluciones en 3D Solitaire. Consejos le muestran todos los movimientos posibles en la secuencia de más probabilidades de resolver el juego a menos probable. Las soluciones le muestran los movimientos exactos para resolver el juego, o dejar que el juego se juega automáticamente. Puede acceder a estas características haciendo clic en el icono de bombilla en la esquina superior derecha de la pantalla. También puede ajustar la configuración de sugerencia y solución en el menú Opciones de juego.
- Cómo alcanzar metas y posicionarse en la clasificación
-Si estás buscando un poco de motivación y desafío extra, puedes intentar alcanzar metas y posicionarte en la clasificación en 3D Solitaire. Los objetivos son tareas específicas que necesitas completar en un juego, como limpiar todas las cartas, anotar un cierto número de puntos o terminar dentro de un límite de tiempo. Puedes ver tus objetivos actuales haciendo clic en el icono del trofeo en la esquina superior derecha de la pantalla. También puede ver sus metas y logros completados en el menú Estadísticas.
-Leaderboard es una característica que le permite comparar sus puntuaciones y clasificaciones con otros jugadores de todo el mundo. Puedes ver tu posición actual haciendo clic en el icono de estrella en la esquina superior derecha de la pantalla. También puedes ver tus mejores puntuaciones y rankings en diferentes categorías, como juegos jugados, juegos ganados, juegos perdidos, puntuación promedio, puntuación más alta, puntuación más baja, tiempo más rápido, tiempo más lento y racha más larga. También puedes filtrar la clasificación por tipo de juego, nivel de dificultad, modo de puntuación y periodo de tiempo.
- ¿Por qué debería probar 3D Solitaire hoy?
-Por ahora, usted podría estar convencido de que 3D Solitaire es un gran juego para jugar. Pero si necesitas más razones para probarlo hoy, aquí están algunas de las ventajas de jugar 3D Solitaire sobre otros juegos de cartas:
- Las ventajas de jugar 3D Solitaire sobre otros juegos de cartas
-
- Es divertido, desafiante y adictivo
-3D Solitaire es un juego que nunca se vuelve viejo o aburrido. Ofrece infinitas variaciones y desafíos que te mantienen entretenido y comprometido. Puedes elegir entre más de 100 juegos de solitario diferentes, cada uno con sus propias reglas y estrategias. También puede personalizar sus ajustes y preferencias para hacer el juego más divertido y desafiante. También puedes competir contigo mismo o con otros jugadores de todo el mundo con metas y tablas de clasificación. Jugar 3D Solitaire es una gran manera de divertirse, desafiarse y mejorar sus habilidades.
- Es visualmente impresionante e inmersiva
-3D Solitaire es un juego que apela a tus sentidos e imaginación. Utiliza gráficos 3D realistas e inmersivos que te hacen sentir que estás jugando con cartas reales. También puede personalizar el fondo y el respaldo de la cubierta con sus propias imágenes o elegir entre una variedad de opciones preestablecidas. Jugar 3D Solitaire es como jugar en un entorno de realidad virtual, donde puedes disfrutar de la belleza y el realismo de los juegos de cartas.
- Es bueno para el cerebro y la salud mental
-3D Solitaire es un juego que estimula tu cerebro y tu salud mental. Requiere habilidades de lógica, concentración, memoria y resolución de problemas que mantengan tu mente aguda y activa. También te ayuda a relajarte, reducir el estrés y mejorar tu estado de ánimo. Jugar al solitario en 3D es una excelente manera de ejercitar tu cerebro y tu salud mental.
- Conclusión
-3D Solitaire es una versión moderna del juego tradicional de solitario, con impresionantes gráficos en 3D, ajustes personalizables y un juego adictivo. Ofrece más de 100 juegos diferentes de solitario, incluyendo Klondike, Spider, FreeCell, Pyramid, TriPeaks, Golf y más. También puede utilizar sugerencias y soluciones para ayudarle a resolver el juego o dejar que se juega automáticamente. También puedes rastrear tu progreso y logros con estadísticas y tablas de clasificación.
-
-3D Solitaire es un juego divertido, desafiante y adictivo. También es visualmente impresionante y envolvente, y bueno para su cerebro y la salud mental. Si eres un fan de los juegos de cartas clásicos o un recién llegado a Solitaire, deberías probar 3D Solitaire hoy y ver por ti mismo por qué es uno de los mejores juegos de todos los tiempos.
- Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre 3D Solitaire:
- Q: ¿Cuáles son los requisitos del sistema para el solitario 3D?
-A: 3D Solitaire es compatible con la mayoría de los dispositivos y sistemas operativos. Sin embargo, para obtener el mejor rendimiento y gráficos, debe tener al menos 1 GB de RAM, 100 MB de espacio de almacenamiento gratuito y una tarjeta gráfica y un procesador decente.
- Q: ¿Cómo puedo cambiar el tipo de juego o el nivel de dificultad en 3D Solitaire?
-A: Para cambiar el tipo de juego o el nivel de dificultad en 3D Solitaire, es necesario acceder al menú haciendo clic en las tres líneas horizontales en la esquina superior izquierda de la pantalla. A continuación, puede seleccionar el tipo de juego opción de la lista de categorías. A continuación, puede elegir entre más de 100 diferentes juegos de solitario, cada uno con sus propias reglas y estrategias. También puedes ajustar el nivel de dificultad cambiando el número de palos, mazos o cartas en juego.
- Q: ¿Cómo puedo guardar o cargar un juego en solitario en 3D?
-A: Para guardar o cargar un juego en 3D Solitaire, es necesario acceder al menú haciendo clic en las tres líneas horizontales en la esquina superior izquierda de la pantalla. A continuación, puede seleccionar la opción Guardar juego o Cargar juego de la lista de categorías. A continuación, puede elegir una ranura para guardar o cargar el juego. También puedes usar la función Guardar automáticamente que guarda tu juego automáticamente cada vez que sales o cambias de aplicación.
- P: ¿Cómo puedo compartir mis resultados o logros en solitario en 3D?
-
- Q: ¿Cómo puedo contactar a los desarrolladores o reportar un error en 3D Solitaire?
-A: Para contactar a los desarrolladores o reportar un error en 3D Solitaire, necesita acceder al menú haciendo clic en las tres líneas horizontales en la esquina superior izquierda de la pantalla. Luego puede seleccionar la opción Ayuda de la lista de categorías. A continuación, puede elegir ponerse en contacto con los desarrolladores por correo electrónico, visitar su sitio web, calificar su aplicación, o informar de un error.
64aa2da5cfCiudad muerta invasión zombi APK: Un juego de supervivencia para Android
-Si usted es un fan de los juegos de zombies, es posible que desee echa un vistazo a Ciudad Muerta Zombie invasión APK, un emocionante juego de supervivencia para dispositivos Android. En este juego, tendrás que luchar contra hordas de zombies que se han apoderado de la ciudad después de un brote de virus. Tendrás que usar varias armas y equipos para defenderte a ti mismo y a tus aliados, así como explorar diferentes lugares y completar misiones. Aquí está todo lo que necesitas saber sobre Dead City Zombie invasión APK, incluyendo sus características, cómo descargarlo e instalarlo, y algunos consejos y trucos para jugarlo.
-ai apk Download Zip › https://bltlly.com/2v6K3c
Introducción
- ¿Qué es la ciudad muerta invasión zombi APK?
-Ciudad muerta invasión zombi APK es un juego de disparos zombie lleno de acción desarrollado por Charm Tech. Está disponible de forma gratuita en la Google Play Store, pero también puede descargar el archivo APK de otras fuentes si desea acceder a algunas características adicionales. El juego tiene una calificación de 4.4 de 5 estrellas en la Play Store, con más de 10 millones de descargas y comentarios positivos de los jugadores.
- ¿Por qué deberías jugar Ciudad muerta invasión zombi APK?
-Ciudad muerta invasión zombi APK es un juego que te mantendrá en el borde de su asiento como te enfrentas a interminables olas de zombies que tienen hambre de su carne. Tendrás que usar tus habilidades y estrategia para sobrevivir en este mundo post-apocalíptico, donde cada decisión importa. También disfrutarás de los siguientes beneficios cuando juegas Dead City Zombie invasión APK:
-
-Experimentarás un juego realista e inmersivo, con impresionantes gráficos y efectos de sonido que te harán sentir como si estuvieras en medio de un apocalipsis zombi.
-Usted tendrá acceso a una variedad de armas y equipos, tales como pistolas, rifles, escopetas, granadas, cuchillos, armaduras y más. También puedes mejorar tus armas y habilidades para mejorar tu rendimiento.
-
-Te divertirás explorando diferentes lugares y escenarios, como calles, edificios, metros, fábricas y más. También encontrarás diferentes tipos de zombies, como caminantes, corredores, saltadores, bombarderos y más.
-
- Características de la ciudad muerta invasión zombi APK
-Varias armas y equipos
-Una de las principales características de Ciudad Muerta Zombie invasión APK es la amplia gama de armas y equipos que se pueden utilizar para luchar contra los zombies. Puedes elegir entre pistolas, rifles, escopetas, granadas, cuchillos, armaduras y más. Cada arma tiene sus propias ventajas y desventajas, como daños, alcance, precisión, velocidad de recarga y capacidad de munición. También puedes mejorar tus armas y habilidades para hacerlas más poderosas y efectivas.
- Diferentes modos y niveles
-Otra característica de la ciudad muerta invasión zombi APK es los diferentes modos y niveles que se pueden jugar. Puedes elegir entre el modo historia, el modo supervivencia, el modo jefe y el modo desafío. Cada modo tiene sus propios objetivos y dificultades, como matar a un cierto número de zombies, sobrevivir durante un tiempo determinado, derrotar a un jefe zombi o completar una tarea específica. También puedes jugar con tus amigos online o offline en modo multijugador.
- Impresionantes gráficos y efectos de sonido
-Una tercera característica de Ciudad Muerta Zombie invasión APK es la impresionante gráficos y efectos de sonido que hacen que el juego más realista e inmersiva. Te sentirás como si estuvieras en medio de un apocalipsis zombi, con entornos detallados, animaciones realistas y efectos de sangre. También escuchará los sonidos de disparos, explosiones, gemidos de zombis y gritos que se sumarán a la tensión y la emoción del juego.
-
- ¿Cómo descargar e instalar Ciudad Muerta Zombie invasión APK?
-Descargar el archivo APK de una fuente de confianza
-
-Descargar Ciudad Muerta Zombie invasión APK aquí
- Habilitar fuentes desconocidas en su dispositivo
-Antes de poder instalar el archivo APK, tendrá que habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones que no son de Google Play Store. Para habilitar fuentes desconocidas, siga estos pasos:
-
-Ir a la configuración de su dispositivo y toque en la seguridad o la privacidad.
-Encontrar la opción que dice fuentes desconocidas o instalar aplicaciones desconocidas y alternar en.
-Confirme su elección tocando en OK o Permitir.
-
- Instalar el archivo APK y lanzar el juego
-Después de haber habilitado fuentes desconocidas, puede instalar el archivo APK y lanzar el juego. Para hacer esto, siga estos pasos:
-
-Busque el archivo APK que ha descargado y toque en él.
-Siga las instrucciones en la pantalla y toque en Instalar.
-Espere a que termine el proceso de instalación y toque en Abrir.
- Disfruta jugando Ciudad muerta invasión zombi APK!
-
- Consejos y trucos para jugar Ciudad muerta invasión zombi APK
-Mejora tus armas y habilidades
-Uno de los consejos para jugar Ciudad Muerta Zombie invasión APK es mejorar sus armas y habilidades tanto como sea posible. Esto te ayudará a hacer más daño, sobrevivir más tiempo y completar misiones más rápido. Puedes mejorar tus armas y habilidades usando monedas y gemas que puedes ganar jugando el juego o viendo anuncios. También puedes comprar monedas y gemas con dinero real si quieres acelerar el proceso.
- Apunta a la cabeza y usa granadas
-Otro consejo para jugar Ciudad Muerta Zombie invasión APK es apuntar a la cabeza y utilizar granadas cuando se lucha contra zombies. Apuntar a la cabeza hará más daño y ahorrará munición, mientras que el uso de granadas causará daño en el área y aturdirá a los zombies. También puedes usar otros artículos, como botiquines, cajas de munición y torretas, para ayudarte en el combate.
- Recopilar recursos y elementos
-
- Conclusión
-Ciudad muerta invasión zombi APK es un juego de supervivencia para dispositivos Android que le desafiará a luchar contra hordas de zombies en un mundo post-apocalíptico. Tendrás que usar varias armas y equipos para defenderte a ti mismo y a tus aliados, así como explorar diferentes lugares y completar misiones. También podrá disfrutar de la jugabilidad realista e inmersiva, con impresionantes gráficos y efectos de sonido. Si usted está buscando un emocionante juego de disparos zombie, usted debe descargar e instalar Ciudad Muerta Zombie invasión APK hoy!
- Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre Ciudad Muerta Zombie invasión APK:
- ¿Es seguro descargar Ciudad muerta invasión zombi APK?
-Sí, Ciudad muerta invasión zombi APK es seguro de descargar, siempre y cuando lo obtenga de una fuente de confianza. Sin embargo, siempre debe tener cuidado al descargar cualquier aplicación de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
- ¿Es la ciudad muerta invasión zombi APK libre para jugar?
-Sí, Ciudad muerta invasión zombi APK es libre de jugar, pero contiene anuncios y compras en la aplicación que pueden mejorar su experiencia de juego. Puedes desactivar los anuncios desactivando tu conexión a Internet o comprando la versión sin anuncios del juego. También puedes comprar monedas y gemas con dinero real si quieres mejorar tus armas y habilidades más rápido.
- ¿Cómo puedo jugar con mis amigos en línea o fuera de línea en la ciudad muerta invasión zombi APK?
-
- ¿Cuáles son los requisitos mínimos para jugar Ciudad muerta invasión zombi APK?
-Los requisitos mínimos para jugar Ciudad muerta invasión zombi APK son los siguientes:
-
-Versión para Android: 4.4 o superior
-RAM: 2 GB o más
-Espacio de almacenamiento: 300 MB o más
-Conexión a Internet: opcional (requerido para multijugador y anuncios en línea)
-
- ¿Cómo puedo contactar con el desarrollador de Dead City Zombie invasión APK?
-Si usted tiene alguna pregunta, retroalimentación, o sugerencias acerca de Ciudad Muerta Zombie invasión APK, puede ponerse en contacto con el desarrollador mediante el uso de los siguientes métodos:
-
-Correo electrónico: charmtech@gmail.com
-Facebook: https://www.facebook.com/charmtechgames
-Twitter: https://twitter.com/charmtechgames
- 64aa2da5cfFree Fire MAX: Cómo descargar el archivo APK en 45 MB
-Si eres un fan de los juegos battle royale, es posible que hayas oído hablar de Free Fire MAX , una nueva versión del popular juego Free Fire que ofrece gráficos mejorados, jugabilidad y compatibilidad. En este artículo, le diremos todo lo que necesita saber sobre Free Fire MAX, incluyendo cómo descargar el archivo APK en solo 45 MB. ¡Sigue leyendo para saber más!
- ¿Qué es Free Fire MAX?
-Free Fire MAX es un juego de disparos desarrollado por Garena International que está diseñado exclusivamente para ofrecer una experiencia de juego premium en una batalla real. Se basa en el mismo juego central que Free Fire, pero con características mejoradas como:
-descargar gratis fuego máx apk 45 mb Download ✓✓✓ https://bltlly.com/2v6IOK
-Resoluciones ultra HD que hacen el juego más realista e inmersivoEfectos impresionantes que mejoran la experiencia de combateContenido exclusivo que solo está disponible en Free Fire MAXTecnología Firelink que te permite jugar con otros jugadores de Free Fire a través de la compatibilidad multiplataforma
-Free Fire MAX está actualmente disponible en determinadas regiones, pero se puede descargar desde cualquier lugar utilizando un archivo APK. Te mostraremos cómo hacerlo en la siguiente sección.
- ¿Cuáles son los beneficios de jugar Free Fire MAX?
-Si te estás preguntando por qué deberías jugar Free Fire MAX en lugar de Free Fire, estos son algunos de los beneficios que puedes disfrutar:
-
-Mejores gráficos : Free Fire MAX tiene gráficos en HD que son más detallados y vibrantes que Free Fire. Puedes ver cada textura, sombra y reflejo en el mundo del juego. También puede personalizar la configuración de gráficos de acuerdo con las especificaciones y preferencias del dispositivo. Mejor compatibilidad : Free Fire MAX es compatible con más dispositivos que Free Fire. Puede reproducirlo en dispositivos de gama baja, así como en dispositivos de gama alta con un mejor rendimiento. También se puede jugar en PC o Mac utilizando un emulador de Android. También puedes jugar con otros jugadores de Free Fire usando la tecnología Firelink, que explicaremos más adelante.
-Como puedes ver, Free Fire MAX tiene muchas ventajas sobre Free Fire. Si quieres experimentar el juego por ti mismo, tendrás que descargar el archivo APK, que es un pequeño archivo que contiene los datos del juego. Así es como puedes hacerlo en tan solo 45 MB.
- ¿Cómo descargar el archivo APK Free Fire MAX en 45 MB?
-Para descargar el archivo APK Free Fire MAX en 45 MB, tendrá que seguir estos sencillos pasos:
- Paso 1: Habilitar fuentes desconocidas en el dispositivo
-Antes de poder instalar el archivo APK, tendrá que permitir que su dispositivo instale aplicaciones desde fuentes distintas de Google Play Store. Para ello, vaya a la configuración del dispositivo y busque la opción de seguridad o privacidad. Luego, habilite la opción de fuentes desconocidas. Esto le permitirá instalar aplicaciones de sitios web de terceros.
- Paso 2: Descargar el archivo APK de una fuente de confianza
-Siguiente, tendrá que encontrar y descargar el archivo APK de un sitio web confiable. Hay muchos sitios web que ofrecen archivos APK para varios juegos y aplicaciones, pero no todos ellos son seguros y protegidos. Algunos de ellos pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. Por lo tanto, solo debe descargar el archivo APK de una fuente de confianza, como APKPure o APKMirror . Estos sitios web son verificados y probados por millones de usuarios y proporcionan las versiones más recientes y actualizadas de los archivos APK.
-
-
- Paso 3: Instalar el archivo APK en su dispositivo
-Una vez que haya descargado el archivo APK, tendrá que instalarlo en su dispositivo. Para ello, localice el archivo APK en el almacenamiento del dispositivo y toque en él. Verá un mensaje pidiéndole que confirme la instalación. Toque en instalar y espere unos segundos hasta que se complete la instalación.
- Paso 4: Iniciar el juego y disfrutar de
-Después de instalar el archivo APK, puede iniciar el juego tocando en su icono en la pantalla de inicio o cajón de aplicaciones. Verá una pantalla de carga que descargará los datos adicionales del juego, lo que puede tardar algún tiempo dependiendo de su velocidad de Internet. Una vez completada la descarga, puede iniciar sesión con su cuenta de Free Fire existente o crear una nueva. También puedes vincular tu cuenta de Facebook o Google para guardar tu progreso y sincronizar tus datos entre dispositivos.
-Ahora, puedes disfrutar jugando Free Fire MAX con gráficos mejorados, jugabilidad y compatibilidad. También puedes jugar con otros jugadores de Free Fire usando la tecnología Firelink, que explicaremos en la siguiente sección.
- ¿Cómo se juega Free Fire MAX con otros jugadores de Free Fire?
-Una de las mejores características de Free Fire MAX es que te permite jugar con otros jugadores de Free Fire usando la tecnología Firelink . Esta es una tecnología multiplataforma que conecta a los jugadores a través de ambas versiones del juego. Esto significa que puedes jugar con tus amigos y compañeros de equipo que están usando Free Fire o Free Fire MAX sin ningún problema.
-Para utilizar esta función, tendrá que activarla en la configuración del juego. Vaya a la configuración y busque la opción Firelink. Luego, enciéndelo y escanea el código QR que aparece en tu pantalla. Esto vinculará tu cuenta con tu dispositivo y te permitirá jugar con otros jugadores usando cualquiera de las versiones del juego.
-
- ¿Cómo optimizar tu experiencia Free Fire MAX?
-Si quieres aprovechar al máximo tu experiencia Free Fire MAX, aquí hay algunos consejos y trucos que puedes usar:
- Consejo 1: Ajusta la configuración de tus gráficos según las especificaciones de tu dispositivo
-Free Fire MAX tiene gráficos increíbles que hacen que el juego sea más realista e inmersivo. Sin embargo, no todos los dispositivos pueden manejar la configuración de gráficos altos sin afectar el rendimiento o la duración de la batería. Por lo tanto, debe ajustar la configuración de los gráficos de acuerdo con las especificaciones y preferencias del dispositivo.
-Para hacer esto, vaya a la configuración y busque la opción de gráficos. Verá un control deslizante que le permite elegir entre ajustes de gráficos bajos, medios, altos y ultra. También puede personalizar la configuración de sus gráficos cambiando la resolución, la velocidad de fotogramas, las sombras, el anti-aliasing y la calidad de la textura. También puedes activar o desactivar el modo HDR, que mejora el color y el contraste del juego.
-Deberías experimentar con diferentes configuraciones gráficas hasta que encuentres la que se adapte a tu dispositivo y a tu gusto. También puede comprobar la temperatura del dispositivo y el nivel de batería para ver cuánto impacto tienen los ajustes gráficos en el dispositivo.
- Consejo 2: Utilice una conexión a Internet estable y un buen servicio VPN
-Free Fire MAX es un juego en línea que requiere una conexión a Internet estable para jugar sin problemas y sin interrupciones. Si tiene una conexión a Internet lenta o inestable, puede experimentar retraso, desconexión u otros problemas que pueden arruinar su experiencia de juego. Por lo tanto, debe utilizar una conexión a Internet rápida y confiable para jugar Free Fire MAX.
-
-Un servicio VPN es un software que puede cifrar el tráfico de Internet y enrutarlo a través de un servidor en otro país. De esta manera, puedes evitar cualquier restricción geográfica o censura que pueda impedirte jugar a Free Fire MAX. También puede disfrutar de un acceso a Internet más rápido y seguro con un servicio VPN.
-Hay muchos servicios VPN disponibles en línea, pero no todos son seguros y efectivos. Algunos de ellos pueden contener malware o spyware que pueden dañar su dispositivo o robar sus datos. Algunos de ellos también pueden ralentizar su velocidad de Internet o filtrar su dirección IP. Por lo tanto, solo debe usar un servicio VPN confiable y de buena reputación, como NordVPN o ExpressVPN . Estos servicios VPN son verificados y probados por millones de usuarios y proporcionan servidores VPN rápidos y seguros en varios países.
-Para usar un servicio VPN para jugar a Free Fire MAX, tendrá que descargar e instalar la aplicación VPN en su dispositivo. Luego, tendrás que crear una cuenta y elegir una ubicación de servidor compatible con el juego. Por ejemplo, si quieres jugar a Free Fire MAX en India, puedes elegir un servidor en India. Luego, deberá conectarse al servidor VPN y lanzar el juego. Verás que puedes acceder a los servidores y características del juego sin ningún problema.
- Consejo 3: Utilice BlueStacks para jugar Free Fire MAX en PC o Mac
-Si quieres jugar Free Fire MAX en una pantalla más grande con mejores controles y rendimiento, puedes usar BlueStacks, un emulador de Android que te permite jugar juegos de Android en PC o Mac. BlueStacks es uno de los mejores emuladores de Android disponibles en línea, ya que tiene muchas características que lo hacen ideal para jugar Free Fire MAX, como:
-
-
-Controles inteligentes : BlueStacks tiene controles inteligentes que detectan automáticamente cuando estás en combate o en menús y cambian entre los controles del teclado y el ratón en consecuencia. De esta manera, puedes jugar el juego con facilidad y eficiencia. Grabadora de macros : BlueStacks tiene una grabadora de macros que le permite grabar y reproducir cualquier acción o secuencia de acciones en el juego con una sola pulsación. De esta manera, puedes automatizar tareas como saquear, curar, recargar o usar habilidades. Eco mode : BlueStacks tiene un modo eco que reduce el uso de CPU y RAM del emulador cuando se ejecuta en segundo plano. De esta manera, puede guardar los recursos del dispositivo y la duración de la batería mientras juega Free Fire MAX.
-Para usar BlueStacks para jugar Free Fire MAX en PC o Mac, tendrá que descargar e instalar BlueStacks en su computadora desde su sitio web oficial aquí . Luego, tendrás que iniciar sesión con tu cuenta de Google y buscar Free Fire MAX en Google Play Store. También puede descargar el archivo APK de los sitios web mencionados anteriormente e instalarlo en BlueStacks. Luego, puedes iniciar el juego e iniciar sesión con tu cuenta de Free Fire o crear una nueva. También puede personalizar los controles del teclado y del ratón según sus preferencias.
-Ahora, puedes disfrutar jugando Free Fire MAX en PC o Mac con BlueStacks y experimentar el juego de una manera completamente nueva.
- Conclusión
-Free Fire MAX es un gran juego para cualquiera que ame los juegos battle royale y quiera experimentarlos con mejores gráficos, jugabilidad y compatibilidad. Puede descargar el archivo APK en solo 45 MB e instalarlo en su dispositivo fácilmente. También puedes jugar con otros jugadores de Free Fire usando la tecnología Firelink y cambiar entre ambas versiones del juego sin perder tu progreso o datos. También puede usar un servicio VPN para acceder al juego desde cualquier región y usar BlueStacks para jugar el juego en PC o Mac.
-
- Preguntas frecuentes
-Aquí están algunas de las preguntas y respuestas más frecuentes sobre Free Fire MAX:
-
-Q: ¿Free Fire MAX es libre para jugar? A: Sí, Free Fire MAX es gratis para jugar y descargar. Sin embargo, puede contener compras en la aplicación que requieren dinero real. Q: ¿Es seguro descargar e instalar Free Fire MAX? A: Sí, Free Fire MAX es seguro para descargar e instalar siempre y cuando utilice una fuente de confianza como Google Play Store o APKPure. También debe habilitar fuentes desconocidas en su dispositivo antes de instalar el archivo APK. Q: ¿Cuáles son los requisitos mínimos para jugar Free Fire MAX? A: Los requisitos mínimos para jugar Free Fire MAX son Android 4.4 o superior, 2 GB de RAM y 1.5 GB de espacio de almacenamiento libre. Q: ¿Puedo jugar Free Fire MAX sin conexión? A: No, Free Fire MAX es un juego en línea que requiere una conexión a Internet para jugar. También debe usar una conexión a Internet estable y un buen servicio VPN para evitar retrasos, desconexiones o restricciones geográficas. Q: ¿Puedo jugar Free Fire MAX con mis amigos? A: Sí, puedes jugar a Free Fire MAX con tus amigos usando la tecnología Firelink que conecta a los jugadores en ambas versiones del juego. También puedes usar BlueStacks para jugar Free Fire MAX en PC o Mac con tus amigos. 64aa2da5cf
-#include
-#include
-#include
-#include
-#include
-#include
-
-#include
-#include
-#include
-#include
-#include
-
-C10_DEFINE_string(predict_net, "", "path to model.pb");
-C10_DEFINE_string(init_net, "", "path to model_init.pb");
-C10_DEFINE_string(input, "", "path to input image");
-
-using namespace std;
-using namespace caffe2;
-
-int main(int argc, char** argv) {
- caffe2::GlobalInit(&argc, &argv);
- string predictNetPath = FLAGS_predict_net;
- string initNetPath = FLAGS_init_net;
- cv::Mat input = cv::imread(FLAGS_input, cv::IMREAD_COLOR);
-
- const int height = input.rows;
- const int width = input.cols;
- // FPN models require divisibility of 32
- assert(height % 32 == 0 && width % 32 == 0);
- const int batch = 1;
- const int channels = 3;
-
- // initialize Net and Workspace
- caffe2::NetDef initNet_, predictNet_;
- CAFFE_ENFORCE(ReadProtoFromFile(initNetPath, &initNet_));
- CAFFE_ENFORCE(ReadProtoFromFile(predictNetPath, &predictNet_));
-
- Workspace workSpace;
- for (auto& str : predictNet_.external_input()) {
- workSpace.CreateBlob(str);
- }
- CAFFE_ENFORCE(workSpace.CreateNet(predictNet_));
- CAFFE_ENFORCE(workSpace.RunNetOnce(initNet_));
-
- // setup inputs
- auto data = BlobGetMutableTensor(workSpace.GetBlob("data"), caffe2::CPU);
- data->Resize(batch, channels, height, width);
- float* ptr = data->mutable_data();
- // HWC to CHW
- for (int c = 0; c < 3; ++c) {
- for (int i = 0; i < height * width; ++i) {
- ptr[c * height * width + i] = static_cast(input.data[3 * i + c]);
- }
- }
-
- auto im_info =
- BlobGetMutableTensor(workSpace.GetBlob("im_info"), caffe2::CPU);
- im_info->Resize(batch, 3);
- float* im_info_ptr = im_info->mutable_data();
- im_info_ptr[0] = height;
- im_info_ptr[1] = width;
- im_info_ptr[2] = 1.0;
-
- // run the network
- CAFFE_ENFORCE(workSpace.RunNet(predictNet_.name()));
-
- // run 3 more times to benchmark
- int N_benchmark = 3;
- auto start_time = chrono::high_resolution_clock::now();
- for (int i = 0; i < N_benchmark; ++i) {
- CAFFE_ENFORCE(workSpace.RunNet(predictNet_.name()));
- }
- auto end_time = chrono::high_resolution_clock::now();
- auto ms = std::chrono::duration_cast(
- end_time - start_time)
- .count();
- cout << "Latency: " << ms * 1.0 / 1e6 / N_benchmark << " seconds" << endl;
-
- // parse Mask R-CNN outputs
- auto& bbox = BlobGetTensor(*workSpace.GetBlob("bbox_nms"), caffe2::CPU);
- auto& scores = BlobGetTensor(*workSpace.GetBlob("score_nms"), caffe2::CPU);
- auto& labels = BlobGetTensor(*workSpace.GetBlob("class_nms"), caffe2::CPU);
- auto& mask_probs =
- BlobGetTensor(*workSpace.GetBlob("mask_fcn_probs"), caffe2::CPU);
- cout << "bbox:" << bbox.DebugString() << endl;
- cout << "scores:" << scores.DebugString() << endl;
- cout << "labels:" << labels.DebugString() << endl;
- cout << "mask_probs: " << mask_probs.DebugString() << endl;
-
- int num_instances = bbox.sizes()[0];
- for (int i = 0; i < num_instances; ++i) {
- float score = scores.data()[i];
- if (score < 0.6)
- continue; // skip them
-
- const float* box = bbox.data() + i * 4;
- int label = labels.data()[i];
-
- cout << "Prediction " << i << ", xyxy=(";
- cout << box[0] << ", " << box[1] << ", " << box[2] << ", " << box[3]
- << "); score=" << score << "; label=" << label << endl;
-
- const float* mask = mask_probs.data() +
- i * mask_probs.size_from_dim(1) + label * mask_probs.size_from_dim(2);
-
- // save the 28x28 mask
- cv::Mat cv_mask(28, 28, CV_32FC1);
- memcpy(cv_mask.data, mask, 28 * 28 * sizeof(float));
- cv::imwrite("mask" + std::to_string(i) + ".png", cv_mask * 255.);
- }
- return 0;
-}
diff --git a/spaces/CVPR/GFPGAN-example/tests/test_gfpgan_arch.py b/spaces/CVPR/GFPGAN-example/tests/test_gfpgan_arch.py
deleted file mode 100644
index cef14a435aa824a1b7c4baaf2d1fe0a2f6cc4441..0000000000000000000000000000000000000000
--- a/spaces/CVPR/GFPGAN-example/tests/test_gfpgan_arch.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import torch
-
-from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1, StyleGAN2GeneratorSFT
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean, StyleGAN2GeneratorCSFT
-
-
-def test_stylegan2generatorsft():
- """Test arch: StyleGAN2GeneratorSFT."""
-
- # model init and forward (gpu)
- if torch.cuda.is_available():
- net = StyleGAN2GeneratorSFT(
- out_size=32,
- num_style_feat=512,
- num_mlp=8,
- channel_multiplier=1,
- resample_kernel=(1, 3, 3, 1),
- lr_mlp=0.01,
- narrow=1,
- sft_half=False).cuda().eval()
- style = torch.rand((1, 512), dtype=torch.float32).cuda()
- condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
- condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
- condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
- conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
- output = net([style], conditions)
- assert output[0].shape == (1, 3, 32, 32)
- assert output[1] is None
-
- # -------------------- with return_latents ----------------------- #
- output = net([style], conditions, return_latents=True)
- assert output[0].shape == (1, 3, 32, 32)
- assert len(output[1]) == 1
- # check latent
- assert output[1][0].shape == (8, 512)
-
- # -------------------- with randomize_noise = False ----------------------- #
- output = net([style], conditions, randomize_noise=False)
- assert output[0].shape == (1, 3, 32, 32)
- assert output[1] is None
-
- # -------------------- with truncation = 0.5 and mixing----------------------- #
- output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
- assert output[0].shape == (1, 3, 32, 32)
- assert output[1] is None
-
-
-def test_gfpganv1():
- """Test arch: GFPGANv1."""
-
- # model init and forward (gpu)
- if torch.cuda.is_available():
- net = GFPGANv1(
- out_size=32,
- num_style_feat=512,
- channel_multiplier=1,
- resample_kernel=(1, 3, 3, 1),
- decoder_load_path=None,
- fix_decoder=True,
- # for stylegan decoder
- num_mlp=8,
- lr_mlp=0.01,
- input_is_latent=False,
- different_w=False,
- narrow=1,
- sft_half=True).cuda().eval()
- img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
- output = net(img)
- assert output[0].shape == (1, 3, 32, 32)
- assert len(output[1]) == 3
- # check out_rgbs for intermediate loss
- assert output[1][0].shape == (1, 3, 8, 8)
- assert output[1][1].shape == (1, 3, 16, 16)
- assert output[1][2].shape == (1, 3, 32, 32)
-
- # -------------------- with different_w = True ----------------------- #
- net = GFPGANv1(
- out_size=32,
- num_style_feat=512,
- channel_multiplier=1,
- resample_kernel=(1, 3, 3, 1),
- decoder_load_path=None,
- fix_decoder=True,
- # for stylegan decoder
- num_mlp=8,
- lr_mlp=0.01,
- input_is_latent=False,
- different_w=True,
- narrow=1,
- sft_half=True).cuda().eval()
- img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
- output = net(img)
- assert output[0].shape == (1, 3, 32, 32)
- assert len(output[1]) == 3
- # check out_rgbs for intermediate loss
- assert output[1][0].shape == (1, 3, 8, 8)
- assert output[1][1].shape == (1, 3, 16, 16)
- assert output[1][2].shape == (1, 3, 32, 32)
-
-
-def test_facialcomponentdiscriminator():
- """Test arch: FacialComponentDiscriminator."""
-
- # model init and forward (gpu)
- if torch.cuda.is_available():
- net = FacialComponentDiscriminator().cuda().eval()
- img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
- output = net(img)
- assert len(output) == 2
- assert output[0].shape == (1, 1, 8, 8)
- assert output[1] is None
-
- # -------------------- return intermediate features ----------------------- #
- output = net(img, return_feats=True)
- assert len(output) == 2
- assert output[0].shape == (1, 1, 8, 8)
- assert len(output[1]) == 2
- assert output[1][0].shape == (1, 128, 16, 16)
- assert output[1][1].shape == (1, 256, 8, 8)
-
-
-def test_stylegan2generatorcsft():
- """Test arch: StyleGAN2GeneratorCSFT."""
-
- # model init and forward (gpu)
- if torch.cuda.is_available():
- net = StyleGAN2GeneratorCSFT(
- out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=1, sft_half=False).cuda().eval()
- style = torch.rand((1, 512), dtype=torch.float32).cuda()
- condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
- condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
- condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
- conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
- output = net([style], conditions)
- assert output[0].shape == (1, 3, 32, 32)
- assert output[1] is None
-
- # -------------------- with return_latents ----------------------- #
- output = net([style], conditions, return_latents=True)
- assert output[0].shape == (1, 3, 32, 32)
- assert len(output[1]) == 1
- # check latent
- assert output[1][0].shape == (8, 512)
-
- # -------------------- with randomize_noise = False ----------------------- #
- output = net([style], conditions, randomize_noise=False)
- assert output[0].shape == (1, 3, 32, 32)
- assert output[1] is None
-
- # -------------------- with truncation = 0.5 and mixing----------------------- #
- output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
- assert output[0].shape == (1, 3, 32, 32)
- assert output[1] is None
-
-
-def test_gfpganv1clean():
- """Test arch: GFPGANv1Clean."""
-
- # model init and forward (gpu)
- if torch.cuda.is_available():
- net = GFPGANv1Clean(
- out_size=32,
- num_style_feat=512,
- channel_multiplier=1,
- decoder_load_path=None,
- fix_decoder=True,
- # for stylegan decoder
- num_mlp=8,
- input_is_latent=False,
- different_w=False,
- narrow=1,
- sft_half=True).cuda().eval()
-
- img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
- output = net(img)
- assert output[0].shape == (1, 3, 32, 32)
- assert len(output[1]) == 3
- # check out_rgbs for intermediate loss
- assert output[1][0].shape == (1, 3, 8, 8)
- assert output[1][1].shape == (1, 3, 16, 16)
- assert output[1][2].shape == (1, 3, 32, 32)
-
- # -------------------- with different_w = True ----------------------- #
- net = GFPGANv1Clean(
- out_size=32,
- num_style_feat=512,
- channel_multiplier=1,
- decoder_load_path=None,
- fix_decoder=True,
- # for stylegan decoder
- num_mlp=8,
- input_is_latent=False,
- different_w=True,
- narrow=1,
- sft_half=True).cuda().eval()
- img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
- output = net(img)
- assert output[0].shape == (1, 3, 32, 32)
- assert len(output[1]) == 3
- # check out_rgbs for intermediate loss
- assert output[1][0].shape == (1, 3, 8, 8)
- assert output[1][1].shape == (1, 3, 16, 16)
- assert output[1][2].shape == (1, 3, 32, 32)
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/set_operations.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/set_operations.h
deleted file mode 100644
index 7d09355e12ad1beaf3ad6af34558d5a8692bd62a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/set_operations.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a fill of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-
-// the purpose of this header is to #include the set_operations.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch set_operations
-
-#include
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include
-#include
-#include
-#include
-#endif
-
-#define __THRUST_HOST_SYSTEM_SET_OPERATIONS_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/set_operations.h>
-#include __THRUST_HOST_SYSTEM_SET_OPERATIONS_HEADER
-#undef __THRUST_HOST_SYSTEM_SET_OPERATIONS_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_SET_OPERATIONS_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/set_operations.h>
-#include __THRUST_DEVICE_SYSTEM_SET_OPERATIONS_HEADER
-#undef __THRUST_DEVICE_SYSTEM_SET_OPERATIONS_HEADER
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/assign_value.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/assign_value.h
deleted file mode 100644
index cf244a02193211b9b4e4f07a6bc9b975d50e5388..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/assign_value.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-
-// this system inherits assign_value
-#include
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/transform_reduce.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/transform_reduce.h
deleted file mode 100644
index a8736bd75d06e54d9158baeb2504162d75312885..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/transform_reduce.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-
-// this system inherits transform_reduce
-#include
-
diff --git a/spaces/CVPR/WALT/mmdet/datasets/pipelines/__init__.py b/spaces/CVPR/WALT/mmdet/datasets/pipelines/__init__.py
deleted file mode 100644
index c6f424debd1623e7511dd77da464a6639d816745..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/datasets/pipelines/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
- ContrastTransform, EqualizeTransform, Rotate, Shear,
- Translate)
-from .compose import Compose
-from .formating import (Collect, DefaultFormatBundle, ImageToTensor,
- ToDataContainer, ToTensor, Transpose, to_tensor)
-from .instaboost import InstaBoost
-from .loading import (LoadAnnotations, LoadImageFromFile, LoadImageFromWebcam,
- LoadMultiChannelImageFromFiles, LoadProposals)
-from .test_time_aug import MultiScaleFlipAug
-from .transforms import (Albu, CutOut, Expand, MinIoURandomCrop, Normalize,
- Pad, PhotoMetricDistortion, RandomCenterCropPad,
- RandomCrop, RandomFlip, Resize, SegRescale)
-
-__all__ = [
- 'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer',
- 'Transpose', 'Collect', 'DefaultFormatBundle', 'LoadAnnotations',
- 'LoadImageFromFile', 'LoadImageFromWebcam',
- 'LoadMultiChannelImageFromFiles', 'LoadProposals', 'MultiScaleFlipAug',
- 'Resize', 'RandomFlip', 'Pad', 'RandomCrop', 'Normalize', 'SegRescale',
- 'MinIoURandomCrop', 'Expand', 'PhotoMetricDistortion', 'Albu',
- 'InstaBoost', 'RandomCenterCropPad', 'AutoAugment', 'CutOut', 'Shear',
- 'Rotate', 'ColorTransform', 'EqualizeTransform', 'BrightnessTransform',
- 'ContrastTransform', 'Translate'
-]
diff --git a/spaces/CVPR/WALT/mmdet/models/roi_heads/pisa_roi_head.py b/spaces/CVPR/WALT/mmdet/models/roi_heads/pisa_roi_head.py
deleted file mode 100644
index e01113629837eb9c065ba40cd4025899b7bd0172..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/roi_heads/pisa_roi_head.py
+++ /dev/null
@@ -1,159 +0,0 @@
-from mmdet.core import bbox2roi
-from ..builder import HEADS
-from ..losses.pisa_loss import carl_loss, isr_p
-from .standard_roi_head import StandardRoIHead
-
-
-@HEADS.register_module()
-class PISARoIHead(StandardRoIHead):
- r"""The RoI head for `Prime Sample Attention in Object Detection
- `_."""
-
- def forward_train(self,
- x,
- img_metas,
- proposal_list,
- gt_bboxes,
- gt_labels,
- gt_bboxes_ignore=None,
- gt_masks=None):
- """Forward function for training.
-
- Args:
- x (list[Tensor]): List of multi-level img features.
- img_metas (list[dict]): List of image info dict where each dict
- has: 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- `mmdet/datasets/pipelines/formatting.py:Collect`.
- proposals (list[Tensors]): List of region proposals.
- gt_bboxes (list[Tensor]): Each item are the truth boxes for each
- image in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[Tensor]): Class indices corresponding to each box
- gt_bboxes_ignore (list[Tensor], optional): Specify which bounding
- boxes can be ignored when computing the loss.
- gt_masks (None | Tensor) : True segmentation masks for each box
- used if the architecture supports a segmentation task.
-
- Returns:
- dict[str, Tensor]: a dictionary of loss components
- """
- # assign gts and sample proposals
- if self.with_bbox or self.with_mask:
- num_imgs = len(img_metas)
- if gt_bboxes_ignore is None:
- gt_bboxes_ignore = [None for _ in range(num_imgs)]
- sampling_results = []
- neg_label_weights = []
- for i in range(num_imgs):
- assign_result = self.bbox_assigner.assign(
- proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
- gt_labels[i])
- sampling_result = self.bbox_sampler.sample(
- assign_result,
- proposal_list[i],
- gt_bboxes[i],
- gt_labels[i],
- feats=[lvl_feat[i][None] for lvl_feat in x])
- # neg label weight is obtained by sampling when using ISR-N
- neg_label_weight = None
- if isinstance(sampling_result, tuple):
- sampling_result, neg_label_weight = sampling_result
- sampling_results.append(sampling_result)
- neg_label_weights.append(neg_label_weight)
-
- losses = dict()
- # bbox head forward and loss
- if self.with_bbox:
- bbox_results = self._bbox_forward_train(
- x,
- sampling_results,
- gt_bboxes,
- gt_labels,
- img_metas,
- neg_label_weights=neg_label_weights)
- losses.update(bbox_results['loss_bbox'])
-
- # mask head forward and loss
- if self.with_mask:
- mask_results = self._mask_forward_train(x, sampling_results,
- bbox_results['bbox_feats'],
- gt_masks, img_metas)
- losses.update(mask_results['loss_mask'])
-
- return losses
-
- def _bbox_forward(self, x, rois):
- """Box forward function used in both training and testing."""
- # TODO: a more flexible way to decide which feature maps to use
- bbox_feats = self.bbox_roi_extractor(
- x[:self.bbox_roi_extractor.num_inputs], rois)
- if self.with_shared_head:
- bbox_feats = self.shared_head(bbox_feats)
- cls_score, bbox_pred = self.bbox_head(bbox_feats)
-
- bbox_results = dict(
- cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
- return bbox_results
-
- def _bbox_forward_train(self,
- x,
- sampling_results,
- gt_bboxes,
- gt_labels,
- img_metas,
- neg_label_weights=None):
- """Run forward function and calculate loss for box head in training."""
- rois = bbox2roi([res.bboxes for res in sampling_results])
-
- bbox_results = self._bbox_forward(x, rois)
-
- bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
- gt_labels, self.train_cfg)
-
- # neg_label_weights obtained by sampler is image-wise, mapping back to
- # the corresponding location in label weights
- if neg_label_weights[0] is not None:
- label_weights = bbox_targets[1]
- cur_num_rois = 0
- for i in range(len(sampling_results)):
- num_pos = sampling_results[i].pos_inds.size(0)
- num_neg = sampling_results[i].neg_inds.size(0)
- label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos +
- num_neg] = neg_label_weights[i]
- cur_num_rois += num_pos + num_neg
-
- cls_score = bbox_results['cls_score']
- bbox_pred = bbox_results['bbox_pred']
-
- # Apply ISR-P
- isr_cfg = self.train_cfg.get('isr', None)
- if isr_cfg is not None:
- bbox_targets = isr_p(
- cls_score,
- bbox_pred,
- bbox_targets,
- rois,
- sampling_results,
- self.bbox_head.loss_cls,
- self.bbox_head.bbox_coder,
- **isr_cfg,
- num_class=self.bbox_head.num_classes)
- loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois,
- *bbox_targets)
-
- # Add CARL Loss
- carl_cfg = self.train_cfg.get('carl', None)
- if carl_cfg is not None:
- loss_carl = carl_loss(
- cls_score,
- bbox_targets[0],
- bbox_pred,
- bbox_targets[2],
- self.bbox_head.loss_bbox,
- **carl_cfg,
- num_class=self.bbox_head.num_classes)
- loss_bbox.update(loss_carl)
-
- bbox_results.update(loss_bbox=loss_bbox)
- return bbox_results
diff --git a/spaces/Catmeow/Face2Painting_From_Photo/app.py b/spaces/Catmeow/Face2Painting_From_Photo/app.py
deleted file mode 100644
index 4c0cb26ab900f67896e49ffce86bca5338d9c4fe..0000000000000000000000000000000000000000
--- a/spaces/Catmeow/Face2Painting_From_Photo/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import gradio as gr
-from paintingface import generate
-
-title = "Face from Photo into Handed-paint"
-description = "Upload a photo, this Ai will detect and transfer only the main face into cartoon/anime handed-painting style. (If cannot detect a face, try the edit button on the right corner of the picture to crop the photo manually.)"
-article = "Examples are from Internet"
-
-Example=[['Example01.jpg'],['Example02.jpg'],['Example03.jpg']]
-
-demo = gr.Interface(
- generate,
- inputs = [gr.Image(type="pil", label="Upload a photo")],
- outputs= [gr.Image(type="pil", label="Output")],
- title=title,
- description=description,
- article=article,
- examples=Example,
- allow_flagging='never'
- )
-
-demo.launch()
-
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/apps/message/message.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/apps/message/message.js
deleted file mode 100644
index e1908bec0f81100cf6bac154e435b9ced4586b6e..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/apps/message/message.js
+++ /dev/null
@@ -1,277 +0,0 @@
-import { sendSocketList, Config, Version } from '../../components/index.js'
-import { makeOneBotReportMsg, makeGSUidReportMsg, setGuildLatestMsgId, setMsgMap } from '../../model/index.js'
-import _ from 'lodash'
-import cfg from '../../../../lib/config/config.js'
-
-
-Bot.on('message', async e => {
- if (e.self_id == '88888'){
- if (e.group?.bot?.uin) {
- e.self_id = e.group.bot.uin
- } else if (e.friend?.bot?.uin) {
- e.self_id = e.friend.bot.uin
- }
- e.bot = Bot[e.self_id]
- }
- // 被禁言或者全体禁言
- if (Config.muteStop && (e.group?.mute_left > 0 || e.group?.all_muted)) return false
- // 如果没有已连接的Websocket
- if (sendSocketList.length == 0) return false
- if (e.group_id) {
- // 判断云崽白名单
- const whiteGroup = Config.whiteGroup
- if (Array.isArray(whiteGroup) && whiteGroup.length > 0) {
- if (!whiteGroup.some(i => i == e.group_id)) return false
- }
- // 判断插件白名单
- const yesGroup = Config.yesGroup
- if (Array.isArray(yesGroup) && yesGroup.length > 0) {
- if (!yesGroup.some(i => i == e.group_id)) return false
- }
- // 判断云崽黑名单
- const blackGroup = Config.blackGroup
- if (Array.isArray(blackGroup) && blackGroup.length > 0) {
- if (blackGroup.some(i => i == e.group_id)) return false
- }
- // 判断插件黑名单
- const noGroup = Config.noGroup
- if (Array.isArray(noGroup) && noGroup.length > 0) {
- if (noGroup.some(i => i == e.group_id)) return false
- }
- }
- // 判断插件前缀
- if (Array.isArray(Config.noMsgStart) && Config.noMsgStart.length > 0) {
- if (e.message?.[0]?.type === 'text') {
- if (Config.noMsgStart.some(i => e.message[0].text.startsWith(i))) return false
- }
- }
- let isMaster = e.isMaster
- if (Version.isTrss) {
- if (e.user_id && cfg.master[e.self_id]?.includes(String(e.user_id))) {
- isMaster = true
- }
- }
- const message_id = Math.floor(Math.random() * Math.pow(2, 32)) | 0
- let msg = {
- time: e.time,
- message_id: e.message_id,
- message: _.cloneDeep(e.message),
- rand: e.rand,
- seq: e.seq,
- source: e.source,
- user_id: e.user_id,
- self_id: e.self_id,
- isMaster,
- sender: e.sender,
- param: {
- time: e.time,
- self_id: e.self_id,
- post_type: e.post_type,
- message_type: e.message_type,
- sub_type: e.sub_type,
- message_id,
- user_id: e.user_id,
- font: 0,
- sender: e.sender,
- anonymous: e.anonymous ? {
- id: e.anonymous.id,
- name: e.anonymous.name,
- flag: e.anonymous.flag
- } : null
- }
- }
- let message = []
- //增加isGroup e.isPrivate
- if (e.guild_id) {
- setGuildLatestMsgId(e.message_id)
- //处理成message
- if (e.content) {
- let content = toMsg(e.content)
- message.push(...content)
- }
- if (e.attachments) {
- e.attachments.forEach(i => {
- if (i.content_type.startsWith('image')) {
- message.push({
- type: 'image',
- file: i.filename,
- url: i.url
- })
- }
- })
- }
- msg.message = message
-
- msg.isGuild = true
- msg.param = {
- time: Math.floor(new Date(msg.timestamp).getTime() / 1000),
- post_type: 'message',
- message_type: 'guild',
- sub_type: 'channel',
- guild_id: e.guild_id,
- channel_id: e.channel_id,
- user_id: e.author.id,
- message_id: e.message_id,
- self_id: e.bot.appID,
- sender: {
- user_id: e.author.id,
- nickname: e.author.username,
- tiny_id: e.author.id,
- },
- self_tiny_id: e.bot.appID,
- }
- } else if (e.message_type == 'group') {
- msg.isGroup = true
- msg.group_id = e.group_id
- msg.param.group_id = e.group_id
- msg.self_id = e.group?.bot?.uin || msg.self_id
- } else if (e.message_type == 'private') {
- msg.isPrivate = true
- msg.self_id = e.friend?.bot?.uin || msg.self_id
- } else {
- return false
- }
- // 判断云崽前缀
- msg = onlyReplyAt(msg)
- if (!msg) return false
- for (const i of sendSocketList) {
- if (i.status == 1) {
- let reportMsg = null
- switch (Number(i.type)) {
- case 1:
- case 2:
- case 6:
- if (Version.isTrss) {
- if (i.uin != e.self_id) continue
- if (!Version.protocol.some(i => i == e.bot?.version?.name)) continue
- }
- e.reply = reply(e)
- reportMsg = await makeOneBotReportMsg(msg)
- break;
- case 3:
- reportMsg = await makeGSUidReportMsg(msg)
- break
- default:
- break;
- }
- if (reportMsg) i.ws.send(reportMsg)
- }
- }
-})
-
-function reply(e) {
- if (!Version.isTrss) {
- const replyNew = e.reply
- return async function (massage, quote = false, data = {}) {
- const ret = await replyNew(massage, quote, data)
- if (ret) {
- setMsgMap({
- message_id: ret.message_id,
- time: ret.time,
- seq: ret.seq,
- rand: ret.rand,
- user_id: e.user_id,
- group_id: e.group_id,
- onebot_id: Math.floor(Math.random() * Math.pow(2, 32)) | 0,
- })
- }
- return ret
- }
- } else {
- if (e.bot?.version?.name == 'ICQQ') {
- return async function (massage, quote = false) {
- let ret
- if (e.isGroup) {
- if (e.group?.sendMsg) {
- ret = await e.group.sendMsg(massage, quote)
- } else {
- ret = await e.bot.pickGroup(e.group_id).sendMsg(massage, quote)
- }
- } else {
- if (e.friend?.sendMsg) {
- ret = await e.friend.sendMsg(massage, quote)
- } else {
- ret = await e.bot.pickFriend(e.user_id).sendMsg(massage, quote)
- }
- }
- if (ret) {
- setMsgMap({
- message_id: ret.message_id,
- time: ret.time,
- seq: ret.seq,
- rand: ret.rand,
- user_id: e.user_id,
- group_id: e.group_id,
- onebot_id: Math.floor(Math.random() * Math.pow(2, 32)) | 0,
- })
- }
- return ret
- }
- }
- return e.reply
- }
-}
-
-function onlyReplyAt(e) {
- if (!e.message) return false
-
- let groupCfg = Version.isTrss ? cfg.getGroup(e.self_id, e.group_id) : cfg.getGroup(e.group_id)
- if (groupCfg.onlyReplyAt != 1 || !groupCfg.botAlias || e.isPrivate) return e
-
- let at = atBot(e)
- if (at) return e
- e = hasAlias(e, groupCfg)
- if (e) return e
-
- return false
-}
-
-function atBot(e) {
- for (const i of e.message) {
- if (i.type === 'at') {
- if (i.qq == e.self_id) return true
- }
- }
- return false
-}
-
-function hasAlias(e, groupCfg) {
- if (e.message[0].type === 'text') {
- let alias = groupCfg.botAlias
- if (!Array.isArray(alias)) {
- alias = [alias]
- }
- for (let name of alias) {
- if (e.message[0].text.startsWith(name)) {
- e.message[0].text = _.trimStart(e.message[0].text, name).trim()
- return e
- }
- }
- }
- return false
-}
-
-function toMsg(content) {
- const regex = /<@!(\d+)>||([^<]+)/g;
- let match;
- const result = [];
- while ((match = regex.exec(content)) !== null) {
- if (match[1]) {
- result.push({
- type: 'at',
- qq: match[1]
- });
- } else if (match[2]) {
- result.push({
- type: 'face',
- id: parseInt(match[2])
- });
- } else if (match[3]) {
- result.push({
- type: 'text',
- text: match[3]
- });
- }
- }
- return result;
-}
\ No newline at end of file
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/scale.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/scale.py
deleted file mode 100644
index 2c25622e939b6cc19e07c485a6910c1a5ff8da3c..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/scale.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import torch
-from torch import nn
-
-
-class Scale(nn.Module):
- def __init__(self, init_value=1.0):
- super(Scale, self).__init__()
- self.scale = nn.Parameter(torch.FloatTensor([init_value]))
-
- def forward(self, input):
- return input * self.scale
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/zoneinfo/rebuild.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/zoneinfo/rebuild.py
deleted file mode 100644
index 684c6586f091350c347f2b6150935f5214ffec27..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/zoneinfo/rebuild.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import logging
-import os
-import tempfile
-import shutil
-import json
-from subprocess import check_call, check_output
-from tarfile import TarFile
-
-from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
-
-
-def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
- """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
-
- filename is the timezone tarball from ``ftp.iana.org/tz``.
-
- """
- tmpdir = tempfile.mkdtemp()
- zonedir = os.path.join(tmpdir, "zoneinfo")
- moduledir = os.path.dirname(__file__)
- try:
- with TarFile.open(filename) as tf:
- for name in zonegroups:
- tf.extract(name, tmpdir)
- filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
-
- _run_zic(zonedir, filepaths)
-
- # write metadata file
- with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
- json.dump(metadata, f, indent=4, sort_keys=True)
- target = os.path.join(moduledir, ZONEFILENAME)
- with TarFile.open(target, "w:%s" % format) as tf:
- for entry in os.listdir(zonedir):
- entrypath = os.path.join(zonedir, entry)
- tf.add(entrypath, entry)
- finally:
- shutil.rmtree(tmpdir)
-
-
-def _run_zic(zonedir, filepaths):
- """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
-
- Recent versions of ``zic`` default to ``-b slim``, while older versions
- don't even have the ``-b`` option (but default to "fat" binaries). The
- current version of dateutil does not support Version 2+ TZif files, which
- causes problems when used in conjunction with "slim" binaries, so this
- function is used to ensure that we always get a "fat" binary.
- """
-
- try:
- help_text = check_output(["zic", "--help"])
- except OSError as e:
- _print_on_nosuchfile(e)
- raise
-
- if b"-b " in help_text:
- bloat_args = ["-b", "fat"]
- else:
- bloat_args = []
-
- check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
-
-
-def _print_on_nosuchfile(e):
- """Print helpful troubleshooting message
-
- e is an exception raised by subprocess.check_call()
-
- """
- if e.errno == 2:
- logging.error(
- "Could not find zic. Perhaps you need to install "
- "libc-bin or some other package that provides it, "
- "or it's not in your PATH?")
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/highlighted_text.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/highlighted_text.py
deleted file mode 100644
index 8d8c9b0d0b34e290f5f86052372587b9a6b7cc4f..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/highlighted_text.py
+++ /dev/null
@@ -1,205 +0,0 @@
-"""gr.HighlightedText() component."""
-
-from __future__ import annotations
-
-from typing import Callable, Literal
-
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import (
- JSONSerializable,
-)
-
-from gradio.components.base import IOComponent, _Keywords
-from gradio.deprecation import warn_style_method_deprecation
-from gradio.events import (
- Changeable,
- EventListenerMethod,
- Selectable,
-)
-
-set_documentation_group("component")
-
-
-@document()
-class HighlightedText(Changeable, Selectable, IOComponent, JSONSerializable):
- """
- Displays text that contains spans that are highlighted by category or numerical value.
- Preprocessing: this component does *not* accept input.
- Postprocessing: expects a {List[Tuple[str, float | str]]]} consisting of spans of text and their associated labels, or a {Dict} with two keys: (1) "text" whose value is the complete text, and "entities", which is a list of dictionaries, each of which have the keys: "entity" (consisting of the entity label), "start" (the character index where the label starts), and "end" (the character index where the label ends). Entities should not overlap.
-
- Demos: diff_texts, text_analysis
- Guides: named-entity-recognition
- """
-
- def __init__(
- self,
- value: list[tuple[str, str | float | None]] | dict | Callable | None = None,
- *,
- color_map: dict[str, str]
- | None = None, # Parameter moved to HighlightedText.style()
- show_legend: bool = False,
- combine_adjacent: bool = False,
- adjacent_separator: str = "",
- label: str | None = None,
- every: float | None = None,
- show_label: bool | None = None,
- container: bool = True,
- scale: int | None = None,
- min_width: int = 160,
- visible: bool = True,
- elem_id: str | None = None,
- elem_classes: list[str] | str | None = None,
- **kwargs,
- ):
- """
- Parameters:
- value: Default value to show. If callable, the function will be called whenever the app loads to set the initial value of the component.
- show_legend: whether to show span categories in a separate legend or inline.
- combine_adjacent: If True, will merge the labels of adjacent tokens belonging to the same category.
- adjacent_separator: Specifies the separator to be used between tokens if combine_adjacent is True.
- label: component name in interface.
- every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
- show_label: if True, will display label.
- container: If True, will place the component in a container - providing some extra padding around the border.
- scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
- min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
- visible: If False, component will be hidden.
- elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
- elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
- """
- self.color_map = color_map
- self.show_legend = show_legend
- self.combine_adjacent = combine_adjacent
- self.adjacent_separator = adjacent_separator
- self.select: EventListenerMethod
- """
- Event listener for when the user selects Highlighted text span.
- Uses event data gradio.SelectData to carry `value` referring to selected [text, label] tuple, and `index` to refer to span index.
- See EventData documentation on how to use this event data.
- """
- IOComponent.__init__(
- self,
- label=label,
- every=every,
- show_label=show_label,
- container=container,
- scale=scale,
- min_width=min_width,
- visible=visible,
- elem_id=elem_id,
- elem_classes=elem_classes,
- value=value,
- **kwargs,
- )
-
- def get_config(self):
- return {
- "color_map": self.color_map,
- "show_legend": self.show_legend,
- "value": self.value,
- "selectable": self.selectable,
- **IOComponent.get_config(self),
- }
-
- @staticmethod
- def update(
- value: list[tuple[str, str | float | None]]
- | dict
- | Literal[_Keywords.NO_VALUE]
- | None = _Keywords.NO_VALUE,
- color_map: dict[str, str] | None = None,
- show_legend: bool | None = None,
- label: str | None = None,
- show_label: bool | None = None,
- container: bool | None = None,
- scale: int | None = None,
- min_width: int | None = None,
- visible: bool | None = None,
- ):
- updated_config = {
- "color_map": color_map,
- "show_legend": show_legend,
- "label": label,
- "show_label": show_label,
- "container": container,
- "scale": scale,
- "min_width": min_width,
- "visible": visible,
- "value": value,
- "__type__": "update",
- }
- return updated_config
-
- def postprocess(
- self, y: list[tuple[str, str | float | None]] | dict | None
- ) -> list[tuple[str, str | float | None]] | None:
- """
- Parameters:
- y: List of (word, category) tuples
- Returns:
- List of (word, category) tuples
- """
- if y is None:
- return None
- if isinstance(y, dict):
- try:
- text = y["text"]
- entities = y["entities"]
- except KeyError as ke:
- raise ValueError(
- "Expected a dictionary with keys 'text' and 'entities' "
- "for the value of the HighlightedText component."
- ) from ke
- if len(entities) == 0:
- y = [(text, None)]
- else:
- list_format = []
- index = 0
- entities = sorted(entities, key=lambda x: x["start"])
- for entity in entities:
- list_format.append((text[index : entity["start"]], None))
- list_format.append(
- (text[entity["start"] : entity["end"]], entity["entity"])
- )
- index = entity["end"]
- list_format.append((text[index:], None))
- y = list_format
- if self.combine_adjacent:
- output = []
- running_text, running_category = None, None
- for text, category in y:
- if running_text is None:
- running_text = text
- running_category = category
- elif category == running_category:
- running_text += self.adjacent_separator + text
- elif not text:
- # Skip fully empty item, these get added in processing
- # of dictionaries.
- pass
- else:
- output.append((running_text, running_category))
- running_text = text
- running_category = category
- if running_text is not None:
- output.append((running_text, running_category))
- return output
- else:
- return y
-
- def style(
- self,
- *,
- color_map: dict[str, str] | None = None,
- container: bool | None = None,
- **kwargs,
- ):
- """
- This method is deprecated. Please set these arguments in the constructor instead.
- """
- warn_style_method_deprecation()
- if container is not None:
- self.container = container
- if color_map is not None:
- self.color_map = color_map
- return self
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/interpretation.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/interpretation.py
deleted file mode 100644
index 767ad641b99a51c08b4efadec350c7170bdc734b..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/interpretation.py
+++ /dev/null
@@ -1,328 +0,0 @@
-"""Contains classes and methods related to interpretation for components in Gradio."""
-
-from __future__ import annotations
-
-import copy
-import math
-from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any
-
-import numpy as np
-from gradio_client import utils as client_utils
-
-from gradio import components
-
-if TYPE_CHECKING: # Only import for type checking (is False at runtime).
- from gradio import Interface
-
-
-class Interpretable(ABC): # noqa: B024
- def __init__(self) -> None:
- self.set_interpret_parameters()
-
- def set_interpret_parameters(self): # noqa: B027
- """
- Set any parameters for interpretation. Properties can be set here to be
- used in get_interpretation_neighbors and get_interpretation_scores.
- """
- pass
-
- def get_interpretation_scores(
- self, x: Any, neighbors: list[Any] | None, scores: list[float], **kwargs
- ) -> list:
- """
- Arrange the output values from the neighbors into interpretation scores for the interface to render.
- Parameters:
- x: Input to interface
- neighbors: Neighboring values to input x used for interpretation.
- scores: Output value corresponding to each neighbor in neighbors
- Returns:
- Arrangement of interpretation scores for interfaces to render.
- """
- return scores
-
-
-class TokenInterpretable(Interpretable, ABC):
- @abstractmethod
- def tokenize(self, x: Any) -> tuple[list, list, None]:
- """
- Interprets an input data point x by splitting it into a list of tokens (e.g
- a string into words or an image into super-pixels).
- """
- return [], [], None
-
- @abstractmethod
- def get_masked_inputs(self, tokens: list, binary_mask_matrix: list[list]) -> list:
- return []
-
-
-class NeighborInterpretable(Interpretable, ABC):
- @abstractmethod
- def get_interpretation_neighbors(self, x: Any) -> tuple[list, dict]:
- """
- Generates values similar to input to be used to interpret the significance of the input in the final output.
- Parameters:
- x: Input to interface
- Returns: (neighbor_values, interpret_kwargs, interpret_by_removal)
- neighbor_values: Neighboring values to input x to compute for interpretation
- interpret_kwargs: Keyword arguments to be passed to get_interpretation_scores
- """
- return [], {}
-
-
-async def run_interpret(interface: Interface, raw_input: list):
- """
- Runs the interpretation command for the machine learning model. Handles both the "default" out-of-the-box
- interpretation for a certain set of UI component types, as well as the custom interpretation case.
- Parameters:
- raw_input: a list of raw inputs to apply the interpretation(s) on.
- """
- if isinstance(interface.interpretation, list): # Either "default" or "shap"
- processed_input = [
- input_component.preprocess(raw_input[i])
- for i, input_component in enumerate(interface.input_components)
- ]
- original_output = await interface.call_function(0, processed_input)
- original_output = original_output["prediction"]
-
- if len(interface.output_components) == 1:
- original_output = [original_output]
-
- scores, alternative_outputs = [], []
-
- for i, (x, interp) in enumerate(zip(raw_input, interface.interpretation)):
- if interp == "default":
- input_component = interface.input_components[i]
- neighbor_raw_input = list(raw_input)
- if isinstance(input_component, TokenInterpretable):
- tokens, neighbor_values, masks = input_component.tokenize(x)
- interface_scores = []
- alternative_output = []
- for neighbor_input in neighbor_values:
- neighbor_raw_input[i] = neighbor_input
- processed_neighbor_input = [
- input_component.preprocess(neighbor_raw_input[i])
- for i, input_component in enumerate(
- interface.input_components
- )
- ]
-
- neighbor_output = await interface.call_function(
- 0, processed_neighbor_input
- )
- neighbor_output = neighbor_output["prediction"]
- if len(interface.output_components) == 1:
- neighbor_output = [neighbor_output]
- processed_neighbor_output = [
- output_component.postprocess(neighbor_output[i])
- for i, output_component in enumerate(
- interface.output_components
- )
- ]
-
- alternative_output.append(processed_neighbor_output)
- interface_scores.append(
- quantify_difference_in_label(
- interface, original_output, neighbor_output
- )
- )
- alternative_outputs.append(alternative_output)
- scores.append(
- input_component.get_interpretation_scores(
- raw_input[i],
- neighbor_values,
- interface_scores,
- masks=masks,
- tokens=tokens,
- )
- )
- elif isinstance(input_component, NeighborInterpretable):
- (
- neighbor_values,
- interpret_kwargs,
- ) = input_component.get_interpretation_neighbors(
- x
- ) # type: ignore
- interface_scores = []
- alternative_output = []
- for neighbor_input in neighbor_values:
- neighbor_raw_input[i] = neighbor_input
- processed_neighbor_input = [
- input_component.preprocess(neighbor_raw_input[i])
- for i, input_component in enumerate(
- interface.input_components
- )
- ]
- neighbor_output = await interface.call_function(
- 0, processed_neighbor_input
- )
- neighbor_output = neighbor_output["prediction"]
- if len(interface.output_components) == 1:
- neighbor_output = [neighbor_output]
- processed_neighbor_output = [
- output_component.postprocess(neighbor_output[i])
- for i, output_component in enumerate(
- interface.output_components
- )
- ]
-
- alternative_output.append(processed_neighbor_output)
- interface_scores.append(
- quantify_difference_in_label(
- interface, original_output, neighbor_output
- )
- )
- alternative_outputs.append(alternative_output)
- interface_scores = [-score for score in interface_scores]
- scores.append(
- input_component.get_interpretation_scores(
- raw_input[i],
- neighbor_values,
- interface_scores,
- **interpret_kwargs,
- )
- )
- else:
- raise ValueError(
- f"Component {input_component} does not support interpretation"
- )
- elif interp == "shap" or interp == "shapley":
- try:
- import shap # type: ignore
- except (ImportError, ModuleNotFoundError) as err:
- raise ValueError(
- "The package `shap` is required for this interpretation method. Try: `pip install shap`"
- ) from err
- input_component = interface.input_components[i]
- if not isinstance(input_component, TokenInterpretable):
- raise ValueError(
- f"Input component {input_component} does not support `shap` interpretation"
- )
-
- tokens, _, masks = input_component.tokenize(x)
-
- # construct a masked version of the input
- def get_masked_prediction(binary_mask):
- assert isinstance(input_component, TokenInterpretable)
- masked_xs = input_component.get_masked_inputs(tokens, binary_mask)
- preds = []
- for masked_x in masked_xs:
- processed_masked_input = copy.deepcopy(processed_input)
- processed_masked_input[i] = input_component.preprocess(masked_x)
- new_output = client_utils.synchronize_async(
- interface.call_function, 0, processed_masked_input
- )
- new_output = new_output["prediction"]
- if len(interface.output_components) == 1:
- new_output = [new_output]
- pred = get_regression_or_classification_value(
- interface, original_output, new_output
- )
- preds.append(pred)
- return np.array(preds)
-
- num_total_segments = len(tokens)
- explainer = shap.KernelExplainer(
- get_masked_prediction, np.zeros((1, num_total_segments))
- )
- shap_values = explainer.shap_values(
- np.ones((1, num_total_segments)),
- nsamples=int(interface.num_shap * num_total_segments),
- silent=True,
- )
- assert shap_values is not None, "SHAP values could not be calculated"
- scores.append(
- input_component.get_interpretation_scores(
- raw_input[i],
- None,
- shap_values[0].tolist(),
- masks=masks,
- tokens=tokens,
- )
- )
- alternative_outputs.append([])
- elif interp is None:
- scores.append(None)
- alternative_outputs.append([])
- else:
- raise ValueError(f"Unknown interpretation method: {interp}")
- return scores, alternative_outputs
- elif interface.interpretation: # custom interpretation function
- processed_input = [
- input_component.preprocess(raw_input[i])
- for i, input_component in enumerate(interface.input_components)
- ]
- interpreter = interface.interpretation
- interpretation = interpreter(*processed_input)
- if len(raw_input) == 1:
- interpretation = [interpretation]
- return interpretation, []
- else:
- raise ValueError("No interpretation method specified.")
-
-
-def diff(original: Any, perturbed: Any) -> int | float:
- try: # try computing numerical difference
- score = float(original) - float(perturbed)
- except ValueError: # otherwise, look at strict difference in label
- score = int(original != perturbed)
- return score
-
-
-def quantify_difference_in_label(
- interface: Interface, original_output: list, perturbed_output: list
-) -> int | float:
- output_component = interface.output_components[0]
- post_original_output = output_component.postprocess(original_output[0])
- post_perturbed_output = output_component.postprocess(perturbed_output[0])
-
- if isinstance(output_component, components.Label):
- original_label = post_original_output["label"]
- perturbed_label = post_perturbed_output["label"]
-
- # Handle different return types of Label interface
- if "confidences" in post_original_output:
- original_confidence = original_output[0][original_label]
- perturbed_confidence = perturbed_output[0][original_label]
- score = original_confidence - perturbed_confidence
- else:
- score = diff(original_label, perturbed_label)
- return score
-
- elif isinstance(output_component, components.Number):
- score = diff(post_original_output, post_perturbed_output)
- return score
-
- else:
- raise ValueError(
- f"This interpretation method doesn't support the Output component: {output_component}"
- )
-
-
-def get_regression_or_classification_value(
- interface: Interface, original_output: list, perturbed_output: list
-) -> int | float:
- """Used to combine regression/classification for Shap interpretation method."""
- output_component = interface.output_components[0]
- post_original_output = output_component.postprocess(original_output[0])
- post_perturbed_output = output_component.postprocess(perturbed_output[0])
-
- if isinstance(output_component, components.Label):
- original_label = post_original_output["label"]
- perturbed_label = post_perturbed_output["label"]
-
- # Handle different return types of Label interface
- if "confidences" in post_original_output:
- if math.isnan(perturbed_output[0][original_label]):
- return 0
- return perturbed_output[0][original_label]
- else:
- score = diff(
- perturbed_label, original_label
- ) # Intentionally inverted order of arguments.
- return score
-
- else:
- raise ValueError(
- f"This interpretation method doesn't support the Output component: {output_component}"
- )
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio_client/media_data.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio_client/media_data.py
deleted file mode 100644
index ecbb7442a6c7c1a13f24418bea01e74aeee4d033..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio_client/media_data.py
+++ /dev/null
@@ -1,8655 +0,0 @@
-BASE64_IMAGE = ( # test/test_files/bus.png
- "data:image/png;base64,"
- "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"
-)
-BASE64_AUDIO = {
- "name": "test/test_files/audio_sample.wav",
- "data": "data:audio/wav;base64,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",
-}
-
-BASE64_AUDIO_DUPLICATE = {
- "name": "test/test_files/audio_sample.wav",
- "data": 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wEtA37/YgF+/gz+hQBaALAABwME/58AVQGT/kQA5P2s//z+yf+UAIH/hgBKAFX+FALh/3UAK/+O//v8cP4WAkAAkQIyAQsDbwFMAhv/c/2J/Vr+qv2BAWUAJQAyAOL/WwDL/OUBGP50/r8AzwCOAPsDDgIXAX7/WwBt/7j7X/+b/Ab/pf/pACgB5AL4AL3/KwCJACoAwP5v/8n/YABF/rQAn/8iAgYAAQKZAFj+6QCI/q/85P8jAQcB4QDTANoCr/3F/7b8r/wv/8P/kADhAa0CTAKlAGsBvwHk/TP/6/83/sj+Cv+X/9oB5P+GAgEACP+5AEP9uPvy/p//lQF8AfoCjgNP/woCov4F/ff9R/+8/rcA2AAFA9cAKwDIAP39zgD//q/+l/26/2L+wQAkAX0DAwIGABID0/6r/QL+m/19/z//wP+UBIX+xQHv/qz/1ADT/jMCB/9VAKsAz/43/xYCu/7AAN//lgCY/u7+ov36/NYAtgKeAekArwSP/3j/zP65/hb+Zv+S//P/6v9iArkAhf5xAIz/NgH1AAYA9v7W/zL/GADn/sYDZf8tAXoCnf3+/5b95P6A/xL+rQDnAQQDrgHy/qgB6P0W/5T+ov5z/4ECAQGeAKABawG7/zz/IAE1/Yj/AQEq/vX/NQFh/5gBIQD7ATb8lQCnAHL80//UANcAbAAEAkIA1v9j/wD/M/4iAZv+agF6ACsA0P9dAdUABQAEAZr/CwI4/hb9q/qT/zz+xf8UArUElQCZAO8CA/7K/+z9RP+k/r8CsgE9ANn/HwJr/ff+1P70AUf/Jv0CAaf8+AIa/9AAUgCjALr/IAAP/zICav9t/20AiP9qAWb+2AFT/Rz+vgDiAY/7fgA3Adz+9QDsAJ4C9v/uAUUAeP8gAKb9Hfw3/wT/QwEqAVoBiQGlAO0AwQBk/s7+Uf8P/noBnv8jAwMBB/4aAYv9N//JACn9zwL8/kcB9wJo/5EC6/4w/joBWQDFAAUAVvy6AKz9Xv5K/8D+YAICArH/AgRj/db/GP7//ZQC8P3YBZ8A7/+jALP/t/27/gL9vAAJAKQCAQEC/sQASv9R/vX+OAEA/3wDhP4mAgX9XwJw/6/+YQDW/gADK/4cAST+hP+6/UUDZgBr/z8AfQJC//MA7/8u/xH+P/76ATr8tgKG/tEAWgDOAu//m/9CAYv/5vzGAdcCMf8v/2wASwF//c4Ahvx0AFv9agLmACsAwAFEAjUA//6EAJD/PAAnARcCq/wTABIAA/1C/BsBnP10AlICegPz/wIAPAL4/N3/MQB2/REB5QFV/70A5PxpAwX+8/65ADgC8f4VAEX/xQF1AVn+6AEf/XwBxv5mAH4AE//k/YwC3P6eAG/9iP8XAwz/fgCvAvkBWABKAbP7AQGv+zoCWv9x/ywDa/2FACMB2PzzADUBAABmApn9HgNv/Jn+RAA+/bf/hQPk/jwDjAFE/0oBRPy1Af36b//AAggBeQAyAd7+6wFk/g7+ov8H/1sBZv5+AFoATwE8/m0CJf2VAen/jf87Auz8sP+U/6AA+v+bADQD9v/+/tcCgv1L/pL+Xf+X/WQBdf8FACMBMAGH/wD/qAIG/1H+7P+yARoBrwEW/xACMP8eASL+Ff7W/IX9UQHF/xwDkwNgAbEAuACn/cL+CABXAX/87ACUAesBxf5MAX//aP2ZAcf/6/9G/jkC/vwsAF0AswGK/00D4QBK/RAC+/2L/o398v6lAnsC7v/HAwf/RwGL/C4Be/5c/L4Asv/cAXYBvAA5/h8CY/4oAXH9XAHE/iL/YwAtAZL+2gJrAcT+VQMg/zYC/P04/+38ev9p/jX+mP2JA0ABXgBwAYf/CP8WAA3/3P8xANH/OgKc/Q4EcP7Z/pX/Ff/Q/d4Aov8WAZj/L/2wAQT/jwGD/x0BvgGH/1kANQJO/pv/i/0c/vcA+/6YAfsCJQGWAcT/JP8RAWf6RwAj/4f9YQJA/yYBkwAg/6sDjwDAANAAkfyfBKf9NP5CAeP9lv81AOb/PQI8/6z+DgCk/hgCWf5ZAG4BaADMAEgAP/7/AZb8qv83APT+tANT/6cBAQGT/1wAwwHl/AYAkwI3AL39pv2v/jX9Pf9i/6cBpwWCAw0DAQXDAKsBgP9T/UkCjP6b/hP+mf5A/0z5ifxmAEj7z/hr/mX5of6fBODxZwTiC/n7KgmSBAAKDQhb+3sKrgdg/Y4CiwEp/mz9oPzB+P/88ve/9OX9yvqZ+xH+Nv4GASgATQA0A0gC7QPoAVUEkgMWBK0BlwR/Az4CTwTAAdMARf+kBBr9KgDW/6QCoP/DANH/Yf5yAKb4e/zI+Vb4Dvvm+vz2cAOV/Cj7VQaJ/JQHgAgB+ikO5QUC/GgMxQOWBq8Fsfy/Clv/ge7vAhn5XfWI9FHxqQOC+GrxRgAOBFj+SgDCC84MkQhUCJEIOxAICGoBIAoeBjD/Iv+v/J39Evho9gL5rPVw/M33svZe+s36Zvqb+az+uPy7/k8AsgCQ/rgD8wNvAQcHagWmCOYEIATIBkEAcQK/AqkEvgGSA3QFLAEWAyL+oQC6+Xb9qP/D+Ir4Gf+/+Qn2lgBt+vD9PQC7/lEFEAR0//kI9QZyBogDwAPPCp8BgPVHAPMDlvIA9FP4Svy/9Ez0I/3r+2j7ePqBAFEEiQJ4BgoIkAyLC04Nqwz/Cw0JoQEqBfgBagAZ+1z9Hf0d+KD6Qvs19nv59vrk+B/6Wfrt/Bz4HP0d/b7/8ALY/jUDKASfA6kE2ADzA3ECNgE4B0gD1ASMBUIBNwLcB7r/kwFgBIL/oP/p/MT5oP7t+ivxu/2m/tf6BvqT/boDvv6i+gAJ0wfZAtMABQd5CjsD3v8YApsJkfqR/bj8KP8I9hbySvkW+v74s/Lx/Mf5UvvN/ywENAU1CVQJagoUEO0Lsgb3ByoI6QRmA/4CAgDT+jL8kfi5+lL3xft1+sb4QfsI+wH80/nM+2/9bf4y/BMErv2j/CwDsgMs/nAHywObAeQGJgLpBncBngMvB0ADRP+PBvgB5gAU/Wf+PgSBAhH6bfsWA074Avas+WH/rfki9o79xQTh/tT8/gS/COMDLQZMCe4JTgRM/s8Cx/4t/hH7yfs6/uv4mfWH9zv1V/Zp88/4kv7f/xoIugWpCX8LUQpHDVULDQnIClAFjwPBAiACKv8r/pX7N/+J/Zn2y/098wf1bPpn+DT6Mvtk/fX+//+i/WX/1ALO/fcBNQTT/5kDrQWKA5MCVgSnBnwFqPvDBMcGYAEa/7EEOAax/4T8hgDbA2z61PnQ+xwBtPeT9rH62v/5+BT5ggIGBR4EpgFgB8wGmwWMAwcGUAIFBXr/4QKs/V38n/ta94X2SPYR9+f1kvtb9Zj95/3QAK4CSQZNCLwLbQdJEugM+wPxDXgElgLKACYCVPxW/Sv6ZP1s+V35+/rz+Ln2lP2E/BL39/4y/AX+V/1WAisBEwHn+9D+QwXkAWz/2wTlB/sB+/7OBp0KowAHAPsFGgkvAJb9EAHlAWL7Y/o9AcoDBP9N+xz77/3D+Hj0bvyu+lv+Sv/bBXcD1ARmBOkF5QUQAzoGwQFEBb7+swDL/OX5APyW9371IvuC8x/5u/pu8cD/4P4t/90HwQVADVsO8AlNEHEIkQQiBG4EFv8fAjEBBQCq/Rb/yf3R+BT94vYz+iz2MPgHACT5F/WGAYUAUv8V++7/WAWK/OT/swK7BaQE2AHcBMQLpgAt/+cDywZzAcz94gckBf79nf07AqoAKf6k/E8BZf1k+6D5+Pcl+0r89/qk/TwE5P4zA/cBowEgB5cBPwYnB2ECJQhRA7b9v/6Z/kb77fho95n6H/bp87X5MPcw+5/7uwKZAlMDgAn9B/0JFQzjBzML8ws7Bi8G7AK1/5EAZP21+Cn+MPwh+vD0y/cYAUP2MfWkAI/+Sf5g94oBfwKg9xAAY/+VBg8Cx/47B2QGBAFB/yoCUAjlBKf92wU6BU7+TgN+/yoEgAAw/hwHDv+U/qf8CfuU+J/5KfnT+oL91vvZ+9gBwAAeA/0DqAMEBhMFDAfPAkkDeQAvCPUA5P4z/rL9+/uD9EL3sfXs9mz2evmD+Zv9+QN+BcYDCAsvCRoICwhVCpkISwKsCHMFSwVLAJoCRAKi+SD4DvmB/cb3mfV0/Kz/Sfzh+G0AE/0M+mb2ov7rAY797f9+AtkKY/4rAt8AoAXqBsv+uQQfBakB5wTPA6EE1gPN/y8Cmv9GAf77hACK+oD8xv3B/BH+uvsw+XT5kPkI/OD+jfxsAU8EVgmKAwYIMweyBmYB3gKx/gQBB/6B+6v/xfgU/gD27fly9S/18feL+GP7cwNNAOgDCwuID8cK7QeWDSELGwc0/gwHfwIEAov4bQGtAgT7Bfk0+s/9Fvai96b8kv10+UD8AfvZAM37qvp5/s0Fzv0dAJEE2wIIBo//twToA4UBDQJDBtICDQT9BOwDCP8HBNoBeQDl/wT+oAB6/F7///nb/nv4KPyP+Xf93P2N+UwANf/1AUYCYwcCB34HIQZ/BqkCOAH3/mb/U/6l/uj8P/zv+F745PXA72L6Hvzy+lT5GwKoDJMDkgC+C6sKTwbNBUQHUAyNBRcBBgUcBP3/Afyr/OH/3PiK89n9bf3297f4Xf3g/or74fsP+/D/Q/46/T3/UARk/0YB/QPEAJwEGgAvBvkDcADRBMkDvgG4ALcCBAV4AAgHwAL3AIf/TQD+/S751/r/9S7/RPY9/0P8Sfqu/Rj+zgCiABkFpQbuBQIGkAiLAzUItQFbAwwBNABW+9n/6vbo72H1Avr890ryTPsvAmsAp/u9BucHqwrWBEEKrQwxDCsD8whkB64BaQHK/7gBnvgd/FH3ngDf+JH4B/9p/ej5z/vp+637tPv1/PgBuv1m/yn+gAGP/vcAyQBpBaIAZgX4BYEBzQY9AYgE6wBCAfsEqAK1AZoCmP/fAzv9Wf29/Lz69fxD+4z79/pb+rf60fs//Ff9IwLpAm0ClwmZCOEFKQYhCE3/Y//SAQ8DFv7X+937C/7H+q3yy/aV+pP2j/EW/soFhQEKAgAJgwgpC/gFbAeNDGIGIwWnBNIHqwGV/ev97/0//mz6c/12/Qj5tPo8/A77o/iA/Db/1vfZ/rEA9/jx/LAD7P9lANgCLgX9BDr+0AOkADkE4gBTABsJ/QOVBeIETQOUA7P/mv+C//n/YAEoAej97vc9/Xz3BfgL92n4Z/0T+wsAqAIsCOQCSQblCbYECgKOBn4DBwKk/YYATwLv/Xv4Evow/CDzl/Mh9DD/tfUa/RIDGwFTBh0E2wc+CdEIjwnqBNcLKQbLAC4Fqv3jABUAqANX+/z/nPwd+Wf4cvZf/mv5evgJ/kj/IABC/pAAUv58/CcABv4oANf79AFyAxoEFQLKBScHXwR4AYQDjwSuAvACJwOp//IDSAZ7/CADvf7yAp74JPpH/Cf1YfuM9M35lwJp/7f9MQW3Bm4BKv7cA7oHPQPNAU8IVwQQBTP+JwA//yb6Zfob9aD7+/ON9/z3Cfsz/G798gWfBlcEWQkqBs4KZwesBLMIggE+BoMAlwTMAO8C+P4n/PD7Kvue++T31/qn+xQAtPx4/a8B5P2d+6H65/2f/xX5GwObAXr98gP9/7IBYQJfBUABvgI9BNkDsQTb/wwHKwPJAlABqQPZBz7+zAAr/3D6DP4p8qH3ofuj9qn3kv7OAjkA9ABCA9UJkP8wBu4DmQPuB639ZQXpA9kBi/6u+yv+H/UO9c35jPIg+Tj7gfsH/zf/pAfZAWgIkAasCsYDywnXBqYDGwl0AJwH7wBAApD6B/1N/qH5Qfe8/+b4b/yW/T7/3PwB/FT/Ifu8/jv6fQEm+7MC/f7jAfIBYgF8BF370AHNAoj+hQTHANIDlgn0A4kG7wFkB2gBaP4iBQgAcf7C/IT8lvts+2r2efso/cz5JPyO/iQGHP4YAL4E5gEcBlEDjAJmBdAFUwOsAkwAF/y2/EX4cfgX+VD2wPqc+Bf42/5n/4UDFf+GCJsESAJeDXoGQwb6BB0KXggmBdf/DAMU/b/9//pK+0z99Psy/U/5wf6q/xT/3/eO/zb5gP5g/Mv8Zf5y/vsAogFPBGn/cAMQ/McFdv6o/4kEYAXPA4IBlgWSCu8AUAKhB+L+UwS4+yoAdP1A/wX7R/tp+6/+j/Xi+wEAgPY9AJ0AOQFOAhAELABsBxMF0wq8AJQJaAQG/ocAgfhn+UP6gfqt95v8mvTg/WP3vf60/Q/7lASuBGsJewn6BhEM6QfE//gJpwNSAD0AKQIC/SsDMwH5/Xv8jvzt/aT3gvwB+U34AfyX+LD/pQNy+ysDvvuiAOf6Vf/O/nr9YAOdAOMC2waAB3AAUQYa/5AC6//gBPMAmwJVArAEBQS0A6wAlvzu/dP8cvuu9xv7hfef/Vz40v7B/BQEGgEbBVYGMwnjBOoBigOHAnQC9/l6BUL/Nf4R+9b+U/aI+Gv2Ivvc9gH9tvvj+5wHzQJ9BMAGIQqWAgsK6wTaCckC9QRh/+sAEAHZ/Vn+gQCd/Yj7MwE0+zkBBPYP+yD9Gv96+uX6NwCjAbD46/0hAtj8dwJg/Un8DAQ0BxT+GAh3ANQDMQA7Bl4Gmv4SCNoB7wImAoECigRKBwz7RQFy/av8lPbd9jH58fRi+37+7vsv/EoFU/xTBs0E7QKyBwkHMgOKBtoDeQbr/WkB0P4m92X8y/Sj9p/zffiG+Bf/mPz6BLP/KATOBRsEfgRCBW0IqAfwBlUHigvS/7kCH/9CARv8Wf3Y+jr+Zvq4/MD5/v6t/v/3lgLh/Oz/+/fg/mX5K/0J/SMDVwExAyIEsgLbA6/9jALI/B4DygDIBaMDxgU4BYwDhgSyBjoC5wW5/9H6yPvE/DP6QvRW/T/9L/nR/ukCS/lYAtr6DgHF/9kH9gMKA1YJsgR8BskEhgac+cL9SP0T+lj7yPed9kH7UvYZ++j5BQJMADr/QQPMBJAIrgdbAwAFRhBmAEADgwWjBMn/Rv7xAQz9zvul/931IfzB/uj12gAz/Tr/d/tg/6X/uPuN+cX/cfxd/kUBOf4KA4/+1gGyB6wAFwQoBEr+nwWe/FwHg/4vBvQGegJcBuIGuAAx/8UAFvgd/9j8g/dQ9V382/gU/HT6CgOk/F8HmwOaArEDIQK2BnMChQmrAQQH6f3/A4v6JP1792X3sPqS8oj77/qS/s/8BQCa/GQE8wGfAUsEywqQBSMFegp7B78GYAJ6BGn+PAIeAJ/8WgBD+wH52/+O/DH/jfku/Wz79fy/+vP7yvyf/kECav3tBDr7QAaeAOz/KvwxASsCqP7kA4IEwP6QBV4GXAA+BcYCXgQK/VQGuP7kAsf9Zf43+aT9x/63+F34Rvw9/F/7+gIq/AADXP3MCMX/oQbYAKMGgATyA2wG+gHaAfv8sgN88Wb8q/kD+Z3ywPv/98r9CPymAGkCUQR5CLUCfAwGBXwLfQMsCbgAlARw9+cD8/+2+oj/4QJUBR/5NgEH+bL+4/iD/hb5Cf8BBPf6afntAMP3zAD4AVr87ACAA3MDqPutBiAEvAMWA5IFKfw/CHoBr/5ZAYACOgRVCFsE/QGcAir6AgP182z+E/Sv+pf8wfqK+gwATf+vAA0A1f+cBzr+iwmS/JkG5Ae1BwEFSwKe+WcEkve8+2T3lvMj/Er4cfuv9jIFS/lqAwAAQAgjAwAHW/+rBbkB2Ab/BDIF7wicAZMKhfqCBUT3X/2o+mf7mfreAvX3ZwLO/pj9pPw5+5MAlfiTA8T2EAWL+m8FJ/2bBTf//AAJAikA1/6cAa8D4f/UBzUAnAvBAJ0NFvvqAzwFsv6L/xUEN/WEAMT90fOz+4j2c/4a9ycGaf3zBCH9DAhz/ZwEN//gAeYIXQOIBFgCVwbh/QP/T/T9/4zzGQD78HP8UPvA9pQAoP7y/+kE2QZiBMUJNQL5DAABcAsLAM0D1AWaAl36CgYs92r8oABI9XwDzPc1A338eP8T+I0BMfkRBRT4BgADAO/5zgO/+1H/xwGKAGj/Cwic9mQP9PWeB1kB3fy4Cb3/TgIPABUE0wIuA/IBLgmB+CcMCfcu+aj8x/hw+O77Y/tC/j4CJftQBH76LgVoApUE7ATHAp4HpwnE/yYFdQGj/8b/k/jB9O/1VvdZ92f4J/0VAO78qAfq/QkCEQX5BSQErwchBFkKKgZwCOUAGwFCBRf8IwNR9YMBsPW8/v326/66+wH7gAEz+3H/dfwPAzr9GP17AGcGePvpBpD8bAHH/FoFk/yCAAADovzpA6MB3wMr/KoHxQJ2A0sAvguE/kgLtvltAxb90ft4/wXzuP4z+Zf83ftNAtH3dAhl9g8MKf6RAyQFdv5tAZoBgwQqB10GvvwgCLDwFwbt7qv5B/iz9WT7Uv49/YkCFgA8BH8M8PwrB08AgwkmAKsHzAKDDxv9ugjR/6f8wQHk9N0B6/ln/OX8v/1j+pAFgfPgCwH4NgDd/qP6VP4q9rP73gHSAWf79Qdc/oILAfvxBEX5swPXApf8r/4CDJ//2QFOCXIASgar/sEFM/w1Ac78+gFb9FwCWPmZ/fL+4vtN+RIAkAB9/iD68AHvCLn5fxAvAnkKNf/8BOf+G/vW+vb+EvK3AEv/9fi4AZD19wCZ8/MCVvvHAdABmAkCAQgKjgVTCSoB4ALXCrf+wwXbAdYA5vrTBZj0Ewfs9S/+kPvA+hb9yfwp+0X9Bv3V/vADePsGDMT1IwrN+1YCUf7L/pr8/ACU+mgAGQUg/dQMUvfWCjMEYgJBAJEIcAH0CQH8Kgey/H36HgF+9X8B//YW/0b6Iv569XkGQ/ZABi7+4QHoCScD1gLPB1gDrwDMAH79pwTg88AFE/WqAdr1+/0o/Wf7ofiv/msBhADxBgz9mQcAAmAEOgGNCTsC9gc1AswLOQFaAM4CpPiP/HH7GvlI/n78/fsuAJL8OQf6+CoCzv1EAsz9j/0W/HX7yP3s+iwBiP2bA24B8ASOAdUBQv4CCeD9qgFuA/EGsALQAh8J9AQ5BZr8IwEt/CQBDPu6/rb3EQDZ+Hj/y/kp/b75cQEM/q39EwMB/hIIiPwYC8L8gAh+AagHN/0TA+j3Jfxe89/1GfvG+TH+KvkTBaT8rQzp+owF4v0hCAEDrwWAB3wIj/8bBdr8mwNwBfP4ngYk+Y4HOPT7BEj5o/4S+vb8u/0P/6f/dPmL/+77HgDy9y4C9v0gAlb4GQ7T9WkIEAbcA0IApPwYBaD5BAHu++kELQLQDYH6txIS/bwEsAES/d35Iv5o+Ab7qP5f958AOfaCCzP4IQph/PADtQCzBGT8tQcKA9UA/go4/vMEzPkrAary/vzu9R3/yPTNAov0l/5bA4H9dgSu/AgOyP+kB3UB/wTCAR4JmvptC4kBiAsf/zj6yAFu8/L/6fKV+oD87QIl+3gEMPrQB2z1SATz+Y78/AV2+VMBC//3/hoCAgULAdUIBv0HCPTwzQd7+F0Ba/9CBLcGTgNmCngBVAajACwBRfXkCAr9L//F+mABg/l6Arv6QvvK/M7/L/tT+0EEevwVDCQErgjtALUIIwCd/y/4jQH59wz56/629y3//fxV/Xz83/o8/R8GZ/rZCMf8lgn5CNkGtgjXArIGzgW//aYBsAHk+dwE6PmC+x4BzPyT/08DzvEQB5n2bgFp/nXzaAKD/PgC+v9Q/XQCrwMb/8sEiPf3BGv8gfx5/R//yPpfDH0GJv6GCL3+hgrt+NEAQgGL/GEI6f9V+L0L3vvN/zEBPvt4Afv1qP2N95H7Nv+SBSECugtSAnEFQAei/OUBgPq2AEIDvPxS++z7X/pw/hP08P9Y+o37kQBD+zgCAP/R/zMNSgX5BUIMugL4BVj+IAFI/40Ep/90/EsAhP/p/Uj9+//m+08BqP8o/wj96Pz3+6f+1vzD+9/9XAO+ABH5SQQ+/g8GIgLq/iUDWv/CAKX+NQA//ToBkgULBAsDBwgEBkD+JwJS/Xb/vgCN+Tj8k//8/tYBy/ej/aADJvW2BWz9MwSaA+sDu/60AvwCOv2dBaX+uwSl+0j/s/vi+R/++vhi+Av+SPiwBbH3wQBDBMj76QjX/QsHwASXBLAIGQLn/ZYGWv76ArYCxwLLAmT+pwOhBPn6B/wGAHbtX/7/92787v7gAMsEdv2RApAGtP7c/moD7/iEB6L5/v7I/VH6hQPYAKQBqAU9A/n/5v24AN0Azv2HApQI6QcBBjcHcvoKAPD7A/sm+d38FPt0APsBBP45ABwGV/rf/ogIGfwkDvv8Uf+sAZX/cwRg/ET5NgEW+VsARPzN9YX/IfiX/iT4tQYz+RgFp/mFB3QCYwiDCMoDvBGZ+1ULiP5M/2L6RwSr9QwIlfZpBXf6XvsDAIr4Q/6SAMwA0vqACwLw4gUY+m0FI/cqBW8Efv5vADYGoPcGBu380/4+BH32GQ4B+RUB+f5TBIf4dxCB+s0KSAJtAIkEhAID/4QDewA4/qIBt+35CUv1ugIR9lgHDvzyBEz/eQAWA1ACCQTp/i4KOPtJAsv9Bv/k8YAAz/TC/zzyrgCh+g4AcgUw/rP93wnCAfv+fwnV924NcfyYDsr+TQ/MA1UADAAgAHX1oQJj+HX7wP8F9dgL9PdMA436ZgBm+aMFl++/CDr2UgGCBTT+dAViAqoEKfzEAiTzswd49QcGSPczCAEFkQKH/x8EigDABMgE5P6/AnP/jAc88bsIg/cKA038lQI4/PIDlvnPAib//flABtr+GAsq/BAFNgIDAU769QcN7hQJlPqn/kL/k/cy+BX8Pv7U+Wb/Cv+bC6z0ngwN/EwGkAkmBgT8tQ/P+gwJBv0M/z8E5veNEODzDAYl++oCHPt8AkT0v/8O+TADpPht/WUBSPorA178Nv7J/egHg/JJD/70DwiOAZ4F2gAZ+s8C2gFQ/QUBrP6DAB8L7fm/CVH52wuW+fEJKfsBAkr5UQRJ/Br80QVn96AOPvO2AZr79gIJ/O8DKfmNDP//Zwh3AYz7AQJk++wBp/Sa//zxbwXd7wsBrPhMBdr/f/73A5z77Ait/v0EV/8VCuwAvxNB/uQItwOGAdj4gQB0+T72vgm09VQJAvKrCNj8Of3B+fEEBPk1AyT+EwCy/fD2pAd5+nME5PoTCFf71gpS7pcIk/QZB/T6oPtqDCr5kgv+Ah8HhvyYCJz6OQr38BoHTf9qARwGav13/kcGXfvu/XH52PhGBZjyQwyH+RoFUAQWBhH+IQmw9TQJbfuQ+NQCqvPWA+zyLAAo+hn9SwCTBrnvqAlK/h0B7wBY/y8CWQpyCmAFdwXW/7AHtfYAChX2OgAtBbIALfr5BtL9hf8/900F6PvB/B8Fuvg3AIX54wHV+IsDNPw1A938LQV+ACEBqPmDCKj0rgI2BI/6eQRSAV8ItPWYBWj+MwaA++YF4QN4COQAJgP5+uIAO/1A+7f/FPm8A4/9bQru9ykJA/jpBrr7j/+YBAMBmQRR9ggIMfgo/ssAQ/WI+3ABUfXsCp3sOQh4/Kn4Lgaj/1AEfv6YCpMCkAQQ/XcU/PoNCk37cgRvBrP+ZvlV/FQEGvTyAVn8iwGc9xcC6/56Arf7egWr/ef+Bf2r++QASP5wA6j/ZARLA/r84PjdCIPzxAKQ/T79gwDpAdwGH/iMA7IFzPzXBYEIT/9QCHr7wAS+/K/+T//B/PgCX/18AtgHX/kT/F4FV/nIBsQAKPdOCA//2vhJBs3+lQNC+WcFiwH08mH/7fcg8az3zP3e/PcFdAUCCkEFVAOzAAX9XQeuAsf/YApyBy8Dngl198MDe/cQ+Z8A9Piy/Q8AIQWa+UUB7v/fAcf5xghO+OX7HAB8A+j3ff7ZA3wDBwWDAZcHCvFaCWTyVPpCAekDQ/rWBhgAbv/OAV3/owhS+SoHMwMHA6IAaQCC+3gGjPz9Ba0A4/fCBan8uPYfAzv8xQsQAZn8GQpW99UOjvlD+RsDIft3+kv8Q/q4//b2sf6VADj83gVg/VQCMv1mA8n8uwQ3/f0OMQE0AvEKS/1aBDsAUgGFAB4FLf2tAEv1ywUU/Sj7of1XAzT9Zf5L/WP3kvz3/7sCt/hwCov/gP1VA4j5eQDoAMT/fAYC99gL5/sd/hD8TfzUABH/PgcSABkILwCNAbv7ZAdW/nwBbAEaBdgAdQPw+FX/yf1Q+agG7/sKCvAAvQIg/BH/4QG7/rn6BAnY+7kBLAUr9Gf62fmR+nj6FQIi/+ECnfXIB4z20PwxB2L+KAW4BMQEKQY1CMEABAYK+u4LL/+f/9kBpvaQBRn98PYXBBH54wRK/qP1GQd3+ur96AB7AEX3LgcY/a39SAOa//4Aiv1cACH/O/tZA0oCfvokBiP4/Ahw/WT/rgGA/nAF+gYQ/wMAPQPQ/0YFMPdZB6b8U/8K/JP6x/7YBhr57gaL/6EDdwRf+z8GEvncCP358AT1+0kBk/wa/n/5pfqF/HD25wPH/db5xwYJ9+3/HwTW/HII5v2pDuz+bQnzBQYC6ASLA7f76gSGA4799v22+ygCv/ex/378nABy/RQDbvo3A2f6dvxNAbv7NwPy+xgEQ/+i/h4AqgCT/rQBHPKsAbD+MPyBBBsClwGaAQUBpwFQBcH5CQZs+o4JwwO4AK4KZ/ujAEz8C/fiAgn9WgGxAZEDpv1cBF4BqP6wAmP9Mgyq+MsHSvjQ/nT6q/rZ+RD8vfzg/Yr2rPqyA071FwfQ/oIGFwMLBf8AcglY+GsMS/+S/wQF+/6Y/YQDOQEpAYYA3/0G/xj4Jwgk7U0OtfpgCyX+gAAjBYwA5/d+/hcAG/eKC6f44wHw/iAGp/MtACoCkAIW+p8BH/X9/ez82QAvAEP9dAsXBIcIKwWDAzr7igg5+bUPTPVCBDoASvpEA3D+xPvvA7AFtfscCan3wg8Q6ygKt/g3/D4A1/cM9tYATP0I+5sGUOzlEOvzsgQj/5D8xvYPDXz7cwYo/gsKqgqL9H8MXfibByH9WwZK9xYNq//nAgX46AXD/Oz2HQYc/Mr+BwVL/yEFAAk59IUO5feeCZ/3CAMGALX6EvpR/xv5l/nTAIj0VQTq+k8AMf69AYL0LArD80kLk/5mAS8JIPzSDmL8awffANIDDwA7BeD6LAjd/04EfgD5/t8C4f4Q/M0CyP1f/UQMXfRAA9X1PwVF9D3+1/2x+7v9Afyo/pn4ZAT2+P8EeP5gArn+qQjP/H3+zAAhD8r13Qo3Al369AiM9wwA+f23BbT2Qwnf+pQJtPe9B6L9FP7g/1AFv/4OBkwBRfnOCHP4Kgsx8NwGVvWPBPT1RwCT/zsFxvk9/VH+2vWI/8b1IAAM+asGOAKbDSz/PA4w92YTP/m0/1YBVgBSCMP4NgViBCYB1wMnAyT4hBHZ718I9/hXAkX9/AGt+aIGWvzk/TD9NPJxBKTu8Pvm+JMBJ/EfDqT0ZxBZ+lQGwAf+ALEGJ/49AiADigRD+bAM1PSREXfu6wtC81L8wvxqAD38Xv88BIwDow9W+wER7PPgBvr8TQA0918HYvakBJX0BwZ+/mf85AMk9FAAp/3//1Hx0AXf8NUI5PQUDxb+FASwCVYDKv1XDLUAdvzkDN/2agwG+SAIJf31Aa39ZwbK9YIPcvNVBvkB9fziCbv8/Qiq+FEDafMKBvnpcQNh9Hz8Yvfw/QwEDP5/+/QE0P4xAGoGsfcYDvn09RCM+SoIJwXKBaj+pAJU/Jn5bP4N+VMB8ffYBqX/OQSuBM0JyPMTEE33mgE+Avf6rwtS9jYG6/5R/J4JxfkD+4IB5/Sq+0r3g/5t9aQDhPTWBvf4af2BBTH85gTq/G4JkgXbA8gJYAOA/P4NKvvzCEf2cA7EAXX+GwUB+4ECf/7V/0j9Zghk8z4FnPYZAMz9rPQ7B8v7PvxrAaH01Qg591/4+QK59wkEhwD6AKMCRwph/ZEFUP2UBZoCgPwJB3j+yP4EBRb9zgAm9b0BygIY++sKu/sXBkMC5wPv/64D4wPKAjf+d/uFAQ/69QB4/wP3Awgf9gIHjQC88PsMK/JGARD3//h6AkH0EwKm/04CtATLAPQERP88BHoE/AIJAz8B+QjCAKAJjABF/yoEa/78Aer9bAWv+XADUgPW+g4HdfxD/5j7f/dv/jf2Kf8x9MD87PqX+X8DdfoqBnX7LglBAcQBLggFAy8A0gT+AIEFzgD3AhABwfitBJ700QA9/HAB5fnbCeAA9QHQBiAAcAaH+6UFIfo+Bsz8KARr/+UCDQSh+Zj+4/gZ/CH9+/hm+YQA8/sd/nT5NgRz9ogDov0v/6wJu/nX/T8FSQaR/sAFbgZhBQb7uwrH+B3+mwsd+eIBEBLd+toGUgJb/yME/PVlDa/w0gFwBgD0BABAAEnxR/vX/Sr8mfomAI/+DgGf+9j+WQAF/ssGg/ogCZr+TwVaAl8DSwS2Cdr9iAVS+7z8Rfuh9Uf+pvqPCBIEngS7/94K7PWmCND5IgLeAO/7agcB/1ACiAUU/LsEw/xY+qEDLvJTA9ztsQQD9iD+2/uzBMMAB/6f+cn8GAZX+lsDiv6rD/wBRBAoBVEH+gMr/9L6gQdQ/ScCT/n0/88GOfvvCGT8lAei+w8EmP8n+1X2z/iM9Uz7KPcHAYP7Rv/8AhP44wQc+mAFsPpOAgMD0QQXBKUKbgGQAxYHFv6O/cL6LQAs9OoDUwLyAd79IwfN+18Gsv/EAIcD5QKuBPD6uQJx/+8AQvckCQr7HgWo+0L/E/t+/Qz6rQFA9aj/lAHo/R8GBu/+CDvz9gLG/eIA0/ujDX/7sgTN/1UE/wPs+O4Nkf2BBf8EoAeu+oMNff0JDi/6jQ2v/Ez7UgPk9SAAffiWAc34UwAv+OMAh/EFA5X2FgGe8LoIZ/jPAZgBEfkwD+7yoxEf+ggGXwPIBzP2+wqw+gkHTfrFBOoCTfSCC2H8S/1a/KIJEftJC6jzWBIa8WcMMgA3+IYJHv1bBJj4dgHO/YX76P8CBvTukAggAE8A//NbBCzvd/03/nf03gPg+9MLo/ylAW4MSwL//9gDvv/YBc37AgaoAGcBAgdJB9wAUgj6/KgA5QHa/VwK+fRnA4ECuvj8Adb3Y/gx+ZX2dgMx9EL/aAKf9pQCJP/P+5gI3f8uBHoGwvz3CiL+8wETBUH4fwSzBmH3yASv+mz+dwLl+F4AwALqAkT+Uwfg+6gEFgFV/+YGIvyUDXj4mQB5ByjxBglW9RP9yv1J9qAHUPqp/moB2fY4AGX7T/k8AfL7aABH/ZsG5AXa/0L/yQFMBNkALQIjBWMDEQIyBPUJYwPq+0UEVgJQ//4MB/s1BHAD4vjHBHDwaQu/82b5yQMy8FIGUPdL+K/4fv9hBFX6KfmBAmX6K/+uBEX9JwUaBoUErwYuBVH+nwnD+goAkPl4AR7+1/+U/zf1jg79/5cLY/j5BSoFrfuNBIT8gwobAWwAoQTr+LgFs/1Q7/sCcPj0AJ72QQC9/Qr4NgOE+NYFt/hRABEDm/xzAH/9DQLyAyT/PwQVAiP/wwUdAtb/DAFnATn/owdP/1UJe/pSDFEGUPt/Ezz4GAVj/BX7nP+P9W0CB/ti8ogAp/OF/yr3e/hJ/5z4egiV/uQEswHyAaEEmPpMCMEDwQKBAlL/Ygc78fwCQwVN9sr+KAXbAMgEyPwOAuQB5fnZDOv3mQrbCSP7yAlg94MHpvVc+LIIr/izAcb+pgKMAY75vfz3/Lf30foJ/bj5k/vBBcvytARRBOcE4AMVAFoJzfnMAVYADwC7ABsDtgRCCB4CgwtoAmn/cAX+/0AAMgWiAdP9JQIF+oEDHPYZAaD6x/VCAWvwUQG88hcAIgFp+8oC5v63BKD9pgKI/a8ErvxHBx4DgwGnBF4EN/w3BQH8ff86ASj8qQdr9JgLz/mwCI74EgOvAtn+cwaw/o4GK/1eAZEJ8/diAAYDjfX4CInqdBIE87YBjwFu9A0H2/Y1/3b3M/nF+SgAH/nQCt787Qa3/68E7wQG/sQA8gSv/dYBsgsQ+ogLtABkBxwDSAcLBXADVP4LBDcA6vPZBQr1NwLV+zn8IwKt9Kz88PzO7QsHa/wz/r0HqPi/Cn35yASb/7MBuv0cDPsCYQMiAD75GwVk830GfflZ/3MI3wFH+MkH0/xpBT37ZQadBgv8DAlO+7gDCPyTBrr3awvc+AMDDP+n+gcF0/fj/Mn7cwFM//787fTeA0/z3wLn9HX/uQSb/dwDcf1QAMsEDAKL/oAJO/vBB9cFuf5D/1EDZAEBBs7+qQof/hgNAwO4/dcDm/zUBw/4Gv+m9nX9wvbl9RT22//D/HwCPfnF/7/7oQJXA6D5ywdRAUIHMgA+Ayf9FwQBBi39M/6YAxX97ACJ/Zb73QAsAaMF2v/8AnADgwMpAj//SvyNB2UBl/tMBGT8ggVD+4MHQPzC/2gDCv1p+ov9Zv9x85cF/PJt+p4BCP1n/eb8x/ypCiXzgAqT/xX7jAhq+tYFN/tACMAA3QL8BDAK+P6LBuIE6ATBBL8DegTMBOT6WQbx/ED1UQS07z3/cvdE/Ib76fppAfj4jfdMSVNUYgAAAElORk9JTkFNEAAAAEltcGFjdCBNb2RlcmF0bwBJUFJEFgAAAFlvdVR1YmUgQXVkaW8gTGlicmFyeQBJQVJUDgAAAEtldmluIE1hY0xlb2QASUdOUgoAAABDaW5lbWF0aWMAaWQzIHAAAABJRDMDAAAAAABmVElUMgAAABAAAABJbXBhY3QgTW9kZXJhdG9UQUxCAAAAFgAAAFlvdVR1YmUgQXVkaW8gTGlicmFyeVRQRTEAAAAOAAAAS2V2aW4gTWFjTGVvZFRDT04AAAAKAAAAQ2luZW1hdGlj",
-}
-BASE64_VIDEO = {
- "is_file": True,
- "name": "test/test_files/video_sample.mp4",
- "data": 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lpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8AAAC4gYF///e3EXpvebZSLeWLNgg2SPu73gyNjQgLSBjb3JlIDE0NiByMTFNIDEyMTM5NmMgLSBILjI2NC9NUEVHLTQgQVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE1IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcveDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MCByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9MHgxOjB4MTExIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVmPTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0wIGNxbT0wIGRlYWR6b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9NDggbG9va2FoZWFkX3RocmVhZHM9MiBzbGljZWRfdGhyZWFkcz0wIG5yPTAgZGVjaW1hdGU9MSBpbnRlcmxhY2VkPTAgYmx1cmF5X2NvbXBhdD0wIHN0aXRjaGFibGU9MSBjb25zdHJhaW5lZF9pbnRyYT0wIGJmcmFtZXM9MCB3ZWlnaHRwPTAga2V5aW50PWluZmluaXRlIGtleWludF9taW49MzAgc2NlbmVjdXQ9NDAgaW50cmFfcmVmcmVzaD0wIHJjX2xvb2thaGVhZD00MCByYz0ycGFzcyBtYnRyZWU9MSBiaXRyYXRlPTMwMCByYXRldG9sPTEuMCBxY29tcD0wLjYwIHFwbWluPTUgcXBtYXg9NjkgcXBzdGVwPTQgY3BseGJsdXI9MjAuMCBxYmx1cj0wLjUgdmJ2X21heHJhdGU9MzMwIHZidl9idWZzaXplPTM2MCBuYWxfaHJkPW5vbmUgZmlsbGVyPTAgaXBfcmF0aW89MS40MCBhcT0xOjEuMDAAgAAAMsJliIQFfJigADijJycnJycnJycnJycnJycnJycnJycnJycnJycnJydddddddddddf//8FxOAAmKZxB5GdbBJ0I/qo/+Ee5/93d4oOmgATyCOPs0YQeSU9gHogQgiKkeTMGgzhtmA3WzCcX9v9GB1FRV6izBeETEN8RUn4Je+68aKjADOf3ubYk08AHEtZSwC2H7GiIqbM8cRd43GpcARMxEOpH4KRIvGRP52KgM7jxi/EBunL+Pb8Ix+/7jerkCz/QtRtUideSfnaYLRJSz3lB1RvwBgazm58BcNnMliUz/zW1WZSYFyQG41SL6ow45c4iU6r7FJFPdK8xe6yyxBmrVixHdQkyeS9T4AwgVDLo7LoTzET0SdQjjirUv+BAXdSd8IboCpR3Im+IIKrnmRguh/9L8WA1irxxWN0JvUNIu8nNqd/b9ddBcVcsuC9IeBMTymfewA8LtG7q2wAa+IwbQA9k65iZLgPob2eFnnDBcagqMpt2I7/1VZ1Vh27BryvRZp0fhRWMBxiA3eVGMJY8H/No5i//gMZ5poHv9ddddddddddddddddddddf/+Tk8IDuABDKTM9BI7pwAHwESgL/56gBTQGTkZfwAHUghT26wGNHy5ieDNIBFU+qSAeyFMKNEmAb0DvqGnHGb+jFMYIAT3YDOggSMfG+GPCScBAvSHHWgsNL8ndz3dnFPgAfIEOeu0Apw+TLDwj2nBaAYQiqTyG5xRyeZgaBXx/gKKC//4BWA8QTisiw11pZXteZnofZgQQR/qMOwbgv7hvNiUQESQhGALf/myLwej3JG1GwIEkX+/CmyBBflXC9Sl6cdQpi59oqlWHzUueWwQe5ggEWJAkH4aw2KPjGk7t67AIQeUIrvoDzCv+899b8QJ4uz7k79djgbBzQnVsOrUuJAayty00xMJlSDV0VtZvIqqnvBs/7ji7WDR39wNZom+DQ3v5PxD64pyT4PuPL/1l0/j8acTZmZp7gQdDHCen6PymgTN1zjuEf0VeQ1JXF2cjJqY8imaqG+4t3t8UdVEOPXNODVzgfbk4h5dvLnvPP20Uv9S+7xQKtxZRuBeKZFzqqMDGhMjcftOTeAdlwGOH+T8AdBG1C5w0i/v7BvCEdnYm4KFog2nYrtyV0EXdxvdebsMw2vne/FK1TK/2JTQHexJdEg9FKaxQt2mB88PJ0av7/AOeAm71/uRNi7ZU3a8a5yI11EktxpGhGl0uLWmGxtN8Bu+rJmjMMXTlGLqvue1sF4nRav3bdVQrv1QxGs0dEPWCMvup9s2pXg+N6cLxIGBZz5Wpmfpt0mgQylEeOVFPzReR9TMt9IYMQSVZaxzw/9TTQyaHfdUFVGovPWcCwM6871GyOSxd/XLt6ziDrViqIqgY6b4GnD7lxqTcST5l6CiB7UGoHAzkoXlcpqNx5mtvb6qhHU8UeKE0OsVm80Zzx+lrNJmPE3I56lGLLSKPzBk50VHw+AmyNP99BHL2Xj7I6wHIcBRBquSR4DLEZGqM8r6v/mdc7Bb1umLIBjfOeglpBU3w6a74MsxqLrrrrrrrrrrrrrrrrrr//yImhAIcACxOAfUhhTMjEAPjEyTgAOwhpL21pHBa4xPz74ADiCcFmJrhUNq/7tNtj+cuoAQGC//nGxva5+690BkbtgEMDwPgiMpggBGINge3wExmw0cfg0CEHIgwAmzPSx/FBaU3yImsz9GFg4ADqmAMsBCoXZqRH/2mNedevwxSI/7aZnj9mNmYT+nh4EgAXist+hzc/NGYb2TeZ0Z7i6aG68KkfCVfskOLagYheehm9P7Pd7skEOz9+74o5EqlVs/oTKb8EGnYIAELrE53D79YkdflH8hbvq4bs/j4wyAwuhGYVtXq7YmUaik8yVHntqbJg/Xn7UaHOID7AKbZHHaNod+ZytfRyQcpik5q731gF67NGY37A1SIdPgu6iT3G7fHi6xEKB8/dFgNXEfqGOmMbuJTMV8t2ZGskPyMfhfrav+3lL8+GcHvXwzokaeCcZRDjbBQI8o463E0CkplW7++fde5Wjhv24r/TED9W1AYiQiMmIn9cfLYTb62/fM1uLwAXS9dq3hunpx7JmC98FD5D89/Yh8mRmAJhuhg1cDMVeGrc+xYMQv2JWgiZ6/7ks/zf9nhMnf0ctryrGXodUbuDtoFAUu9tPf6dZDszkjO6BLjnb2JpF7vjm1Chv3i/7/MxZMFJ80CN5PFcununmH9W7sHXJ8exHXU+OJrLru+QOfrYjkWu24T2DO8SSuApgRG0fEd+hKEkoTvy4MLvdqxqpMBDGNBdzPv/sf9lDfjYXYzX1jfoewVr+UZGTfMqmhQD0/QY+HZ1P2X2mdQE75GBXXHHIGEYCgKJDhFqme6sSEQdUAVEnI/d5r5W6f6Nv2Yz/NBD1tvOEladUlUtBf+HKo26DFSmJ76rxu9UqGo9l10/byG85jdRNDWlBWWAAdQm9/g29t2NnNUGpwELvnVspmMYt7548FfGs2E1eY5lcd7GGGgLQ1n+ulqgwBIysonwZHmw8dIBL9Pa7fndLPH7KuO05gKZZT1vzI0M1Uj0Sq15ntTDQLWAVHCU1ypQ37EcLnbXfcqulbCXD7ZBEbHF5IOl7cg39+f0ME0seX227NqSQ4vapL2GaCtlzgx3Wu5973sITIgqbwSI0+vh4UWomuuuuuuuuuuuuuuuv//s2HB3ABE/8r4gOAgcJllJjJYaMwxK3/4AEuRGO5t6/7/4JCHb1QOSG1sORf8EF3YIBIQvAJjWwP24AUtzcIIZYmsDMdgCXIAB0k3OP7BWF10jBIE0PQp8FtY/Hg7xiqnus8Hz2oWj3wQj4r5sqwDeyyVhuy3U2tLgn9EUewCATFvJ36lAqDuQVrzveA/re/6oIH2/JHp9C2yb0b1pGSQNe6vBGAUBBrCAQcJtAEzNtsGgkFyH5rw65kFGJ7FY8IIPkXt3WUENwFDMier2666nTIF5K4uc/NhdpP6RgyGhlsqdiGUbwXYe3rzw78yb2Uf+TqrQ+Hd0w5uptDCt7/3XcpHGgAHfh11xAtRfx+nfdIKtYfZq/f3AsMQnfFy0JG07qvzNIv2KjfHH3Arbier36aKYAJfocSzuMAy1rcYvVOKmbPudrvCH5qhl2wnMtj5/dYexDpqkGrPBB/oEcXu/gFo2mD2pGpWSl0DZoF45czID8c4IiawhTAy7pQhPyV2VSrlyQb9s8ogwzgCnkQEB7vaRQu8vp3Ba2e/kj3YhrLud+6kaC6/BXvWQSrevBpJCRX38RPqF9CwlAT1gBNI40Y6J+hoYDo/R3kc1iV7clpjivESd0EziRAJN5NCOeW5ADPdWTMj/wAbVV42vSm7B4ZP5eJ69wBZRtw3WYbq852n1L4m3lwvoAk/luOr+fZJ5vHDw5/UKN6sW1NGPsgvEsVWvRWrHixH31CfVbkhj5IL7TFpZxjaq/Pp3FGJ5kWOW7b0/cbkLhCZBWFe0xFa31I6v7Vz1HuO6fJtQpz7BEMI2UAGrlMhxd7ZnR4MZ2g8Q+PZ2kH0wbGg7ke7UZhuDUrhbl0GOuxsbOhOzKDsSQBz+lsUL1uovzWFPyBhKkX4AJWpGRiPeihqpCf88MjnUS3GkVo32pvrW/WK3clmOe7ZmPVN09//3u2G8RC5iL3qGQJUo/hqKc7KNC2sc6gUWBIxYjiSbmVqwtzrxeNoDnRGvq9ckRyk8QAAPKYuQdadKxPIk69XfKR1K//p+/VktAQ91nn7vCKdNH5f2i3LVP4XA2ya24NNT5meN6XJxilH7POb8YxQs7kLtdOhG689vjSugJ9ks4FzmH5eNvLcmyhmL/INtO+FT4Fu8wdoRlGHcmuKFowbfsGXc5W4D7vjLSmvVTtesW6kFmgVeHRST+9CEfyd3RWqxvcnARmDUwIDJsfcI3Wx8Ku4AYRXkhoxmxmB8ikV1QlvxGleNcBdRGErhoNn3ysGkgGdj6vq7SmkHF6wd/ACZEI2M9fqiy4aURePJrTfLlmlfq2gh/rNM5IDl4Sa75QJ/cquJXDff/0p9gtEhVXU77Xru96lrrrrrrrrrrrrrrr/a21vJCXAAVwk3KFWQIsmykBaZ3S4GyLNV/6jCJlFdH34AGf0f9+dQqM2Nhm9dygDK1bAjMPb98AGEeU3GcSIRPUigHbSBf/+fG5R5WnAJ9pOy8N9ZcuAcdhlBJa6jYJFtwfhZ45Sj9hG6LPPixVmBmrYJsA8Bbh+z0S39d/t/+JEVfv5PiH8eX5jZ696xZPn5yXb5eHlGJ9rjTDUpgRDW87FHUGxSwG9gYF6jL+3P5Nyo58irDt7XmmoGoSTu994AWqeEACm5Fh3EJ2vyimqrZOUI+MRQd7hh/7bL7EKdWVHv4ISgDCIdGk32oZrhfOa2zkkayFH6wmvsHNyc9zkakIpqjjIIOJImguJJfJISdC+KLQ6MHrLYAN022D6h8cpjcQ//FmV+nWk89B3e29RHwffx+mmkU2V7/BS1TT1cGu1mRsdKAd92OuvRvaEOXoPJp6ZearPjgWvg4UgwneLmzvoslIGDLMnaWAef73UTYhUmRkvzIq3uEzhqfgCH6p2d3/lt1fhXW9CZbwIuN8/DfjbC53srRhBdQTCtVr3HuO53C4G/tvT+Rjwhn/12h5kahwKM/1ng6KVd5ojR1+CAQYgkIIVbt9N/8As/KQY3BXmrn/GlDI+QBkdP6bXJQQYXGpPesvmiL7t843O+3sebkM7Vox4bmub+nwk2GIEgBQwBmz6/PnM2uydR7EWFep1gMogY4q9MvfUvU/TbzhjmRmXxulD0Q51MUtlZA+YB+oc4e3FTqxxfWJ8SWn82ZzazWt8MQpcNOp5SCFuWdAPtc8DZfF+n6SE6OI39TsuPHP83lrlv5UKqCiKvt7wYHdlfAHgwLmEaglstB0j2o4hif95nE2J1FqOSQA9Zcx+FtBou4X13oUxMgKsxkYJM6v/6YyJ745iXvbfpJFjYWP3eTWHLkKNUSLxp+C2/6lVG/73Xpygx6VRn/YqmP0yU637BzYVfA6mnNlE0OW/wo/7MSFYS9p9a8/UlOk/UekYwf04ztrMd00Xiy92jARVEa++YY4HGAFCc+o+tu3DYqTc/J9HMLShWjInpOWrgiBqzLJqHMP4x5PUoEmLfg5a2P+8bLIDPrdcDVjN0ygB/R3GzQsqYNjWG76yYkHucSuCb/p1SiY3q2xUYZ5zA5lOvy9LTfmxDj244S/n++3YsA5DCUXot9q7Cr5dWd9uJODe5cYDBb/Pk3sVs9pNB9yJgpDWQ/yc3eGgAPwyBaGTOH84/jHn9X6Ue5V1cG8mjASmaqxYT1/UIbQasFViFDo5Nfy02NE60IJlyXRMm3clmF0vAcGfQiBb7STBH0DC063kQv51a+FPubwmWQUdS4EOdGCmDv/eEcQaxw+wGbP/eR2ikA+B0+5YRzohlZgXWco3v/2S0toh0VPf732vAS3A9l1O7Fg0rAXwFTrqCqwD0UNdpsp6KYME4cDIIYAKzy+QAip/oLyBm7xblv8mg+QaN1CX4Hn1rKNaeKR7smmCOos4u7OYF4EzfxBR7XTf/a+N9AFriI/W08GE12omN7/jqSAdU1AUKCEHJ/u8JLrYn7x1gH13pGzTGruJvWv3t374m/DEPIDbhiJPuzascX9zwsuan0Dc5uV+XwfKgFMFOX6c3nj2e//LncJNmC9nnu2zhnEcAv4QLubFZojbl6vLwBmJrYzPAD/A+8qr6elPgwJOx85+1bGMrnL/icYSYIwMI5/1VJPTLqJrrrrrrrrrrrrrr//+EF2CR7tAB54AWTqAcLGF0icpdAHAR4EZTee4A7TNFnrW9WsAdwkAg8JlUkb9SkOLG++76VmEDdzRgGclYMOeh4TU/kSMajwQETCSpDJ3qiETUGUKYPy3/NpKASdgjQ/lh/f7+SwYal/hm5hi3DfKNEwfJ+GcnCf/+OQVJ2k0bAlhMBANX30dmXSsdhTCMkG1myGjAZ48sXQAB3s2pMfbL4eMoU1Hhe/Vtpe0HqR9UEgV/A4oGLszpyOfBYZ9R4vYmCevV9/dyHYJpN74UbDfhNwL/shdxuLlQay4Kloks0ryqPxOpvczhH2/ESu4OAMiA5tqVRj/jeD3wGPa1hhC+nmih1yP19IKLKyMzOog+GjeUwaA+bstms6ISokBZyMJVDFyKUPm2EQcwtKLjdZUSI4AhlYP+XTbvqXbA+lzcPe1y7aPcIic2rXo4AtYVR2A8jAzgs+RnSZe+3aKlnz+y1a3c2YGJnEHdR4SuFLRYUr6pfY6xrGvUxk5x0m870Cz0zWEyvd/YrDWuJHOfusqyC+OcCVbz08gRVcJT/Uy8v7hvZPVXW8G4RGo6O4khC8ONSk0bho9MWMK2cgKMHGBwEHnNzt1iR8W4hm6Vk75ewbNZoufDxsdnugIAzqjuCmvu/ExRs7+Oeqgf5r9FXQn4X/8qkV/JZg87mH8Nh+xq14nyq2DFVcvRaDVyHv90TDWLMF/95Gz/OCSsVyLLKUJufo2JLzDW/uPIrwBw+/lDtXGOkXe4KBM4HBXHYV8QlYSsPSlHdroAqWzcVPyTa/BlMVowug5RWQfqHQl3K645i1662VEm+YVeNWPfgzIiAZkPT4opmbFe5AosXU6yPomNYvOsE88X4uxNHbEoJohk+qYV+juC9jpBjr1yGRbO8qCcdcumZjHtYIZEKSYhBAd75EZmtd/VoACaRGcEVxEesszyJhlxw64Z8DilVzUjrcqbYGvw4W6ASdPty77y3flfebXV696rjfBqRvurSCq5zqfAJ6/KvzN1gYjBBLgDPRU6uVdSzrAhHv0l8MP4RA9TqHsoK7CyaOd67bGTx998iqO0AJgIINQ3Gc9V0uJwdK+/bRB1ScTseR4/7+l0w+Scf93Pmhrhcc1yzJFCvt7hC+b7il2bOscIwDJaaCvWv8yN7bgimvBRQUMYl+VNb3yklBL5uQwypYOSX9P/Jy2d853MQxVOlRARGcHZzZSRxFbFQ0nu/jEzeGWNBu/qvUgfWo/vZTutj1afc6CM6D01dDOIc/+ODTmuW4LXfgN6+fK/b6l5nFVeOftX95Gtbh89m8JEISFcHdcWZ9gHslMm75o7GWHEQGUi4GUpkJExubyx9SZAbyp//4EgEgOsbp2dpph1/lOU+6GBmZaa4RpajZL8otpE3Vnc5mwkqti5r9Y+b/nGcAgl/UuvGe1MQ4sXrjUKhl8thl234XrbGcs7RjvSt/e+k/UfPIlB8TDT4a3a1PJRF3QScShrBCCzSXUJvIu4qrnD6GQ3zKz8Mo+/+7lzrv6hCKUCeWMoT+ATMdbeGUdXHOvDAQIHkzyfBJ/PgGMB8Ts0v4q9o3H12Wg7iVu6JrdHNQIDPnnpiBT2QNPg1Uz4WLX9WMY7UDZ0BCCt28XqfPyeWzceg2qqXLVvcbY2sPNkJGavtJnKVyrBT10/rn3phWXmbINMS1pD/LAN8Xocbt2ZB/+3DJIG0N7UIKrfb+7y/vMsbfEuPlzyiiywlEZEQCTzH57k2HH/dyZEa2k4ur6Y2uuuuuuuuuuuuuv//9hwd1004McEPOh0kf/QLgDs8BA4nhUmLb/oQ+AIMBc+wJf56EgqYKNvvxB8vBWGQISAQ9HY00HNgP05NoEHojhwG1ztt9rUciqIkBaxWw1EsFBDTwOLlpBhZM2Cvo4aLgsDDl4NTC2gd7lHnqgBPRUG9M/w1AgLAxXfhjNcPDD93J9f1sxVRptmPcsmD/9wJBbBbq2bhkCBa9AAVWTofmSl0GPSTHgX6QBXLfKaQXiPuufNkCSxoBS3tNf8b5f+/LoDMoGYFzlri2J6v3VdcZxHhp0BXvXIh89FtsSJ9Cz9Pp0G0v29s+Mz/QvwDkPR2fL1nZqwGMwQptdeCTPWQTeCKTN7rmyKRKpFMekr/csHaE5l5f1QW1Bjo1k12M2Ux268724p/enxYfMxGm1TtitiY+v/SWKdHoqqeuje4bpmkOP/NA0HSZFY5/zYCqAMBXPVMEjqkiiqPUJcxcpsoCStAiuYmvLuVxjcIKyk0B75qzJanTeXbN5426v99n9sWU5OD+fetJRYMdB0MP/TR0Q10eTXG9LtT83K4+jICv700ZsXR1Y3iwfBRiuSb1X7/CTXiejkTo9Bx5A5keLATqqNj4YH6L3qBj/bmTuc93E2RAC6leHfAMaAAEAHKYpJDbX/NFmithh/Xqejl75dSLR069imrJrlsi8c3Dr9mg9KD46a6Tm7vaLZH/KAope5QVj4uwD/z4ruILaB+Jx/z1rxPY3WpvzsCmD3ClBQyfVTb0U1UYZjlo+CkfrPVtj8CbRgF6w8jWl/grlvPS5yy1tI8q6YD+xUpp658YdruJkm1MIMCb7iV0CHQ0FaGnJqmlrHUZJp47CYMMWFRBxLECs/avtzWMShzmmwBrJ3RKVlCnqLEQhkDCzWo2BN97TwishFAdAPrwBA33Y3MAhF1P7ZUiPw1UgAAgB7rx0O/5dv7HRvYqQjd/+v4d24ytV2OnrHn/T//eRNCjsYActl9/Ai88MLhUE0KqGNBNr2QJyD/nCQ8/HrCHmn26PmTkyMMmncnJ0SwORjjLRP6M02GAdNPNz9vgaZ1DxIw4gzDfOQvIGLNIHqfe/TvGrBSBQHspCLPvJnjPCiXhLJF/xh087PxT9vmlm1G5d2ngZOEldhREjiYF3P1G2k8gMZvQ77ZP1FqSi/mnds6zdp91cG6bXgsMcteksosLU7tSdhYnXMGIuCt6KUB8Lt1n3j/DK3AlcmgBSHckjtms4XgURooYvTWvnHedJ61SBuq6QMXm0D/GXF3BJ5AbHXWe/1HL3miZ8dncvbsxqB5p4HtgLKratT/jlfdUVeDVC6UW3S4NCIwEV41Zj4IvhTN0kKGgKKCK0jdh465s9MFQI9mITOt9Gm7F7akt6tV0O29VZMqn396xv6ujVDvv6NrfWS5foAkahN63eX1LvxBIBFol+SCj6aG2vV2WNaaIo9basYus1U52UDORLrpDdSh1aqI8r7WNmnD/go9/ZM5cveED0DsjQJ+C8/AsN+n2/sNi8JZYIi3V8NxI6P3bfyQ54VPXXXXXXXXXXXXX//+w4FIfgb/maPRa9COTQj/KAsWAmTCpjy/7MV//7XJaKi06x3y33o61lFuKzn1/kyv9Fs1jCQcCzHfiLg8jft5jJ/l/Q7uUJzWce5VW0zji/rF05QP42SRajyRBhYshRfxqIis1G5gomc2b3pmbjj1+g9iEtsTnak7aoRwQpXCR0EPIzCm3ko0nNha28V/UBuDsRKN2QkkU1W//nm3ZXdPyLymCjWxzo6+P9otN5hkzdZl1zKWsm9MPS4L2F7MH4wtob7RGnvjCfHZhfX1Lx68Y15n9IItq4NhhwbSD7V5tl8hmPoxCBObuguwer8DXPO+XEBAnkJ+HeyakoDLECxV+2KipMb9E5rGjJV8vGU2FJMMaOVHxObn0p4DEmOEiT1qAAnL7Ndwhi++INuJyagmrILIQA6pFsSYJgi+oUUdcWE/Fso3XRH64tHspycp2s1usuTYlWtvqxo4bmtBygInReM1WavwQAQNYYexvrCT+Co5J8CRUUwMC3wWdzCnoXQeSWMZ2eZkQNEOY/sSCmSKUZTmb/JpB3cVzbONzf1BT3MLKeA4vSPTu+uZOCE8MPQAU/8oUQobr5uBi2aXHvQ4FK92YQ99KzLeVnW0vOeWvfG0U3a3gqJFQRRgT0RaSSHeQZlwrYQ3/Qo+2wakhvzYUb0YG4G2ytwimmw6giWwlqR0ORA9z31L2wv027y/ADuYqYluPU3K+1C3JPiKJ0Oezc2K1G7Ask4oU7N5nE6JadNIcr9bYapaxPWZDH+BmmDuN6nnLnQ5rEd4g5UZGbc2v3qHQI+wRHk9gdNJMd1JkfxkvGST/Ba3HRtNcqhNKELLXkG2N6e+ML5usgY4w2X28kKVZoVuSmYjOJyfxpFhQapPV3eC9QJ6weRuHhJ+hOdG483aATTkq8NywbDkj0Ytxnaru1f3fURDJ7M3JhwU1NFe7EPEQe8iJ6fIOGjJF5LxNTSIZUjHjSeiyg6bw2h4En6gwwUI7Idf1mAMmVFBbjyRqHnrr+lb/OcFhB0lCRGISV8U2VrjxhT0QiUwr7DV2q39MsvWNBdpAr9CylD+V4FzrZto1eLuqG0dNo1ksIxzuW6KhWdXVXJmN412zncFPZ5U90evsN7lQGzbCZwW6DmnB8wIFCDfxpvT1n7a3r6quG/Mnb2stTVJRaFaLc901W3hpg1lsg6CsIwXsae+dvh/YbEkw2CoXslL/SUzJYO1+/U9ddddddddddddf//0YcHQbnIOUEW0i//noHeBSA7wMihUq/v0CwF/oUAcMVaIDd/6DkfCBQyp8t7+oJQnCJgYkPFVN4JNArbTRFTAPPf//cQqs4ScOaSkqEa41dHsyT1ajjQQPI+vFu8mduAnDSwtI/jsPOE1REJA00yavMJJ+lujg9hp1N0VEIJkldlyBBPqAHz7OQ1sXcm6nj0alU+Ao6h93B09ecul4sE/9g7wyrfqpI6A5I4gjMrzynHi+6HKDOE2N2nGUXPk2kHU0rlcNyDthgc/j0FqJOLJoNQeZlDzIm9ZhfQ0lQaJBYPGmmb03YYH2DOw7yHOk3ihSl4tkrxV5LRBTRXcDFXLKSKaDoecfPc8+j/d2Rt1dfznSxJRf17kwr7bfV4YRUHUxNOxzR+JP4m79FiqHlBGEXVaWDFDiWz9Af85SLjL+AEFzS7m7mOQ7FqfrgEn0/IQT/dwwJ2paV3ehvq7oWR4UbkE9TQ1mXok2W0+Dv58Tfeu7JZNK/beEAHIHDFuOL6/+QfdR+SxMTd1V0UyJK0FEcFTLOyb/nLy4d9USkkjD7xMKIcm+nHCUu5BY5KbPzw/fsTDzHtjxW8qHyMGuluA32PAyqnqo28TczdzIhFAVlcOY1UbTTtxVuS27XBlS0HGuvoExvtyxfyrrSfQvY384y28j6iZHU1TVtK+hD4qzfZhQf/lznEowiDzJ0Uxpc8s2C+lcL4JEp4Cuawu0Rsw8jU6Rk4qwXsbv7WfWM2iamGP7btAQDqwdGTYWopuBQv/qnS/gm3oSeWhr8+lbaYYabTHYHPTkxuZTNj+JX18VrP377o/PUCOotT3Gggorhic7xHr7H5OjJHzuvzSTPu6CciJZEHtBv3jfvQVt0iaYOY1+8cHW2hbcMYicPGOr0VIjrow9wOwe36LdR0+0psa7Xr1YkXY/NEar5w2daskcFwOJ4EuSDybizZ+jJkhcXXcjIUo8wcrH260g9O6d9MdtN82Wbo38JOznx5Acr1v09rdg91cZ2wfXPO8xpNF33FFSyW+aeg3HnuLvQbMt+ZxkEWw5JoO1VYrrrdMkZiQk4uTmDl/xAf7ux3QURR1xomd0qVOUSHiFvaTcH9DpLllHOzS31kAe0+B5+Wymw6JYBg9b5eb9dezm4iLkE3BEQ/IwzhpTSRAW6ePAOCck7fRuAV0YQ1dWrOWug1lCYOdWpTuAxW1mJwvUtHETJxMJCfSv9unADPoDWgLWMcv5P8z8STT4cXIWcgEWEnAxu6b4kRr0UgHKNxEj/D4I2sJFWrP1E8raOgL1GkqOkdlY1Pmav1bc0msOagUbT3LhSs/BFET7e4zPFLV3Mgf6ueWVNrUBzsGZseuV1oOTHDNHXm+wsstoEyaKY6NIYLjIM1DZKM29kodIikhcHHuRHvdlT7q+v98xWK4wAgGoxb39slOrviPtD/+w2Ll9CvfbpmVE6f9x+54xc11111111111111/0/9Asxl9fDvd+gEzXpvm9B70RKJpT7hDgTBkxrQxVH8HwSIj+eIAOP2GAAxEatSACWH7hZf834uCnqHhw4WExwPw8OATHgFYwKYR7QFin7X0micWADHA5tJlztXsSCeqJeNWBDT1+WTkBp1HttooPdHjCA4LIQCoaDUHRQ9mwCJyAM2VfWDqQVn2OSmBXhM8440BmNQQKMNHt0OKxVVqso5LYSkkQAfSDWIMjHIwm1qi23oWNFkouCePwlAQ4MADiMnPPjOwO8CZoO8LvMTckKLmDqU0qqcOMoY5U9lFPLSjBO8hqZ42SnwKVeZUfkR2hkeM/Awb7Zwgk/UcCKfuMLc2aX65Fe3gjPo2BkRGb8ZkrwOimaEdi8bI/qSxcgW+/IbkQEjmbo3OnOsNNXcMa/wTWY7j3mmk0wrwrbbKWC0xNaXl+Gj7k7Dxe+LIWJrg3hkBHtImGvYfjPv9wDk0QVyITvRui3jHeYABsTGYRdDW+0kSN9ddp1pkZsbUN8Q2Fa1PVGfuCMFkJq8Pz6voetMZyj8l3aMl5oMem6kTuoTUjMt9CqbI4WTxNxDs8R4YxTtapNMMtYhO6sxqiqodRL11tTo6ET+Q91dtynfU3lVVPHMJZNXNuff5Bms3DZsEIL8Y6t448WkkpvXRkEaqddNuPvjpZXRv4eFzXigzamNhMVbq8Mx5B1IfeHrqnavdtv1/0bCiEfiquqgLdeC5B4QuNDIgmcCRbdAMX76u9r/VfRuYoYkCq7ipJsrkuDcZamjAr826VPLc7Cw3QCxDPpooGRDBbu3QmB+0gOOwIe22VRP0z4vsbsvUbwZOrDX0BDzIFnTHO5hPgzgcy5xCy4nPYZXpiueYjXwL3TxZrPcXQwq2fwmgqtHSqX3W4BCmOc1t5dgmhCq++9aRbw7M4ntwW16WLtL4dbtLveB35ZSJsG///8tmQig89hC7ClZ2jX3ynFhcyohkfHrXy3LGCZsvw1qOJ/WOSodpKfkrqpw9iHLMRuAzVc9lP1G0+W0tVTNNb+fsGQFObd6VeGXA0WzRnZFPgAvkdJBeqRZrnARopbLiGb/iU76iL4v5+0YlLLzCc8Dd3kkT/Wp4/C96kIsocYr584QBUNxqQY9U6a/zd+1LZj7nvhJ/XITBJC2TOnLvEo11QDDnlvBQ4B1dUdu2rk/BiAyAQBycFcmjXuERJGG0TZgUgb5Flvq4wM8YHNsoLpWN+xbfNS9Pqqw6XYLpwMVnS4t03b4O6mLBq9jJ4GVsyn6KYr7wAYsUGKyma00JNJy/NCuuxKqHvwSDrC+K8/4HqAMMXq6nJMtkjfXXXXXXXXC2AiVtIOr+GR98scd5L+EeNpsxtjqdM8CJArgVcWCj340EAYEsljPAOyx40Ay4YWszGZfoT+Lrrrr6UpSelMcEvAzgDM3VWAchqx1/6AsyxzM4ybxdfNZxQH00m3i/3CeVlWG+BhBBVZM7D1ar/8GYALGFXkwlqLRTf/zjNbmvsETBiBAoMSG2g0xsK17YaJEAZtEBRgvONTx+3hHEVQn01P/+5lpslkziy99MXpR/zVP+sDqIQjXaoVdL+FdqnKnGdAL8AS4k9nAPFd8L3C/h1Ur1/TijBk70rL8EWiSUzUjPn2/4qZk2ARj407Wj2FDTqTcl0wuK3FkSlLefW+BpSKIQ1LJ2Y7rjdJbkZI9W1r7qmHTpkN+AKWXISAkustM/avToIrJui5sX+9arrSIkO+5pzc7H/D37qcLMbKN/L4ZUAUmthom1u4FWcgtNKUUxfUFW5P/drtrD0/HEOXJ20cT++nbuJj2xiripCp33+TXDxvpzU53qSsSKzdq7IiMU0Y75r/wavedhyiPyN7uhho5Nu+Cr+BuCrbjIWcvj2bx6op4/c/iQukFqU7TTqir3VGLD/4fxDOJcWbPguDiGERlnz3rnlkClaLuPZdRnpWjkQ0J+TqxE5gkq/vZf7npgh3QoI2MH/bb4dkYPdpNQPk0AAxP6U3CLK4PGX5UFeYGEb601/7gbX5O20CPy/W24BKhY1ht1bvJwzgbeDFipa6BS9rg+L3iDGbUoI4gXxkZS9MfBypzJlGal9YEvyLE7oF2ozNqCzkRJDVda+ffTMTwJKYGsrAbMBmuOJBa02s22ZrFJlCGHQ6jPwN4pDi1xjfkfu1f5p7/DWZcxulJ6gfL2NCIOqKUkjvn3L75YPMFLeq79YzMfM4x2wCnMxUf0i9TZrGAu1eyelbOpUvJtPBGVQH2IymzpdRoErAZeQN4vQXtSDumagf1n+CL11enRF6qf4iOyT3acPNAKJssnLad1UfXXXXXXXXXHqGMtEd//11111/mk/9AsGA4CkxQ5GQoEylZB1X/9kBoyzDZIGraCBZ2CAlIWEoEsIFZ+MooMSWlcv9Pmip7kbs0mg/RqYbKDNSgT+Jh+BEsd5/BPgS0AQsmDYUsjATcbqWRss4MN/fVhcugh/FKpiEufL53k7AqecAbNo5HFAf5c4+wQABTBzAgACtYDimxpb8LbiqP+iDxfixLmYGJpc/v4ikQskHHR9Li+cGXAAmcYXwnJQYOAI4nJ5Z0wcvs5+sy/8zKqEOFQojlUvHRfED5c+X5+c5m6b+rhXlu1bRc8rzewYXu/XPDDREm1ytufzzVFdg1gGsZgVNq8FUsolOhWdRqO7jrxpBHp+GUEUr3wgWlmLJM7Hop8TAycDNXjHoeIpElvu9ePBwc61pKkuxuF7c8Z/CcAq37Z6InW/Tav7+p4V2e5up+NLwCQSwY4htMa9CIeYH2gfOTWyUeiOg/0rsvyu4mYKI4jADV0LHKbBvmJS/ECBNeuwk/0oNC/2XaE9TC2hr+T+gzxsfsUsrE8ahnomCZUq6hUGUsPCqkEL4sl8oDeD38FngXL8OOP3ezJGiIJmKE75fPhke0YVojNztJuRClL85mwq5R812/KDc9IUqNgIUMpW3UO22I+88bCc201BREyye0xa32WttdBmDxn/xiTApFzNFnyN6wu+++XogH5pPfAEKwm6+41f4B1ps9tidKJx0Hxgbn5XZ6mZ3sGVPqfHN2JWPkUIzl3KNr3+Slv828pH8PYA6Qevxvze0+fxWS4zFLPe2ohIQcMYCv5PT+oNRBBYOlxck75meQfu2vmP9TFDJMFyeb3si7Ug8GEjeLF33+tumMQ3CR6neJmIm8vQ7z+JxEJF2ljjqTF6iXk4ExNkt1YAkf+2TlZvNc7CEgQ4nCEZJANrllefn9v//BXUbEh/BxHEof8f76i6666666666666666+fn+ehwQzHut/f9/Quzg0/0NCNxJffjiDaQEZsXit08v//3Bk5rSmjg0zDgfubF47XXfPDCAAJhjTAgACgOcJ8ANJ+KiPAxGsNdogdeykDgrpbar5tM8z8/vOcsUON7l7ovhgaUTbiCv7e+4MDKNhLHTQpBf+AsgiXNQrxF8tWfmkZrnEvAQAVUDBACRABSxuF/BpflgQcAsRdg/D3wd81QeHIkWFf91p8puR8HuM51gP2Bfa7LlVIf9oSaW/g3wIkY9wv629R59lb+SGiVBD4lrdZB9UULHPYdoq9exIxnp7faG6imlHtqib2xPdyyOKrB7Bv48AEABAUBf3cc/O0jPAsw96Yk7PGixP6rCTSURtSasLq8fexPMtO/e8arDCnwdy4VerGKIP9+MAWojE0lyrYlhwXyvkzpK3zstfj+JlLH+Y7txRmwJqF7qmkY1C6zQ6oIgFuON5vyXqOOAeMJTOx0p7yCEzV6YfPxqggQbvHLRiPQvE7h/lMYdf0Y6wxgN8JrewXiRBJLH0FJHMW6BSFtogpum8npM4hX614JWgSKjTCc5szum2faAoO1XFlE2Uk2LFBEdCW49W2dWkkaIRxfvrsKDojOtFP++PAt4qudsu7TgIitwPnn//I+TV4vwNRMn83d6ySqTaR+998556Jm8gFpLTxxaf7HaH/CWa8fEUVlaX4g4FDIpBZiIAMGuLqbOaqg0n0fXybO/jlJ62A1acvfQB3IxSV8+75saYQL5h9qDVVQL0PpNuZ4wmmBpDUb8Y7JxzuBFjl/ho8yXLfCEzMQkTQlLf8/9RddddddddddddddddddL19V/0OHeAVFNGWQaM6Q+YAvBYDtxw/A9YR+fTg6GN5AwJwYFOaEfnHPXnxDPeEAAjPcEAAQDkF+AUCtBGi/z+LgD3BpJQdxP3/VdVXHX1XJQolloatRIC/Z5gzZDvn1yQA/OFmqTDZ0oO4eAfnZlkDQhWGEALsKIvFhWl3Ht3/6TIctb8Vj1SnIOItDqyKVEWHv84gk6AWWbCC3TX/BABFsUEAIzg8cbJjQLLgUGMzwaCJlhifCcawQp84JRtRAL4DDsE46pa519AzD4v2iqqp9V8mXTMzOHljecf0XLbzhY/VprZexC1gZMDJZbhkgHDHBbdVkUYSx2kbuvEJb+GA/orVbuvAAvx5dLHVXd8MmGlmsOx4XDFVuAb6n7dlcRqUA6g61Euwxkt+BK2WwkvLcRHuGYw0CjRKpvImn/Bf3NymVc1lQJSZk+b/TRb08FvlsMB7rSalj9xJhGrr7yoRlpk2Q03XXUyrUlVlf0qEScP2fbDVv5MN59Sj/TO7xi1H1yqiKBZRFlqISJ+ePMhkOVdkK0LWMVVqGcd59J4g5hEAaxEF7aN9jqSQbHHbXcLUN9sYlxBeeWDRZRr4EAcKFAgUojwtU3CkZcfwIyPd3cpMmSf0xw5vg3wi76Ii+bqMSPk11gCu3064goubUj3Uvh7LcQzKa1nM9e3mTl//BWU0WM1rCipYwJCQ8rwhMzCpINxKT+VfvqeuuuuuuuuuuuuuuuuuuuuPX/gz/+uPX/hodpvjIHpUFwt/wGAJldByIegM0v/n/rZCYIvbpB/J3/4pbEPJFegGZND1xWI7MBurgg/EMlE1BfvgwAYOCAARgAb8yBKjYT0YCs8/wGjBOPyNgZ6HFYHpXz/+qx/46mWLEkdLqCZSmAK6AlAeuU/556QgpxSb7wEjEd8wF2FHQ+2FIEv4j9JFf+dgzmwYN5ZTCLLDiEJcMvzAI9BQgQxTf1/geE4UHhUFH7Ot/kMl7BKQza7CAgX2Yis6eVqIGH/W/5ZQhyIAygaEoIIBwiZZohEB6o44SHo7aEL0zP6I2Q0QAQOyAmvg4+YP1IbIBVz1jSQMWvnn4bOBwDcTn4olT9QsLpw8/isHLfdMnwi+6/+uRDJFFHU46/DNOj6SAxRIJBqKmglQcdfh4JngJuEvwPBBPoz+Jfn+IWgSSNn22b4JZSpcxxFXvv1KA/X5z7qshSYfD+hej8DVp3zXEwXS0RRZVdgASSnTz6gkLSU6ztWvtFAVA6hO9GSIpb/s94GZjdnF6ufjsus/Usu77v/6DVMzqIPG/M325+W+VzwJb58DAxwOGstBgy0HvXyj5nki/XXXXXXXXXXXXXXXXXXXXXXXXXXS111111111111114AAABEEGaOAr4EjiwlgIWl/YYFzo2lePuE59t7yhXcX2lFiIg96v3ov+/CxffexP/JXwCPp6e/7jih+Q69WO/DGHGQXbtXH/BGaHy86Xdl+HUY/vvy0pBXubyvr1TfCW99pZf/WFC/+2LHcTEXezPt0xN8y+QbOFhD+FW24y9u92MEfalw7vBKqS2bjcpbY1aOT6X9/EFCY3y904sbx4iGu7zGjAcr0pHuOK3lXMEm3Bdq5gyQFZztvD9eOhM5PzOx6iEq/w+iZHl/3wV8Ewnp8C4YLrauFdhSU0n7Akdf6hV+KEO7u7vvxF7wDP2Jj09mBKL998TL1pQlurV3hrxFqFX3TbeHHpwQeHvEyYW8EFZJsCRAAAA10GaVAK+CvzDsttLd82e8NF/3bFkAg+P2u667hKlKvB5f3vH30+HoNn3DaU5nFzJl6ERcNdwDDc2v++fvl3l7lm+Fl7ZRgo3Fb17U9wr5jPhlKE0Nu4rfUd3CcmVa/hN14q3bw4k+4LRveXcf3ywoX/33F4Sgl9++7u7755UbkIfk/uor9KE3fV6H4iOuJhPWKn6oR8TrWtb5cUd73fUXWXPPzwj5q136QT3u932Ur39qJnhLyEV5f296RGrRVwtPLH8l37SO0Ku0X05rhqep+S9P+Hvh74uAAABekGab0pAK+CzxYrF5i9Ge/lIp3evffrDPiyT6ATepuPAT1aZrf/z+/gP7jITus1O7uHodNcOJJPxlBJ+lbn4Jpi2LkqjQ3EFTovrBEVz7mbVb5Pr6a9fhO6PNry8LF/vLIEHf7b996t+/J6bl/9PeWQ9794TfthEUKN3fgI9bS2+yqdHYmJB+78eZJVi+T3S7fqi9NL98K+bEKCF6ZunxhH7ef2+3sqByqWPapttJ8ntNfv09V9P0/br3tCTzKUll0me8uES/l7gnijsS+3G0Dkqg7aW4wgr3co45a6dvbvfJ7p+79fT1RfXk9JL36S8t3Rj3uEX85DX/pXJEbd3e+k+Tp+uqfr6+v2ruoRplZHv1E9svXNVdcK+UTP77Xruhuqvq5H1wj4jGqfk9UmW8Vvpu7OunLxrT0/XXJTSdKEl4kJ1TyUvTyqqkhR181DYShnB88v76hUv6+Yl305ZZROK/Juuye20/1C5fJ/rIuva71dQ4X8lVh/42AAAAbpBmo9KQCvgr8o7h5fIn5YvjTwusyFLXL22gx5iYKP/Mv/uXgj27ASm9NPd+4Qh9H3d9737hG76RcG3VhGeHW/y8n6XieimbssJ0r+eHcVd3u47pry8IHlYVX2LCDu4KO7D/+4ks8vvBH4dsejvXdZP6TVxeqErrXLol33S1ynFfCZf72wiOe/ke+yqZfp9+mzefO9UJ1vC3m1nQcsZCNMlLstuWTBqu3ew0kkQEBDdrzD4bsa3hiU/1WVdC3035fQ3qv2kLVgp3lWyfxwSu/eLF0kNIsJ+CfyPixwfs1pcsdFatCLZbcFUaCz3bveT0t/+nJrDckWnNIelWlp7sThLy5//GXd3qld977q66uXyYGtkh3Q2YTe9NZDbev0oRX7FXu6Hl6auh+Sr2mpcIl+L/CUn9a3xOCcr3e2/uz6ruvrqha6L64S8EkzE39v3H3e7tvd3f0Ur3qz4hpP/cI+KIZivk/2Zu3v0kd/SnoWunhPyHm/9kLzMboXkob6rrMe99EVeyivhQv5fkHbv1YskfVC919iHpcRhhV8l/UkNZpDiOL1viOXMu9YjiIV5vhDgQNuMreZ9JwVwAAAB8kGaoBXwV+Ydgj3LY2/iy7RkBqVO/5SJTi6ov+/DPhEmMxe4CRcsngjl5Jrj/fzPL9xoO3Qe4+G14v0OlGYCcixrpZe2qtsFctMgaKr+Iysg+A+7XgRLo+cm6/zJS89lpHJejxFJ383k/Xf8vv1lu+Fy+920CUZvHRO9LGD629rp/Xs/Sa/Za5Qt5hA3h+4TWXW8sTd33s5iu6e1fd9E6S2iiT/1dF9iUK7u7+yOEi//Yo16J2++8IkP8okG1oEA3dE2hkMOpNx73fZ1qZEX9PmQC9h/vVUfaV/SW4TJnX7Zsk9psvfeXHj0n5MJv3BZFbvrV3d+9oFN0Tl1E6YlveksgaP9e9u7Oit0yHcdFrmzk+kq3fJ6S/ku9Hk+m8saWkVzosmG/e2ht+vv6ve1JS+oR8hI2k7vvBNvXmhFdP1XX19ZMRj6S6b8irUEJbvlCL9BEoh7/PVaPXmVWJfdLXf0/Xa/Ju7hH2QdpwZvf0CQrn97VX2Pff0IWnydtf1XZ1dlSL0I+C2RmT+b239GI6TvrEluK4o4o+q7T7eqL6+vvtr5JfP4R8ERFTW3iQj0MSv2vf39fk1bffVlWT1/9nd8K532lZP0tX+iSmRhibO6owl3wq/lyfaftKW8uJblontP3/TX3Wn2sMWJ9Edl/9YEiAAAAlpBmsAV8FhfyfFis+hN13Ehlr8WTfrmp3rJcpOW/lLcscMeYmiD+T4INupihWmX6BveltN/vue5siDj3WapUH9+2MuiwktmvPPDzlNEi9+4elNdxnt9W+9nCQfbB6SP2fVie6z68pzykNQqX+3KwiFHsMpBUz47lhb/uY+HWydK2/d9fXqi/sTXKnl5O7y/7oSU77hMv/lYKBmTgFXr3+ne8t4t2X1vy28otL/uV003KunrVf+qPl/RfkNZuEvMS24bp5L95U40g0d03a3vsyB93t4CeVV63rXHytZv+i2ecqMu/Xvdiem1wXdok8Y3tpvwmTOvGcebPj+ZMEOafPDH8Jzim3ot9MC7RKqXvCT9IVP29xPvvZ9l3Pr3VuM2NlftLJ+RZ6YsWmdmGH+1ev3ffVJ70qKGu9iyhHe+5fk/bThPf0/uR3Tfovd5vDLO6bctwEcqzlr/03rk9q/Ffq6yLf8Il/vscQ/Q0Jt7Kxdy+7pcd3OMvu3095BN77ft+iLrNlyZ1p7iDbsjJ8j7bVZLFohX3010ZR/kEU5f7HY7173nZnf7OE8jL737+36+va5utcEZ4b779fXZZN779uqKbFbwkT1XKaVMJS9xDS5dn9tukuWT2k19+r7Llo76S2nvvfWoR8E8nlQbqn+sv29rmV+LJw+j8/RP1fL/J7O0eKskmxnxfQI+Cc12OpPX/4IyPL/U18g19y1/mJzqKSE1L3cK1vErOn43I28/r90TtJUtZJBPFYW1ExyT5/es3xPsTqva/C+SQRENOsX/Wq+y+qhzxB55bnlgsgAAAoBBmu9KQCvgr8w7cJHnZLJcXRk5AV8zfi+a0Fl8NL3BSThTOawq0+8gQXgzuzf015k/Y3pIq9x88TZk3pw0ko0pX02W4u9fLwm+nN19ffZeX8vylt3vJayenRaTdZP68ssIU3PVlY9j2WXeIEcKeYdxOFfYkkN6O728ntp24l4Lj/L3s6dOhOvqi4Vf4LCD846unHfSDtar9g1R+26aUIbdyLn+vpkHpi4bk/vIsxXjI6PoSUvBQ+pyWeCTPGA38HuXO1eq95dDmH6vSBDy/qE/FEpytlU17YUIG3S7Lbu6r9u9wCd6+tDtLBEc620WVApO0aBG8dSygz98/uwny2luPjP/p2bPw9Fy3KrV8tCg4jRd/8v29KL0CeVcqmMCRYnLrsaf9pibj7ZXwJLRLPzzOl5zpL6mjSV2XanRGCnMajP2VkoS89/+t9ZT5PCPihVjvCS1nPl/e3BTcMpHXvfQ5oT7SSdb3y7cM2Au7ajPf/b+rocObVCO071Wte6LW+lcCH2er9/+KEO/w1JeljL9ie3kcEgm933t4JOX5Qi/yGe31tEghu76uz+/uzCeGEkqs9iir/QmIEk/d/X5KvSesxL3CXkvf6rT3tLRPu3/7+6tpC0vpQi/cOzP+TyMhvoTXGF97rfPf4Ii8uRj2+hr92Wk9XfQmbe+qLd9U17wl4JDG/Ta4QwpcVu7ob8/uHjpc4tdnZd3fZ6LXdX5TcesXWnWXvrhPwvUm/M1v1mnL7oS6iSc/vvehxkjt3c9CeT0kqVUqf4ku73dwjWTDcmOShL+JuhPabmkNP/UlE/X+byYUdcfk9KvCC+6vSppWRIveRm3f6hpd1pW+Hq3VLBXAAAB/kGbABXwV+LHccj9Uy14vuzzzMfXl2Zcwx5iTphN5q5jKX/7BR4wAxC06P/045bqgp1L+9uO8tsvGxd5fpMs8XwDvWKfZ+H3KXtK3CfdXu924npcnfl15T4QO6MKv3CQUu3a5Y35Uzv5JaqisnjTvoq22VPdCdZZN2nCfm8NJMS/+WjPl++2tqcvLYuCTwwkj7WVX2dFLKVjNV1pLqFH20CgQ9zpZlnLixH4JjwRaJeUj2jUW5PF9BOUqd/KENyKsTBFy2YPPtMlxRHj59ta92ZvCj9xUVu93d+0Mjq9y1If13gYk/ZcHp7ve/RWGeAVLB9n1Ge/8n0lnv7TsbBLSrz04ZPd/ssEZ51zOatF1ZZc31uBH7Vcf9OKESb6mCUnrX7iSk9C4BI7uqve8tFaEvBUTbbhN287tbGvsEfh+tfkt8ntOuWKXX1kJHaemhu2edqZf3u9+iyle+1ka9oQR93d3CPKLEN2/N+7LZ2/5Raxresurohsyz717yV7v6L68vv37ptkhJfKCq7xDjvbtE12Rg9tXtVZWUr777svrtpF8I+ySZ35IJ/M+P47ClLa6fqu7onrlar9/XdWbe9tfCPojry6J7f3/ckm16e2t0foVfn1yVRdWL6pM3TRWRfq8LeCO9315dV2t1WtLkwzp3m9ZClW3DWaQkma8l3wWQAAAolBmy9KQCvgsflmGWCOXaRr1NQMbffr3/F5LDq4h1cpcg38vOjaDBf/cEBsdGXMdcwoAc7Ilnj4Q++9Lfwn++Jv3GlMFM8zXwQ/OWZCw4FUv2ZstqkrMtPl9N7oFZtxZu3IqRxFmAwI9LZy5L5U1r1QnpovBIXisjZPd7umoWXlgmCEwse2aJH24fXxNrdMUeCZ8/5FwSw0iN3399b3/LWU7cW4T8KDOHHtfU5jwmtbtCuMM+6ibeXiYrf7s/8aCCdlr2vevBJ4bi8tUvoxbvCl4rO+PnCYslINyaumzlGGDahsEn3H8t9+77KyK3sSr7cFJawwlphsnt+y72KqgSXFrat1Iv71gqhfqgSUpKX2vgqkTBPUpR5awGmtspyhoM4juzwVYcu/ScrrUNCOR6bwggq4mH1k93X8JSjkZl5kL4UL/eWK3tdvFdu/x5r94Bjex9H65l+T203ztwQlwZsHzHQD2Ohcf4c6ZoFr331guq6mmj3w+lyp1+0nUhJK3N3RIn390hPbe/2gQ731CPhIkkeVjvvBFe48XNtpd9fZPf3SPFvusv/7EGXfu8qZQxJ/9ZN6UIeCYzGZ7H5/vWJ1RfT1Z0hMu+1vSv2WCLKx9tISiQT3d+79CPYgRP9a1baPl19P2bV/f3+R1T+2vQj4qT1t1GKX5YTy/e79st79aLbv+qoT7/aVu2+i1b3Eb3vfqTe4R8E5qis32yest4kIm6zOlfJ98l/e/f39DV19m/o9b6y5e7+FFn8jr+3y+M6pgnNDsy5yhh92F+yftq2WvkmLe8n9X4RDBC8ZXkwp5hXN/aPLsrqhPquvaf1RMLZkQl3+/Ee/sR79dV1sWf8N+yB4zZV734K4AAACwkGbT0pAK+CvzDsljMWX9dy+hGiHC/75SBChwhepY3Ee4UKdr3t/mQMKEfpn8o7eP2Bfe4s3Z4BxyNTavfLCZ+TUQ5L/LUUp0JjqSd73kS1+Czx3TEOW81XO2/oZev73feOTNO4V8w7hvhL+nsoJCH9/8v274o5eRSH2F/Hf/vf2Ku5i2++jyX3a/Nme/XJwq/bCJhI+3fZDE6/fuOiu/d3IdIv2ke5uGkX39fRchPey0vKUMoN1z2evbSGkNU6bX3d8Jvehhnfzn7C6vZZbu6R3bniGvQJsah+oEx9hd6socL/sF9OsHVVQ2Ce2NC7Lfkpcyfr+4KuUPp6MwRDMpCE2vvDo/QoNKL4U/LmOJtw772YKOBI//BqfcW0qsEuU3BJ9ahCV7F9dkwmX/3BP4erb7vWt8OkPQ1+23XaSXgOGimB6d03gxzf/7povZw9a9raCXvk/tZXEylui3cgnt8Ve9VTbIMPH/FdpCZO6aEkR26JCe7G97hHscR7eO0973vYLqfdbsEL7VCvvdCy6rc2795T7vvCZOMtzdKrPcBxOiXf9r3Z9L01f0mNedeP8wQm/8EO52Xs9reEQQ3vd2JLSWi+i+7IXhBw8XTSngoK+46dfI3bghvvWT7cyr6dlTW3Usvd+0FLvdN93e79Qi/kBWItjNNvN5uT2e4u2yRRXiu9+2lb09t7YTKHZP+737x2773af3XTXa1l6axPe7ZPL6dslcoS0iGe5vd99CYIT3u3Tl3vuu780Ehs93VQ9VUhBL31aiP5LCcqLCPkEKq15NaXLYtZcdUXRP6fcslyTtKiNGKm0q+k+5YT8RHFNb9ahfZ+6+vrsn0qtLiyXnLv2roU2xxwgeZL7vnNu4KXugnd+X3C6TsWy9N9at19dVZCD7v2+9ehlL31fXTT3MW9w35I90+T1zMbGRXwhJLjWCyAAAAJaQZtvSkAr4LPFjM6o8jXfxmL8XSrJF06tuHgvLeiNny+WYX8WbmjPg9MtHfhHknIFnwAk9oqb/q9Du0s3uCkoC628IcUuiXT9uu0fFM7ZC7PCBrCHhdNpwi4HXuVmMhL4fHd74w9913v/3klu8/oTNq5kVeXvfE3e+WHuW93CvgpCVEq85lrJEfnLbcrcIncKkfx+Zv3nm8gqNPn0Ur+QuMNfvre9ViWW766/ZXd96EYqlDXWvx/8KP3FCnH0J7ufoun8IS2rQiw31fMP7su39VT6rrVFWr+2CPmf7s9F6FPMSZe32woQ+O72kSavdvyLw0k87Oz3f1cx9/o7Ne2WlGHpqzSF5fpzGoRKeyQRTG2UdFfXbgq2G/XXyqQ8RZ4we9/vLywRSL5GeuuEfBFrY+yf1u2cgKe7hD5nF2LzEXal54Rf6tagvXeE5tg+t1QYe75PdvXf3BNswenH3vYaGvrBGc7U+urEkmr47f6aehRIMaT80tHiaRep4HyCSay/LR339Eu9wj4JSEf6dPLL6uR2C29vz7i3utdVv6y2Xk/a/wR33YhLLBLfe989WV1Ynv/3RX/XpX/CT9RZOXvvoWXtv3vVX7P7qie9ly/9b/RehF+2CfcsbbTeYbL66+T0qJLLLBNzSkjfXdAhvv/XrukxOvBITD+x71QtdJ2vIwRFl9+hHwTkp05tmYbXjw75JLpf+heR1r4jr09epS3voSRa9ot7wqlzlksvSQtKYg+29+q9V0xOr05Rmyj+PhXXsv26vteI/JDC66vRoteT5NfDd7nmS9/D3xUAAAK4QZuAFfBX4sdsgR7nqa00NtF/9y2DuyvL/y8En4bDHmJKFSAZ917jfDy9feLGgCDhnFr718WTGCxnx2ALgkH69/rdwoULrL57uYNPhrAQ/9ePv521dnYw1zGpa34oaQPNcKKSbwXcn00X5Yw66rY+Sb3b7LUYg6PBLuXJ84tTd0Jj7vs3lpe9e5rvcLP8FoSfdtWkv8vru4895zZb8ZzsXDsRz3/1WmuxHcrn78lYq3+jFd3ryxu5u0zwl5jVnO/FkptO5EYBPl+aqs7zL/vYLN0Pr0CxNI3SzdHe13qqrwTl52OG5Y2/BXw5fdsDCTo92Zwc/raR6+XCmWFBDhE/o92W4+7u77gri0LGq2+sglo4r9bSZ4uiNpPwXedcqAhyUrWmz8FXOpubbDhGQxzQKZ7Kb8t1YIpHzT/2pP5f5ROC77zhEv/tIRXqCm4cHI+glw4W9ZoQpLwT/d3d+9h9TNxaukJ0/u+obArB9tFD48qxbVhana9HgjOMur9eK6f9PVCRAZJp9AtzJfa3tCzu93fnYXhFbcoLg49Q7z/v0+01+q00d4gr2d75PSS/frBJIv+7GxBpn5L8ntt7fs4fvRf/J7/5d9An3vd7kI+CYzHP5VM/3fvN/RfZX2/bXXk9Nrcl0Jfo1e6+xuqKTflQi993L/s7HZ4QfuCEcp/u9fKkfUm2r9av5Nu0jtld/W7361LQj6I/4qfxXcfXFXZa9W2uX119OE7vd4SvX1aj2NkzL965bu/ydFKYt76/wQ7z5UI+CU038uq9P8vVdCSlEu/8mfHtWX091iTO/lDT7aLu764S8mNL/iO5n5P0uINkzexfJ7a+SKmu/txMDfhY/DNntW871q+3fhbUt7yUq93RN3fZFpNwgyT98L5IrPlqt+oJtp9315NUqKVNLkm7lz0Xd4a93fBdAAACtUGbr0pAK+CzxYzOiJ4++35f98XkfLog/vpcdKxeWa9g/i+TQ7qOSjwY8IG5bILuBUAIp/VZm7tXC4WTl/dvDxQneK6rIPlBvLhTapOfOCd7kLNT/4QMX/gUbCheFoqBDZ577x5ePN3axqlrz8v/uqVVTiYJN3M9KpP1btUnyS3tZSjujcuMKv7BYFHd7VRnnTfJvt7gqPw4YJgbcD3LPKNCuQo7abKndIj+tyzn99lqVO/vy/7eQr37xG933Chf/bMKd78rBVFH352CNe3d+ECyby7vKPaokX6Gy7309JrE172gtxktqPhJZf46vLT/ff/7vahLzEvDsiX2ofIfjpy+725JAhbHvx+Wec0P24ul2/Yt5YVdFiRLQ2Fr5I+4bIVE99xlmvq30kr7S3GbyxDkFpaZy7Ou/D0WLYFL6wQVaCO65kPwnOXOwUfWO0//Bbw3BUXMm0MpIjs8KFH8gIt3lW7yxu20aSSwxJ/oEkXbu93q71n/+CaJg+WrMFXvhhfsN+tXBDlVQFG86hcKTqmLPH3j732XpN9m516tuhJE3yhqc47s6CQl93e4SL9PeCQmfrFvloRH8du96Wk1d2IO7+76PMQ6enzWZ17UvZ49E+X/L7ftQkt4sEJHvqaz0dv1rq+s136rSov/pAo7ve/Qj7EFf/ii7tuPzrt1bovdlfbXurd6tWu/r7p/SvCPh7NLMxbyd1lE36zQvtEBVl+W2sve7t2dgm7vit5fb3d9trl30T7/taL7XvXr1ZK9CPgnJXXF6zdN4kIiSa1L6/xC9cnveQn992f30vowl7unyYmN5X+fhHz1+O91t+Zdyy3iycVBmKO8cvQ1FK73k9eu79QSbv70SFHfE7sy3P5/0Jav0/SXWvdVRP11yQQ73eGFieidJk+TDa/8REHNfZPoRCsFUAAAAtpBm89KQCvgr8w7HHJtJf1+l7hje2XB33bD82N/LnPYZL/7YRJlizbRwBImSW/rb+8vvaThQoJ38fWU5d83tf15hFxamvxpsEFsdsuX5j9u5VTokXQfXxCslty6zvd/UWeifk3J+mXl4iaHOZtvr6BRd7u60y+EOGWh23fnlCvmHZ3j9de4JiT7vso12t3Z3yHr37vcwYnj3CRZaTE+be4nbmXbfj/68Ft38/pfL10WS4ZaLmQo/xQoS9E6g+e5Qt8Fe7e9G7xBXn7+4Li5I3lHzjT628vUSkvd+t9/RZzx0w1Lz5PtvyyrSlokKbYUEBl+u3LC7HREO95M5Jgslbft8vur465bpGFA1mB9H04W6DrvBUJKoEQ1mvpv9u+XQuGCFAvGy3vdxlmvvcmZM4a7c/c4Xf7gnhtLSw7oRJHrRoej4J9jrdO09UEqThuGq3Jmz/ddlwl4JO5V6/BTPtvBO+thCVWHrJix00Pct9Y7VaVtngye25X6QK91D4PcY8eB8YaX3/pB05cJHH2P6cE+9zJd2Cw6o2jv9F7obBNgQ/vu3+79VC4kQlIdZ5pBnDdrJoWxWma73vk4R8Nm3fL4r/cIbvd3dLVNbFeyRZZ17yg6X7py1mAvViLvvf8JZhx2gndPZ5BDEPCV97PBALyJVy+fWky3d7vwSb3c232iMQivlKYr5WMv+uEpfvp3V6Ozuu/txF33f3q3etd9WJ9CfRIIt79CL/Ma9+oJCuyFfu16PBGW92NJLaxdOvbSt1fada+717eSmTe9KLT5Pbr3bXuyFXv1aEfFS7+Vq/BER738rBJd93SYIyu/br8YumtvZpSO95Pb/+rfvRy3eTy7TkIIglyb3vlCPhcy5PKgovZYSQmhd5eT1X+gQne/K7qqE1bVVRSXvzOFPcnL10/Cvr6+kSq/FeyoRHS4/u8MZclfrWoI+7zQyT+v/Vf30X/SJ9WLDgsz4EeAIRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vAAAApBBm+AV8FflHbut/y6uQc+Ll8JP42VpkGPDhJYCEe14fo//GdI3lYDjUlzY0aygoAhHq7fTVvU+sv725CzQv8PmzAUxTD0G6MeVkER9Xt4LKSs9r8vve+tyTHz89b3aNwTfr/6CW8PLs3JneFvCgQh7M+XQrAx1Wx87QpdizvhxV7jS7l6jcTveSuxAj+nx/S3kh/dWPcYXywjE77bnRcg4t9yTBu/4giaWmSWPrfJ+3Sn6KcEK+KGT7beEfCuHhy9tsEu4PO32motHv16fJCfhF/Dv1tevKRehPcKCHcbNvvfu8doc2ji69m+7HUc5qa7xfiVaj21eUpSv17QIyhBx+M1RcZRWrxt93k9psTfxF9jlhLO18ValYIGuE/m3L/6gohmWl/SfIKlh6XCT+Ze9sKXP3t4dYOsBKNJwdxtMRXezJ9Ll+HqdFzr3rfQfyY8bT2l8g+/h8tb6+vwYaJz3OHZx5fGV/e29etdv2rI7/0Jf8EV7/N9Pr3NoZn2EfZqunX26tMW/3sS79lkaUfqW4rlXlXu/WV/girW50RR/deQxP16PHa9671WdLnmJ/XXItQh5M3/gmNI3Y25/Lf2Ccr5dz5qvRXN5eCPe+WT7vHvf6BDe7t2Pq2m117tasaQEl7v29qlr8FF373eEfBPXTk9tt/QJyPd3d3b8mnfuCUt3u//o9YvcEd737/Ge3q5afeCTe/b+16EfBIRVm7vEhPzcv3/1yUXk/p39Ffb0mbu9a5L3+2Xd70spLl+EvBERa2urKV33fVFgkMzcw7H3ezlL3/FZf7v0gT73d31C1L12117shHQ+T20+8i2u5t374WSkLq7X31L5NWZeKmEnzr4ZX5CPfWuSyRUifD3xUAAAAq1BmgAV8Ffix3CPz7zX3Q4l8Xy2Qs1VFktI/i9XP0DxW/BJw89mFC/mJI6Gbty/+2GMNQQ1IAZjT1qf7jfgyLDrUl/17QeLYShm4OfDsWlLMJRr54NPD2P1l3ljzZLEj9vhqR0oQ+xNFLa3el7ve9flst+5SvG4wthV/YLAkf1uYRbqaR4Pur7yx5bfdW8/uLBtmvojEmH6SsqBDzv2KWnfvrrIV5c6PUdvff5KUz3l/yvLd1cJeYRLGaV+2Ccglx3dy0f7L9O+r9Zi47Ed5HhO9+m5C/+WCEvLjddvk1qEvManG8r28YR3d3u2727yMtfBbBI+f1Xx5dLBesoNJXaPFk9V99Yy7GxXG0/3jTdbeo/Q2jBc/50emEPElbXQSM7uOpvIe9J7TQlFvguLcu7vmePs7z/yViy/5fCPgpNKx4Oqt9e4dhlWXbiJli/jo/D6LqDST+3Pf3NyP/tSvBDE3885uX3JLSV3T5Pbc/xFkrfuUpV3otH7T/txVPc1Z/7dke/tkp1yeTCS+XfZV3fd/fWhL6XMxnd8w2U7eZXb1vrIIGK3jf1ssEu0/PJ6bfy9r0lYhDohJ2ZV/jrco9N6btfKxvZafdu7lk70eLoi6+qMV766dGwqq6u7OQmX3CJf/f8El5Ye6OvN3VfkX5T7v1RH/WpPXu9fQi/1f0QhH3tPOxJd0TxW7say33fi77HcaErLVAiuX/Qj4J7ZoUy78vwRmurvXsbd/UJ5++9+77+j+sERE7zA1+9pWqjol70T1y8Qt73CPkx7H+iPfs73+WuTXJ701FGyg0NypPWNz/qyl4CD1Pj/pbVrhbbF93e77G+5O/vWvUJay13f0VEu5f9wU3ve/d4PZoYuUpdJLRPpGvafZ0Er3d3vyMnisNeII586pVpkuN9+TBXAAAAC30GaIBXwWeERnNmabRB/zEu4Q5tpYb2Z3tyv/xPnpHflFY5f9S4Y8WZngBi95TfZGBL6M/cFJQnejxvj9u7q/7RSfCBugIrGQ8GkRRzBd+MQW59gjO9TOWduz9ef0WInzeife+JisOD8/cOOlhXyDOG/fG5bRK95w8kt3lqjSakKNKXggbu01Z6vDjHL3vYi/FOHV8E0C/uMLKWj7QcN7l/3lLSf1e52S+wywk9tJt/fd+tU+T1yVeta5bvy+tfCvhQVqicx52myeqngQf1/sNX9nXlr2wp3tMb5OK+h68Q2f228u2U2/V2Ji72d4f6nu6Kgj3fd7M1fVku/q1f1X+ki3E3fdwEX/Wfz71Zowd/tf/BHM39Cb7woIcvcw3r3Sf7vpsnw0KZWqzwU8gVOWNcP/P8YJiHqxlG+gRHnBlzJ0dgm3eYuNhqXZVF79yZ/PLYuKIZKGH5CejmXnAtrZI7lBP+5MfV+bapSoEvO/nGj/P5PXCb23CUgOrIL15l+tz5Q4+0Iwkktw1WPtLmFPJ/7p9932WLK07U4/vT0IWT2/1wpvemU49d5g1tPrT1sRGafl7/7FoEQm7v28+RXhHxJCbQ8m+rGT93vd3d3e9V9l9EyWL3bgiLe79KCLMy+xCHmNWb+3u+tZ1f8WL3RPvqxF77v296giM77dl9tXdb1zbvCK/ZpGU+vzFy99jZS5n9U+771y7ddXbrXdG7vvLe+T7p230Wsnu2790vFYR8E5i+5/L/vL60W3WCO79eqv6ffp8dBHyAQdu9k9tpPpSXe+jLt+1BJvftvnrqEfISbrPwRkdyfarPlVb5Ppet+7BYJ3d7Pe/dGfZFvaqEvBCQnaW1LvIJutWL98nQgkOQ67299JbyFtZQsT1Uvny0tyWL0q3XsvreI9MRC2aCKkF235nkgr2l2nSffDW/3+445b6W+71qIve/J7JZXvDK/8RZhHJc/c8VGP92w5o4K4AAAAvBBmk9KQCvgr8w7MMu/Lpka80u4X8xLute4K/LIIEN7rtKOh0jP3R5l/fw8V8xU8L3e5cqRev6abOxxndu5XLt7BK8bZ3bZXSosT1Vf5iu5o/ylGmWt0wqvcFgSXI9333ub28eWEln+/yrykXgioFcd7e79P1mu96q2vKqJ/b7077+hBbfd3hR/gsFB9aDlQnS4XnacbveD2xMVu/Db1+iFe39oJb0+MNte9E+n/L3l4JO0YXr8ucNAbUHC6817/rUJ5Y0QK7P0DvJRR7vbucput3d1r8E3cYry4EMpkPpezguYbc+xB4RZ7D7ipA7/BHu96y92S+T3a/LIQwb37PCF8XMHfOrNsicrFNJFQJePZcjQmed/z0X/5IS8Emte28uHbu5+HNp8FcXeGneMmUEdfh377sRRyt/x+cHWv73cEhSr+svu66J3qLK8hIiqQs/y/X8EUqvKe7J6TR1aTYkw0seilgzVifrBMJe8gazdtJmogI93weWta9IEm50dtCHm8N+l/9d7uzXerP7oxee63cJ93u/qvdn95DE6FeT0m6+mUkUyS76s2l639Ai3N7c6WEF7ZTPfW+7u79KlkuxNKdArlIVrfeje826EkXq6Lu8Iv8EZJ/dO+mbdzxT239wTiw2hyX93i6Gt3u+66EsT3edA+qHoVu/d9OW7u8nr/vTU6oFl3vd3e7shF/QolOTpzN/QIpfy5OJffVS8udngnLu+726UEd38u8vljyfxZj/8N6XU3LNPTkO7+kxPLTd7L5G9ioLbG7u73lCPmNGse/lBLvbvd95snSTEmp9331Qn2SiV1iDpJZCL94rgrI93emUNPb9dDSFlh1bMOtXve94S8NEErNu/w377ouq+vr6ol4sECfy+PWviSufBdmIz7lmhgDKbRSBQIwp4IeT2rJEb3d/ZfZfao6dSeviOl1lFPf8EJX0ntTzeFslEjrX/k1ksTdYY8RjS9n8v+Tkpuv7ujfL+P+p08kloxNngsgAAAlZBmmAV8Ffix3DFp7iRLv3L3IV+L2cqBXt/l5dJD5eXO1hfzEyCQzM/BBwR7K5P2jfIIXSMM9dkbSydfLUG5eX2nuw6UlOEHJNHmvc/2NuXqhxyAS/+4RMjv4JtnrRoaXUH3s7BEVbKdsvu+frzeiydXk+/EiWsRzS45/+Upz0CH8d1/Cr9wShKUUrduwmNbuCHf37fuCUTmLF5z9YUVnT8Vd0ngpwVErelfLuTfcEeeFUEK+54eT6//6LedsKv7FDHLGVl87AIX2Sl/3cVl35bqttld++9ZSevZPSy/1g6S8uEy//QIssH633Y824mluXe3e3Su4dsj4EuKnPZBR4/2dyYR3Us37hnq7b/9e/RfX0/Y2SG+R6TPOr8RyP8NyT+CHqRMcu3YtOGOj9LFCby8YId93fP6FcV9FjdXnmgWIG14Oy3XId4T4aZvWNuEm9XRkp/SupijTb5By06Etmq79RZx11vQt3ug01T95I/3+5jXMFP6EvT0SRehLzGz9a7sEtyCb3d7DT/jCu/Gp70333f3+SYWU0/sENivdvpXqjoTSOg936rV7QI77sQh5DTevxPG7jGW/L8n1/4St3zd5031Jk/Wn8Eh93y6P105jvvsbqruipG7J9eX73d/TLk8IeKGPq2n37fSQTEvZ2V+r81ev1frkrd7wn5CT+90XgjKfrk5k9V/TBHvf9L7V61yel/909evQj5CLqsyBJd9p8vetIvenr+FtPsXlsWiXe61hT1eifd/OT31m76Wnda/baIuGCfeJsoh95f5u78mHuoe+MgAAAC1UGaj0pAK+CzzDGdzEr9xdHaivmXl/3Lhnw4aPTJRqp/8Z/L/7gg5Bogqgb34R7vG2d9us7S1/1+EC5cKBMrL9zjd9wVmygVyBs7wf4ykOcPtDecGT3TPzugQlSb0qk/b+/stUqq/hbzDIbtcVKwVl/u3CnOiZCu++Enc0P/pOe2FMQvIvr/rVru7+G+/ff4dO0NDkLeWu+nt3/Un69wnfZFPuS/uIvmQYSclT+bceOOifVfl+WKny7u7/kzIjpX50i3scJl/8swxoqR+9ui3frpzFfInJ/XbjWHOHmm14z31W+++7LBN3IO5Vh8//l/39+Xv8EN3+hL173HGFbvd8/ne/cE0dzfFoFwl5N1x+hi6+icbLCQkZBTi+7PSoz1lVKsbESLGC2ZKGL9SFye0m59Y7aa/MjfCJ3LrU8JvuXtwp4InjlnanMaUBNtW3een9roKSC7n/touM95P38vhHwQiKd6/CRONje5zb9PbiubIfz5+lv6f0mXdLQ30/WCLOblBvdOY2HZXftoEYm737K133cpO1+EuwVkLzhvL3u97Ls7EnOoob5Q08nu335SO/V1ornWqXWKuGhB1ZcOGfsWX8ntWRNeCIqKWn+DtNX71foTVvaVyEtFZN3T++/J7f5t+RbdPZ+GkrvQuvVQn390vb3wR+XKhH2ImZv8EhbpitzW5Wj67wUd3u9xvXu6BJvfu9X7wSb3fr/X/X2/eE7u7vftVeEfBERSf78VL7vPl/l7vtQQ7v/39tghLd79r2pq76dGirk6E2Jd77rJ6bVUo6CLL+CErOPFjLk+tfMIRX7bQmw24It3uba2lc6+/J7TX+Qjv7Ku8Ee8O757J6afffcsJk+/rwQkJ49TSez1afX19/f3Zrv0nkgjKZu/dydpQsT270t+/v6+17SJe+2nLV28xa6I4ZtayfIoa8Qe9J1pV/4iIKQusYZnt7R39OP7+CuAAAAChUGar0pAK+Cx+4RGc2YzFqjZl5fJ28XxnwqfNKi/5eW4XULhnwibOfZy6MzwEI29LZ54vO55N6X93sIlKNGl7H5x6tdtB83BF2PXLrsRdfhxvpxmC0EXHX2R8t6EoFpVSKVsxckfz2/wRT506prL18gvSqzPndl0Ju+7TvL/vlK93CnmCFSPbGX/2wU807RZd0alWRY1u0CX8v7cg/b3EFRpIbABnf6svv0n9v+FH7ihT27n9yne25YN+CX/u8spzq39693/gjJOHJdtWlv8EZWW9QnthQQ7uSL33s6iymNsLG7ywWY+4Ow2MFjizK0IwxF/Bv/oa7oTBEeG4MJfYye6W/6/GEw7KK6dXDqSjc+vXdx67vywU9cLu5ykOP9IzkXW9P9OE9wnEPsQ+z+723uPsU5h8pLD3N17X+8IFd+93Ivye21+J9uCiG0Xv8qjI/rr6yRw77Q9ZZVr7S66oEl7u/Sgj3vrsTy+X/CS6lBUR73vKyXeu309J5PS10qRa6cJQ0X+vu99kq/iO1cVBEaGLZ+D8SVye5/+rd99avS1wRXd/aq+EVnUeCW7N3LNP1SuSyF5b2Xk9pr8rp2pb2pwurP0tEIid11pL2vWEecEnmn4XZTNItdHYI9K7HTQJLv9r7WqofV927XRXqpa6ct7/Zcv76wR3vaEX8UCQlt92+7CW98n3UmW+lrX/16q169OUr76q39r0I+CQ1a5teJCIIq1pWsn1k19+rReukKhLw7VPH8Tc3J/Yiv++T2W9LqxXHfNHVaI+lsL6os50o/el5PSwq8p0Tydvy9d22CaZvu9mvoXMP33eF8kF0+EzvVmn+9Pv22ZFXSw54ga0twxPT4K4AAAArlBmsAV8FfmHY41WxD8XwxmXd7/NlHnwx5ScufGeH5wab/IzFzlu+jN6ykQK3ejR/cPFD7I51v2BXd3d+jPf/uMNcwi4Gz5fuYTnBbStKr/8FZ7tZqvu5ezTEir3Dk9vdGK/Jy/li/NUh3m8vqS1IW08wsX+9woEOaF3PTHlife5v8E/Rc49OvwQvH+/f+ynZv/R4SnFeQs8aFjyel+Tk3vr3v9Flvf6ovv+J42u+WdoS8xuNGJf/bFEh/fzpF0e+JcLncd7f500b76PRa6zc10T6yF1f9aqsT2zhecOS7rBLd+5Y/bbFpJehRbZYdMJe7lyr3e39HOv/2gRyihEzBofKH0uyghEmqcW3/RmKp9eWCEt7uaTcT8kEplrOzPKxraTpAm7G7lFX6668o1uGnsI+CQRN/t72Nu73DyLRuRGlm5z7j6YfvrGtF/v0QEpEcn0Arnj4Rth66syqxr17gkO5l7/EvyvdL3eSNif/RJMOwZnV3HiMbjerMv46TO9rX5jy+87DcIeKDir7pdwTSsJ++fe/rJ7/+i9XgmsZj/u7VX+Ihu+6+mRCX+q4UT9fE8EO9317QIbvexCPjhBGIzSN97k3f9eYbe+j6sfXLrRMOv6LvfQki9k9V/L0l4q9xuj7t6JBJd79CHkxq979xAhkWPe/qCQ/FdOMv/9VgkK79fgkvd+6sEV3e3WrdiUve6vtLl7LJ3fmX4Jd7ve8J9gm3Sn93YcvrJd/WrdfWbGNfrMbmp19tH7uy3fupSol3supN7hHwRGrX6yf1/o/6rV/Jqvr7q9Xe7YS9E+XUsuun1d5x/osWd73d9+Iwr5hF736YIz7r2rxNcuurE171Nd3e9pEV2X+pSSRkx/kwu/FsXN17pVfXQsr73vspO6honp5n+IKa8xJ9+SIw7H2RDji/V1cdFw98bAAACtkGa4BXwV+Cgdw8Ny1ZweUTJbl2DmfX+X/fE+XEr/i+yLPnoF/MRJIhmgq9wUQIPj35v7bRYdvea2U04kDuNKBOvdr/rdK2EW+79TgXf7z/fuEDZ+VR4Q8eyNqZk6tyywEb13/1/1vXhIuNyeXPxZSZL+acK+HQlNF3jauXoLicgrkPh7bkiV6/eXh61v733CT5xs91/Z2JO9zFh0l3+T3/8vPtE9f93e9PyxFN8v9/ibLe7wr4RFNyhp05EN8CTb6pL+Vu322CMrU73q355ShPh9v3l/+XL/ln+4YNmmYMkJWeuHvv611rJ75f+98Ehb36E9sKCHc/c1+0ve5/c/u1i2T2kjtJ8Ja3LKhHjdmI4JRNHedYIf47Mv/bYKDZfac5G8Lf6KlWxcUST8/9Hh2yfKGoZ7LvnYMDdcPcpn/8O+GYulXmsp96rGw5t/bVl9Vwm9uhm8Mo7GC9sjHzzt7nZf76wVWzrtSlMA+EXFK7ixyJlfLpwmUj+2dd+7UbBIaS35d4Kz5npoLLg1pYe6SHuO4JJk93N7qIve5QK/akmiqbyKbyr3eLQREDDx/yXgvjt/yi3u+jwRXu/+ipdZPVV+oS8FZrcfpt3Pm9daxaX+/onqmU8fqy73pcyyfd19sgMe9+k3yH3fWW99KmSiOV0UeykZhHxwQu7uT77eqY1Hv9Eg7chnf1XYmCE9m/K7yEvfbJa2qEbvc/wlzgku8Z7vwW3fPmX6q1rq+7RWyeqn76s6P6ykffXbTegQ7u7oR8EVtY9Sb2JFE27u974qgQle3ccny8v/pq9v16rr7Gyld9E9Ul+QEV33ASooRYy7+Sit2NKjv3SvdF9fZff2kIu/d+0l6E1iRujWPJ9uqLu601Rve7ucfu3sQTyOFV15PSrf+z6r9PyUd619OGPkXLIQru7gSIAAANIQZsAFfBZ4sZtmwpFVKpWVfuHLmOyFXP6lr8pLumGfBJeYFATf/Bk/BBx0NLbIEzGvHyWrCVgRM639162/ymp715ekyysWQhy+bCgB69zX37Ys7lzzGxXsTBJTeQPIxUn79ZYIt3cYpl/3wrzTAl3/bPaNXlv4V8UO4z1qE4fSXLl/eysKXKj5b+d7vx9f9l9/LBLf2dqi+QfvX+8ivospQg8zH+0Gs4duvq6emqK6L/168eQhS+fuFC/+WER1ryiPkufXfL7/Qy8Yafcjr0zsOG84/Xui90Vi9ylm/DpdV1dFhG93KSe7pZEp/5oJN7v+Et3vThTbChBW7hGbXx9OFt/ur4bTF2c/ZZe0NtnVjwq4ozFOtt1cHBCPfRjZ7Ne3oOpUbqTgYRRiMP6XxB07GdzCz/IgYEaDqvSWSi3jqR+kgkNd+kWOlv/hEYdQYczN6ETvcv+9BScLphy+1NDkC/l6mf28utB60LRs9sZx4SFHIrdEvxsHW6AEV66916S9b/TQlEv01wu8tKE9w7t32oRcm8CX+f7qX2W2Fqq/orDxPvIC3C2a8DrOvVVGPPX5f/fJ9K7f+4UO++a44Ei4XjKdZBdCZc00+X7d8E+R0xV8pU67/gl3d53NtDUWmxasUIKZt/pkHyL6Fnd93wj7sott/BhL4JyBqEGnsPDwHYkp0/rlcEvhN6Z3vXTe/d5F79Sb36l7vWbiyve8wfq8QYJdTfHRPv15bPd/a+wT+fv37e00Ce2Vd7vdMwh1vc0SKdy5uxnQP974Ij3fr3BCLn3KmkukSDJ+l/aNlT9Wd79H9eT3XS/sW0SqqlryS33CPgrMnWx5WKdW8d+CE7r15K1T22UEN9+6O1r3Md9/gkIbf/s8V3d3fS4pKxp+9X/Z0vbdXV/2Pz+EfBEEoce3DbyvBSXlxuW8VxXSho7Wt5Eu/VXPXovpzG2yx2LRD7usn2hXl736wU3f3u+7gI+Q1NV7Yvdb38i8tCW2lnf6t1L19ZLv19WJ1pgk7vXSM8nwj6otfwUmTjVN9vDX3+XQvVX4z603pJ3jYdd5B0Kk/r8TKXdu/wne/d1l+Rae7VyqryYV8ENa6pb9V9essRXvJhzyFU5zBZAAAACl0GbIBXwV+Ydy4t1Sj8EmkW2OM34vgt+YP5qQyX/2wwSkMxd5trjLKr79sExbIPaSsT69JBJDs7EGxDEAi3fTFz+1LLE7vBfnMW9O63xB73vffWI2y/y/ua93rdwnu9wh8D/hXzDqmQfwWT7RBhf549O0UTNv5AZft3oFPL+YfdjQp7ft0WC2OOf80cP0hJbzFcbDr3edgjjyd8qdFis4dvPnVXglvdq9yp0fCy+woKufY/PIkr33D29Nvdx1/McOc80+ozfiTuiu9/rJ68r+be+mwRzDz/XV9OsWT1T9zratFm6afL31ghzsP1CZffpwUiHdw/jrvNuRW+9xL/+N50oZitGiz3nTY2Bqdd+D7s3y/tPkEj/frv+mJRXbViaEPpNcUZxmwgLmDN7o1wS4Zl93u+18TfUtU3fjw3+s0g17wntjhgh7l/MpnGXuVeT0ki/cPd2iFiD7sKsJB+dl8NtQ90ePmn5PX/UJlPP41P3TQueQ0vjoWu4s7WYPBH0vVPtwV3vuntO7nioJLG92ye3X5K1r3QiVVoXbsWghfe993CPivN5v/Zlx73xmN093u7snv9L2un33/vd9+/vk9NrLayEh5SSHe9lI51X/75EWx7WvuP8hqHOp9CZGcd35fW+WrFlqjvJ9f/LY3S28E57b+Xt9iTl9+fwiX5fKUwi90T1yL6Jvf1+CEp2P87G1fvX9Udaa7Vjb9b29a0rZGCK+7t6wj0CMy1+/CJd3P7uX39H1dgi3u/fVjaJB4iXlHnT6khaqR28lX7lVvWvX/tdi33ICS68um/3mbhTMvL6+tFSp5P7EYbiXetF7V5RCJ0LeEuXeXPJlJ9JNfqzJ7afVCJbvhjy3uXLd1sv/mo9aTzilOkCRAAAAvpBm0AV8Ffix2kG9X2SUv67ZeQ2dvzVNb3e/DPgkJKPHk4+VPwjzRIfDv55dqB4f5+NnGX/bwgVAka+Noj33w+3x17hA2mV724Q9/i9SEQkgyMn7e5+WOkx2JVH3IfLN6/Ll/X5e7/Lz/8scjtfkwdCpf3fCgQ5u/u9vekYs4v8E8Y3eWN7v7QISkc0NbtRPVL9z+pm39l/MtKi/EZcgIjvGph2gtT8sqSdmE39gnHCRx3cVu4eSGMfuXHUCn9HYWO5JvlY65cb+7xbBBdz/wiX6Oc/KSw7Rt/vutete3+CTLw4kn5fmIHJV1//TX6L0J7Y0wl4rFZDRYOEiF+Se9tv3epqBya+55/aCOG4eTzc6KCaUP+uP0345pfECUl/78EP5rEf9QSGKSuG7Sw+xI0ELx/hfd9+6IlVaZKIkVKfhMlNLIimQWni0FJaEF2aoWFw82n9z4S+D3lzDLz6TfDujhuS15S2gMCb+IuVtDzQj7APKZT/6enl4kXu824SXRYwZdz9z+xLruO1d+/fSleFNLew7cEuQsOaS/QoQ3/h7hMpkvebNLXEw97pxsY/IHu6xjsb71i0n4JDZx8wLmFN3RBLv7Vbf75NkvcI+bu3XKWGb3dfdO37W50ezl7b9WY276rqzbu+qRZeKhMQ+93p/J2VM7t+ipWOiV78otqnCHlCVtW67iwQn3evzC73pVrq2M5vrMW99OEi7vy9LXBGaUNXervp9WvtSvVkI+PNPsu5/rVej97gj3fl1XVAiLd3+dYIe7v2Pr/o/rJu/eS9+l2k3gkvfW3fBHd7+Qj4IiVr/8EWGh7Hn1tlvz/sWXbeX1orHmfe7680xs16X+9F7qil3fSW0kzMFuWne8YBHwTiD6ra1rL2wmS93V/NBIVdX7kR26lV/pX6fvIV969QREnztRPav6rzFXX0+73ycJdAlMuTl67aQveaiwle9yEvyI96o2FchVlr5Ou6zXf8oISOluhjJeP4/6uXSVWi90pvFfJ74e+Hvi4AAAMNQZtvSkAr4LPFDMttHFr5f/cXBm9wysv06/rxfGIqCXx23C/mNjgUXD8v/uM8IfS8Ej5ZBmDa7SUFnnrtotOFZf38eWc45E4kNwillJE271u40meB0V3PtHVHwzK+Tk1IP9TVwj1CWXb9wlpFJZ7kDSU5PWj/96ljy55eS51qXfL/1gjlln6UZf98s+XcKeYdDsSBZJ7vZf+2xud5TvySIfbR3qW+lsH7fP6vL7e+C2POnu7vb8Ee48XX2T7/u/wUFMJon3dyppM8sEWdjSrZ5i3ekp7iSPR+dj8UW97v+Xtm4TftCggf277ui/u1o+vo3GwpLQsvwRd3qiek2Lpags4QaC355mrT9X/snv/S+4I+UNPqFNscZ3cO4W58727y/u+M0Jw3LknAm94QtrRWoTsDn8eJCkM/yuOul5B9uqxrKa7yXvjyV+wYSX+iNFT0yV/4m1bOvcdk0tJoovdwm98ICne7jLdWoLp1M66x+rE8wRXC7aEUjHCkzbnvM9HqwU+CQs7HXWCM27919iWi10f19ZjYczVqX7E7ve0Eb77vL7hLcKXepgl2nubuHI+7+7w3QSmq8WW2+96XxJnmNve+6ye09dm6XdHi8VFCJnEC6qYe+2iaDMYJt06j/f+0rkr0VyEu2I0zs3rkO7f5WLJf5aJN3+TrJ6J/f57I++nCV7y5evBDu/Ci/vyO9/2cn4Q5PYkEohM/vu/dDa93e8z9qJerhNxfRbV73vXT9lr2nyQQ3375FqEfRLfKCOT+bdFMouf3mf/WX3J8pXf2W977urPFm5VGH+3qhNH7vXqyVeEfFGtWzf/fmStvfR36F9Ner+9ZK/8nqjEyT74U8EM37Kziev0e6G+s18l6oXchJBJtE2+S+m+5AQlfd9vxCJHhXie63yEouVV9ZC3vr6/MdCO7mhTyCa1Rf/2afe38J3vxlbp9MEYt9xSniLn/1irNK1w3pU4iMQK92nS2dz20Lh03Q6YQCa3fkyi/ppncP/nwyXzJvyCndVrfdb+KiLt5Ot7y+X+97w98bAAAAs9Bm4AV8FfmHaTrcJ8da6v/NzTy+uXi47GXszX4Y8xNwl4Ok/BBeP6ZZHnwnzWBv0ktNk5eO/+X99scVzj/jNGC6v+Ezbw3F23YQ366Tc7BHHnrqn7plf4844R/dqp/8v/qEtq5YP6LC84y9j3K6C2lX5ff8IQusnTW6dvb3LPC3hEIDee1GZophE85T5Tt1uWCXcHIKr0f39wQlL7Z33Cepb3vJ67r6uXuuoU82Vs3l/8sKE53spB/7IWqYLe3Zbg3Cse6dSfbWe2yhS9mms3ndGLG7LdDSx79/H4kuCZ8xZr0jp6SysJ0rtXIDhAz+3di293yfv7WJvedWYWpL12fgszuePLYdlBkjvA+LJ6vSQlFhHc/tt72G5O369MJ733e9rBHPveoT3GCj3u4bedh9OkDG+fvb3qs7L4YyfFCWz6cNfPX5iGBNxvGiaoshcNXT+xvJ9Pl04JTN5eHZQuz8N1bQJof7nTEjovyQ+R/dJuReEXv1hN/MsvbCl3c/c4fry+3c/9hSW4X8BF7yvA7aug/4od7j/37YKTn3+98Idk+7FtCO76ovvRe09ikC0z5T274arLCYvP4y4P8lf9iVCXgjMnv34KbuX297u/q3IJvf1244zt73vvr69r2CXxl03exsFr7Xkgiu/W3F8Ed3fqEfEmTp0z8qO6+JBCfd+9owvmbo5URu1FiL3e/WCUtK7v11r3WCLjr3/ujkBdJ/e/VTa58v7VJwj44QT/lYy/X94sS97vb25r3yftk/ghmr3pcuv+xdXk1BFvet9uvQj6/rUt792tasUte1Vll3u3+idq0hq/Ra6O65F9CXixCdb3bV6Ev4iyu/qlbouyftf4IcIfb/btLInpegREu9/oXu7vvuaE/IIVdfWI9daRRLu/piCZgeQPucf/tHbJ+/vULF+XCFV7psEnd8uxO9V6ifX+Iwz8mTXw98PfFQAAAA2VBm6AV8FflHcMMny90fyykrBh10qvLDGUkVD5p+7kzwz4cJjpZWZON9/XuEPD114CHWM2+NR9wIUOP3mvMnY91u4fKF3ftQ8ovSTON7/Hu535Y425TQ/GdoGoFp4w/pPp8srcsecO0vk+1Xc8h9Vl/ovJuf9bTglufv2rnFl/34V8wyHljjYnNkVe4UJIjhhPa+E2fl3d3uRsH4LZg4/KcEdyb3bTR2eCModrvfLTdNlK9905Yjkbc84UT9UX2ea+99YjSe7yofkveFPGCJN87zh9E2gn/hPYcle22H92g6f/0U52qapze3uvDp3nIMw+7+5Hr565q8kMU5+bcE3y+g0PF1/jP/i99mOdxGQJ16lTSeJqcXWNkJW9mNkHmstqptwzN8D1qN3081L8sVLh0Mq8sn79oI3u+HZJXQYb3Chfb9oFIx3dt63vc0WOjxlzstuAx2J8iSFLOYnp0uoKRIGdla9eLzmDoZw93O13dPVje/fSbi4RI5HkDXMzfOrljJ6bTnTWFNExAcUeWtrac8svmTHFxbvEoKSpysCIafnHb5w0EPtr+8bPxFQJW4LO74T8EuZtN79ZP692UFM/uXEw7Rrcgd+z9+sFxL1gcWj5geEvFtvb61oEZ1La62QzZpWnZrb92Xu/LR/i6U9a6wTTBm939aTUqIIe+vcFAt7Jw/F1XvfrX/UgLL3z5299daJKEewVXdM/bX8/rdFWCmm/3GTh3cv3ne5OzfqYS+y0d9Y8Q6HvkCkv9bkv++T+l89X6PBKQmmHDA3d/K9Uf70gT3d97xQk67Bdmve6TPRQkd72q9hF+RIU+/tF76V9/rbrLcoa9dWLlu/shDcv6aNy0hHwXCI3Tt3sOyotUXyqloQW93fk98aX/8TDd0mP3vovsTBF5Y28lZqoq6rokm9wlWvfsmm+joExc/iuKPf0gRXv7ouqLLu9UeYmmb9XvfVItdlKYrv6lNe724kmKuX3u8JeQY9/TMXde6Eopaonbl8ircxqv5KtT/hLSDunJ4q+Jseb45pujr/el1XXLWTvfBNyaQO5hYw6x0WPO5hLvd7+T3wrkgo7vbLO61+CLe8XW7363d/666KmR39iUCK7u/ZPTT+pgQ3dp5fIzjGfcLrr6ybr19fS+b6cN+SrX+HvjYCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8AAACikGbz0pAK+CvzDs1pfN40Wnza3vfNx9xq+Xbthj0RKpf/sXLJmSb3FH6nRNn/wqWcCpsRFPp7v8v3vbLCBnw3EZ3Dt0eatetoFrbUvD8tD7cJHqfpy5yz3P6k2vxdN9ByxeX/6fhpfF8s/mMliFPFDOE3xWhePCdl/u3GRlewuE2LpvQJElJqtwf2n3eT3Utz6D2+4KvRuC/7vhzV3jHf4jaH97B3Dy6buEi3Ve8od+EtBFQQzLnE+qxMEk4KvPayfy96tAolg/d3On5CnFQy9/vy5CfmCXDtya2i9deX/dr6mX8KPfGT93HDt7XrvSdCvf42eJvDckp2WFlWuB0q2EurN6zQd7qrO9N06F5eXVDZBAYSQTW5/4Kq0P3l3KHHx9FTaP9nhaHZCStTCKKt35lqyVxqL/b1u88cv/0hMW9qEYS3CAq7u94ZXZ/ZWPu42E/laj+j7twTPme3qj33fq4TPrW4FvoXR2NJ5K17rlk9JxtJREwjmXk9tPxK0Jh96tk/c9vk9fFYRXtokPoWXrYkyG/RbEG/+W7339t9/ViIJd55JWkLFfz56oWV77v1XeCPu/a3ZAQ7vy+IhB7khARd3n5WbyfftXZ6K/rV6t5tWR7oU/5ei17t4R5fcEd74tenW4JC3v2/Wn+C7d77v0PQru976Um79JLVVYKr33u7/Qn4I5/dL1LLl3kfaW/onqlquSjw3CPKLBEtc3/L8lb/oW/m++T21VeYEMmEvfqXqQEt3e93fsvVkxz0ncK/Wb4nbTo0cbhD7FDa8vbOUD2uMoS4WQsKv9S3pIneXV/kk7v9a9MFG97veF/IQmXrTQmcon0v/VFJuk0oaW++XC5xERu3NyD2CyAAAAC4EGb4BXwV+LHaZ55ce/el7ltb/Ndo1frC/mJSO0gj7/+X/3CXKcJMgYTB7BfaC5f3TLBcUf5XVUw8O08zUpl/XsWbngnQu+dDlh2y35Fxubvcotyw/2eCw7W9OlV3Ft+L7tWpRKXy/wlfV8+5O7hbwRhDYYf/d7q47gmKHZOOsd2H0cv39x8JFzv3yh+qy2R9+pd5/L7teLLy/CDbfv39y8j3Cpf3yx4zmofu2DYPuMyvbvZyDmx0VorjTuHoaXt9Ysstmj7vyvW7audHqzr6oQR7+dulhTbChnd2J6s/t3e/LdbjsvoR/0KQWgjDY9e6xBxl8dhJ3cZeN2tKIsg3evzPyr/Ve8khX32JWrJzGOsEHv5Pn7OgpnCDG8yHq+X7gxUj/Lpvk+sHw8NY6+npvblZpXwQ7lEdii+I/wluNu5facPM5u7vBzL7Zp3/WHtxsBP3dvjQBkD5ipssH1LY9Z2JHo4R8/5oycfaFEn+NciN470ntpFe7l21xmob+k2yoLykg1mA8cZK/WMzr/00NtAr8cL+3p3jLq1ZdcXnSvvVsEZRmB/7txJDBHPGhnl23TYwnbeE9yWF8m5So2V+2twRluMF9kHeog77u7unl8JOvBGIL/XVgj3e+6N9N2+2mvr7Kyn5XyenRLiujMOK38JuuiXu6oq0r319deYpzz/pe7F7fJwj2ExBmH8/35Cst1qmwTFSvPHudfVgk3no6vBdd+WX/osEO73+wR336XkhDoEVs363tGsl73nTghvferEwWn3d7623v6ve+tX6zGxi31+6P2tZCc//d9+Srwj4JDVq3qiMon1T1/X0V9aFsqvyVb0hRHf3Tv8Ee72rqEfIWGan+CU0k9b3ip/1V9S+T3fYlEPu/TBCTKCxkDLq8fvfd3vCyXFi+tF7q0U9eqJ7ovJ66/gt3vOSf5lyYWxSZpf1Qniy20nW2lJ+1/oqda+/tp3vqnS7JhfyR7p4MIAAAJXQZoAFfBZ4sZUCTfK2oCKT5c04vMTHWuUi+vLzAwZSQx4s3NG4ev1sdL/7hDw9mYsV2ihal+G0YguvwkWaBxvyuI+lfGG/Gg/stjo2Xtt+y3DstswOVeeCOU3eUlBr3Fnu73PPvcs29Kvwl58ZK71+EN3l97mFXv5eGHULagqX93cIhC3yOG97z59Ngik3e4qssWUPr5P7v3BKU8PO9OqabL6PBR59t2k6RPr/oTd3e97rkfmiFfCIoqMxaHl2sR2yTpE05f98TuY3BDzrvL61ylDc9u/8JwxJJ/HbY+/Tf6v2Lk7vV9C74K+WggrVuHqfu867UEcsYZFT7b02CGfnY/wmX9tvGCuEVkw6N8mo3vz8//mjYWHeVy+v4ZOGIsrisRtf/L7e9gkmGiqvpLf8xMNxfRkKSKncvd+t4blI++E3vYK77u+0736KxVzFx4L3tmX63USXCfhvr2f/KS91qj/Fbutafy3fk97pq7IbCCqXk9J7rNye9eTKhbK2h5rvvdy/CPaJ267Ne83sS78n6Xn5CXoej9NVuRA77SaXrW/SIbu/iI/xROX3l9+46dvP3fp3CIzeTvF9bdLVzF3clnWT38R3vTEZt+7/KdO8IeCUZemtO/zhItXrXo/6BIV32PdXqxpXd9P6UqvsEO76uxNWAU1BHLl590coJL7kTJ6/+vXYur5fd9UTt6llvNcm2Cwt3u/d3aEn5Qrr+Sy6rIW7y1EQmusgyNWPfEdWTKt16KQw19JWTCnPr9Uq3onSUfraFJEryVrbfwxko/WvJN4hzkcCRAAAAL3QZogFfBX5h2CX7my/+WLsrmGh3K/8vlz5fG4gMF/9wiTHyeihppwaOGtJe11f+4woJ3ZNT5/10929tzB68uVu4ICNbleErlpmLpNDQa2h5/lu/YmFrvcOs0WYZ3xh7SsJNqv9OqSp1L+9N7+iluXX78V0atEC64V8w6BMf8/Ll7kl/u3BThluBuJ1mTq7cLHtXy2xwx3HbfhlbPzzvLncEuNr4FcwL/6mPcMJ4DunspI2Lf6aVhrsvxGWJkO8qGFPFjmU85pHdKEr22xG3OEuk9P27y+tH/7mI7AjAVidb+9/9bQm9toOkEvd3ewgeJW7uxNhu41f6rKw9KkQsyBFXtmwKs0edpvf7pzwYFGBQdhvd6+OL3JWi/00e5CZA+RVJ6Sn+SQk/KHrk9JLV/f4IcWMqO2KXyFGuVBhJ74KhUfEw+/m5fhH9U47CktwndSj+YfMDfTlvvrBR3e52Lrpz8L2l+T9f3RaZaLWvJcy/tJ8eaYXXPj30Z/J6pf4QLhJ6Nz4yeLr730XeMwh4dtR65fGljP9vrJDaWmCeDHhFrTxv9z9x0hc6jyRd+vs/ryekn+J6aLwQnd+F26NLbXQISQbmdSGZhs6Eneze9/L3WcwIr7sbyxtYoS1MR249c+CE7y/FVZCXfzQR3u7HX19fbWqczBGXd2GxaBde7vv2r5AS3d93udND827uft7254BHsQKHqR/TbVFo7dXVUyvfa2RCbv3vqVbdaJ7u+3Xu2kXtuortOqLu8J54JSPe771VgqK77v3v2T0m7o9wle+7vs8m95PeupJQQ3u6A/TgkNq/ZPbS6Vy81kAlpEpdvpNl/aRX64Q8NE0Su/xD0s2gT7u7q99/ZRObF69tibT7vfmPWSqdV8gIibu9JqWl7oQwQ5/9uvhLwTEbXm+dKy2er7w3Xv6rrtNiaQQNmIh6CYvyDyW/JYkkd68khs/hfO1rr7LRX70U926M16sofupu4WW+QQ2ver9Eo/d/evXZMOdO7PD3xsAAADLUGaQBXwWeKGZsWUmU4679xdLDLTIZNa8N5iJSmv4/rhjxZuMypoBGa88VPll/CHaGip03xpp1+ci6beZf3srCBc8pbvHzoxvKcpfuNJuHERh+94Gvoxoq7quX/XuEu42CQoIfzsxgbP3vhktndvls96eWViqTu58sVvL/l0CXyS5cRavlpT5CnmGWiVaI37jfh25cjmOku67zzIJdRyS8R0KXLfqd9mRKapUp8/rdxt97cdClnKAy1GuGlrQFPd/YYW8/63UFNEni7ysr6BkKXkXfb3KXhpch8Fdh2nK8y+OBZ30pzdZ47nIvLC+R59l91fEc27kQeizdN5f9oveWo8rv7mPz5/E8saXc7azhN+4RCB/bhN9wtOf7925977fR2y3LN9av17y+svSVWvZPdPJ30lRe1XWUJvewoZ3d3rDeX3MIn93MWeG1u4LreUsQagLZSlzxfiChWB7rw58x6cf2eCTMrkRP5Jb1KTqxvvFZzsPyVK6/k7SewpbBNpPy5fj1HEpC5RTvCbTfb5PaVzXwQ3bCDf1/nuF8IT8EetYsv7+FLnX3/Ovd9ex109TbnNtCWWPIQPovQHY2K8w+BX9gdR2VbxJcwcduvOXvX24JM9Huu8EJMt+G7fBae93v7uzXf0T2ePNyVc7/w03RunQpVn+WCQW98XY0iJ3tAk7u8JLdsSQVu+27336SW/FLWuYrucd5f9cEeX/fmK9+tG7L4rq5CH/3+jsW3OnBH5fBCL20hIh3vd07+wRnTb4evIVFy2nl/kJl1MldF/1wTFve79/cEN969MEV93y/1kwlt6XcEd3c1+PwXblL73seoJL3t7l7v829/QJN3/9wXXt3e8fo8Efd++gR73r1WtqdZd7+glvd9wi/IRE/Xgjve1eCMt31vLwnd+9+7u+kupjbpawjvfOrMPXOP6L/X9JELe4T7Qjqy/wSDWn4K933110CGky329LJ+vnX+Ccjv7vXkcJ0xfiP82mQmZ+/oSckvkv68VYsn4Vfb9N+Tr9CJqp0TtbSYId79l9+0RehfohH3l/+esnSVCH+LO7/Lmv5Ib8kur+iSXeHvjYAAAAx9BmmAV8Ffix3Go7Usm4vLVevf8vUdIYY8xFO8wo9iX/3CPaOZLEpZcEozdjnX9748oF317/DzH6T8vvluESY2fc4Wfy9pqfctwS+H9qvSc4n9/Ly+S9KyrLBfhOnPLycL7jxnZm2BN/z//un17YjjfW+il/Tr9/11eXCvjDcrXvSD01nXlFVAu7XVeJKPlTmlgsBeXynHmfQr3oJHdjuGEmGrPffwVwTew/PvcOy2ngJN+vPDfgj8v3567GxfLco5fvKV714JzcJf9O0Msk6Lo1+EC7EBrhOffuz0dMj715YJN76hLzEvTvbaGkd3cV2Aj6m33uET7U98K9fl2uisKQ+u77mUsf8rBR24li24ktsrQHgl0d2++nBdGRP3Jd32snrm/5Prz9RXP2GO0/SaiY3DUPaGnf6sLzlpd6tvgqUs5VMzZ/8FnHzz3HF84MGoV6Fh8E2dc87t9/CqCkqBIJd8qQm976yDCj4ZfAY8TGoB24bKHpVvY0KFcfRP6t3BFtlBTj2V79u8emPJ7v12Cry3KfO3saYcgisMn34v4I+NaF1sZ7pwSFe/vKiUN+Xy/SRGhHyyeTd14JczG3fLq+36Inc537hAsw6+7wm5LG+/VEr8ERd3/0SCQkj793Q4lqCT5LBK+r/5Y/94IToosF9jrBHvfuk1c3+vQj6EM35YJjzd+927wR8/14glUXXgi83fr6/NBYV733fdtL6xdYIr3v6QS3fe4Q8mN5f4QEVl6+di+uiiSXV90MViD932n6audiSq3pu9+i1etbota6wR73fa/3wgT6+J8Emq7b8h95SOCi+96T96uWT0v6y3v114Qu/sQXtXz1ln/2UmT9LXJ3a4Id7shHzErbl98vZK16lKu+mTJ6TZZcvepBb7on1XX9UCQnJDtJkK4vjTP5WPgj3f/f4kt3d3wlogJhVazeuzpj8vk4rs53Xsv65pS8/daN20kVMPeX4yX180yv7Gex+qkFc2bv5MK87O7+itG6ie6hG/os5eSjVuvyeuX/C2ZEuuq0WrXkXyeTDNiJJaP7ySZ7xHZXLaeH/jYAAAFAEGagBXwWF93WxYySxnJTqzf/LvD1K/KR7lj8vd/qVIX8MeOlPaDSoXOZEEDzLxzo5f/bG+K5cDF7vDU88IH+7DfTB3IF9VezRbiQxwPfE6+2f3l/e3BbLB+3t0vuFKZaf4CjWf0k41u5tuOXfcA3dnPBnYXoRatFlI8EX+SHKa8sx5/Sr8J5O+fMufCd96NziPxnTH+1RFrb1s2+EbWeYV8wyZIaSES65f7bLG6meMTKTmXU+K88HjSz3p1AnV/dv2I5T6LBNrvmD9oHxbqnHw1DfPu+78nrVvuET3bu9oiW9LRYjvd3MdvWp51J+kq+EJzzkFreYzy/Vfk/VrVSz0u98mS+4Uf0FBnLJ2sXt63DKUj24rdxO7CPnX8+n8v/X+zwxJ/9i35NVk7vrCO1DCSHLK8SDv94H5S+WLkL2zLbvL8n6yhLwRZpEbde2NMhFZdnc43xCXXze9uD9fzxfdhuk9/J/S+dh7Xwm6Q9L3UVtVc5YI+EtJR3cvqpv8cUfL0lRbopHOWslCfMfMZ7a/LptSwUx11Ki97J8X970q1iYeR+46HLaR/Z2n9YSp4qR/XTQm4KjPqVKVyd8wP0fPPsvqtKMjEObLD7tQet0OR/gIKvrnzeaci7j3hpFDn87Q1c34JvDlvXRUN+HFxH0O3DqcO+EO5oci9PeGXxwQA9yrNf+2oRsyn0uSCGHpb0JW7oJBCtdGdyl5ZT3maEtwVjnt3d3d9jd0Vgq4Tb25R/w3IQgPHmbEFHh5Nw9e5bf9P4Jy7kF+V790yR+US4eitFbzexfL/vicZdfvbk9WrsWtkT9HX+hN8vja/CXghM87ct72EreP0u++u/Ene7u/qkSq1WvUxHvprch33+CG99fgh7ux9Aipvesv++UXnUR/YoVxlZz7/Ekbq0z4rOddtaxYKjn8+6TuX3v9r3R4IxO7kusUaZUk4qlhl+q9j4+8LcKPNjcV9O7+x/vCRbbgsN13264JDOHUFR33RYUu9933vnzBv3IW99HRt3f0Lu9DywZ9sX3efL9XOonXhDyDDs5/8vN95TOQS7+rBbau77ue0CS++Porc/v39E9Eq3Wa7+nBDfftr69qmywQ7vSLpC777vyYQ8FU/XL20Xm/63BHeK7a/BLlRv3ex05b32lr4qCHd9VXl8y27Ly4Ht4fCV3d93WS+7rEYQ8FWRum2OV61iXfRmCjEPeFLsV68kKXFbu7+7d727F2Lnh3RkaCq9EX3vukCGyfVCEvQ+XCs2y3Fe7v7HwpNp/8b9bu583emHuqO9iZc+P/HR/2+W8o4HTYPSV7a3U6Pcv72RhMm5c7uYS6YKJljxXplknvgHcxFTSnajrlV3xvrPaF7+qKL6EvBCEFU8Zxb0rCc3t3a71WrJkyao6R+hTyHXX4Q59kwXkxcmxOlGwgfNms/N1exfwmIl7e++ixBbRYe7LooK93ek/LkdjvqKOln954V8gQEcBDr6P+u0eK31jLnWpJw09u997+ghuX3bu5Ne/Vx+l3CviNarVfgpJCrl0r86mnd3zG/xhUhDKXC3txJq6S/XHvMntV/iekk9fNRzpk/vLJoVp3vL6SWhl98Z8e+k27I/+OhVpR2PefhR5uO/TZw8uo94wv4gRH2XYTD33+I7R8t+Fx//iMSVndrW+7n01uu6UUUQUtHGeS3TQPSw8n8sRNp4i2+L8EMB9fiY/BVAAAEkUGar0pAK+CvzDsd9FL+vhEvHd3DcU78xl8e5STkr6/Neif5ePYVSfBHxo4ZkhjykqHpqNvuHihOvJXhjN0GQKRRHFmxumnHT1Pn/w8SmicwGPvR8PK0iuWv4e+itM8sIT3y4Vi2a9/16YRK7V3nnz/oTyfpdUoIc6otPQduHlmdb7t+Rq/msla9Sz90yxCnmHRvK/b9x2WW7s9CbYmhk7rdwTylSJ85dn9v3BNe+p7vT1KhZZA8+SPdfu77wq/sEBg+LhNYu1JI7+xFBH4xuK224unL/u4Rx/r+4xWwu5199lYKy8w+T8vPT8222rCW8hwk96ySxfDN/N4+cvvs8vc0d/5vK32HMwfhFy0haxf5Pdsv3LUj/Tntglu7vV7Qn6J29txxHd3esBfJ0e7uf/ClyeQLSKZT96Uh1sOYfl/wUlBFo903GUrge4a8fatI7w7Y/BdMGtVZ40Wf6RqN4rxs+vX5f/cPZgfR61hpLstD28Qhp3yzIeo5f98Z4ViXsDRGbXhtDSb13jh48n21tbghxhAaV4u8QJvfd78VhHwTCp9q9uZt72Mu1S9Y3TSp8TR/Zbt+Wh0Ev/uNptyHYvI8MLkwBp9XVl/Tbq7/7KhaN/FekbHpuCzEyxH88b33gm+3v6ZPkFNfWhxen+UBJz8End29wVyB8Ir7Mo2Z1dyipSDmL+e6v79e/UI23hdC5Bd/bOXT3b6haQ71S2gWmU9cF2i5drL+09PSdf/wQle7+/Jpvv9EeEn+CIlCh2IrB9mKX/4Tsu937E+/uwle+91ZZfP95DGNIXS/e1k4KBL7c/+hHymOzOyVB7Ic1L9DaxVlRCx4Sv1LvMl6iL3uehTHrTPBEU83sZEZye2/zGghM+MpmBnbJ+5O6lrvs/8EV3+p6/ywh2FDN3t29Sxz/sfi8n73rssEIkrenXcFHSe929QRzEdeEPkkHN77ERUf77vye3/lgq7uzvPD7bfoEcTPnB18zXuCLd9a9QRXd9dDe/oVu9j3+Td4R6WDvBMZU1TpvZXQJDve55YTvh1przhnvusa+q+zE4BL6odumL+gGON4bkkr6s4D5Zd369Qhd933vrzxV93u+yom9wj4XrOlPJ4m0WXhsooyiaL0jS/BNe+7vP7hIe6PNqOjrVwhdFyytcvvbUFeWlE5aXfFaf1BhGVuhMyR7DyZSrw16z3+fn3+CUmfN9+ny+5RCtCrndynJNrKiMb3L93SrckRuvofd4r364z3j3d6ZEE8jef9enCPhrDt53AJaU+8Ep7/8v5x+2NM1k71WXKToPwavRYcfy7tkbjCmoV9cOGh980Eumfxphx1u9fiSpofLnviuIe3XgjGihOqqveJG2II77v9RO9+aar7cE/L/LyiVWolONM73Hs0aLsgq8gP0ugm4Hebq4r4hqCtEI2nnrUNJXOLRnvpMaQqCha2G5vniMJGW7H1SytVsAO9u5U7aHEvbvd3Lj2FX4YL4wUU4/T3T5Nke9blPl+8lUYyU/UI7u/LyG7uF1rgqm3xnot4voBvd93rvRQBr1+lRWcmGy+aT8FkAAAFUUGawBXwW+LHEuONjymaLy/+4ulNLG1frNxdayu3vcMeYmCT+DB4KK9xngRNemS9RgGCDhn/gwf8VW6d91aXpS/27YeO0e2kcacmCy9buXKX5TJLXrdoYbjqYfco+ZcH/fqLAJuOqvw1V54LPHC2SdeZb289l8vJfo/dZ7K9/w3eYbTVaa/8up8wp5h0opGRSV7YU22zhvuoQb14l002Tmoe3bh+draX/HsEu+4aaanaKDe+CKQ+G8x9tJO/Wr6W1ZblVen15fRU73hXwWGxh/m0iCsbgXhAaDvs1u4K5bf37l77Q8fs+hBcz5mmJ8nrRfbZMu97aQSu78M8zl+XbrXlhM70r3deCQRPsPShMf8pR8U70/1TuLvhhdBvF+EuT208J7YwUW7u7gq2lu706XktynqrPGlsiwQPPz6044Zf2zYFbcN4JxHrNFduCtiLDiuYchP35f6LBDINB5ju+2K6Le5wtoXFiXvd/Ruk88ImQyiZFVyRvsUCz9jVXjciY2JFOphwegk/cWI5sfTe9R3BP4dSVqdY55f7rcbwm47R7fNjeIT6+N4Ru0WIj2OdfvUjxntOPd6aCtxLdmm6cY6rUF9XvlnKHu1n+l8kI+Cnl7eftkf7vcKEeXu5cG7LgIX2X3W/mH+Wl8b2w9qjR7DUnIbi++IPmOAXWP8Qb1iP9v53v9IsN9U/Em4/OhtsWrBFe+X4e2ofz2OtH25VTlch/YeXv6cEN736ynDnVoqLv7wkQj+xSC47OtSW1WFrNJPlCJJOKHRoWEOX0hq05/zwTld7u/WX8qrBRpXu7sfRSckQj4K+bk0epf4ri3+HiTeb2f48/X357jlz/6BIWOMu9dgnhy/48M+7vBk9d+76sFOcvMHTDZmbdFbv3WJK8VxPpmQcVBVveQmzrlUFh1v1DwjLpe4eBBTlO1X995Uz5/osSfCT118/3SFf0vbpNoEWX+57YLr3d76y/E/sXnyEPGBA3ire972mN2/bCBxL91e7it6yzH5a6OgREkDT23WI7nq+/IvJyf1+ecijOn/012diD7ve/rorJu+unH3dywvfc/CK7x4imV+f/L2+oIrhfj1rxKYlRT+rH3Z73M27J5PSqWjV/E0iu+Xqjx12nnnu+90/pLsVnx+lL5PWr/L2QadKlLSBLufKt3SrWEbpyxe7z97k/r/CVaPvcI+L1q7emsThS8Q4Z/Lglyk7tu3Peo3csN7977vI7k6/+hpyHy4e6j+Y2erdylvlm0CVC0B72m0bbfY2Ebj74v5Ilk25nw9Bofk/f7wkR3vkX/csfyfX+o8k/7or5pewV93LR+NyJ4JfDsXlTL/9BQr2789DP97zt+Eru7pb8kIXe7vd36tWRy9zPxGEVt4UEW75P5mL0/OsE1585e7e+T9Xrso825sr9yItj+7Ct/XrNpXqss0ZsdnlJ9K515CTf1Uib3aF1SAJq+3E58JeG6UsFtl95NS97y/IMFp8JePIZaUipZfrJmT66CIIVxE9+87W+tEKVlKf3/FCd3d+t/fL9fbsj3y/E+piN0vUIkwEfvQOzu0bKJ2DtmUv1wtv4k8rS2PY3+EHprl/7MZGTwn5Cmesu1mEYqq7VLL9/oRF5GMHu45277DqT7ueuixhn0SHvNtEf+9Ulku/0iFve/ceW7933e6V9JVhMQ60LPveX6+i3fpWQGllO28LL8hnQtGq6Swlu/P9qXglLPlHn8qeSJO5VI/c/LnJCOO+7N37vdF0CchaFplmf3Km0lIgR8/pQDHqkS8RGevccjYevcVNRcRJI/qS49CP1fGR0T5h/4yAAAAFBUGa70pAK+CvzDtmO+vyxZduNSCF9+UlVhjzEkPGOLg5y/+2EeMTa6anGnGmHzdxuH+3v3BWeMkjoWZ7J30U+4IDSwM5WWGGUGstz4cRcJVCGeR/2mJnjvmvJIw+kKcGXVNeXRP0msTwmVrdmvXlhHtJLd3L3/lpPcKeFxnCbrdo5WCv55heHv3/G5xZXlQoSN0wSvClkrTeQpR1xfdrXvpSAmQXz/L+7uCK4euRPttk9Uju9ydyltYTLcYIvh5df2nbgjw7J/0qS36WuTd9fk3eFC/3tgsEX7nefZmIXVSsllt2X39sTvQIm1W4OvHCuwd7YJSlzIPhteZ8dsnruSqlljL/SLecVHdyf3+dmjIqvp+/ZUEcIvpfgIl11Svhqetu4V2xogVu7u4O+27uCb/4exYK93WYVnz+0axkKj575Pt6LyxxShEoOIecV444tU8fEz6BJve4l/l8NzIMRY7jDXdf8nob/FTr5RKRZ/4zi6xXELcOec0RywtciybXopOvYPBhQk97r8KXDjSjhuy8LnXTiX8a28hdrAdk/3fiMy1x2vvL/5cJerN+oexZRP/cvcQ+hF6DVrlgWf7w7lnpMs6ThsMbhpsJcO9WyKxm8uaZ3vye7XueEIcXue4mWv7deo8pRAletrqDub3q7LBbtNPw/F04vbGzdBdO/CD57bk6Jpx3b15BRwfralqhobb/8Ee6/e3uCQoeRaT8XuCIj7TtdY/eCrE/MXnu0C/syt2Ib/oagTWsaUr2/9y9Lgf2fm3KtFi91g19CyXfFdwk+3BEQrLxNDVgktVHm7v6BF5V8uhr9Vr3/BEV84s/4IjbNXy3eWzx8Hr91b2RWN/IvQh5svp17hAhF7e3c/ve/cE4l7kt88O2kugVb3u976/EFfezvfLjLu7zB8pzu5cbbv/6CNp3vjZxe/5f01LCWck8Hoz7XyhK9K99fIEzSJhxLyOrPvoagS3vd+X0Ei877v2eXdD+Xf5rR/X4mx3vl4Q8EwhtilZLv5a3y6yfqLEvfc+dqLLI728v+k0Eu7u/8k7chK0xZTk/BIVyxfOLs/7CRNzzuzf4re7u79P6NPL1rY/bu7u+7hHxMWP839iSbaoaoay/8uGyunvWrus376y8Ydoos1eMxFI5xtEGYdK3t5PXH73BHLB7lBk99VXBXIbd85t8Vvot7cRMg2ywZad4q4JDIh4/dUqVVuof3kR5CSLXWuzNrgl3tiTr/oWV5cR7v7fL2/RN33+EZm3fd8nhHzEc3l1F/k4XE3PJ7e+8hcSJu8elfvvJKav2eHKRhvb5Np7bbJCXPBu9Llgo1enqm2/k80fUParS2Pim77sPz6f9Nnq6vr/xZpzwdp9hH7R7/Ce73u8v+R8I+QsMIlf+HjQa1WTyenf5frynfHcusr9Jl13XeUTdyv+bGe+iXu/5N4Z3+T+93aGG3nQI9NwUFED+ASiNqf10DV08tB1D4PvlL9fQKSiP2dP/++7IMJUnwgtJ5GEiDVG9WcctM0QqT9d+kRzJ99P0Ua+lzQRGe9MNWrid3wj0sl7f3CAuWq46tNm9yZNKL0EhWErB6nvf4Lb3uzemXUfvd9zKrnz4ISggvXlvF86QrkIiCu80Rm3c/b6/NdEXHX1LafevJMJ55V5UH7vlpnP/umGdP/VEMmX3iDTR2ReXBnTm3F6WF/VOviPNPI1pauK/yRHRuMd8FkAAAAUwQZsAFfBX5h2Y0/xZaQJf8+9PuLJukt1rJxfjvj0MTr9f5uNETC/mJhE8A5f/cEF7RA/D9NARvfa+wLV2t/WVVGu+vsYfPqQTeSr3IIT8NS4jD1tpeXh82uncfEL/gR3dHX/h9yHgjslrLx//yftrneCO0ZQ8EvC57s8hZWv/L4/2xMKbVKK720h/EZq7GqZff0hN9znL8v/WWJYF24VfuUIcPS5Jf7boEBUGAQ7qHH8gbe9w2tRayDrXP9NnuP/I9ikLIVybui9qv7+++qEnvd3fL8u2kXmkWYT8FQrhpJrMle86KQJ9euH32e2On9o05DB+lKn3QJIaLST9ac7xZTJxxy93r3BPJHJF7flv2wR8o0sVU+mzywSeOPPDJ/RPlQJ9le5cSwa6tUlhO8iEpN7/gm3HQSPefwze+97hMv5ZeCwQ7gIl1vnSH15h3fx+G3Rv1uCT0uNDibbVfJ6r+ogsBTi5iZ1/7EsFVx4L7B7wuSfe9xS2IQbxta4K0X8tprN6PXunGSI8q7pWniIOjttSKb9zqZPSr08FPLzQ2T7tJ8WG7usRhE9l06VURZu5/9ZhL3/LurYSfLihS+naFwzJ1J6Vf42Hui8hTLwAp69c/e7DkJq/9w08pZhv9/v8Ff5KVCGgXnrtnOUi6bs/4k9f9QsV72n75ks8/+C0j31PXw9QUFMvMBAQaIxj+a9+nJSv2eJw31v4VvCT1Wt8KX3fGecDru74XIbuBxsWgTEAs1/Sd77fXc26UoIzvfLaTqS9/cUS93vCPRSGYmbv6BPdy+X/ujsFBZB9nzF7tVHkIT7dr2Cc7mKXuUX361rzMTJLad2vra3giMPCa1r+/ZbL31kwkX4mvDEQ6Rk/c7P/fWJ/7GFcu6In5fd7+nvq/pFODL/vvsQev+fF6T6V/u73v6GXHSydodbR34/L3rROk/VZ2UiVe799HRu7+ujxN793XlOOIPj/NjtP7Dwgj0loNLPfk1+3Feri/+Cm8tmfPsuNvZzD62z2JuFHT+CCbYhy9ySd7P1e2ZX9auGOeu5G3Bpn9X6Ox93P7akm7v3u2Mvlc135u/f2MK6O7uPwgxfAx1D1LcvhXfWC0l3wk5JIoyi80FVz99x/rfSuQDmT0lW3NEbPn3vpx197pctMn0vJkgpl/er+M1DtveiQNf1PtBHoQRz6/P7/BcTmYut8v+ZbK8/6KzZ/bvI1CBxsuF5Ll05K+letLH3sJ733eX8naHdjNs/3vvacm9+oJSPV58umH4KCMHI0oaw8girc0lVDjyNP6V54WMuNpzTZAbfVExW/qCvu7vvSloSe3glEO+T7ireUogr3u7v6Hi2stbllvnvY+yan9dVTc9wVnza7q+ZA9otO6jFPxRHyoPl/sSgnZd7vZSfa4tE0JIk6KcVmND7+ZP6SfSZNXknLu79QkcJ3O3d9wk/scIlaUlM+FW3JPvWcJ3vn5N6UvCZQ/ibAZ8n9vJ9du5IovPA6V6D7i/L+XrwR9U0UrTgkJjOM9qyMqBAQ2ZFwA9/lWb71MKXN3D9zvlbQN2GOXan/UFZ0srroZ56ii6Xdu5mp4BoCvUExE3vl8goVyQRGXU6b19raYs7T/P+uT3ovokp3ebe4TKkN8O+735IKBGXIxd1dyyi1qaFM6ZpFtqC9y396OVt8lBPLUPQ+Zhu+/1IkLeKJG+7t/Uvd0sjdCSJtVc1Ml+qCmzfbl8W6WJN5k3vghvumQY9ESJk/oR/8REFOXLkbbYTNK+11EaT7vr4e+LgAAATUQZsgFfBZ4oVzYW9/cWQu897hsv/uESXvIWBO9X9JmE2j1G0v3ElCdbK7tD+91ZYwj5INEkaLfR8Efjsd/7lkIcuncuGy6TFmJ/8IFySaPv5cy+5dF5f3WtfhObHa8VhXwSDI2JBkLWJ9cy/3bjcwjOfCaY8Mvem9GteoRqtr1lyS+33hQ9RPT/9x/VE4SLl7UyF9/cFMcnXpMkp2O09uiseVgpAT6peN78aP/MFAzd3eqNlf3l5r302dl975It7zc0oVL++WCwQK29+pFe57d/dl+vcFPfMtDyvdqHcO9a3sExR8Zrzwe4S+ehhal4TuVX7s6S3Bb5c5xUPeik8VlC8hzhL6yqyp8o2US2LQne94fXxrFDoS2woId3P30g1dJ93PgW/h7Q+zbIc2yLhmHh/KVKdfPVgsOOtbstA3L88aFx+5koX7gllG2+ErzL/2X1KVcEcOLha4SVEdqy+xsVDK361/pHJ+G/eqzQVaIdPzhoUcoAo3qKcbLdNLpeuG/BYTOuVTx41dmbSSg0/vcmWjySL8uNEywkvLCFxA+Vh7e+V2sfn8iXGHTAS7dR+rCIKDEluT1+9wTfjeLCtjMNNTFvbxgy/d04op74etz/uFSeTAT/9v/fqa/4KClKNbnKvbaoksEd3IR4tPuSNn+xdHeTgnpZAecltB26nLo8cTD7JvL7xwHigW/1Xv9ny2/dYPRB+X9u978ihBd4KTYq7z+b/LS5Y6K3e+90z/Xgl3u73kT296Tora/BVfc7rryv+/fd+j19+HjczyKjTw6I2G+7fWGVJf9i1ppTQSHvexk+9z/1fICKdm+L7hB+QQCYkydDbTbLGt8uU9NfwTXfe3dvpzCXv0eyHLNfUfKHZRd3vblx73wVz5fPDnqM6WLU66J6Wf+Yj37ctu/WLka33fe0RbalSHlTe5lO75eEV3jzFYpIMkXXL9N72dQUWWu7t9GEvv2wld73krf4+jcuV3e79e5II/qWd6PqF9bpMXHprq0m/l9d8ERTuvTfiyOG5J7gdkqf0S6X0/SyelTSWo68607+0nv0mNJ/CHjBxXyt3n3jeV973JRm39CT8vjOD+b9oEMv8jKEvuT4SLq90dryCBGr+fapywvaeJT8qzbB5z/FulPUWZz4Ag79X373TC7xBI2r+ILHwemewlf2hcvu7u+9LNcmv37wj4e3wyIus6Geq/PCCb6XPmEXT3lKoQK8svFaSV8vtJ2VlGpy06PEkeS32ntxvER9fn8517SPd2Nnes4lMxblKR0luS++T9afxxO4sotvnuWnoT3cYz/XaYRjRexsZyDiEASq5NPUb/KfxghfMKcA/l+/zE3e/sSfcQ+7wl4eEJrbJ+5mNeYLnrw45/ev8n05Y33v71m/tUlNXWqjb3cVuCR9bOUj481I+05RdZHsTv6/2aRX0y3Kpk/Xo6UYdDrDzlHTdt3Buet3x91jXxwfNb/3fdLkI8wReT9RD62PC2V8KE/bv9BStbTiRp/93r0Qpn30fpaEQQnvIfTjL9e4TK51ty+91lihA2g0qTm44zTS7JFbvDraf2rUkLL/X4sl6XLnzlq5tb/5YLjt3fG5NFfkmpPyf2T5UiFFquq+FqyZb/um/yX3al8nJJd6XkqdPJJVd/D3xUAAAFDkGbT0pAK+CvzDsvMR+LLj4UOUGEIXtLy9flI71DHmJgi+KsHxmX/3CHhRW/Qn+X1iNvpAxnFX4oo+0IVJ8u87+4eJs593jpzfDSVQQ/D/Ly8f/9Xli7gG+4dPPb6Grfe31BgXLu7tdzal/UI3vvfd9lu74XXuNHSwlDfn0En1dvah8tob7vzm//xjTrdwW3CF71eY6YGb/YKytKY0lzDDoW333R33rxM+48J587/pf6/CRXS83hR/gnFCXCsXd3+Zfu3wS/KPh5dM+1Bl2eOOHBf/tgbXxEiO+4eIgR38vQJqaov/ep2VfDF8seWHcI9z+6cXFXfsQ3NXXtlKfH9VjSGJy0q8sEnaL0d7LCPdzL9yt/J7+SWondtz/ddtEl+4TfqCAU7jytOcdoaL3CTVW3et37ncMX5/d+CqYscG+NXQzxaB3OFW0+ry0+SOPcFYH8Fk4d26ua7dHuT3v22xGVKUIvfKK17hHu/DjWXt1UvjIqUK81zNtfClyB5ClkRvAyVFysHQXelV1FFqE3tB2Q/rIYldki/prFoaTrZvWCX6Tf6i0xuddBF/FcPqcf6vokjb+sJ5WOvu5hzzu7u/ZYXlXImi5sR7tOJE5u47e9WOffq6cE27rvsFQ9f9uy7+zfnXr8Wfcy9zC3SQt0ETZbzfco+yQUFIVa+ftk9/y19yRsS/M74c40U+LL7/bkZoKyQ2vMtDlDaA7aZHCz99ffZaLB7m8vCPokW78nGVaXpMt2UMdT0/wW73PF8gi/s5R+96ds4+8fxEE0CV+J1/8bwxuHqFY22/IHoZi3VMt/7P7wTzlb3cfEv7b1gnIhjNvGzQNpZ/vY6FoefdJ95e7vfSitt3vfuW98nrnS5KshHwViMrJOf59n/78FR324X4734/1faQuU5Rb3/IR7vJ+7qJbghI8NXdh9cO8Fx6CP99zp393tvoIkuXLe7uj6XNCV7u+/wnfcv3l+vSNbvCHgiLNs0kv2FDamXYiKxfljdz/0V8uyiQp3bd3c8fVN3dzd5WJOPTv33+CaU2/oj9Iqy3F23u71r1EZKcPSf/6jsd1eeCEpFp/Tq0xNwQXjw3Z6Y/GppP//dMbsfbSiUEiO/kf3hfd77q63+qyxcp3mdu6eljMr9Wg6733J8I9AmvdS+PTqsh8FAh2/V33RY2Cg735Xzp1YJ7V3vciZf3yMFRXu+7Ge/Sh8XfSvL95qV34iCHLecX4LKLKkfd1c980Wnx5tyILvzKg3FKr9RJ3CLgOwdnPvn9LI05baU/YyaNK7c/S32T+hkvf735YXc/CPiiY2ve38Fhlri9Zf//GHcqJ+Onbhmuvpe/oYLd93vSLSlZu9aYhIhxZP7pSTxGaek6vdY+W3d++rLL9RGhvX+/Tl/cmzaT76w/u8rDh5IWAbdCtlnYbiXI/KumH6cuU5VQyz3On2xh9dQwPIZKbeP+nL683CXijZPHLnPkhS65S+tTR0Wsab9T5weVUT1obXfoXFiX3p0tZeK/JLe+/Py+/qERBA9gzh8e7jF9GrTg22cvpX4w7wbHblvDNwnz/PvmeL2qUr1ZfCBM/93d3hXymd9VZTp769YmSip18IFfZS/P976xRk9sPOi6n2VD933p93k+vOiUgRbu5mQbVKoW8hHCa2d6VcVae89eT1/JK95fpx9xAl9zNv8rBPSMZ+KzC06ZI6t5cuKz74Y9ESImoJSkt8I+Fh8eRILIAAAAWHQZtgFfBYX9/CIrNk3mVSUxuC7iyJcxKeW9S8kyGgv5iVDN/J4v8EGYFH+RoJdr70Y2N2c7T/NoHmvLVXj5f/y/vbiStDJcgkeuL25e+bd124UIR2Q1sPe4Jeh3m+wl0+O5Jxq3LF2Q2OC3t3v3Hl47t37pI37vNfL7STeS79XuE6JSyacvcK+YZNsJuXjb+wpmQCYiisq1oSzV3aNk0IvOkv92sJ+r9vftMi1sv274zxd3gjuu8cmex8S+LZ/y/T7gm7I+9k3m0k7gkLJelUnpJftmksrr9y6U4vosssPW/tMssFU/973ZOlQVf2FBFSHI3Tt/d/KiUVh+RPs2vIf91vaaBNv0widOFtWEMvq/b1rrBWVrI4jXocfp8xhS3bpjRc+8jovKSBZsohP6/KgUcoXK7BW/tjatu+H4bz9oFG8qi6bua9/I4R8xHta3LG7Pj2e9/M7hQM2/rKFYrFdOWsL+aZJzqQsG0UZSk1U63cb0Kk4ZWEHy50MvKIu3nfv1I/1hRAYP29l/8A4nEWXPQ3yGo+WnwTYZRIWjo+0/YJ2En10XlglKMvn5cIPzoMNq+CvoxpttHbKffHY/BCd28ovJZLv0eKI23Z7v3ClQJaSLn3Bq+g3SF0FaG+5T2hHOWmufqGr5vUFJHObRL9GLKVYyDh2CpI+l20zzxHdooa2w7Viz/hJ+RAvNbI3e7buTv9/j8NKZ5HxXnsDIbk+Kw6oad4IY6P9TorBATVaZ+aLD1mWF1hrcajneqpMX79U7hDqnq8FP1X3TZFh54SfvsW0dvf2wUTAU5IfbFKCW4PTDpwSSP+dl9MnUFkBNr13f2gKY7GYRZAfRPo7sbFQjaa3/HGfRAlaqqbSoFZrTZWpSam48WQ1iPd9sr69bS5xIndzkHLfeiQbdLLfcI+CgjaZv1k3LdJYLN/OPVe+f38kFZTr6O3vbTu99brwRWN9yi/+58O60/qqPEbykG++tYvwT+PdzP1u1wSmkOZdhb2tLncW+MaExx3WXqy3f0hRL2N3dwh5CcdT69wV5GdN3d3d9eWCsr3c/3n8V22meNQJLN/EL/GC33d+ld7v3yfSV3/V/giIe+lV/ghNw2k/y6PLMGjp/p3v9V4q+9316src/hDUFRDLzJy/Zol9rZ6Yy+3d03d7eXvXZUEDz2/ZPe/QmC65fRYzposVJPi7WY1yXaSTiPPkoavJ61q+E97u/VLQKj2c+8vuqaRS/9YKCTx8+OlCnHrF33bd3+EZf3yBPcaWnFrCW99tHk9uzXvE03e99KWIKM0O4zka/Mf9YT/vuEX6hkmOoNTQdl+X+WfBCRu5pN6Q4uf42r732qkQKS1j69vHrdNIi8pVKeojU33uNfunFu+2h8tMkYRtMd5K90TghxUqUKXJ/DBsJfnIrQBLu88zXe45HPN7R/8In2lZwazmq6LWgMST9efOmW/Ob+pwb+4R8GBIdXMcrxpU/ItUNO7z9daNb7CB3nxluM+HdWbfrHC2r3e930rpIgtb6ETR77KlO8l5NdP/J+l1vpoXzaTvJ+l7ShGVk4juyKAIo4rAP4WAXdq0F+ogly0z/6mhHwRYYHR06+Npohay4ldDukJqnpDu+qQaWplwNcg44JfnDpp/KyP3psSxUFx8UfLGn26I7Fo6rW99idbszv23ifmihE2FD4Sv2v9ICTetXPds98IFa/1yeqX+HzkZ9b9Z5HlbcRD68O98TXFpjS/1qSFSPvuM8/3n8J+S3Ty26f7+8El9yXSoqL9ZFbOXf1IIL3mP/Jb8dlddEgulnzx5WhbyarrvFEl9q6+kajyXT+c6ubP9anTa7mK8ua9IEZJTjuqVNaWLvu74Yr/EVZ8HfP4i2uk59mT6/9yTf8j3tEcFcAAAAS5QZuAFfBZ4sUlL3xv1/KQbmHWxuvLnONqffLlydCF/DhON+14UZ3/hjpY2RDN5qNZGvfFMH6bZGZf7bcIlHCPu4WrS4cM3Hdv3GkctHcP3uQcNHHIvUH/YcdCKT5vR1eeEr7ngU+2DALy2VIwycJehPXuLu+9/xdkpq9S7y/+pbz5CnmGXh5Lyy/u+C+Hxf4e2s4JGZYaQ9vNjtvTDbYfy+3W462nwk45qf+7zkfhP9kUuQ2/T24Li5apNTD9O6e8fvblpsCMHj07vPH+GZQaW9E/e961XQu+7u/lhHNve7099lEwoX/2wTiHoyH3CrvKPf2X7vcIXu1yAwoFrSYNdqVuC0s9N30jS9xfjtjbnRZfLj8mSHJ9L0XieBnhH47nnk9O/ykhR/mEBoRciO/aFTBdyoHFZQhhhKuk9JN/PBMWYGwQ+W917XP4usERQT9rPxYGf8tLaQW9snh6VqtH2PDvd+svo8mRehfBaQgVnZnO359pNpBSBN6y/Xwguaj4fD9LYOzoaRBbS1JBG/8vprJwk/sVBRxZh7N73DmP9Ye55CEfkLxnlhRjHdECfvOw7XO/f9fcsK2rmQ3gBxgi1A/ZENtagSwJfdJ7o8Up669jPYG4OGY48vb04KymTf3aucc22m0LYJDDKXT+XqHTvKKPu/KlIF9axzH6csbVxFyv7u7+xaBZH/Pkx2Qq/wm6FvovMwVkwQ9nXJp0lCG2KCHmAqW1zsMTl5RlWWuHyAkLc7twEn9b90Tx1q45l1YIrpcOq+ta9zmWNe/0kdFoS/6p9ciRiu6b3y6I8IebL52L28FZHd5bct3v7f4TO7u736wVXvfe78ChS+T6sXd9XY+ESHv3d3ezk9VfV61qkymDSS7p/l077GkJtv0VZPV97wnP++9fUIL8RFGeARJ+ndy/L5PthK97kf3vglO85Jy9+Rt6tE4++PrBHavSr4ve771vqnUvur4SKWHnlyeln7qKlMpvBIvPDX5PrEa2gSEsaun2vgk6ltFtE+tvrBYV4IW/Vxuv8YzVsoq1hDxpi/qRn5/SWQXysSCL/EEe65H799+okS+/NesZjrt7cP3SPmlveqfCN8d/ex9yi9V0Ft8gKya3hN3/6kvfWqRefMn10t2EDbJ8w+FO7pZJo7VpfCRXv181yeuq9m3ve+OK73lhu7/QQnth9kaOXP0oR6BOOu3XfvKxwlzLvd13duT2kxdUvTZ20yyoX7S85XPlxeq9kd/mlPLT+W79btBLsld5j+X/koJ6YfRvcv7aEosIGy/bJohwhUcDBvtJVH7vmmft93LlchHCPqij8eSsurZ5vJnzhPcvf3fk/qYqY09lI3+xI0Y7lZG+swiWafSRd9eT9y4Q7y+0z6Ie59vJ+mKEUEUAkd4DQ8Hkk9YPcBJv5X71GHaDKoG75/h003H82+mvGsoZN9+vXCuxInqszxXT2VBe9w++1Wu6n/73d36sLniOq1IV99iYJzXhNylzK2qdWr0XcyP00Cv2jsJXeXHtPtNdynCbhbFURMaURxdM7ZSc8eqJ/cgl6e3KxV73pWiwnKit3worzQzu/CmbE/9cL+iJERa4gslYzCNXo8t1ciIiDxMfD/xcIRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vAAABVBBm6AV8FflHZg+nL/vhAvNg2QvDjRKR3+YmEX9v8u7hmUxfqaGFlcI99YX8EhNIJPAqly/+4Q87zGan2ajZB8pUzejQG9xpQRrZR3fTe7vkHz59o8JF5pgMluTcv73hEmdfIPXQXQvB7FJPptzzutX9LLa1/+U5iJZlO0gp4odzNhjZTzWDXX2NywLR0NG8CRnPqfwp2WyjtlLofjjzDhEzn/lgv2Q60cwvOJYfvjX0Y/z93nYIZl5o1zLrvatU5f4krvdJ31p/tkK7v+Xl5kQTL/7gsGMnvaLkWMi13cb9TPuJvUv1xNIgrVTil8v3dbKc3fsSwRQEzKz/b//xVvVn9Hao7k/SuvBTD3ty/P4Bduuv3vcL57bnI4S2350D3tboHvhLxWtOTb37gqM3Rl63ljfxWaLxAYtZO2hrjN0mdidOuFI8+9Ducf1jSzAuHb/LuRtG6fEfteaQm5Sn+5JO4D5zOH+1fGl4R685UGMvXu+7fSNN/5Przt7CFvuUavIHMJcA19UrYk90Tlhv+Qm77L6woZtsZGfDDihPZZYetJc01eJteuc3Czx+zv250RjabH+R3Z9y5w2OCQB2Xb6PGZbM+C9gEtHwGyhfEQu2RobbOf9gp6jA0QIr63HICcZKH4PSpT+1k1zvJy/5ehKeEfQpmX/ywS92jqeVRWT6Vr8PWdwyzK4bSch/GPA+i4WTeevfWaF/rInCkjyTzNrFafVbGtFTwq9G+G5/6u+3pjB0rPT1VO36v+CCuDHaDrnDNhuzWh591j6JrPZKU/lKEvC2bz5SXwxu5Vyr91xlf6azwRTzvSq1KjMFuE5x7IRL3cpV0pbaluFDelv993u8q8pWB1zp6p3lIvbWThHwTbZ/m8rOLdVhe+7u9n1desEk2/x2LYLZdvKXfi7ro7BEU+RbdYJdO+PFz8usEnc6eXWCK7v7rIIhyj+mzqwQid2d+1V/ynveshMEZnd22hHwRkP76/GCR6v5+raG5oP3fysJdJ3st7vrSplu+T0uqXMZ39ZCny/Xk9qxP9EKDS5pcNQa36ylJUpTVFQRu93d7nTfk+umugV93z9/OmXgOoR7FECnP6Zm/cZe6T6dmq/LBO73rorCgnPnH2VTuXLPYcJ0w0rkQe3vD7Ubejpl+bSVpex1lBd6DMSf3JLThG+yz+zv17yc0dEi4lh1wrfLvlGbXcXJKz4J/rHLumsZfdOZDu9/0NU4q9x24eW8I9CZl47SMz5zxudvZ1MITLzMXdROETu72n72OT1utRbDO31n2vrw8WE/hM8T38+XpHQDv3dZffVN5do5PSr/dFJ/UOX3Mc/pjCy/v4sQc6YlZJtfRAk89F0p4493eQ+PR++lhPdrWOve86Fqm73W+T3cv6BNnSXeaM9CPh4lr49WrJGtGidjw2uBfVHf4JzN6Vzb3N7VBA73lxJ3e78tC0U0/bHYrfeVJPy5+gkSbbvtdwRFyZRWtH5ff8JaBvB5N/J/VSNngl6WS3W1AaXiPaTUsImd98I0h+1h8hw137LuQlrJI2W5JYSL6NXjxCnXOEuEf41Tr1+O3vY7xX9MIFOwG5P30UFo+fp5/7iBuq6ZLvdfcwjPevJ9S92+qpO8eIlwrG42cmPg/jFvwykG1N2eg70JKEO/v7GgouYPJeVYdcVTpKu3KSgnLD3fvY8Pus+FMkgWbJ68kWNTbdIRNfezr9ogouxcQXNsq/0L95CF794jeuyQU2z+YHPuqTyYWyQiRoZ7yfjPf4S3fFc0ul3sTmvJ7T0I47tczBFfc7Zf+pIYr6+sJE3ZKn6/ESF5cBZAAAE8UGbz0pAK+CzwiKw9m/Ihu7Jm+/ykW5iVe5eTXl/3y733wv5iTzMg/jeYeDj2Jt/T9h7E2/s8Mr5dy+a60xYPNSr+HIlrl/bdRhW8I7x59MzTr4muN3eWMIKPQ+vw2koVTyjppdgpSkdL+/hi8w49sITxGYu5P9x9z/7mK+neqW8r3au/xnhI/NbK3u9uHRysK+YZeOm2X93xnMcKTd63JlX9AJcRvtPy/v2FdXThxbdgacw92Sf/uItv0udxEey1139/vl/vfVl1Cnm8ZvS/vthQllO91frZOTu+W2qHvvsZ7uwyiX3vd0WQ8VFaYllhAp57bnJc/9v5J78n6XjdhK6Xy00JZsj/SlK+X14mKkeu+UF3u0Mru5IP3LduO4CLzqaox962VnIC3OM3P5P7dpy/y3NrWE3+NEKWVlb7e4I82VIptWG7PXh23/3TTQKpZNHHpPEUTk0UlVNF12FG8yBbNfyLHl9+CI8OL0U//6/9+vpr8KbHBXFpI5HI9b6q/M3Bls+/g7t6gtMYIq/ln34VLKgOGXKg0U3qYzVR/v9fIYS94S9CHb+wT1q71F7lH+3eWHso5CN5mYZEjtSJPkBH9m7rwX1U0L/7BXEB7MVbp3zs1xdga/2dXzof7QXsAl2l0ML6/9iYJOWd8v37gnlWJxtECNur+kulaAvx/w+Wl3TO9vu9+p1f9r15fV9QjMJPLjYZL3IHb5FpPdM+hqxhoaUzfIoua2jz13B+4n0JjxOG5Pd3vdOl6mJd3CHmys0vRiH/v5PcFcDykzilTJ2uv3fLoX9uvWLXuixW6osF0DV9Xfe9M9qvbvpldk+T7/8ERN3eEPBFl/WX7XiAVzpyaZS7OM2WMvv3uFDisVu8ao1G90i52fpQ22HdWuGaD8fSv81U6zJeMIiH4KRY8besJ223AjuQXi+NVs8EJrT0veJNDqL1aPd0lFvCp89+XqX3/8NGdy7rCS7G/3+Pu/d3vfp6vPLffuS9/sFhXKw9ux93SIL8s/3H+TGcbr6BSIb4HrSLCs6W3oxC/wTd3cbG/v3lOePS1T9SVOe+r5zr90VOrycxBoSXfZ+X9p819+myyB0/9lITu9qJJUI+EiZGU4+md/GEddOT3N+a79PftCxKNjfLr+zsIz/58ufC05P3t+nvOtXeE7re7f4SveQvfvye3+bizbgjV7+LRj/szZP0ksalElczvAJ3p5v7WxK+kyb33Y8rKPf8r3d5YfH2j+dYN5z+P0wj4ITS+6cPQITO/48rEnLh+5bCrcstvzJi20uT21Wqly/SklYyf99y0977PKTmy1F9nyVyfSYbl9BG5E5yl0mruz6giJ5aIx3+J97Bve1J/brbjCW77ub8At/Y7+/y+T221a0h0sPDiTF8vdLD6euSoS8IEn3G8m7w7TNVTvspwnve9e2OK9KynpP3Lb0opuxfF9+6FSl6+8puf21japAqnSFGkm5liuMCM1mn02L71XlLbh2UX2D1fA/PqDnu+RJcXX0b18a3+loRBLzb3udIVWi+T7+URJKUs3Q+7CF9+HHqPemqLFUqXNfiJbzz62V89OzIxt3k+qGVbreZ5LzhTWmS5ZU4WL9N/rZMJkKjt3u77xB577fvEdTpqt/JBFyynBtImtr1C/qkXyWmn/ET55LvL/+Tz/iIJYwy+fvnTdPvefA/8ZAAAAE3UGb70pAK+CvzDuHEfxZZLOcs5/v3MQN5b78vksL+CQlouMemy/+4R3dmQcaIL8T42vYQJe8iHZf9ywRF1zpp3J/TrnYQI6+t43AeRNi3KXqnw/nLkQdyBMyu4dv12RuOYv6/Elxs/vVHq8W3mu1V5kF62e93P2i/r3dnHFj+CPpjuhRQp5hk7VRRK79wpnSTxTjTG9uyNlvEOqOa85cuG8iPx0QzSpQZfu3oEu9cXcq33g7UnJS17jJA/3d5Qy0HOV0SGjv6acydNgvWpr77Lyff/0X4zaC+FC//YJyR3ZDizXn7M/j3dsGX3d8Tq6ZR5oaYd9aWCoqKhO+51s5KcChsrEZ63D9/v681yJ5LPBNYeaZZPsJvYu2ZPTacy1ef4UW2WMEH7u93Hdu75g3r/wpMP3vnfnPvc8dFZGPE+fvDpZh8+uN7Ydn+upIlkiH8ty6tfc2e3EFaPym2FYmHSVumqPHx9+fJF/DMGT1l14KicfEgcNaad359N7hTphBs/DezL1qtzdiDtfhH582l/90Fbt5B4ArdfrM9P18/XXRRN2zRCNYKDCH36m7N5uC0gPa/resn23bpuPyiE6/c0+Y2Eij9WZYK6j2xW5NgTVgTJoHfYUanYC8XWKO5X3yj7fdGzeos46GlNIbkF+9p2XSHn2Ji5c7MqDq3wS3f3MOTmn8FmNBZsKDSbe93KqY0kVOMNoJz+8Z7zkndMduNSGQ0zn+WJEtPTy/J6151giJe7i+TJOvuEPN5f8STI9SslNT7/BHtv1+C7huS/1JmHf22CIs0qcG7zwQ3Iy7F37wR+HV0KMd6xd7vvbVWCc0OPyFkQ39tawQlWFNv7h2VEK5fcIeTeXy/7eCMjxW9b6xwm7u7vKxW+/dT37FhH3gv77wVEcOOG7y5xlki4v3MIui7cYUxpfMl988d73d+a7v27RmCWleHmeNHBurM2rzwV5ZXhvZtVMWfkF9BDu77N7vSQvYK+Q+25U7u+UCu3E0xNOuR7/oOXvb7t1CPYKjOWRIorSn+2m3dtdthKX8+F3fuYr712mEqKcfl39nvRvt6y3IcXrX7sxX30eakBdUU/9tZ/aQ/n5w6+z984sleIVhOkvu+T7dz8n6GXKoe7uRS7vuw3CPQdjJjb9OZNFmTf5YSzRLyz+/eqUdHlorv1Glt33cvyCofSWJ/4rk2191jWEJD/MNK0UG6u77dTfwXzlCPua2FxT62n/qCAQ7vmc0Qv2Kn4g3F0oOG8DP/YRve9ITlKhKfvTtYot3d75f3096WL7vh12MI+KJjc7qQRHn8v7+jXPwgd7u93RO7cnt2/kKDAWRz9p2jxSs/Z/3IRw/dv15JO590PYSzPvd/VlfZd/cX03kRT3d7gl1JklvzxT0WrqLNw3FGkUuI/////wj47CEjcvxkSF5fyYR8mCVtNf4UItrapfB8TeT6cgEmOyRnJzC1xOSrfX2YuX+T1fqJLl/P1T++hPyRRqwg40w4e1KOQs1vr4Wh6LT8VvO05f6J9+Ld9DDi/9/gj3cvIkKeTLuifWv+tzutevpImf+sIlq+733vXkgp2/dx4T+XpxvS3up9hb2abtddYmt5TOc0rfvCIt0ezfKOL7pEODWWS8Zxwxf+IyQ+XZfNJ/J67bioqJ7qfP4qpEgsgAABUNBmgAV8FnihWYEIdyxG6T9xJNfv1Nd/F85o0pcgl9I58sL+MJy5JOYtDSBNsx4MUzUbH/GljL6tFl/mPYXTyOcmz3qcVPbnr/3ClEURvb8Em0nbhCsak13qti9tsWI33joW34k136vzRzuU4z1/4qnvqz+Er5Z8E7adcvyX4nx4Z02z/qMUhTw4aEH+mYBrekvrfJQhyl3kuEA9Or3G4azaCb+qsb4EX8Xf4fXuWcPUQeSU0s23VFwEM0t6rQ8xfe4spi9faaC9kt4tdyzJl933CkfU2d03+CT6OfKVJVw+lw07CdtEK2CdkLRv9ypvtsKejMmZfaqr61zDlp69C9QKq79IwUP+wykEq7hQoRcbhSOxjLG2lebTPzR3uct8bI5abHsMKHbSRbjeEH9W8O+cmN0QSf6FcVuDJZi69a0zlQrTeffk9JdfG0GQTjcjnCRarYRUkXEjvvjRafnSagxr/y++9BS4QMOXW/vwtwywOjvDq6Lfrghgt8sEs7c8xfb0j2kyywVz+/3l2xwwvaNwV344oim+N9xkk817mjtr3LG8Gb7Rwb7CfgsCWn/XXT50BjvCbbquOlYt72J98ZQv3uHxsve4kuifPy/9ZPpOXs827aPWa8yBh6LqizeNepp6CHKoAgPToy834YrBv0Lb3+bhVTPe1wm/seIt3e+AJNy4dn+/xnswEe+bJwxsrgjgeNO25fr3ChQWdYw/ekUeKV0menniA7K+rfcXuQX4gvHB6wqimaOfcIbIi3znyvrJhfTWzn/STxtGOmmz6BSTZjx/Wmx6ho+YksLPtk+nI9cKUVh0F8dsb+5QKWrNuDaWo+51JgvKJJ1ze+T9aCBhzHlhL1dl/3bBPDSThGjUI9t24OjwvhL3y0Y2Uk0uv6y0PDlmvJ9qo0r4U03FndwoBfyz/fDV/15p/d5w5LfK7J31amuhyqMbPoZWD+Z+EyuMa3yhONiPsWwRGoOp23y+pZOCg6Nzg14Ve8OYOnBHHBJeke7yaTbye293ikKwDH6Lp2kknhmXLUnC5jlIZRLOki5OOKXV28PLcfSVtBMS09N6e+nRNb3qEl3ghI5+/Wkt1w7E9e4Ijp98NP7JOu9/XOda5d4s7yxz8sWL7oIincK8/zhve6dMEJQ8kk+x2eFCu7923303bpvargr3d3e72+ZCHo3fgrJzMPve/t+WhPda8q5PrvfBLmf88v/q9erh6/S99G8vCL+wpTzoCa327fdsvffWLyqT/3eT1//3IXd6rP/N3fQmE5Q0+8/6WrF9X/R1pJU99uTe4R8TL/H6bv1BES532/soQE4Vcb+4rd7vsWw9z++yyelE3v/WK7vGP+/erG/dU6k9PJ1wiR3e+E+HvfEDf6lfL0cRfTRahE8xR7e979NKVN6UgqeKdIqDbhHxBoyY/P/wRGp0/HqJOK3FbitxWK5PT1y613Lr5N1ne01Vix3f5Pt8XXNLvpSokEvb9sqK/x2pyw3vc/e7XMxZnR+CSlEdVlQR3JtjrLb3d4h/kZSfuEfPFV+Nd/hc0nXhzsKgpnNV2SJvliy9vdMV9IcXdGYL6ol+5d6QbGvvUOtX/68sgh6t7EyybdvVKmGml/UvZrJ9vi1dAnM5RWEWHZRbksh/a3dOqo1xMJeYwLhhv9+78uoOe9akbu7wtWJLpviHH5oIsrny21JEXa/T6JVP5fki+X7u9uSmOxvHjoz/favrBPxuZ350ghgn1WSTeklrJ6dv+pBdajsq+bw4PWkUivCypcvmcL+qRLERE8PuP+v9778l135LjnR8FcAAAWuQZogFfBZ4sUkGs4a7h79ykJf1+Xz8M+Ykh8Ee3MQWvi8PsOhJfxfGxpLE6NFu4qcI9woUfZW/8J32b+fCCi8fhk90Rx+N56bZWiDwmGgj/dd0vdLfJ9lSYXW/3bn6TfLJX1u+T95MTLHXfNmvLlfn9ezP/CvizckNR6Z+FMfnHIXmOgkv43u1CbXq35KHEdlAE2+dNKz2Q/wxkMt+I9/7du1+7Jhwjb23G79y/flEKYY2vMj77iNK7BDt/ktTr7/m4bijF4SaY7/GbbUZi6tsMw6G4O48MjIMr/2BW/uIKNifQ8OTnrXr8dIDjM3uayLuaeZpSh+X9fCUoi7TjAZ/KbrXBhixSjh+9vCtU/+FpDuj8j64338vyeoRy8d9zInp3feliSny06vZBPzCuNyX7hQk7jGUyUhmnzuyPy0yMIux6VsnNEjv7i+8umxt2IiOpvAW1dd/csj7EvezgI/47yvvNlPktsZQ1xbUrf3VFYeKGmetLejR3p4y7m2np952UfdYS7n/WMvHZlUCF6OtP+6c8I325H+EPHonkeWNJEy+KvDbJb0O46j2LifLgyJ93Rf18FF4R+Hz6GbB29wpmgWCl23eHccE8U4s0LXB/AJwm9zptJpsKdDHxULt97eYk4SOHoZb8Fla3go1hcvzghPLGigyOImYgt2W7lx3dzxEZbQuf6TXhX3/G/mkG3akD950G1fw1LBkifqxHrjXf7BbukjSIvoqE3VUWXx91Qg8WHINM4y6U6TvY9StkjMO9IbZe95E3OdTOlGqLRUut1ITdH8EpLHw61JQZAcgnuui+6yxtoZUHtdfNdpVPr6EEP1LbdYd7x1dHk+lTp8Evcg6ndrprs+19b0uEn+CexPXZbvMmlWisb2jSmfluMQnUp3AjflnP+ObaP/6D9Eym4EHp+ogvj1kz465t1dVc0drB9dy8wzlJyZ2/+mCQzNdeexkx8n0ll3mLKIw63fL9v4JTP3gqlFW/BcV9u75GdXWX7kwyt95f31FZRKX5gc+mwiQddH3hqhkCEycgWqukJODJ64T/onfZb7hHw/3Ddbs8FMmPx7oP45i+UgJ7sY6Zf8g2luHamAOKEsvO972tQ4Kef+T1fc/E93MPsH7/IUj+gr7VkZl1/R4LZB/fNHnNLmgh0RX/aVf8FPdy61ZoRRRXV9320dYfokPL+u97u/oTv2+GPv120r9F+0C7e93f/5xfXnh8I5ILBG6u3Xd671O4qjuq+vrWONfXVVtayXfCK7wU2g337u9G+5pYZk/29sFRN2nd7710XtL9LRpRMvva3iL3u969p937WT6T/E95NCt73L+l8Vml3n+xPqyZf9o13wj4JvLw26PK8lrlwWEuHMJdk93em2T6zW85ARieSQtH4R7ujvvPuxsTdy/vvr1qpY6vWTdjfX6izPhL4sPmlyLJIpVvKXAj17ffvQ0W8V3p2be99b3uEfC8iEkw8k70jceCftNC749100ZnQM+zaxITbBQIf3vdzL5O+ET3Hct3c/5c37QKBeDrg5cL+L00uQhCTR9ZLvOtqiUIz+T2m/lJyf15JPWT2aPVZIQLaZ1y32976d1lmEPA1eK+/sXPvvL+kz5Iwk/UICCxDFaG7n5/vd7yiZx9+43VVxW9ekUrxW/J7GiTHWu6Ebd9/qjFLzdze/TGm4c4djYqEemf7iDXzv55jPWv39DYQ8fWwg5unMNd3utXtwfCPuTt3uO0PLxZhzK/qvBZe4r4cZ1P7zpCmSE+SFaTL4gydZtHqe/ExImHWSX6mu9hWnlm4rdWJ7p2gvvDctLu/Z7X/Sm0nLleSCr3fPy8dbPpxuiQSWUYldD4V8QIVfSX4qbXJlqaXJ6qVmiOLtj3vuNTVO26veT5Pr/JRzMy5ove85iVg/deSQl7hj1Tr74YxX5KnqyRBSbmZIco8FYLIAAAReQZpAFfBZ4sVy4/cN+HBHHI5L/iaWX/2wjtkOZqlkwumzu9vX4IdZiyXW7Q/e9oOCSjC2TtPhuL+Se7Ynvh7d5cfxq5v/LVe+9SxRUi/3b+CPuNtqd/fPbhXzGwwvdmfKYl/d7G4SeQNTp23LJ2IJEiXNrstqBfx4cvXosOwlPocDnZ6aB8x+sF9IOjRFNH/4U24NsFbsAYR+vP7dlTBv1VB+H2eOoqV9x1tGjYX3d91tJ7jANLAu4M+9bChQl9tby4Xsq/d3e+V5Z0zFqlLCHI3ODzSvgm1PX9Hu9/LGZ37B+N9uUOtL9CY+NxnDlO0zj8OLqPJ/XTliru8yKCP4GWPtBEpeNrLbsyqN3/l5nkaE3+KCFqzgK3Jvu/xnu7HYiYXfvOFzjr/XthMrFfcJ3gNakhUfcJ0V+dl77aF3z7hN0v4Qvve9jvf4SKY3ecR76o0/u/cFG1HRL0eAgEvv5//El9wgQcE962BK/itl+/E2vCsj+/aCfdPGxAbv97u4TXtggEZjju7vgpOnCPk+PX+O9L730Cu0BDumDZwwxlfZ/udmDRkPXRYU4yL73DMW/1o/qSkqN0JU/ZP38byHKKLBPXv8EFBi/ynIQvbBc6AZ8NqNH36OwyJ3d/6ab9xd33rqiXMIve0j1CksFjOpL/768wjLj7M7GSBZWJV/e9INVuMN6BHi4eh2IpcGeG5J0lTXpXwS5oxDmLTRTNknDO4675Mot6UI+CMRPvZ+C2GK/+d3d7iT6r9wWZVwyvlyE5d4I9L3/mMLolwVkeXbAO1xRCsVffCueVp65S+HuYTzL1TngiERurudYKy8JeUXq2j3W7xzpzFPQ72P2eCIke3/94IsI/NO02/VAgpTFbcEvDqgQ8zvMj5RJYdsc9JLZvum9mHM85m/pUVKqp0xG97v7Qy5aPz+97u9wkufBWR3d73Xbr8E0yCHYs9yy0T/s/Lc5vvvCZyRe93f4skOIs/xvf6xb9S035PbS9UumySNDNSw7zMEJK1p/P8PwSHe9qf4Iu7tCK9wViuVcV73vrfuFBLivef7p7v7sW1z1Qn1S12Pq/f2quN139krl2kvtesI+CEj7b79Ne0/Iiw23eTs/v6EPqu/v7T035C3vuveEfRH/Rn/KLe/RPilVfX15PSXrNMbI3xVe6SrkhHzHL30/y3e9asokWmZY53d+T0ndzyLL6/EKnfXrZFt+SipBtIskK9TrTd9fvKxJ+rk5P4oQ+qwKKwIr2pUi71iIR8hZf/CZNARGEieTy/r4jeXn5ffdldgmLuYNn+5JD7T4QRBeb06Xus5mPzd+T+mnkcUasYEO5vy1T+kJhLw8w8/F9Af/U8F8EH1Z+T17rJU4oW8pabf2Xd9fIGJYbu70q9U7y/kKiydiZe71o5UCW73czOLbosIXhC00/z/d+oLrps9sm6QenC662Z71ejnTVYj0oK8/e73d3pVVavQx2TDHqkSzRE5E/5ryCYRgqgAAAQiQZpgFfBX5h2Ohbi+CAubOX0a3X+LC+LJlxLDjqL2Wex/Uvq/Fy9swrxL9eUfh8srC3hwJY5k5EvDuT/hTzSNF39rZhECT+Xb395mEysYsG/LET94Z7fR+7y/veCefLwH3Fr/JK/i6KGRBYK8AEFCcjjy2UhachZ80T93xNzyp4yv3vhDY7p73DNwu8v/yllybQqyBTzGlo0Y4vx19jekErFAYu+u6geGSzf4C1VSNRae6VEve09rboz+zxvxQOea3kVmz7YU4gnu7f34b85EGdyVlpxjvn/KUMLW20PptJWJLuhc6ZkwTvm3LCEiL7QZKaN5eUx8Sw91uPpJH/75a3Cf3u+nEXPry7Zw36wS42nlX1b3H0rqvFTxlGXhxZn+a+YQ1+JLy/u4U8EAxzBetxVzkTQKeYpOgAjq9rLz01jml631uVjLtXtyxADI1/VIhtrvlLs2KQILsOIrAQKv+0M6WX5S7fg+6HRfdyvx0Cv4IDgjv14RP+vfn6bVckmdL+MJ+JTRre+IJ8g6fy7/FZacv/lKYPFPkOduJiIJbvngEnrXOmuyxneY5ZNmvA303ScS2uDfSk/Xz6BMQbE/w+3zQylp5C/SqT9dT2zR8v63rR3tZJ5cJrbLGCnsVu7/u4lzcBe3U/Txef0N8OGL00XDCTsjv/vpfd4aa190zKqfwQeETgjPD0meA75fnc0hxjepUka//L7fuOPKGkkUGldpDBX+6cvrt4KomEyBvW1w4g4OlpCUPjhOeNp8kng6v17gpGMXYBSKva86lWcYefd+qCmJsLv09h+CMbXLpkXR8MO4uBLubq9/lqnwSz3wh4q7DSjASlf/XzAt+WtNxIvJX/d69FCe73mjCPgiGZP+3+CLTuUffX4bh+Lpat1au7/0dhAkO1P/z9XBPvjAl/kHiLadCX7F/cEJnvy70V+/vJl2Nx/IwUGzPhJw9T52WOrQl6evCWUlV+ZL34gnMu5f/fRK1k/UR/9TTh93elfEQm5D/3vr7OSEfBPl/y+v2I5unRXoSy916kvosVUdBK78q/oq7LXL9X6yS+7hHwSiJn67t7db5P6+z6X0xN77G/dXrX6XqspASXf6EfBN4/TXd2/sEJnvrf2JG3d7T9P5JCO/1RTgy+i91Vif4JBBbhjFV6L3R4I+dVJd2LRDu+m/kk/CRfv3IKe7fwVne70nduf2/EC7ve/v9Zf3dOqE/J6r8EgjEQjlCj+/wXb3fS1+OLl17o73dO2hOmv+I9jf5sFneof+JtmEgHV5HPhSseSTMfneE/Jh61mlyIt97+UERsueqnKJu79Sd3usr8T9apom9wu6zMtV6pc3LnqW3m26yLdCNBETNupfucp3RLQom58fvdLhK5JvYrd4Y8EV29Ok/Ncblf+6Ov5Llb2usFcAAAP4QZqAFfBYX/3MKcKvtrymy916lSF/BJ47ImMv/uCDKHy0eLR9QM192ludUjeK8+9xZwlFxt7NOc+X99QtjpGHm54YeBWT2nb8cix+X033GzalTwn8EVvSmnBfoyWCL6Z2SM9fl/23Fkf1T+JENPXuGTsJxifi9MuHr/y96tD+je8+SkT07yd54YV8Ehrm4n12kxr3GY/OVcSEG93+vIFy5uplP7SetBNdyPczaG4us/audFW7gty5btmKaKn8ZCvjfoOjyA0KxStp7gvGJnR7lQXlTlaqsJlOJTXcBH7kb6/tS8F2fZKJHeTc6eoKcjn3l79mNH6dVvl5aZf3dy3DjbAyJr6/hPzEk29/Y0k0Ebpk4CrtcB4++HfYlUWK3XyKS9n/G7IlYNlTwclitbw9mnszG70a60W5h8/2755RNfeavthCWXMCyHGHd2Ocf+CUpSZS+ErTHbs86bvOwSbmQO06PqyXDG5BZbQ7g6nm0/r3X9YKKsr7T3L5W1uWCHjeRPxfhtJH0YMYHr42iwwluVsXaodgOZDRxcQCAztub29F8cmpCTc3nzTRU4fu9vj03hP5761omWV6uf/VdBC78vu7u4S8E4p28nb6y/9Nh4mTe74BTy6/f14btx/bBTnFIbuND8p3/Tvv1Sd8n1WXTgt4aQxnMDi6Am17f/Z+IEucP5rRFT+4LiFDpd0t+9asTZXMg9HglIZdoOlzjL/+q0gwTjJN37XWWnHu+CLLWLxeSYe+4S8EYq763+Cre5VwfLXBPda3WWCDjLpyOmnnh+EPj9f+PVV3eNYUJwQ4EWB/ieQdDcOa3+4gkuIibl2BY/VEw47sN4TFnpMe3cwSk99/cEQqau50e5/fVS6E6V0/IUj31Zu4520t02Mly7iv7qIF8N0xyCGkHqDovULRhdHuFC6ZVd7uX73d2hPxIjKxk9b1yXv2Lbu+YqqPR+6tkKP76tddfXtPwQ+Xt3QIZLiOqcqf/RSK8IeCLy/X4szj1Ho/P7/CAm772z69vX/Y0vVdifiPX1qw1yckI9gmNbt5ffXzi6b5t+j+/t+/r6L7E0JerF+0l6nrrUI9AmmZdCbn8MpEZTvk7+9ddfVq/fXhe9+SwQb+1eNr9dZfERZU5Cvff5ru8I+CUlbT8KtO9PzwUb3e99Vvt6FdriPd7dJ/J6P7ErvNxpO7T6BMXd7u6KdrjMJeMl2/JInTe+RIlOo0E5D5e+utfElOIcT91k+T230K9fk9ubIUfpvJHEKF7kH7e6stdrt5tQsT6q8Y6qi9NCf+jluvJ6pNEfITuWFURmTt6/ThfJKZ7urE6L/kiOXyeie1hf0JSJZLkjJCi+aT5C54wWQAAAO7QZqgFfBX4YHaQ24zU68c7/iyh3NHScVkJN33FkJhrYRs2f825Ub2/F70uHM58FEt3meWamSGPCBOO9UB4Qyzu9qS8f6yP3YN/spf7d9buFL34bk/c9bzRYN37inTOX7/CPd7joGzlwEjfzd3oBO/wmV4bQwWu/Lpalgj054koV8xqTQJPD+6l/d7G5Nci/J9hYVvsJ/NYEvjkqi5KeDx53l58iHp/3irvZBAbRVbMOITiUY8WZkn7GUruP7d8swn7R2H4Z66SkKzZ7j2VRTR2WMhPpj73BnTGA1tfZ2e4dgryfbaYnbqdPcQVhqeK55Xrgp0ZkDishMD+EvBM+ZzpcjfjNEvPuVi9VPX4rn9z51W0W7js719iSmQbZ/lISf5bDljCfgsCEidiepSn/ytTxef/fhTv86Bv96xqu3q4cT3Op1dGniT30fvoExaFuY0QWttMtk+kres3HZnk9JLL3CfmvK2Vq9BbCenSBN+x+uHuV/J+lqyWbjomXOCfgnp1O53tLCz2/sFRkM1ciB33PABAG7L9duOXrL721jeHqBz+6WyD6RIz7b3qL/6Mg/tHOf09pKe/VhSH2GPYqZBjYYTDj854PwFd5NaOmELZVP7vysaoTyIeJIPhFpnuNNcvwl+sXiXc2lha70m41hUiInv/uQfuiv5b31QJhbnve9FfY2KpNNOt/yGcwkCB922LSfbeLVtDDHXd6zZ0BDJ44ed9910+1vCnQPzBhCLi/pigfk3etQNpugHcBD6a/fhDfC67f6TG0noO5tHTr2JP56RDeC8xNIixHT4oYdEP/L68S1r+Ei/+XXYKxR8qkTnlPLpaEjt7ZfODTjuG96QuH394JBHD8nDvcx93VtOeT9NrizZcG8WPD8lnlEI779rXKpt7hJ/iScsdN/qzy+8EZ3v7on8KXd0D2dm3vd3s179H5PTS/wREx2T7vZOMzW/BEWndupCi8Zywh5QkTZf5YJRL3vfhJXrV7rui/zeXhHyEbdN5fJ+NN3TJ1djdbXXCfnEu+imet/ijU6dN95MvYn2/eYXe8v/qbd+/omrFoJGd9424a9NbXZIS8E8z+MmGxwq3PVlgpK7l9t3ufuc9t/fa1r3++vJ00uxO795hF2I/7m7veqlPefhGsmEu529fv0wXG9Ny973fOCUj3d78N9VaXJqxPtcn3+vt+gTFuNiwLB9g/z9t1cxb5f0oVf5T1ky8Rl17T3vv7+8WV39tVkkIfy+GHt/TXZPjv6phvqob8l1/s45KngsgAAAA2RBmsAV8Fj9xYqTC4rXsV/KTHemvdUw/L3TDHgkJDfg6uvf2lNe4R0RYZghgiROqW+ZvyvrhmV3vjoLrqXco+PtDvqlXiTeXl/sraFXcPO5cGkfcXj4UOjxe7QvgJffPtd6Abk/S71/KVJ5b0WJ0y+FkqlJ9eT5OM54V8xtQT7xf47ILk1IzcY3OUk5bfw78b9ITPctKDA/uV76FuVk2/GcePYIcNNQ9w7cSVO0iHi6l5UO/oLV3mrj8eR77nBHa48e77rt7nDa57pptpzCz9pueCgoTct3fxyuxTdFjKbYrkXQz5u/VpUuRC977lFq+zUZ2Lrw2XIH3XHKfhPxgyGdxQXv8DsV6yvDO/fJQSYO72Q7YqI7WGXVNcfjQXttjJ5EuDsvbbAx5XJuhWl+a9k/MITm+s+Yvi9xnHGuUFp9p8i5CPjIeHRWCkpgzTpjcw4eilv3AblbJ+u7qP3t3viUb0u74f3ddJbvkn17SdSTl1dPqhvCXkpXlGit26SKmw4y1/n33X8MuS5PSSTWsEd70/uO54Ht8blhvCb9oeOs3d/d/cwW6t2hn4I4VWzXwm3yfr5Xgpp4YS/OPTfYZvi4aScu/ZPvc/lIJx099Wronet+r/gqJtIw1OOGof1+kT4fiLmLlByhxQb7CfguLI1bn37f2C26IS8x/HRTfS7rcb8/NGhzReGcGdo6Rnj9LpIdDq7ol+18cSsa7OTI/xf092/DbfMHnkcWjsFIl5QvMN3n2GVDg6rVN4YMGscQ+UNvVi89P1+Upc77Hy0N7ye6pZFhO7/DM+d74QMUFvfdzq3OLsKoEkKqioEYlbF8lS8TEvNN98vCJf3iSSCr3vIrRXMn0l6uJ5gvbh9J/9nZe4/7eCO99dfuCPe6f1d5kfV3YlUqc8l3wh5Mv7/KIdv7sTe+8l37TNk6u65L+0tqvCHkx2n9DiWdKx1h/3xukfl/Lyx297vit+mTquxJbr60JaqOuqBFm3rohVfaSogIbvfoR7ZJn3dWtV4IS7v3T6yVV/J6tG7svukftvk7S5IR9CL0/PV7pNCXuxfxFe/X/k6Uul6FMp99Jr6etXhfslU+uvrqxfr3r9bPhHwfId5Mnkwst/J/TJiOUkz+T0/7JFnuk7v7+vVKSbd9eSJ3e+4YvgwgAAADikGa4BXwWeLFLKSktSP+5iZyxxpF/2stprDJf/cJEWiZyiw6SkeKS/vso0sxIpczbA5XqLTCG9fWcsp6J7KvDIb/KLfqNf/jZyNEM430LY33bgCdq7fDr3BWuJl7i7+HR3FynZ5jDbK3Z1b7PhvB5zkv+JTl7vXpFvf8ISwzyl1thmZwS9xVyyvlhtqPqK8KeCQ1I+pq2ZMv924zeEy4+QIV/8bUtqwZf3CrnAtZUEdiBGbWfksZrZDd/5Pi3Y65PbUpSroZfFoe+WGy8eLineELgy+vn19jYI//WoUsec3cInnoRu/8Ymv3D96hmdd/9N24MChu937FxovOXi//L4yy+SFMzSnLa/c9Td/btHM5S6U6w5bIgIW19uqy+SlebneQev3E3e5f0L2jcVmTv/8pZXnSsBQTf4LAk7WHakdcqlv75faq8E2/vUHc7ftXX5j5+XSbapVXeCu+KijYkg6kq6J7Hlbs6FXveXwoX7rbGmPhGXU4Dy9o6fC66x8ATLcV/rO+OkGU/NssKSPUbsPV2h3JFL7vK6H+e6kvLXldv/uMvXKItMCR/6/f+T1X3wQTLQTPHbwh/lmY3KHQRPWpRc2uf24I7gy5qiVX/8Ep0pu8F3SaYiN7hMgaSTWlf3kKvW39H9e0tQp5rKnaOmXu5i7U+7OqGkyCw6rR2Z6fHSjLvc36rssZOjtjRS+T00v7BLbed6BJYTIkn+0lpBMbLe98n73iSZfLwk/arwgEjL5k64WKESO0f+TqHqRFd7lxb/o79i3Xi+de76sSgQyy9jJ7TX9mM8iEdTGvq4S8t1qvZY+gQ31T3kRepa+97FEvd79H9vk9q/Gfs8xXd32kvQl2LFbm6eN76sFolK9N9q3mo9e7fvJvfRF5SQls/yzVfbm3d9v2/yq0JeCbe3P4aVlffWUSf0z/6EdXghLu9dj6t1+T35Pbb9VKZ5XWvJIW942F4viyEPCoYonvXPCz8vlnr9FBHu4aegu5evsTR6ql7vtI2ZNyAbMziqoyWL2aEfOVY73+/sPkbhl1nhG+0+WNu/4cX7J3qvFEPjby+/qUSF3EHek/6XsnomqI9tyObN37TNSHEFc50Oyzm/cP3Ay+09Ls1jRinwnksUtbpes296TywRHyoTSKm8kR6fWb7pPHZf3hm3lp+1kJhXyGXVeXbvy+hPSRZPrWRsWt/4ayUQ6SfD3xUAAABEJBmwAV8FngoFGln8d0fFYrzGxrTDPmyCGE59m+M8M7umNGXJagvb/AIPmZqypoGVddXvd/jL9Q5BF8fMU/BoPln6XcdBO/xWFh/fj5vxpl/uEeMnhrbtwI1X4ftL/L6vOxZM6IwHKboFLaf2JhM673svvNJLr3CHjMraO98v/0C7liad7iu/L4eXAFV7gtHFNeZSGnWit7xa3cQXeH/d/cRAT+sL3dravOrdeqY6L96y4V8KEtFxzoDVX/y1LLGThl2qnGrodNJtiSgrljPl7WE344sC+f8Y2EuI62k7hMo/l3KGr9UKxpn8ssn6t8TRiu6YTczVhK8j320ekm6CezvdIo1sTBHu86dHQLNz/3CPz1zNZJWlVJEaYKDWg97e8qatpre+Xd4TflgnEP3cNv7tAr/hL5VwYz8GrcN9q5EMp4pQ7nXGRfdgs5+xRfcG8n0/Y3ho+VM6V5y5f/yJEyuy8nt64q4ItApQ+VGwl8nf+wS2x26gJ/edEdoV15MJepLf2KuuFRdvfvrNhx70niYwhe7Xc9hejQYBzd+p3RuF/0Yeiwhirysnpb5NFO9XXuPr6oEJjpwzus9tX0fu9dd+7UWgVkKPsjBHxs73KFyFvvyyD8PUzTzpIEh5n+oQ83hx70JNk2Xsex3VWS99NJyBDyry7fhl06a21f6BcXd7yu1tJ36+rFb3e77LyfdCvlQSzlDEXte/eyZaFEnj8vtbpCD7vd/61vkkNe5eEe2Ix+jWfgrPL+XLuypv02r+pTkf9Ua93povIQ7BwnfX02rjaq/bQJLlb920ELu73fu9v9F/eVOEPIXhp6/bGkOwHn+2ty9MtkZZy9uNiketvlWL/ukcwkFGt7gl+lt/Pcr1VSiT5/RZ81hCMq6alldV3feT0kpdPDG6T3LCyqHzdPpK6HXDaSN/d3e77xPd7v0bp80Zuc7u+G5aV277PeEN3d3e7u3tIJXZvxzW9FhLe+79II73Ivd7u9bpwh4JiPXeRfLbqVxRnwQ+/D/3N7yMqKNzR20XkM7+kwlvfd6TcWlrt1hutWta4LN73hBpiXcFNb9P9dbLd/wlvd75f+iEH73e3CjY+EfBOYuxoTG7QR/+UwuQ3CywhT3FZce8V7Sl+/TooU6b8gl399WNRb37TBHO68V26qhRHvEvj9B6H7Xst9v8tXTcI+GizU6XTr47v36gqNc4rzNuolhl7J6614oj3u33dkKJEp0rvL3dl3vtaL/4heSS8xtK/x0cE9Ei9QKf10RLv9Xqdh7rZD+e4aI8JnLh5G7n5fhahMvFfRS9093fqUps/foTNu/RoISF947V7KpNwr54CxbL0d/pZ8eR5ZFubXlWq010L275cIa0IghOfJS9OtiemsRIW7+IiyXhVueHHuaOufb7uK9wx6pF91Dn4O+/4ifQu5Jet/iMZ7Nt+hF1JL+IjubN4/j+CuAAAGREGbIBXwWeKFc2LmNe5SF2Pki73zczQ3H814btX6l80wwX/3CRLjOmrlulL+94KyzI385Y/cNt78otOfh6ZBOgtNndgkbEoBpqqdnrnP2gQeq15q3bls2Qu9B9cv5e4TEmvY8yJF3Nur17lIcx716ZS6bhTwuI4b0ObXshnZmz1Hm8qeEXsXDuAV7Y3CTqbdTF5fb7AWviTzf878V3f7uqbzGSTc7iHNsv5JxGdNkaGj5WUhLaF8b7aLbsdh3oN5OG3MNvBF50zDSPYvtja92XlfO+Ntn37DcVkYNIZxtQsbut4xE/TZ7jeGmlXhL3/tfTIbK/zuV9Rky4f9NhAsJ/ZKMT/y2Bnm75x8yvyfaq7uOhO5bC+8ILRwg5NDAP6SLcTysFw8aXSXgk4fXMaV2m5Uby3vH3s+5x/dK2yIhQkV585eN2fl4Tdy2GE/BYENpHW179JaJnfxwF1Icf2y/Vbh2W6WFtTLlLGgV3AXvZk6u/YeP9+T27EvtwgV6nlnvmB5C95tEwmw+b+xMVu/KF0Uv+/l/3yyrhI5Jt0rv5On8IXM+CFv842JJEzGC+J/2Vuvlwn5KbTTdbtjYhgd1kZn2CoqrpcW034XHLf2nVmATXpdtrDvd3v+VZZdp/SR2o0mBFufO9Ub978wlRXM7n5O/qAySv2L6+CLcULjbvGcXBAQt919Q53NdruNvP5wxLMJsK+1EHcUCFt1b0Efz5KHoN7ZlW36NfIf+ylDkfFqY+ycl92uNEjkTiE6ue9xP3UxQv+3HfnTDBW2/9i3/SZLQeNKLZx6wkNML2URMnXeYl0tf9i4k8W/CFwf0P1qjxhp/G+IyMiK41b28xW9mfB+3SS42LvDN3eOTpb7CgRipoRXh8aBkuyAvgIXa14swyOvry6m8dQfaWkNoaxqmyFbUYH5ZKqfJf6/S7D73HvmJ6CYzCw7xqfrbZt7XCPgoh6tthHq68z6G3t7oxWh6DQy7Yd+/dmL/3TuC0hbdEBXxJyHg2SvyZCeLDaaPxuoBDkXdejm/P9ZoW7up8W8Am6+N9WMBf5ucfZGC+5z9KzFd+/ibHIbpmQ2/6m7J5I9uJKmnc69tvUSwa877/dfokFhMOfvc4/b2VLB7je7hijve12kFigeQWl8I9ukrdVkPKN8M0b+hMOFS1X8ax8sFO73fhuJg+XZ5pZ+qNNChMIOKZ2FtCwX+1RvMtJx0ujF4xtJ50CiHoP5zqhzzvfqyWdx1eEfBWYnpsdak39z1CHl+F9WXNj2LYJtBcyOyaGOaV9m7Swl6T88dvioTLqw77u/seR2JvuH7SQt31jTzC90qBefZ5PhMsepuXZwatM1XbbbLusFJILuZt22R/JvsdYQLck93vm3a5oojucs4LO0GvdL0DC5H+kht5dCS+blXd9Q1nxn4zKr/xxbvnzSu+ixdp2t72sRIC695Nd6fL9dZRJZTJx/gnHRy7czHad/woRfTfvGKOy25dUU43l7gqE7u4hzvO3bT54IbW7jbRShzWfXglJd33eL6BNIW+y3d28uxPrBLd+8uRfYISPeF9eu68Sd37ny9UEbvd3fd3tt0QZd3fd33Y969yw2rXwi/IzCHvr6RXvqr+ul+OLJ0+933+9ronu7/hG+73d7wj4Wy7I2OnCQs/snipX/fysz7d5hmhZ03rmI7/p5/7GsFt33JbOzJ24q7vfemsQ+/o9E78Fe7vjCPww4U+3+OEu7y/uyeE/DxrV868JbarexsmBYLDt9ZoMqy/O7yI2t+4KD3oqTpt2LL3iBM7X3vrGJwRXe6dfH733d96WlBH3c6dfv9Ah2+nXoEhrw4CB+lvsYV3vw3T2MP3cvd73pIU4e2n4nCPqkG/xxqjqousqibnO+HfZOtqhxRurvtB6ew98n3rjTxJT5vl+j+el7L2JXkf3rTSCAiY4VMLbH8IG4N7odz27+X+SuE/IUdwgy39IIea8sebK8okIGFd/F1d/jhAKxYl3vcummY5n1Kuy68hjkZ9Na0kWpStb6XsXCZT2r6Ut31TNxkv5PSp/mBF3IXSqX03XhfsE/c9mrsiSO36pFJ7f34kqZy21fdGqelX5tJtLkhm76YXST/WnRCcZ8GK/JJJrzW8kQU8xj/dEbhHXUPfGQAAAF5UGbQBXwWeERWW9KXG4Sl/xZDAo4zSmJBx7+Xmv81KXIY8xOG8XL/9i8PQ/ET75dyB0/UVhO1jWCEdlL+740rw98x8f49HqViLd1h72kbuPns6Pl+8tw9xmrczwDkXbDSX2kaT/ct56O6wk3v6rPBWalu6WwNuOlj4+JYtHRYQPMwykjfl+vcJZ98scnpKX6hnlnivbb/4QpValw/8vhXzG41ES/u+NyASCH4HpQS2r6Qe1aACPx5JuzldgYe4TBQ23200XdX1D/Y/xveu/96ZfveYKAlfORx4byP/f4yZccktuvkh9LMKPafPL8OZpw1Kt8gXbrLyelpOuEy8/aPsk63xU/Pgh/y/YmJ3u+/xheeAe9/kfaIzhPzGjtJpZGX+NJLGBG0l/uEikM/bi2KUl2z+x1WZPu9uTd/zSecfeY9zwsrkbYf7G3KYsTVelzsPWfZKjQ03fXCRpUrYkS7xBa9t/h/z/dCVhL3dTrOkj/vpsaWsypzdw/WXF5DDNN6r2PrwwHQVQrMb7yWkJMr/lSRLNFwrUiRz1xaRO3yfS5XtjbVlfluIu4D3L/5dLdu9KA7hnSDD7n7WsFfSOxXsnSDcnywNSdfBXvPF7x6OLje55dKXgouUeetBDOvOnX1hfe+odi/2E/yV+mmqG46EQX2R/l3T8sLmXFZcBVUZ8tL8+wEQpVuvl1dgwvlJeGm6CduftQWXl59dEEl6SGe3+Uu7SIzL2tOm3ksdvcnz34oS8gjdu/wpownfoXZnu25VLF+w+Y+dEkwdvaBBc2H1wncBGq0aalW4M3Pv1U5+Yhabvnn+8M3W6bMPZs/jbSI78CBf++H5di/dvHWg1ISVh4/V931XrBctFgDh9JW23bVE3UbEfpZseZYPVQcl3ZT/JYfub/iC99w6i4HAW/3V1/5H7XuNINFLzizXX73fNV/l6f3ElPnvIXZ9hexcl39/kjZVwCffQ3h2mgOtpJL5FvSZF+gN8dX9pTPIauy0zFXsKYcuGW4/402Or7fVQ8WQfn+D6znmYPP3+CGprWw/jE3Mt/Gwk1a13eJP5u0aJ/I3hCPUiv3m/6B6Pc755v/3j9Jr/J9JJedMTz3rliCz4+EfQr+9bBFdoZeN/sMbw/b+et78c7/+FDTov78sbuv7kEZnRC/65/f5Rlbgcvfc2XuC0SUffGZWrvXSd/wTbu+p1eFP1BJePov29y1/k93G9yzbvrckKEod6Snr/CT75gG6sfpKqY64/0CbunmdlyaC9kt9b5S9bZIJz3um9OUJ+CQyWGnn9y3eDt/iDvftORbqCIQnHu/b+RCdn72z/CPojflIycvf7/BId7313q+/dld230V5P6fysVPRllJX/e5mrWi6zwjszi+jve/WCK+5i62QvsDnWJpXmEr71cUV3e+6fbwiu8FJnRqw6W9Ne5du3+Mn8/vduX9v6E+mum+vqiHP5w1tczyelX/1u+jk9Ut+i3kd7hG77yhq761NvevGYQ8GHjdFrX8RUZf/xRnP3uif5WLTd+sxHf1id33vzIKF3fd93u7pV7BRve7uQTdj4Tvd33tJ/J68giWoJBD3DKSxvpfBXve08Lo4cliDfXakm3pNBEXu933vSRSShPdK997i1CPgnMfrHLhJthdKvhEzt96q1yeu8hLYUPLhYlt94tyw1/aWmu4SEuMV97v3DV71Pi/19hLMv3d9KJl7v572X3i93d77p6F5+f3u/sxuC+Jz6GZj79uX3P9Pu8n11MnQIy3n6eEfPX4Zqv8aYMvbAlbtrz3Whp63NT9ZRLYJyPwbq95/30MEzJtLOgc2c4+f/opqq+8t0Ttbzb1VP0kDAQM8RHzcBiT0XXd79Wa/yeu66fTUKZJNR1bvOyewmJFjXufbSk2T7xDbLXolkpp5PdLkyfUSVt9IltfUSZ3Ku0HPfXZITj5/7yb8jhdLWEiPpUb90kSU/G/0v+pC3Sf4JyOWny4lXxW9xW+GPVIlmiN+Ll31+93LnJ3dCxDuuCuAAABSlBm2AV8Fz3zBDHSThnz0VeHGm/4IMaDQhHy9rG7IId4FaLnzZWb/mm6C6NacW6OZ6fy/27YrOUvK/7gsvlx57hLwcszl/2n1bngoJzChcygQi4CC05bs9Tv6TcvJ+/rT3vL/l8LP7GiFDj1shK/lL85CRQ5Q6GArrQG345K0ySH9MiXruLbEraz7CShl3WB7/+2NKkZ72HbEP43B2MF60a8OxFnPI0RHGthDrpMP/H9ZfafsZBHL7M2cVCuOIf5ptAhO3HvOVHyDrXdqbewlosEOzJx4rGqXd0Euh9F6aXUWjJ+tvWpC19Zf8tv8p+eQTL/7Y0VaNLhHyeIX2UeFHjhsNpISHQePZbFb/TWtbtDdZZIquFYq8Il6Q1t3+vbv3CtpB2Pk4XD8Fr2lbfXEay+13hArEb4RKPEEHnDoS7bvT7I+x+ifBfURPY3t/JJOi899aZfdXrtwoWyiyL3MRIW4S5a93uEXjobPOJXa6jd5zbjMRe9reSkScSZ8avWBLgL/DbtYV+o2jUPuc4nswQ/Wbs+91HTitwi2y/veg9OdLhYQo/ce5c4nTkrttm3rI7G1VffPnPBLtwQ/Vom3j/Pc+pFk1C9x4iF4cvMMpPGpF3kV8XLrXBJ4ei7pU3+JKZFb7xwl4oQ99Im13+M4ZEWfyxn7k7iVtDtCrTsD9aeCCEXerG9AHivkAGI92RkYp2C42t+gP9C1JV9tP1HvONGNXnr/e+NklMgjrS0G1s5vx8RwaO984+7XUBG1M2cLsrAz54D1gTv/z+qx2LnHfrfEFtWe29fv/m93gkH7+tfjyTkU7vDDuu/vwTv/fThsr4G8flnp1jPf+vqKvaMDWr72sFRJWJTdXkbvJDd3MCENYJ48vxtoqHZYK9BoAoP/n/xwHTIhb4JW+1siW7zJ8rFwT3a+3LRst+w/PI+kmnDsAMrp2/vduFlc28Fo3ET0O8sbmsC4336j5TnWP/qEvRKy/2vk9VK68uOvjSMVk9UjtvwmaS3DYHx0rtA2V9dpAjOH4j2gvj21ViavtLcExM6+Oh4JZI0snH3WUrzBqrFxOHLzt4YcPTje+bqmfzdKo/w4JIoel5mT3RsHn5VH8n1RPuWZGBpODQXsPR/b3rUJdgkmR3VjJ6Sb3Z4Jeubvce300fQRvvuU9jsC+qfJ+lk9mLLD1ojnX1gnyjqXKF791YgmzDbwMgbJ3tKlhN+4J93e98R6QJS3u97dav71uQsq9AyamJKtf0Xe/0VnRU97+gll797/fP4QL+/hkxn7991f7fP/TKXd7/Md3SfY115IexnN2NW6z0RyqpXbVN3vcI+Cbe8rFOX4K8/ve7zb1rpoEZ3kQ0o70Q6d+6UtlGfby2X6Vd8vfqJz/e/RfWC413uY45r56K1aZUCiGXu9MytmelSF1pJzpsWJufuL3d+RkvfyRxHja7e9yoIR8Lky/Ha7AzDyQuWrzDiXtic/Ll3ftQgd993vcq9/fXkXSZiSoIS/0T12e9wl4dMULxnvKybMfGTGQf4cUNwmbfgnIXu+963b5RLu+T3/NUEJZsy1O8X191S6bFq8n6qrREHTTB8An9/a7QTHs/Tpb1xfGyux109Mgcc9/fqJ7l77v7YskZ/CqxJdGTJUVhKVhWnd/kXYvp/KVK3tuQi0uMKUh9/JZbDHp3C3iBGq8u+3d/SpKjp1fk+Mjizz6u3cl9qKjkiHRRq8SgntXENH94ZOFbTzXx/vhf0ITrkTo12T11J8QWrVGfvgsgIRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwAAAVpQZuAFfBYvcLCufQwhiMlSQ56/hd/vykzaYz8X3fSaDBf/cIkY5YY3KCPz0UVZpWWTzmjXFZf3txpYeZoKLBA9dbj3P/7HWVx33Kk2wy4v5PbTbfKgjlq00GvgeL2tLik3vQLZcsE5nu8NEPsKAyWK06b0yxJ2bzXd2Svx/H2zPRr0iSry1GsVnwj5Z3caIZr7SCvmMTUgTfq+3qkv7vY3xvATCzce90+Ep/QphRiVWxUEz2jV8cn7VSO7bd9PuEnzn8v/uMnu+PffY6HEUvbHLYR+vxsKvHHuInbCbWGEGfqo5Y2B9gOYvcpeqaUx/7vbGFgivV/vYH8N3+cMJVFsHrzOe5Pu8/sJVztOeaOSHRL5Ppv3UKXq9IEubceAe/g+2ijfsS4+UDhVlUg/e4IzZfD/u8jqXsfdp+5de31eWM+PaO+YOd3dTctBd7WCYrv74yCoL0hR/hEcWz3Hf+d72W3l+/cKaFpvfnDR1tZObwzTs6cYek435bSK3GFMG7yCz3YBDbwSaM9X71hLDqefu4Q+fuqT9JrpsJlXpju9w+lvi0WETXDK8i9VR4V1MZ6U3lv5b3bCXl06y/+VjJmRG+sdm9ujFk0+Q7e26porBASUPhy5eAHPbeU/dkb5nZNv+Ro7TP+4SculRfpwjlhKyXnEYIvC3kH2zk+ixc+5k+kiJt6GiftHJFX4+HWrQyfZd3r83b6VsiBUSAdlVsJcKvuWtIjxW7l7BdKq/LwkcTK7UssJnbfu+8lxVzlvd9OMM5Q1hqk1Oi3z2H3IL168tWdwfJChLIHx8uQP+59lZsQ5PVoNl725GwSAUpxSY/wT5QxggdvAf75+ipnK6T/J+vrlK93CRPrr7BEKdt+wpcsEJFH2iNgb2NNnuFJHNgX111/QcYWDBcPAnPi+17BL5BVnS1wdeQUiDxS7dX/VvwW93cij1us8pTD0oYL7PXsnvXuWCrH5/m+dd9/xZHzLzBkEfu/+jwW3d4cSvuHuTOW0n/4QO7733P8I9AjNFWiN5Zfx2QtFiZ+gXEmlGVPHzt9tAoK8oLezu/eTe+3BESxuVRB+JOnvkMnYl/30RjJ9/9FOZHdlb7gtJshud5X67FoEJrLGSy7bWoTE273vujPBPd979diUCXe+9x2hHxIg/rP47uf32coKD3P4y5u9XeT2095/8El3d+1rr2X9fXvUER3fTLuPES8irfdyhb6gi3vLJ7VfuY977Ognu+yfbZqhHL3tJ7yyuEfBMIuf5or3FeWLuXiu3u9Ovk+nHSxtowvdr2jd0eW76oTXul6cEed//pJe0mpX0kCTLv2msmEfEl4cdFIplv5DgkI4f6X51dlMLIJEj0Tun7vtwWU39zIbtuL7rD0iEv7ZW92d/pkvr3G933PDbavdKi+aP+4q+aIbXa8/1ll/7E8npO64qCI2VikfNG3uf3vjthcHnboyhFTmNB7PXye103qJEvefvveQsgIubafL91kIYhfwj4JyH8ZO9bR/xLDKntnQu93ef6dU2d7vSWZGu/q3e77EvrLfesISylP/L4f1mqW324I93ot6cUId+Pc92tJi9Eie78v3MnlOxvhHyYJm+3+IEP+WNSu9FKKIXuX24rvXkgkE3en7FtFNd110vN9fX8YaswFMg7HygdcR+3+/JhPyFYQP9js4l3o2GT038VJ3jr1YnrPJYvd6/d5c/HeMitvb223yetFIRXMIz3mjd/ufZtmOFi/5iOJM79UvNGZNuN47vusuENU1iYIjnvp1oR3lkgnK2++4AC1llgg3eVhvZXd0WL28m6ZBxG2mnsi0sKPX+Szhih3C/sQ7/xHkiQjf+IvkL3tAugAAAR4QZugFfBX5h0N6l/iy6q6Ri4QcXb/iyLG5K8aOuslwUd3cuPisZf/cvMfY9cL+YkxuBA9dNL/7hHmRHgU09uDqCD9fSNmL/X4wvLZCpPjWZaSgx8z7XHN19gn9w06wN6s6p+Pljztr6Fm4dg3CQtnyTBe2KO7O6uUrL/u+Xy3JUNcuXP0lLcv/Tvsw7ufyxnumyCnoxk37jc7oScHbAT9zOlO5w1/gSey9bdwdUqxy25t0LV+uw1SXvQCnTi7hrDOZ4xwQvA/+/sO5PxniJu/f8F/zK2PDy+reIDjqhUq7T8v/eNhF3JdgQXzilx3xKn29zL/b4//05fp3x5bQditGUbu+qV/haJd1U7StLq+HIOC9/idUf3FwmxeSnSUbI55yb8Efdzg9xeXHvSK6tuieX19m3J+vyld3NoTftgqHUUZzFBuWMLKYCrQtlsS4lfxvvgxRhWGGGIvJ9/Xicbr+hIv28f/r3lj/7gj3e78pcZfu6Lqj+qBYQI+SpcZ08IeWgeLl+0HXdnUKP7BVgo9s2rlu+5dvsa22gXkwolLAVtdwQWZy5S+B+p8X8eq9FYLOmV+4aXGQ0lzmCo4YWIXEPh0TwAsd0qc1d/yIwDqu+/B54SyTT/8JknXyg7hyJ/aR5YmPNP9OkVH859dsu/Xl/jyRYpVRAuD4MUsXpSpX43sI1iLGfWCsm/Zu2buZGrQZrcIfia19reFIJzScR7wze5MW8GamZBvgr2db64R8LdtpUo+vuPaf3+CIrlXfMJh1glN2d4Y7X4ZEtfY3k4zNRUx+B2+03SqotXEoCPaiq7qQYudtIsoy4/SeMA67x/C3Zh3nCAkdf7+k/e8ayoEdBBaejphOzpwWcd6xoib/baXYWqHRa7ktxY3dHlaa6Nn/UEZTTkHn1T+gVXDVp+3IF+8xdsn0t74ICYSXDG3sC+3Aj9i9m8M3d/VkiUFt1RfMKsavxfXV7H/9hM97yR7/CchvckOES/++T+vUXBQbmif7fLVVYIIZotha0+TotPuqeH/cFO396Sn7vbXTVOuut+PtF+/qtb9Skd/6Py/S9vky73rb4Q8IiM7ckud/glPJG93t+Xd9dTPd6L/umJK7vffX1iO7vuvRDtJqvflOT04Q7Eir35/f4+973vfesqLUhPSr10WLrptj0pdp+973k9ttL8vLLJ7v1+X2/y6dwj0Xe9+oJyXrz+qeXaLB967/yleVhv4iqfe60tfYsQEuXy5jtWtd98I+CcoZWjveVJs36/YQ7pu7uXd+kzlj1Y321r11/vIR9677v8Jkvdsdjpq0yPZd7y+unlPne4Sf2IEOvSLnfys17+hIm97b65PJ+kxM1k5Pr17ZWt9i/v0/WCfu93lbqh5H0ZA3+89L+SFF1iCp6vvyTGvfr+xu76L7yke/Xq86CR0793pPxRr3e+9bCMl/Py0z4/evCvj72E65dJBq1PqLIkkYe1WvJLc1uW/ghPtkxItPqlUxU0r8kVmcOZfUm1pk+TDSxvZjXHffEWU3SwWQAAABI9Bm8AV8FnixVxl8bMUR5a/KR+683LkMl/+wiSwo6RmmQmE3upN+b8578y3cfD739ykd7+4K9zgzuc24ZQfLtP7h/xnK8boaPicqZB+lbIgIlO2Huj/hMqTxkTGU6+i3mFr15YJ9zAvu8wvwnKLovn4V8EhpfAQ/5J8VNe4UybHYQAGj6X72Up6K5j90LltrOdgowv3yM/91a6x1r1ldqdbA+gP7jOeVdRErPK/c4a/MPw5tdullfUv/uNsYIv+gWrGz3fuXfaF2g7qODdSOnVr+jxxTCaFvy0/2e/fG1Sbun1T47p0rXsl9pZI+ixclU+efST4LZa/c/FqUvzZ2oO7y/7KVVN3yCXmER3E5hf4wktYetUELn2PG+9/X43S2Xp3ZL4wt3s5RkKu2lCL75I73bBDY289BKfCkj9+Bd+p3/cGo4HmQ/LYg5BvWT20mtaGFD/DRHskssfaoOi3EZiS1EeS0V7jcT1hKW+eoawL7QbSW4iCRsbr/n+k7PLwL94U+pc69Hs8pXlMp230boQ6DQaaUkIiHch7u32Rfq/d4if7XaZSShIoYOT+SsJk/d9XGGcOpMn7EMA+Mmm5N20d9tPjSXd0Vuk+XYO4Arp3F8S1UjfeDPzF1wE2rqtZ9PtPOwWU75ykKs5sXMWw80cfhJc9X2nxqCQR6aLr6TxrEZtQoNY4DlTsDu8RlC1NgbVzXcdcHR96by/02tUVLtczCJsaV9khM76xhM0tYXuksvplZ8hMvTPXkdDpOAm9XuY7T+Z5QNsF/bg/abS0M7Ly/E78I+t8n97pUKK8Lq/dKT18ncFJiL5Kve75l8voYSOy+5+G5dCsgZIH6QnHvv9/xvLeCMpAXeB8evNe/qvdP5oIcjbv+CvOPSnOGnRo6aeB31eLQm0rj4n2wM47L6ydF79ehHxZp2bem6L+/Qq1ToRJcn3fvou9eaXke/o8hXZI9buiMVQn24IiMOX9k9NSXxFHfJ9fv5PX5ETNd8IeCIl7v+CYg/Tf7e7/goPNE/3vf+qPqvf691lPu+qRjtk9pL+1i3lqCMr3peir3BPdje5l7dQj5BCdv3E3ve7utHrpzXf2dgn7ve8ffqv+vS5qsZP1NyvJu+X//r7KgSb3fsQSEPBNlY6cerffpir3vaqr6vpfXp6p9K0aCi00KyW1NKhqYuKh3T9bPu9viMI+F7DNoyYicmvMSDsUyO/nE4akUuUIcb0y273C/w6t6SLt3fV9UYr3elzSc7ar6k3qb04gl72n6whu7lObzw95cjLjJQWEi/v40gfcmY9wK1L2Vmq9q2EXPz1PB2v96jPckIms+Oe8ndzHFcvk9JbSicSJt9ymzL7lbqVQhys7yDuqdEMW95fXkXJ+vl4sj3n/1IL3e1kr89/1dPv1gpEXGT9bYMKERvDrwE9NSHx9F5VSyCt5fd4V8QSXeb305RJ/Eve+Vl0tkqnUnuou7fW4TLPfe9dERjXfWnRL3/ZaQ3bhZ+4g2rW5dtfNy5SSpfEfzCT+fs+Rgm3eHHCq6Z62VZKwzkkED+U9va3K5dfD3xcAAASXQZvgFfBZ4sUNRHullTl/1cxOMzFauX5N+WWDjX/5ePfwwvcEBIcdIEm9LwUQbtHuK/JcrcC4DdfqS3f40uM+vBiRPrb54INy8uzrdG2/astOMfozBaafK/tsrLGSYzcolbtZeH9cwCi2kB93MXcn7WnlRjOPQG0ck+3Vy3BGd31UTHRYZ3V0FJ+Tv8VrXV5f8qotopuO4l/+y8Y7CngkEUiBtpoyb/GeEvBLA6yxuuwxPdZ4+Al80buGBJecPgRxJRvDXy089csutfiPEv+9hDl9+qDnjFpK732d5BL+NtzDbmsrc4Z3B7eXjYg/1NOT/69xBfG7mVbKo1TlgkvCRh6/SrRYSn6BYegxK2//LJj9PVZeT9PLy4V8YaYSd5nBFvkQ9NBpBNDMN8g6qRcNM++4KyF0+7sknlxK2kwZckL0nftZOXzi+Y1+MOdlKaVdmDpCve3h0p7rsrCWaR7xumsDdlddG7vyxRA9Lz8q+UNlTv319CS7u92QS8E5A4899scuthfpiLiT8MV/3DiIwi6O42yM3AQ+rmfS9yjrXsBMJOaVGUyt18CXrhsGO8kYstF/GyUylJFbjbMhDjd3xgl3IoQj8ru3uytGwjwM3cddhG17yUO242UL8vXYmCmjdGUu7A8E9i/BB5Ye9q/8PwVYLctAjWf+SOgpYQfgmnm+/HohrSZ41D833+GH8E+AgedX3pZ/J+16njJkDhyTz0Csh1zPszL7e0pAJvSTm5qrSGwAmvuV/1tQzwSDtwlxNjk9fuGhd767/e/QIt2ucrm3eZSNmBk39tnzK268WJwmWbvNsJdAiEO/r8EJtgviouIawpuHyO1TZLGIWZTg/vDjRg0QezNiv+4Z0BuNZdGAu1n2Hx2k6q/TQ/MeZ+Y84cks/1699tZnrNbCeYNyLyxNieKR9gjgdcI3V+sWC3gp8lAkPK9+oS5QR3t4sv/4JDDKd+XZ2yu79tLLvMSXH+Ziy5/pnr/BPMLTi7vd3O217WkV7cXyEAXf9L3j/4Iyve1eS5fhBdapPwSGHqc7eWCEr3du/oy7b6Mvb3k61Wr3eqOile9LJUT533uEPJvMz8aR3tNjPp2WbsZ0BM7qrsv+UJ93d3P6fwSFNLxd/ikO3e7819L5b7+la6Pyeqr+buf6zXvtVd93v8Ftu/d2hHw/5fL7pzPpvrND0UQS93vtMqyndIrHt+/utNZvbVLS3u+2xqUpihSa/Myicdgen/CPiry+ewTPp6p5aGumyir3v2gXHrXd5ZPaojMRk+22JGfX6fChCXafbuORb7236nFtr9Lk7T3pXiSBElyJtSmnIvfvyWUHH+hQlpDSDc5ON62lPTROT8Kf6kUJnHK/v4sKGTrnzzfem29GKgiJ3cZnPu/shGXBfi+PT5UQTmvd2SzPfeOuby9rkJ8jGCBvBIpFwwk2+HecC1xeCB84tUiQsoYGnseu39T0Ke6zIr+lSl9ifWUTeqfqQllfpwr4ilfJzw0vzELH6VHTreXl+2iaBEV9RY3tFJe9dKa7/Jhf1In5KhS2f7qOduS/iLK6V0qL6r8PfGwAAATqQZoAFfBZ4sVHNlxuMqYlk1l9d8xNsxsMl/9wiTnfKFnWlkNOHW5Mf3HFD49iDWZZsDWDMXjBe+5YTYvJ/b7nh7a+c/jKTEDUZaGl/UslPk92xPvFG5+7S6GQXpJJ757ro4rHqEK35cXl2vaBZuXN9V70r+/LYV8xrl5q0ul/t3G5UM6QC4vaHl3b4UcvMOguQCO7ZWW0STLpq+/R6XRlK35k/ImPFpQQ0UL0/aRW4zbzdFpJu97CPhsxunAz5efTRXjuGr5qCPc4PRTrdyt/hK7jLvlnYFpXycu3S+S+YNSe1Qll77SdIed2OXu/z87EntJFR7agol707l6cilNuJ4q+9V/LehtBMv77Y8Vw7sxWW7cS0WwgV3VWWMILXo7QKeKRJOCpQvC773adtL4mXKC/osYctfzgwNTj2sfZav/J6S5eRE576U3knXuL4R/LU33u8b1dfX1gggj6O/24TC6JmA9BBrKDdNXVSY2N919JJ2U8dyB1n220oy80efWmfmZvgrkBkt7ye6rrie93nkyCXgiNdrj1vjrnx7t3OXROryeq6T4IJrMFkmCoMXmItbXglGuVDeiqqvdbv8x7tiExq/+EZwfDK/LgkNnS24b45BvfjCfp37gp4QccMMfMavnAvpNH36//BH2bv39/kr2l8fwRmYt2vRqWsbQjoYI9ibXkajbMnKow5LmQpSo4PdFbr5Dfz66+8s9uda+a94R8GGd8j4t/h+WCpJ48oK+nWthNzhc/vhybNINti240lzEsggomzvTpoIkm7DazB4fFlT3dNK7q/Zw//EPrJ/3h8txK/vV7CdmvFGbQUcGoWAzjPO8PXtfDbdv8Fu972YdbT+ECOWq8+9yXS3hE7vjz10amJ+lxvV7go52IbvfXvhr3CeO07v6OgpduGm3FZ60qXLXsrG1xo8oIrK2zBtY6TGzMeluFPD17Bqu69VZizqv0Bptq/r39uUHB3dWMWenzd/iuZAh5Xwi+8cbYz+tXfzu5PXjvEzHciA/92KIml9aTXxhpEfv7cIFMM93zj90w7ezp7EIFPGwiOz2icV7uPhHH5EbxF93fX+C7Bz089zCWzbtjIJbB3mPThsoXYH2O1BHulZpLbFXd93yfbZGJXYg3h1ClZltBd3S7Pfv1Rb731IsoQ8NZV9byw/0Ck137buXHLf98s4KC3dXvfaqKILKeFO736lJO+63ZRv37TEvRJd6+GtntHh80EJi/KEX6PE3fKrK76giK93OBHrJ3cJP3KIfbvSIQIlEP3ty4eHnj2gQ33Sgt17rEX3e+mnSX51ii3u929b7vrFXfSf+/M21P3e7y/30Xd/ZBVN7u7l4R8EhFl2/2CTblf3otL9f36v2frLS9QSEe5B7sv/0UTd37QT3u9N5f5VIYt3wm9tQRmffpb9adVr8mX1XI16E/ECQ+i4Hb3y8P7Y8oLDXvjaTx1e+uRkBGJuK5J17ZXuPFy0onl5cqv1Bdd97xbfYUNOljInhuKQUOT2ctajjp4NL/hPx9Jlsvrqo7edzvuSPf7EvP4Tfs4kQ5sgKtFz5kjdvFnyi3u+sxXvtoSyym5JaxN3vlzJ+vJkXTRTu/q0Y4NLQjDCUs7JzV1iOCMu5CJQW0tI3d/knhdwxX5l5Ii1eP49FL/kkkp5KckRPoezr2Sf0Iby7grgAABHxBmiAV8FnixUPM92knGknNYC9OUnHffNpGuoM+Yl1DsKZL/7hHpHGJBV0t/IjXK5juvwxF2pf37BAUw+8sh4Oi/1zl6J3d4JRtST/+FOcXL397bTcAN9x3iPtXtGuvcPk8uVsPZ8jPa3CWT+7TMZLw92PpT9llCt+W76y+f/l9IRc8J3fPB2iA35eSEKr3GiuEnGEAxVilLcz06zkt9Z4KzJbEVYjvkfPsNZ0XrZcGStf7hAu9OA8R9vPPZAcQfDKHA3AxBFzdw/rETbbA6esEn97rf+YsGYr9+gvjlf+myssJcZq3DwYlcPVdPGI71X9njL3eRfe/c6/xxV0lZeSa16gl6pZHhuLHTv1CfmNxXf43w/BWguOC3UTe0EgqELhY/NhbMu8fuxYvP1s+E56R/CBiiO658I19pyPMPgn0r3/8eeXO8gbSu41dk+3/yyuoYupmye3W715JeV/Tid7u91RUqVV7hGZYpWxcEA2rchDh+opkpy8l5BjX/9u1b4Tn/zZW+8JVrztcJeCc0+547wZfu5Cxk/NLC6Xy24rOdlp3bv3CJE7Gfh1pyFZ6J02vwY1pvPorBYXhyK8eMw29w7YOBGYiJ1VqeJo54u4NFIhj2br4b6+xrBLtMGfnH0u/e5vs++ifX2v1gn7nDsIvafL9r4RtDB3BRD8FnXfcV3e/aCkdFylEoTm760vowJahN6Zo6oCFcwXrT7/bc6wQ2Ovtl/y+EfW2YLM0IbT8ol8nd83P43reBnIAF7/+C4hQjWH8n0w4RX3f+PV40oUJhl7AME6aV/eFzJUkjiJcvXFuQXn41VGH9zqNu/dihtdtAm7nl3cEslbYVtjd1gkO6CKp8NPikQ22Sdf62vTQmWCwodkb+8gm7/73C8GY7398EOX5L/rLvUTKvAc2Yo3hzsQURXZf2r0syCzz3C55PJCRcnz5SCPmEE8n37aL/0gVZX5G0SaB0qozQe5fgk3r/9Xqj8n9e56v7r/L4j161wkQxuV+cL3vpvsSq9j3pwh4IwhTvSj8FJ5m0xXvNr7fkzU/gku/lS9yFvRUuoITFPP4uxbRcQS7wRE3dsvru0Xe9b4TPu7u+7/opAnm+9/3efwh4QEXcr5lqR4/yxhXvfd3L73Mvf64dHiSu+Tb6S76xF77v735oJPm4V+vxW95f9P8m9+mJ7u94R8FZLy+O0uZrvX21y4Jcv3/fXtFjdXb6vqil5/sT79arRfJJy8n7aVDagjEBuf70vuuxsJC3cvu7vsiNenveoR8E4g/l+N3Lf+tRN9u29VfVVQn+hZ7tPoiJvfS/lMUOz/S0TCT9MaVy5Scz42cY7OfxLCn+dWjN78oLTO980nb7MJmj6/J6/J70dMO13eX0mFBTvgh+fL5k4t3LzkvvcdvkCBUOfuUL3zS4T8mlNvIQF2e1lukeyJCO0rv4mYfyesld3/jJrv7S3Ri5DPDrZLtaIXX8LaiiZpyX7+/wUCX3eidIpPq29/Xk6d3Ld325ERlpP8jhf1In4i2lUupZ6AvgAAABNhBmkAV8F/mHXGob+bpNBjwiTd5SpenXqnjHT6qQN0V7gpLcwu4ccaZNDWl5C7TRb9MFfjee/TOnd8PwBFhJnyeSb17gnJq8JmHmbFKVbE1Tqq9lXf4vx3ifl2X92lhXzGmt6pf3tsbPOXjIEAhcF0GC7xGGmX0hDeZ/RK8W3b9RgHwKfUhbDtjJv2PU/crgt+sZ+BvnMdJgJtqPDnCd622hm4zdfbOJ/6N6CmqeX9Ydf5YhHhz375r0x9b16UdZ3qXa0MX7CJ/daeuvfcYV1soIhTKGroqaE+8DOQ6LNV4lfJ92vbjbgrnsGenvQTbFUf65o/0N8j9/6xt3a05g/BN4+EP6MQmLE9Ps92vp8kFN5i/LhrGFO/yxnuixHfkmlL0r1VqE97ro7UpEzbscKF/vbCIp3cdpKptluxW972N9srEgDWXHd/uHBKa07KPD1d1Lsfv8i9/YINLRnOHzFbbovKc9uMov37Q8rGva9usoUIPj5o19sCt9iWEr3hPp/+c96ccEjS+nE3OVfe+q8XfctbL6cpd1pJpwT3d3pSkkqk+vfxYjKGg3IxA3ZLkST/grXcffd3d7tb6sTu/LCE1viCCstHu3Kx9wwQkA96rMOhuvDcs9i36LBIVmcaDE3yPpOaXSEyj2ASfnOf0yBu4A9HEV/guLKudejpU4Ne4vlHQm4+ud93uCeUBOUkQNx2m9xJ6VHTrQ4msMtF1HnOFoZe3TRVgpp8+SyGA43n334IaORz7/lEtpvCNYIzG+9OKrBVGwRG4EZ0e5Suf8fTgsIGqk092J7I+yt8APbGYUt+RWjt8PBk/SV/BNtS1Pzn/VunDNjNH2PpxZcExRrf73jHdCiLkgR0KfLpxLBQdw4lUfyzphtKlJ3JK/aC8Els+DV94EP8dnXu2P64fyelvrY3udIOileF3bvuk8y4JlVHcd6Y+Cr7SfEkNMjz6Ge+SoqxqDkpJ51Wt1EHfef3CPixE/t6b/BQXdp2N7assIlBOQ60dpORf7pwUCePRPjhdI700jfq2utXrwUFe+967F2bPMoM015flr0I9grn+7T7t1ub3LGXb3ve7b3/ECWnd331mI7+y+iMpX319YJeWk+bZga1f9Gf8uHmOX+og7vc//5cve+sI8wgQVjfcS/7Qword3ckn3d3NJK3uyz1fxC61fyenCJVfdru+lzaWu+7qxsdR3u7uf/RSE3uEvBhl+P0z3+vFf+Q+6KsTSTeivVUat/UVl/d+l6+t8N9HepevVCO7oJCbu9729PqqwlcvvlyEfC8Ousl7TK/XSou5Q8sE5J3Bi2ky+l/kMIda/CAl2zwfG7cVvFG+xcSeifP1um71QW7et829ZJ6WHpsTUJSL5j73K3ZPv5L3fVZYQlbvveaR/20S0Cndma8+QY8su00XA/ry6vnsn1VEzbu7uEvGEG7lcIfMTmcJbE22/Hbn+9skERpfd8n3n/lEhkaH0LHd4y7K8hVlzetUg2n/fv39MVlx6V76oeZy/Ee99N9Nx8VksZJjcnpekaone3b/9OFCf2bfszv0p8rFjWt9V96kFp8Z/VOuSKvHX9Fd9OJRoI5efHs6Qt5MXR14gmb3P+n2wRHk+nHalvfs3ySX3l/JJ/2ULTH/wt6MdJMllj2nvyb+HvioAAAExEGaYBXwV+LHbkPjXlTRxdwiXhJrysBne4lGCj+YxSWHbl15ev4Y8X40ZTg+0Zwr3COUVAkeliXj+AQaeDM1VT5bF2LvNNJwdlwPaUfcOxr/Uvll5iLzf9z39wTzt3wXYCpKWy2stRlM46/inXXnDsbWEXBswthC/gxHUEfp55cEo9VTquR8vvrX5S5zbhVe4SGAi3Ja3qYEzu07ekPnOxwidiQcar75vgw7h6/u44J7m9JW/+4ibhL/Mb6IOdxSvUSW7op0Idh3f3dVJD/cuHoN1+4is6+dR1vRi7vW6l8/Xl5cD2gJeYlKM5fGE4BH+7IY373CfeuoR/0Mz2elfMf/xsIXvj4GU9er2McEdlrlmpGImfNj+jJhKw7F82YL/X1Tdnf2EuQHkv6oob+CgoT+TSJft4fGaXyp0WJmOHm/5WNfYvjX49yOvfCV7uM7/V4uEymZe7yFCsSe7nudJgk6QD3ZKUo00e4sR2QlAI9y3HfgtfqmlF/u9E7dqVY7t+T8z4S8E8q9joyN/VMWJvtsZDagEQ9uu9+rJ4x9T3ma3+V7mTm7GbL5fV/Gmp9pFC2bpTbuTtAB166np8alBMXEkNSnPF+jCF/U9akitxpV5KaOd3O4Ay6xyv8CLuMzPtKr9M4cbr+v3Bq/vzqtjScS3rpmpoVV4mrabcgMPehNFxfyO3S5YJe0dw60cRXlT8N7mCLwyo7AeP/dnOFiXrJBUbctWKCsfe6dAGH4NJ4tDd2qULYGHOKXBs3Vko+K32h3xH9Pt62ZaY98n2qZ64eI0Nqc7VtEYD5Q0/By0f9Or69owt7wm1K8KCB2n51SfKvbvhhTXbrBAabpqtxTHRfx8UvxDuAmf/q7H1/qFCq+xG9nyf2J3u0vnWYveeg5y5hj6ufmnUBi/3cHOoIykPZ1r4zRi7Fw15c7jK/17+4oieTz98JWOPW04I4CFVZ6/sGZb5T7DbPdOCEuVh5b9EhDyeXov3SRaxbovBEZu6uyfqZd+9PWuxcERZC06V+j3nXHkTk9tsbd/ola3eLQRK3ve7v1a2IEPna30SdLt6q+kRehFb4Tu7u2Rmnf4JS7vl3B2NL3+VdWUr76sIm5td97d+T110CO5Xbm/UEZ03dztVZLqnCL7sWI5/u95CYQ6R/e8/e6UmwUF3e7/qrBJd/uvqlrr61fr6SX+/6d7UJVir3bvPiaXXWjnBtcyy+r/rvHXamChfgXlnr7LNd37ogh990U5aQuxeT1TOvzd2V1JITL79KEPC8OrWceN3Nr8VyKPQSM/fhl7l+VycIHtvjd1dkr+mU73LW8qT9okud+cup61/W6gi6popVKZlQm7vuZvzP7KYSgUfmx2Ad65cQ+fhLxtGk2ZFbSl5VwSd/vavF1vICHe729f+C00vdvsnBmo3449zcl8/907Ky3PhtwLAv8Micv6vL//BObTdO9OOxd7v+qYbrc3kzrtI9IIiAD/+ZpwftQ+o88z/eB779YU9y767CR7vef9N9/fk+ncqJ1SDV4nk/WVdQjPFDu77dXvyf3avCzrtiNV+W93t8VCQk/736+iTFve1cQjctTD1emEiFdy5lh5LOZUsUMYiQQpN5fETfEdEyUdW4deQpMhYLIAAAEm0GagBXwWeLFblxLfuUy6QbL/7i+HoEYi9m5uM4wO6aCa5fy3TGXSsJgi0KRcuG9nR2KYa/ft+8v73gq5yJ4+8OxdcMbgUnNbvPKTeVBvBEUl86OpP6cvLBHfcqa9VR3L/l4u774rCvhwkwUc34a+X+Obl/e2xsqAJO2TKwRgomXCRe1Tq5vej9wF1c7KXhy4Bdzm0SWuHjcH7y7EGv4txpwUFDc3Hyekk3W4f5hZyh+trELv4QatbufYk9tWe4NB+3/gSbadw+o30nu2V9uN9DPwL6UfJhSvrSl95go9r4Za8/3TRYnnDb47YNW6S3eQefpL1Sq1ej3/dU66bEnalhHyD37sj31+bkj+U9zLsXCb9wiMCoD48ZYxLQiHadb5z3/GRP/ktl+h0b/22B+oZRwbpacPx5PHLAnXVnOQsyAC8rbShy+v1TvegR9itpaQW0ugVlhiX5asbDPa273eVNLkiITfbsFveQGckIzHjx8NOt85Zv5qFMgIP1p4LS3m2X6dS/kl4Tuf/LMdFx+CARMnP9qwdkpjYlI81PRP+5lmuv3ChXoHZYRcnZf7dgW7VKpfk2pwW87dwTdzHnoTf4gVMv3blBPcb7/znzQRAay4h8A0e6jHHbEZqSaDj3Fk/PqOGSa/L/QUn/MJ43X4aUxF4AraoTty1+CbwRfVuGFtIwSYt2t8aXHiu84xDbKfelu6m1L/R4bu4duigExG//97gih3bNpJ/bNOn4UuBzgJgpyfMO7WgfScVXdCeteBRsGPwOPwoZsYmPw3501zOiRFcgW7udnSvCfo+HRWCsnu1P7xkuisdZPtuxb3CJCe7p782WQ33+ZtQhd/f9YuYZTs9u+CkqZIz25ff5q9BlFgQ/LnnPm77HOm17p+sWUMy+TAc8bevlDRzqdFZZJpNX1mJuBAuTak9JpLPwTeO1eBDvG9/ZRbIXuyn+tXv+Ezu+OmfahHxYiVhvar+CQsBJv15q//qixbBOThP2+bI7aa8FYt+4fv+9mH35eRO3FCA7e093v5O9J8nr7rr3qveSjnF25BEvR9GaOVPaL5f8E5L3unqEfD5Hv5fU7OpIV/8Fh93P7338ZPS/yyFng76btEwye6kLXDLG3falTyW6HP3+3ra0Uva3wSZ/PFwEfGGdvff74/S19p/4KSm0/unu7Wwrz1rv8hKo9a6/dW1ZIua++8El93rzFRJy+X/yS3fhHwuR4dVm/z+/1zwVOW51ymPvyft+XmLd6rCXkmTb1eid4r24KybvAdVy1s25zl+qikMJ3e9PhPb3RZN1y5OT0u/k9YS8V4Qdv3lb+FNPTvL7ve8gvxh7klb6ZO0y2XHf1SvRP013GpFRqlT+rvf1/DwgxXKDQN04bQqa9+XXWI5ALCXx5awzt/++nCHXoODxXd+tizoLwmX9arfUoIh131vMYyCAnP8/8V7s5f7FdoaMdU+oQvu++71W+k3zFdv+ESYEtWB/1j+f/dZPVS9IkTvc8KXl9Ql93fCvkx+zi+EDOj8u6r1pZuXMvqkuxO76+vdkshO70l0Cndz0cvux3S7nv2cqzsfgR4AAABINBmq9KQCvgsXuLFBB57Hj13OWeL5TTUw15smkIUq7fwQUNIeimLgiCAUvQSwyD86JhHn5uNC2TBHaXmdcbiUm1/Xu/UMh77hDP7vRO4bi//0JgrmHjjZcpKxeeNqfojb60udy2T9X7UJd3xM9o/P5fei8EdvVIvmlkeHXuXu9fZb7QU8OEpSoii8b38v7vjfLwnTzVkGdIvUO9RISPPeJH8pmqbpdra3xr6y2mvgHAwGfeuv/G3TANLUBrYOLXp2pLO2v1Gaf5e0NNYcs/qz+z+vcO7uATi+4a9lwwoODX5EPQ90f+T3ztuqFdyLyL/oYXluHrEqg2A26UuFHgytvyHqV1Bd3u5Sfh90eiQH2tFjKU6K3aacsHuy5KDu4rEw3nf7pToT8/ej5PSoVJLPF33x5P/lLUvwmX/3GDLzU53uaPab5OqErXt+Le4pL/7jO/U2SgQJzO85tiRL0G6SJLOga1/sei43jq03bOvEQAvNduxDdtT9nYKZb941foNyF8gvwoU8EikbjXdwke2n4Zl9gRtva+9m7TmT1xL+wTYcStfoaI5xMNe0ErLfudRSvl7vtQkW7Pe3b7hPPX4eh3ezwwI5OMwR3J/oR6n5UA7eGdu+vsIleCnhy5+jzYRak4sY8vBL7N6lvBGuJSwntjBj3d+zdz+7n97w3Rh7Fa4TufjhkyH7wSSjkzQjcWDl0LYn4mPwxeX6VzIERTlMv7qniV29QS07XHD0qr94LLTKbI7bBH7/vkF+EfHhkN7jTYclV7l+R1EYq1uUa/T6tsg07L9F/y+E23uCEpuvpp/0luCAxl+OMhwTxDC+r2lkWh6utf04KygTf7n2Irb2kL0XBfaREv9Hq/jdtLiki5ZPSSf3IYg/daXFezwSHLMyL7VZYIoLs5/36GxRDOxwYcgY/7QEK5SPllDIN4PXh2Ynp9VCPvdva5aK7pQRELqUtXaoZsSJjIlc4/c60nqvl9WeY3D3fbyX33ghnf/ZPbb0usn7iq6kJvvHQd/9lJ9ovQk/wUZYvvd7mvLZ7v2ld6NB39/mgtu4ZRd9vugLT+u7XtvWva1eEOwT20isVG3t3Zf/K1bXfXgjLe8X2Td+hNa8lWye7tVarM3tYJ+7w1Eaxu1rgi07xtUotKynySCt73fL5PfCHgwy+ZluNoI1fJD/ZDPfywRldid708mhRX33fWSt+tUvebu+j+s3d1ZUUhiUMXG/v5CX3CPgnoEiefysQl6TKfygrub96b3e9OjO8nquEP9fS9iO2snvpuVS69EZ3vCT8pRsBF+dpv2QhE7g7epx3+w6LiLTh56S3l/j+ykpyS5EEBLz+Kz97rfthPxwS33k/rd9i+XpJ53k9KlxGtp7kEO/dCiDUL8/z119AqNW6QI/C3h1EV1Ko0GIc3lnG9cJX3Sd4UyXk1/kmI3vW02UXcv6Wc635JiXfe5PfpPxZ217r+Qzyyy/3ZPskLasmetaiRO2txnX0J9+uyS7rl9NaUJeMrG5f2Szj2G9j8M5JBB8p14go3APqnk+HvioAAAASiQZrAFfBZ4sVu9J3+Y3HphL6F4/Vt+Pf5S6CCfjboML3CBuPcCCwZTV8UH57zhqX9tOhxY7wy0OItNFJOCna8gkiyQItp3C8+XL4Bfd8oqL5bw9Sf9woTLS8T8hVXYwlSvtCgxBr3BDd8G7ZfvbxRXe1ecfrywVe77uc/E7o/8J+EeXcI72wK+CQk0Q9FnPDJhZf3fBAQVhwdQCb3nYRre6Rspp/4BtWBMl3ZaBbwolG+jZG/vsqHcEj68MGtz/zvUxn+4T7NODAWVkvlSaWfVZWMKYegg8dvheYOj6Z2c0pMMdNDsf3FQxc1/M/b7giufZuikv1BPiYn5/O2T7VSo/BgUMY993vLc1X+rdsWTDvR3PvfWWcVf+U93QYS8wqyl8v/2NyPMlRrNrScdlWiKvO2r9ehtUEBTPj2nXfMlmfX5f76CBLKqyFdXm9jtw/a2skpDfExoSvrCPM+9k7zAsw98WUM218td3pflWuCCyBC8rzfV2HYcpAJT4S0XHQLj0tYe8P+6npalThG93+E32py/e+sTcve1qPImxNsJeQ133+CqfokmT92O4RzK6zHftNCWeCgkDq2tns473NplYtjT0ZA60Q2hh2wjvgNKf8MvbgS2VlVkF8zJMPNmtt7h32IA33zcM3wl57CSVeVNt6lm/+8EGYFomDz73d1Nun9X0CvH2veQZPe75imkJyfgq87QdS51+4cve9ttC04UvDydD/jA5Mks1uEGo8Fj2WRLZmNYxY28cQSwXiyz9lBk+lot8aRr76JNjXiQaF/e8geHy5HAT/eabgi36/9YIaRPjfbXyCxbl9z944R8EgyrfqrCcrMFQ3l+T0r3JEwkSaGvciYOwxnp/bvlhf9NZEj5snvTu5aN2T9J/0d6Tvq5rFPFVwt/JHgO8kLaF3BVvy+fzj8xnP0699ii5n7pwj5hC13XaK21pwREHc+znXdFgoFhqK0/uHF//T5PaWu66wS43V9E9MupTO79YIs6u9XbkJZH3o8XZEa8/+SEawVmI2ViX/bJ5b8qBaUm38u+Ndy9awdFTHn8Eb8Hr7E9e6Gddtgm6Tw5F0Ox0rvvt17b01CL9wVEl/e99/PwV3u973u/Y1o9dG+Zm58a3YR3d7T3n/pk0ryeqR/qrCT9f813ek1nrG3RtBDd3u/K3xHatZL3hHxXP72/IkTsn1Te2onN92T+lr3ye+rqSPK97vn/6kK73106pLSSthEQ73hv3fdTh5JJsX+Xa3iS3vd4R8E95Il2HVxn0+vyiOf73wRnll+241kfRdvp+x8SJe97+3u+90Mn7rsIkd3eWd73l9uIT2W8mhLwpfKcafBGqWP8JeWHq1/igO/iTaKP8N28SfWUmnIJEt53vulrNfftBsXjvZ7k7L9Fijbt3v1N5f8t36fd9FQIBAykPo0i4Fu/ec6w1+X2XofuMO/u7uf4T8lo8dZEKIXbmzHF78kSLk7WQ5t7E9vvpT1tJakI7vk9UkvmKW79U4y+fhYvvrglES4/nylVPti5t8z9v/a5JheKy5J67akkghJLVdOvUMZLyXovkjfvScb3xEhQ5o938PfFwAABJtBmuAV8FnixWcXdKyL/mNLY2ve+bidmwM+bZR5fjfD0t+4TdbEP9qbtJFhqdAQ5nwuc8oZO6pze6raYfTD/Z8v7veu3HyE1nyA3uXyrpXyykANpLHv98/eq3LeRPVOrO+Ta2lCd39Vr6F+PUvHl/2yYU8EhtxvIote43O6OkEF5/Rssy2tLZyxaTVUlXf4VHjVcBCf712mFmJ3cl99TQM2v/9/4T+rRUZwR6MLoD3TZkHXYO1vY3nkSyzuuPvOJPhOXWs83d+T/OUjr0g9Xp16J/po7LG/CfTX5NOJ7j9WEnAEuz9/6EesX/9C7+VbD7ak0kfX8aWE37z1+Mg5W9PjM9KPya/auoWr8fcPQbr+ppK/0k7hWUflQPCM4X/9Ljuv+T9Sy/CFovfnjVl3W4vjvPPO795fX3E33LHHz+qvxZdWsuQmX/3FDsSTbiXH5f3exunx07EexMfCPYyN7ptcweTpIODWBF+LXsLqg93sr/oel1/+lrF6cZLbyXk9JJpvzd31Qgrgm9P6/lrpy+a6rXJ6rZNZS7uvBYY5UoalWypnrAW/DX6CU8M5Af6v754wn1TTQnff+O+rlsXKGoddPd8Jv8FRI7lyJz+i2Wxt0DtM3+9OvxpH6v4fRRhEYTWlErcN7w61gm+ha4CT9Ue5+vw8d9bhEdEgcWq7JXzyMG35T08rQQe08OlzLSJ/6E6SssLYPGwJ3+nG6oPviTeNviO5WjQl+UtW+ELafqO86wFX3JaRDo/YmQ5G/fuizwiSNoPe7jbo/xtwezjwim1LiL14Ba0wOe+C3iTyVppb/ha4dz0f1rk268KG2RHz/vT5vYOUKfp9x8PVI53fd3vUf7/hLwQ7Um2KorDmOlFyXZ9//BAIw1+OqeVqx8SurxiB0RQ7/1+8s9jWJ2ep/95wPoT6+sEZ7kDe/X2JS9+KuqKsqfwywrdbgjzBtcPRyfV4ISz//CJf9cohaofwSFcZ7v/p7cpR+n6xX+utkl6/NNsn61rrRe7GoeZ37kUoVveX/9Fy9+ime9wltgmu73u9zsawQll/Xv5v3VP1oj9e7Fbta5N7hHwTS+M0o6jJww6BR2/wpyY9y+58+kft5F5av7kLl7p++T0tb/rNu/X9O7783l4R8Vbt3lY6lJef2XK8/7otxR7vdyS+oemq77nzeTKVemq/jZ6ammwdvPkyJxvblZfNH+4Qve+7363zOILayEvft/mIQcNgJx/5O3Y+ECu9w7lsZx+vvo3K5d+gRFcuvjt5IQl/0zrz/CPhcwacm/D+XzJsPjqXn4sRj9N2/2Cg7J/ELG9KraIeldOu3u/XL4ukBN1L/GcSsbP3t/uXyRBhIv0/j6B0efulb5fvxBARkq52XMn1WWuEBOf3u/LH3WWIPesuLHr8j2u+0nfdZUFxRYBleQ93Dr259S/6RJ/0wpqGdR/Gvp419L2xx88LvefOqi2vI+xaZHX35Pprr6XkiCjuPq8rvX8K+Qk37ytSEfSvSv0iCbsu1fnzQj+Kl/3Jsv5JOpmRBnyUhHDM/JEQx86b7lEH3t/EdkQlZL35LtV/JGF+CuAAAARjQZsAFfBX5h2MSu/cIl55bvMSdhz5TSkp6fN3KMhnzeEPB6aX/3CPDnmAkiZy+hlGuxo7dL3CkPsSvwnQbmPJsQch1vbkL3CF3yqPgl+PWP9fmJdIQqNieq8pwo2P1WTC3gkJkmUJpcv7vhEgrPHAjcbm+KwTfM+CD+UoQzP33s8dLHPkrh5f73CExcry5eUx8FHVf2R4f+HpTfsq/EalXAh286/f9fpT8OlzmiqXG5NhMI+P2f5yQ7U1PX/4Xgu5Wv5lO5aXnM/1TnidJO7/ySaZm6TnSRbu5B+X39/uEvPS/4R7C/TG5eawJDfpzbjdz1edhwngPqv+6oEFl1k07WfXAbEFus+pGxR67im7lgbedNvpT4yVlVHGHk847eu2hppjtPgt+gfZLA95d0f0stzfdnsqWr1WIlfYEWvnxVNMn6ajh6H9hLjzo+mjs8adhU5NNFjQcdIgTccE3vrrdwH/+o0fH9H7y9EWfuNAy7Q1rUweSL6pPBbIzLYEf8V0DGLlMg+SVFLSXlvg7ZPTcsW8Ugtc303//PxeuHuly2Or/TYqCJ8q1u3460OGZfI+nDF93OXLmWrcPcn/DBc7kWWbo1X7Z86bE2ghNph673d8EHy1KciEMaam9w5C4uw67m29Z/j1cShiRSIBHrpHva3LSKjrh7m3ZmP1W0J7yAfdPOSbde0C+7u7uAKL1WreKww4r+E9sFIp7d0zhv+7uX9unwQE8fYw36ji5ecgovgj0j3yfpEX4eO6i3217AjRdAZurfQSC3rH4Y3v6Xwp7cz8B/uYfgheXUxRYpPTd4OqBDfcadu3kWC7bflAqf1+CKO1WKZ2x+Crjx+dBhSrcRLRoQwdS/2n6bBaQA71oxL7zX9qW4Yf3CL/Rbwj4I9O/ykluCLe8vcUaFx1MrTxck8Nj1eLb13OqRRMol76aH6IIuumr6sakdsn7bXr1QskZnQm72vvKTMU18gknrCPlES+mqxWOXcvfuwUHCHxx/NeXXpJ72mpZuXdtF6915PTa7/vFEc0i/Ke3tOzkRX6LhLwmIe+r5PqtefpwRjX3rv8RZ8Iv6+lr6+/stCn7fxVXqtXe4T3d93CPYIiPe/lQJru93d36Xp/oEZ7v7quteye3fYrav3r/3X+/oEZHe75PpP3EkRe617a7S9CPmlYlYrLLe+T6+rb9/aV/JXvVXeq90W+76rWRWrjUi9ra4TfbgukZmRXv92JlO7b/XYsrKSe7r8Z0k2QhNp9Vy6SuQpitG2XCfj778Ee8N7bLvcZzPbG7/ewk4ry+/IileK+ilCBeHUlx2YTLl3J79qT9iff0vYnttaNd+T2l/JFGdxDmNC0Nl2JydidTaqmXX39MaEfz/hTTBIEi/9k9a6tRI3PLe6vITn+nJ3eT6Tm8/J7b/iPVqdN9kYkt3bLDfaUhLl7cNLC+oSM6Wr1tM3Zb0+T5ISKfZL5rSraosZMX7vZAREvdOq1DWGcRJySrJZXD/7gsgAABFRBmyAV8Ffix3LWG9N/lKs8my1oBeJ+UjwVapOgGS/+4LySD0bLiSVMJHBCYcdesf9xpQJ17ka7dntPSPuxUx5TJEfFeOj8O8BcjyskOM6/iJZcWkyzs1IrJo9W+LMYbyy4Zh0dfgj4x44WBvvLBMfPunIho1Un914nvLxNwo2LS97hXwSEzBsbyTZf7dsaTNFxLgdnqEte/ysZlD4PErKg/z7oDDnZrbrOWRwiZ/gzFA1o8NV/3G1bgwb3VVbw7NZu53440XXtWMXZ+NoP01njuoPOrCy4fvOM28udUZYKSvsblJWEkIdATUPGV7rKmqfNyA4vpJ8X5JnQGHvpySaZy8ntp19Pm2lpZL76yn3KSCb9MUOC7S/BNsn9sC8v122ECd35P0JQmcIkOyslocWo/uUrvn8VkFJfDbZWaVJiT68tfe+Xh9bXCXgspn4zj46r/0xYmX98sFU4uaOAm/b9u663fCNlFt/fFAy+l+GDFkdh09uAK9qtjL6l+w/cb38FRTRoMYDh1lZFzU9jBpvt25YOe4d9grgzHZb6OJSEPiq3ETcLDN4f9i2EiycI/I0m7v5hh8gdf20Ntgg6OXGOI8LvXP/x19kKTQcHZJ11PSVPP9k96G+jof3KHiPH0y/pEtS/G3paRdDN0uYqxghWkWENnPNcAhV3uDd6EsR7o7R3j9i086D5rAid16/HeV42guzu6RSuBH54U/9FX5RbK7hOisw6Ws/tJ8xMNRbz1i4SYZcvCWGmwMVk+l6cJibvtRs/k9enWyCNv6+kXuhNFrS5vZ0Ym5l7a3GZlo4J971OEbx5ElacmQSb05SveEi/++X8djiy929niiQjeZ77vdmR69pvet35fL69utda9k9a99HrL+v9/a7enhFcuMEZfN+Xtvz9fRS1vJ739/vfhHu+7yh990VZP7N3Frpe/2oRfs4Kd7v/d25e3tmuyf6rfWuU7v6PCeel3f0/Yn1eT9L6wREvf/ZZC3d91+Cfe971rdYQfpAnu4rd77N9o4q5+nd79sXe73vo5eq6svd9fWruvycnttif4MLvTBPsb3hp3nk13Ln6NLH3p/eijtk93WRVBF5WD0I+KIX4+Yt/zj0Iy2pqMOOZTmRcuvTrsr6ryenKJTfpK1eOoPeI+vkp0T6SJSHd2y+9iGEjyRC4DN57Ng5pITJt9N4R8hY6v9DSN51Jqm/cZ4cVLZYQu74ib/nEjcbPpwRvULJL3THcdsWa/cny7e/LDol5d5cc9+3K0V3/1BDe7pg65PWsViUXpd1spyr+mb/F3Rvapdao1VLREPEPu46XQ20dY6mEfa4go70rNKysa9vcKL93n5Igl7vfX0OFvPS96UV5fr1L3e91FXugs/7EwntvPFOT8nryetU91vXy+R8IcL9+SFu2lhpuWts//35Pev63qa7u736gspXw4LI/b6RdOF/BDNrWRPJd1+SImOn1DzwutakpagsgAAAEYUGbQBXwWeLFGCt0jSStKX/zymHH37K8vF6lJ3ml+buWIY82O+p8le4zzZnJFgEGbAzujx1rL3jcIe528qJ5B8dS+46E7rfbxnrFkip6q2u4nZ/54Xtz8FJG0i7fI8MLlMX5QdlghtrwSj8JHk/y02JhG+au6TXevaCfn27/l0iSwp4KCc8MwhILpd/jCctAqMYeyC+Zi/gbs1gJW7AHAYv1EmjHO/2wRRllNET+W1L93tCbRRClcinsNe5gv9bphWSXNQ56nUqvFH0N4p/5PpN26KwoXMPbbT0P3UFL5pxfzj+MtF+FttN4TxbvLHywSTho6XSq2xfHcfE498Z7tn9pru/30m8hefkH15TzIE8Jv3Cg7n0V2W3nzfhbcNuSWX9t8FROy4gP8ma+7xvZ19+jxJSLnQPq3psn00XavrZYezf9OTd5KLFEUJmGVfAl7v8gSXH4zWFW00X8MUXf5aFLQwfCXgv3nEb37H0t/+4UIK3u93u93f2T+rVywQbuOj/4J/C5O0LYTZHjeCFe/2cDfmuT00s/cIzr8ht+WI2iSde5P0iP8TYk3aw0v/6SLO7Qw7yIgS/VvEfRESG/e8jQ+e2/73v8O8ZOT7b573bDy3l35Kwy2nY2QpLa9/4IiTqVaCkg/GSODiS1DLcyX/zSJHfFr7hrYDENr/1Dk4zZLF2i9jTekUrgNfxJDF+htp0Q7ByBoEG1rjPZLg9x6oxol+zp8/pYTfklpun1gu1Qiaubj7pkb6l3sn0mlueEzbmq9ezwVx2/rRfAYyA2tzZFDzF4Eb8WPd+CkW4aZ4uO29u99V4IxDT9zSvgjO5Q0/FqnLRH/Fbzlc7hmVdZY24tuRvy/nC6stduCHs7rMqfuzXlstyle+TBde8r9j1CPgrMfZvP+9un/BCXNlmqLFwT6XCtLW93rafgmvHXQRcnev/ab7C/PY+96xrv+i+n/BHu/9PrXsnu2Nv4qQtnSjsy7wt/BDfFkYaa/KuQRu9zpHwj5TW73Wos/Lj7fm5PSS/dYPIii3Pj+nrcEQh23v6r1F9L3Cc1eSz57GoEMuX9qs0EN73GpOkoRf48l6b3vfsaWuxB7u73ftmpW6sbBFI6SPXf3mz/bSy1rblkwj4qf3vei+X7iad73y+LBQ+l2Lrl3mver++t+xaBHKLbuNtbdcvl4R8Lw4kw2O8ZMdZp3/5RHP9+oKBJsVtXhQavD1+xJXfvfRUEjnxmns7692O7JP3vQjpLkKWSXuuhBQSeNeKnmY4273kA7hyHWcLpwl4TjJhuZ4Da7Aa/wRiOHu//ryyn3e6X6X3IJ2N7dFFfJ68nqn8r7S6C4hx2Hyks/Td+yUnQff4T56bv6cKF838hnvvlk7E9dSmE8nS/21k9NGufPSiu73ffXC2SMhCpnK/Oa7O0ab5h5HGJXpXy+XPJ3Xspdmrk9pRMi/X5io5ojcRWZiIi7uXHb39Qtk/oh0rJVIksVyFW019z4/Xw98XAIRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vAAABItBm2AV8FnmGTkIJf7X/NsGkNzi8Xxr2ScrLhkv/2Cw3G6EkaY00Y2OnfQisiKMv7tqEr78aayekkV1vr3FwETvl+eOH/mXk/SLcuizADRoujfKgiVy+9yj3u964JJhZOqOP4yUfyzx3e9f1cK+YmM5EH6s8v7vYICBd8lFwCJ7ev2GxWf3lmAZlHF3Oheo2Hdg08m2EfC6D305Vj/v8Z7DUmtdo3B5a24IH4qtoetIMe0aU1eCrl0KSbxPCgF7nHbDuoiuDT2luMLmCZUDjZbPivIIM5dUrCPzTcU8Ly69wl5efX9OCLu6VV24Q5l732n2kW73feXmkhPyDvaEzRffW/ynxp1SEvDhuAM+uT8AOY8b75f/cKEnThI89aq7zzPC7cb+3p0W054R1kfGV0HvVCgXInTe3jct2ngjSUxn2DHMq+vYwvyW2ZYYmqhwsWrv30WxrX7Kzh9w6yE8MuqawWv1Drerv3IXuojoN1yE049UIX6FVZWFOp7l9yzOgvsW9gV0dOtOH6CL79yBO4iO0g01dqf7/3HFmVwakH94zuw77eK37EwU7hx2+cXf8unbtxfHweshYjPal0WY9fpol35PtrEp1CJIqCvjTvwSvLcAl9l41BtNq4zj5dNGZfPjPBL2Bcoaob5P39XCG+4wZy5dKkRp7hN+4IhT2X9fQveaQF/8b7LttU0r2Kvjt1CER+1uO02Kyelf0Jl/IVOL1q+6slf+aJF3fu6S7giJIvsOZgoIX+YMmT3/HVXYMGd8XTPGseqc17WbrfJwj4JvO/l9ivCmcMXRyfS4O43i+vWiQ3W7xpQQEwEbzXbeG6YEv7/n/df9mH6yvhP42+r8FdfvQFMDfldoUGuNfPR1OJHHwZNcYBZpvvxYvQtKnpGKSemnZ+2QU+QW3goj8vZd8wac/BQJyL44WPy7FwkRwhZv7uL7ye7ru5MMyvaaWWrk/arFyw/tg5vhSVzDg/yIN6nv1lSsPc1j/ulPBcVN3vexvZaBPzoHeZnKE9sEQhyj/220qCBTjsQicIrBUvfmj37F+3029gru/d/P67cJR2l7mTf7y9yLVYpYrKGrjMAvye2n9OJmPn36gue7wn4TMXJfTv+Us9V68S9/69/bq8siDZb1bto9mHuV/vFaWvTKW99jSby2l1+W94Q8ERnV70ujwkJbtJbY8h7F6Ti6ve+jIXd3539a66/rbi+jayf08m4Liu/d/d4JMv69V/CPgnvK+8v//BEQvf7dloqLX4IhpNbyNr1IKe9a9db9OYgJt2d6XUEfdG+qy9eTCWkUmf68rZ0J3P5PVfdMh9p+rlOt3W9F+2/++k1LdeT1Vp6Udu95EDuEvFkUl5snpZ0Y3vd3CXj5g+Z9+G6evGZ9eiHBYId7gn7ZN93dO+6yxJ3ve5c+Qu4w7ui90sta5PaxJOiRRHB2H34Yg/PtfBN7vsnLwW0KZkLzw0r0/sJl2n5f39Pr7E6WoiTuQKckt9wt6nBWSC6+8ZXaf70lk9EmPe9rfl86T+GPRCJ6kqTNrfEFLQ/l+MsS3f8FcAAABG1Bm4AV8Fi1cowEGzP13+bPpx5gkvF+eOYVUMl/9wWGsiiLBco9CfOTU5DZfPP9MMv9vKOvcSNanGtnvOL02dtZPu3E9wUULLhjL3M2T6fLUv8pT6v8GHdmao7vC7eb/XuXO4WXuMEZmuEvgSAEH7tV/3AHPV+zx/riSHYFGBHt/GDqLgiakOAduY2l4bZ+61dcvv3jp7ZOLtpLGQcgRlMuy/v3G/CdSbIn5SMHHfKjkskb+4WkLdPMq38n9a5YSKYeKGZGQ0gwC+mnSReIlhC5AVl8n6S15odWp/Sntvcoa5P1dTpxMg5u54fLLd73r/lPuiCb+wWDBIPFGKMVy07G6hWlEiJXX4ynRKBGxLDqaZCEX3VZaUHodgjom3hVv/3a9nw6aswqiLnZxnIsfN+X37wlfc1t7f25/9yv4ySPch+/OqKneif4gsOQ7j1R3S6WTmzLX+WG0kP69V4okPL7vaAJddpc7v2htXi/L8AvVTrf6BdE8uP3PF/v8LbJHeVQPgqLh2XY/CXgiJd3ff4J4cRdzzZflXZ8f+9w4Ro3BCVDI+my8JOv6LNhlKDTXv74woyicECL86h3ltT++tU6XkPob2BmUFffQ3x93jWP5eH750JYG7vfR4Lr0+dRL6q+nJjSZ9pXQKefSX42LjNtA8Zy75PVJd2wqRxfUovUaFSxnrnov9qVCCPVPl/y8wsr9wkuWIBEZqr91olb1wT5X0tXQs+BFiMf3CxMaC67pw4xEHSLIvHfq+Sf3XYLBb3vs7u509wRiMMMweD8EM//fgkPNv/WvutwUzrkq720mMJeCvUoLMf/HkYs0gcDQwzhRF3pkOf19AjE6R/LyS3vCHmx2mj+Cs1OnZNI6NvJ6praSv9haHk+Xyr0X95H/0euW+36PJeRdDl9F/BEUlvd/wXYQ8rke7u7dOfr2gWTD8YO9xmp7eHihG9/wQ2opkbu/BJ03lCT6bFGeN9t27paBWV9IjNxu979FZDuvek5DZc15LFu99S9idKqEyiLv2LZCPvJ92mN9FK9/aVx8Ru7ufp8vta4KN7u7y5+Jn/P+EPGE3bfbHVa/L4qw30d1kIE92kz+5cS7L/BIJd0bmF1IIu56W7v77rXstE/0T1QKIRceJHu7u7fl7v8UV3u930eE93u9wj4JyXeXy/VbYSvu8V6xHKNd3L9j3trLIIe/Y2CPPeW73s76/Eexcpr33k8/07Lu968I+GpnlyFW4cS/kEZun3itK73faZim1fRpMuP0lo3e8/b9ZM/9P5f11m82fCXknDNx1HgrkuyZTCXgul4VVpVrDwtFxOHcSIdw4uJ2154XlcxTvfs7IXM4hbhD+Y737L7+9ESrdfkNDS2Xl9J1V0cIz9KFF/rZdGy14kT2Efd+M0776ERAm99x6G+blh6spfwstGwmZ3euXeZfhcTd63dfL0/7vZ7Uto17/QJLvcqZPXqqUstD/y+q/C/qdPyTF2fyREmeGlSoay+omqifidzkfiJMl4LIAAABWVBm6AV8FfixWaIJ/3oCFlIjqdK1Ooo6NfNGFYidWg/i5xonYRjqdzQNL/uWY0vs/hE+WnMwYVMFbpfm4+XzCEL+HCU4QPLnP8PvfG+GXLlJIkyMPnHNfrHhuc2brVZidKjja5I65Ct3Vh0USEocfkOfFbl+42bvS2FfZ2rtKfrcUQIvXaYpH3y5n/mj2snb96lh3cfKCab2O5MKERf1c3rvG8oyHW87CVvCRS0v3PcL6ruF2+bccLMfHZWpz9v9Fgg5moj+2h71hfUqOH5kETCQ4mp7+18uaHL/iW0WfN/ieTL6/KU8nD1PnaFPMK4dVa/cIkdILKAS/+vd3Lx2MSssGbyImbdf90XaGLr+CvxArwuhUF1DnX513Ds5doouoot9uI38CfXMfr65X3CJZYEXYGjhchyWDwFuWyfrq+I8+TLzEO/3NHQ8sQfT+S+b6+uiyb39lPmZO4J+MFXhBxLtUsrRjdH0bHq0oQva85JEo1sPVV/tbZYye+TtKETudZuJ4E2Wn4Gw6gi+iWt2dP02YS9WvHbovTn4zni+3CW7xCPaP3l/odvF0kVNjSki1ZYJsvdqfSCfxMHkpnYC9yF9X58JfLX+myc+6V16cVe996c8ntIRvPrv6ptMI4bRfv4ag2TitsI6xyYxO6WhhOcfP5firil8zv3v27dyoO9rLkkNPbguEy/vZYUM9Lvs4Ns/2nvs20cpWF+u+GmWyFlUeHZeAn1PtfrG8rHIGwivdoBB/p8dH3Iq9yCE+33shlf/fzy0Km8tOSjy+ZbeCzx2Sr8m8fvZ9p5NmixGYX0OLz0c7Teh/76ChuTh9i4+CH8/g93LBQF21NL1AQ/+5tqGqt9nlzabOnCghonn4f2m3qi0TStGiLLvW0M2n9xpMIO29sgUMgf5eXw3F0l4PrJOE/X5l/8RBaXP9711ojunDk5WVIGLqvm53fJ7bQnukCsibWaSkrYXaD8iNnTLy3rXdCRp0kfy+Mb5P66z0KcfDEOy+v7y3GvP1/YuCM7LmXyyf1+eWzf6grhih+5ymmX9aSLoWa4BNVvZAvnWg/zf9KpJhPLGT10qyTU9wj4IxF35b8RKdab2uq1q0hcFcP8WikzUCJILo6Y3Bl/DTZsduC2pTmgO77EI+ZOjsRWuDpb3Xgi3dpsn66n19aVtsf5lw9f5VtGwcjsH/ghpr7S57dzan5JfvCa+gS4+vtN3buyFL/J7af7+M+19giI92u63xux0ubXguhmcnYXlT3yyf1+Un0W+nXVq6EnssgzdwmyHlu9uVEK7c6NHFpk+i3RT+mvl3f1+xWXHspq7rXfvTrs18J+fPZbv9lICHna4wU1ebCD+w9vU+Y3L9/iZ+t0MV9IERr3sfiynpd73rKsw3JV63a/HCLx3pc+7XsdfayRzEb+wT+Wj2X6XeyP2PRiO+lai0Ms7N294+SnD0O97+TrhHwTy87N46vrL7zl0URbit75sJnYj997+4JDt1pVX4m73e737TOYy3J/0gtO+547v1yb/37lZJsvv6MtvTfRWCzw/ve4y0PduVNLiG99wk/wVEO3jInN/j5js/IDe6YKDO99N29Ip3u9XjZD46tkW6Rb31rSpluXqxRpcnHoyH632qWJmDjL/fbx7hTJeP/f5IRz1verbr6YIxLxLlKu4Ju08d90qq2hM177q2glLjPWduT1tn8TLPnr0xO9LLDXZKlTS+Xl/7hp3h7twr4extfkOXjRYe/4dj8vrpOONm1Lu71Gn13/kEzduD5Lugg96Satz9FyfbllpkgkruF/fjobWyiaJKfFLlxxOkHFqNfwx4IppivInkkIkq37O+MBeBZAAAAFY0GbwBXwWeGxBqKw00z+bY/Oy/+4shP7uXF5pi9DLgY83OfDd3PwUEkBqfsjV2YJOKwEvvN49wvmb0tjy2X93wjozgxh3t8qZN+B93qL63cKFbeA3NG/T0gSe/XDsfssju44vvcIEx0SGqUxdf867yfvufYL97mPu2euK+H/vYmHSufDba6kd5oMrTH2p/7gnz9HE7u4rtekH5b5qbngK3uLU+nNh6hXzGpD84wPy/2240m5eFxXWyHue4XUeSC6Md2CSaAznK+bmb8kWjkKu3FnwMi0DMXRAheqs5r9bbQUpUm1Z44xC7+6lK/OFxD+j7GT93vUUDvIjTDJ7udruHfSmTwBu76FW43utNyrfj/9cn1plaTjC6t1uD14INzZ4aiOQismX7D3uCnoJ+8OH/utgf0vWOpa793ljJXk9NLv+iwnL7d79iZPPOr0he4Zcm7z8KeERGG7EaC02PP3pHgSPOzFXa6bCk2747s3mMSS0PGpBzIz6S4A8cof7LQMD7VGxJxRda0o2T6S29y1t5IdwgUoFbzAUNZtdinlC5kEn9/i2be6r6yd316o3BZpPykjj5WW0vgMf5P2ypvdwUEEIAd5F/PDfS2FqVZdyFXBcJeCfDLlFxfZRRj93wKnL+34JyXZ43GTg6lO73Bhvu1AVURAfxUd6OIfw3rL/Gcszisg/Dcn5gNmHno4X0Uu9Kd4ILsVB+Ge1PCt0ZFW69+2m+5bU+f8Jn4bXGXs69wVEmXw02o3efv+Vj/YmExb/y291XuicYIMyiQ5f1B9t63xtA+p7byBn4ICJJOgzeRN+w3ULaKWvngIdcf/hc6KaclZZ+nzkNKJSQVy/5dFFsiRwiX/1BEK0z/sfr2ki3BP4ZgSSNNy926zsKcdbP7kBpV+4hFtg6H0XJw7GsFxgboToCEny5D+CGvVjbGWPaZ7YSEVSSygs75PTojxSwYbw9hswfvX1k/qixdi+T0leskdD8tz28w/eXCne2haLBWSFyPxwvc75I++wTCc6jRXT+SMvfc8crF+pcCL6TBcIfJ8frkqfTLw1Wa1b3DsEmVd9u7JXe7Yg39x6TkfRP/6WVVhqst1Jul+tW6wRXIrfZ20ssnuknkqCLbRDBUd3gl903d91eCze7u9vfKEfHGNWXj9K19eu78sEZU5O71R9k+sX80FJXS3KGu7WvJH7ru7V7y+teS7+vJ7SZU/j5Rxxh9WS/LANxXbfwhffe97/Fbd3vv8FF993ZCPhQ3P3xpXU7U3bHcy/yZZr3qve+YXSd+3VF9aJbr6xcYGb3vJOlURBFM67sp/gntv3vUJdgi3V3N9UW+80cyYw/RQiKD42llj/DpWjdqoPVYS1X37ye6QspIlZB00n/Sp0vBJzwlB6ku9+pN76wSEggvfu352/oMFk7lLQYe1Dv340viq1TuCsruYp7u7u5zx7euEbZtbu8ZpeOEfDRLsY3cf5lzFaO/yij+/liTuRd7P93vyxAl+13fqa73k/onf15nrUst39H9l6bfNfdJdS3C6eA/y/rSmBDQe6Z6EvCZC8/zw95aYUM7u6b5/u9zr7+2CM7xW57emqOn1Tv2LWvyXd+vSWRaollFFd937UtoEQqNo3sXj3GZGz/j/u8Q99zvu/khLyFhVq8vrf70Ty+UnOpCiH0W7M0UEnd0w+8nvi11go7ve5xb9oZe/d93yw35JL3/BbmfBI9O62mjJpJJIdP/2p+8sH763vUK+fU1p/9Ixkl+4S3vuXdi0Y+7rdSpk9fL0hZcuXu8v9bQ/u0f30cfnPhj0Q6eSGu7u+fWvmgljxEj+a007WIiCohuWTd1p6ExsP/FwAAAFhUGb4BXwWP3FiIfzZetjjMZcH31+UReY/L/vizx8sPs4MfDK9wibjoQgBBvB+4eOu5XqIOvbTwQvSlX2M7ip23c0cw+vxfDuy3e6h8r6UUU3vlqWMFtJf6cFZMJODEtJF5GIfU7jazhRdOWcJ8wer+QtNfcE0/0dy5ozr6BVvJLCXc8+YraFfMS5GpYj1/jSVuJHhdkCX/nzG4/LgR+9etCFtIhk+6G16eZHJ+hreKx6ZOPqC/GT5aCSinHked/h2ez1aOmHLPgIrmPleIlcTgiIPwC0lfH736e4XasXYstrH7QexMdNtK6la4AEfr3Yuq89Ts68GE3Tf/jC7QaQXprNfRGDxjy7eQP3emlaNIiEYL1WVh6YzY4hcZlkViV85SHmCToxB/+b08vq6eM0r5t7qj7HAer8nvYu6qKsrxX6bOsIR2x1lc+Ytzw/GXd5e2XN37mE994SveZDvL/vizuO+rY/ot6wm/cKBDn0Lvu7H5HSJlyVJCQfvrfuNw98Jry9ZaFMRMkSw+WoR5g7KPWBB0J6eQPKPdyBeNx3RPHkxsR7ul8dE+vh+zFnuJP4F0/x8utFYkoXi3uOQNhnNbS0npLXk9UL8uPdFOT0vXXosT3c6d+s197VXH1fe8innBsOxenRUCwkzL4ROCy+ebWPPgl2PtrYVSqXd2IJvvChHu7wSdn9Tbd9lh17hyHv+IL70+3R14a7/9Y3xkuvml2Xhx10LfDkL2V/1ZEJhTu64cz2wF9blr6GGUB7p7j9Xizv4bSbccfswH431os1/UFxBl0VoJyr4bTmt5JRemsOn7+owQhYSa8Z31arsPU0Wa7jrg1OLyelT+40kz95mpfe+iQVnjzhqXjT2BnJzLVGr2GJz/uJtz2T8n9dtPCPgjyd/5rXBaWeMvVJdfQLJYwadEB74iS06z0NMPvkEw17jfhP3MEf/jM4CXa2Rq1ASjcA9zj7V4RWZ0Me2HEpt1VHvBWIQl5Nq73JdwF96NDTQSfBFhGTU4hvKqbCm7xIkoanzprP/BOS++clcy/cP2Du9OMCPy6nUa/oTBGXDcoPi7GyyF7f46Hx3nx8jvjqmPYJP0u68FBjhyH+dN4I3idpTzoHNfRBL3e+sT3eXvhHziF9u63y5i5/rrBHLhT939QVwmf8zV637CsOBW0ZTvljsarEwRXs/OTw6PBDTMywPspLur7XNFSI78sMnv/uCO78WrxsEXJZwvFr2QEM35m6LguZv+Ey/yttAsvexu93e1E/r7ElV+sSVbzv+v8kt5a9RHOHXeZPe+CO73Tvbu738v0Ett93+/oI+K93u7fxM/8/8ILlxQi3P9ye98f3be96VctWNfRZz9y6ev17gi4wD0zCp2627UnLjye3yYp5rul+O3uOr+9+/5ufwj4Ks0e4T4/RN7Oz1FZ/GabtfE+255vy/yZZAifH944dzwvfn+rLu76cm7vyQuW8f0d0aa7nv6pxMRup67R4fcu79jYoRnSPCH3hZd1uLKkZYS+aF0JpIr3S+y/Ty/rfCPgiz+5UrlBQS7vbd99dyFEjF297SENzXe1SdNdW9u+vJ6vSlrtSk/XsWnvfJ/XW5O5QV1eNnENbo8JeC4hFnhpUMJbpwl/6wkbbeXHu6LoSfLjb3fWQ+csUlS19l5PdPHVfVBiLm5f7fXtK6FEGScxBmJOrr9/ojIUyIUUJ6gxx63l8RrkIRu21J6+aZOEBu5GU4tHlyT9SfSVMr1MRTC69aSYIdzoMXptxNQj1fCPwZd36kLdKjrdLWu56bhTyYhprwQiIbOl05rrLK42pdKS4WO98Z7qftf0nl9EiyvKS6rfmmrrXLj71tYL/KPP9blMk3hf0QifqnVV4ikiFGF2Id9+snw98XAAABYJBmgAV8FfmFZMMDvi6nd9J5f/cI93Cbz2+YuUUdn+LNunjMR/MfDTmYY8xOCTs3tS//Y3yTjUs82wGckjM+ctN0mCRqUHB7c95v+w3Myxm7oI9tC8v7bWN3Ty+TTlYfSlZpr7HnF7uv3bTtStx/bl3x3L8n7ep9i4BX1F7O/mllPbEx0j9HZjZV97QiGYIWT6rfLCJYpoXjuJQ35P19XG7kFqc2ulYzL5mv162PvTDWeX/KTdb38SUpi7jMta8Qp5jZgM8v7t4ICCRxtezqQB//pV136C4KkrycS4RkpunzzNPuWc1P7w2Un7nVPOzjxzmoffeN5/w9R9WV7/KWMPysTvcKMXsUCUexn5dJ9j/fbQJY6TF3MgMXACitXrseQX407w6+TRS8dC1WbWCz7Qrnrp5fWN/XjsjPlT8c5aMlDJQ23f8O7MxRYLcD3FcUH96S+Li1tT0/u9xl4bymx+px1dyA6+84EQJLHp80fgTm3KUjOw7sIPPzF2Yfuiet8bD0sjyJRXdvNt+Wx9sLF9uV/fRYvt4dWkcubbfD4n2MlZpWs2PF/lK8zYS8wzhN9lL/e2KIJepLCPmw0/Z+disJr9nxl9wce/1imeVDGPkH9mfgDi8MiP/1y3EfKwtH+hQ3BRrU0h/SX93wR1oaP0w/f81k/qvKxZUePOsOr+1/qtx/DUuPM+NyfvKBro+k0dFhi91zC4AtvVRv+EeaVON9Te4Rw4Jd/AS13fUcGm+X7mB6O8BL7H2077je4/Ybj90aXYty4C7ndr79+3ZxrTzTcJeCwz3end7X++6CmVFOgde+Jn9PXvG+2Rd6vt1ngw6xr4wKnXgYrEY+T9afxsNzSfjLC1bjTTcIw1fScK+HaKjtHVmnJ8f9Ru9YKaZUyARMxxYie4CF9+UkIX6Y0w943s+Ky6PFX4+su1pdx58wazVoD+RHk/0Rfz76ClA9d5C8n6vlahMw20fLWGkRhwFr6KJwIZ7GeXEqcRd/jfSelVa4KxD85OWdzX7Pcbiz0Y71tDL/NXUVhIi4Ydo2cSDsxL/Rc/G+ob7m2d2oy+D+RtfTCXgh7m9j8EV0C5f7S5WTAGyBLclnH8FBMPJI8j8Ap/il81eNY0i0RbVRgZaOcMJI+QM7B2Img3sL4EH4yRRm7vFBzv+UWG3Re/7ca2zGe/QmH+v7zkaUoXXUJ/pf/Yn2oKDm0ZA+9veLV3QehPVpsT3dHbfKF1H+/9JF0ERHci15zwCB46lBlLF/2shSghEvvLL795svwj5TEb0ujvJ6ud4tlqzX5uPIv7BMUELxmJ+7+7XuQnOv6BWd3u897RVHNRfWi8EV3yl3giI7CnB2oJL7y7JLe/TJCPhMkzZ/k2rwSCSS7iT3T8nNZs7161pKO34u5+96b6PNfdE9JIjrVE7xDhPwWy/l584NZeE93l7905CCS7uff0X1hMo/332fXe26N36v1r/68kEeG1vPerFkXtfIvfJCHr+uwW3ve995kfW6v61rW5iRtr6ayopVSS0twn4XLP7xnHWyhp99e2LEK1tWbLd++xMFZ3uZu6J75svvS2CQS+6ZV67rPon0uf2pwfl7t0u/RyKl9vVW48jvef48XvfWJz/d8JdB/hHbk62ct+HB9Rt9DqHlKfDJSPH0SX8l8ogV3y/dMWkQ+T+oKTp35Epyb3p+xu+6bXmK3vWnQLBFKULvvINPgtG0loExAnsMGfvvdnywl5Cy+iZfuiiXKXLHr1UW15FvckRy5e+/S7zT/0k9G3P8Ll8yX2TkxblEu7v1BHbp02X8nJLNn5Iwtp5te8rG45b7dRRQUkkt9p/d6L4YrUidYjPGYiS2D/JBFRh5edZhLL+NkkjtzX3N5rOZff5Lo34LIAAAAXJQZogFfBX4KBWa49KdDoxXxcbiszmsekYNdNS/+4R7vhvLbraR90X/fLDjNBraO71y8tmeGF7hE2rgEGvBv7iqvl8ZbatmdfX4u44Xq9UEvke6tysPFMO6ZZM7R5SrLG1iVuT/2JjYz583SsCm058uAQOlz+NTZP53S4E5bTAGuQSFhfvCREm+3YcMOK1LCB1n1XPzBWxuS/8bOb1fCivhjj8x0qmB7KnTHLf7iCF5peXlz5SjmL4U8xs5iEnce/xpLKEX8BcSETMkp/VBffv+ae3AYAYBe3Uze3rMOv6UVbL+00BrMN4UKFmqphlZ+M3VieC8zvWXWH18ICj3X1b7c8yhjzJB6F7c26dsaUPLou7BTaNLBWvYBecbeKP389ifYQ4DsuruGjzhd4yJWTi6M6cYws/pDPDSXS2mU+OiWx6WQRFuU7/YxbknvBuT1pMnaE89Y99xSFye6v+O7veWBTu+T7az9R3SMDsh+X5yr95eJxvPcrHL65dGvMZ8svn4T89eGXuGbmL9xpII9pkC2/qpowfAaqxdNP59Y7XxAm17aqaPu8dmZPObY8eHICZfrPWfZHZ6G5qPh1u420Nes2PCuOJ7Xh0/Z2i75O23wCeu9vf7iIi3BlQa/52oPUFBHXgr84PHUTNcL2v9AdgjtYSfMRqDyadumm4m73G7sEttyP1PNxvaoE70FZAfPDAymOvr7/Bny/ivEmyx0sy8/g7yyR5xZ56mq08aUgfVZsIy/iTkaLnwT/Sq3BRiM2N31fjEaLgtrWzvwvQ469eLVqtD8ho62Ma/8O7mnxQMFFWf0T0TQ9y7vy0FeWVSsy//bGbvnnpnx8uY611gs3It6NfI6zn3yiiVUniZZjxf5PvJE3O9qVYKCbkBk68BPdVyHt+425t5oHn70rO4Z6xKXQFlvRlOGBvISPfen9io1pw9R/cNvlO/v8TLlhMiu8PlzwFt19fvoS84i39k179QSEcve63TZXus8IXC7QXMIj85SRrcwrzevcZzCjbzn3D2Y7hmXbu+Qc33y/94SE8N9+7u9/jybw0cH0dj98Zpfqz9xaJ+vv6XUEJioLj7Sw+NI9Xbi7/wgm5JdASmex5tqWf3qRnh/wz/AnhLy7n1GNP/8WNn8fgGzYSL/eIgjEcvrovpwVVAnf3IN6WBL1/3/zfbQwQ3Va3pOz94W4SOP09/I2807N319hRO92Jb7HQ6fxJL3zIXfQSO+9yLdVlhCHoMdrm/w7J/3n+ioWIeVuiIY6RC/T/hLyl5c02OPlzdbv7OxIgaTfJWjm3R5OnZb63aPvrwRFuWH/o8RIfyhqQPXTTc8EV+X1ptcEN9/Ja3kLuu62ZYtVoRCHgmNzrp2+t1Sgj3W+1LMvoE4nI+99S1m3vrFd3ve072RZf30+yEhHwS7k252U+W+zx/Nu9+72q4SPd3d7Xv16P/E73d36ckqB38nqn3TvI/ugrvef62z//ahG933fd9YQ3nX3d9wj4qySL2xn8HX1pAjiX3mBVWS5JevrLcpr69X6XdmETD99GYQLd7aDbo2WraL9t2rK++sm9wj4ItuXorVdYKTXvz7l99fjju9vn9zY0g1Ksos8EZzqOK5Vqndu+xL177tR/S4QvKx6+2TJ6VdDSPk9tq/JBZ4ZX8XvAOuuZx/F6ZPVW45it47TsJeKI2pc3mKnh2wobc7N3j9Rls32hXKtsbbL65OQ813vlkHnd/csH3fTvjBQaf9ZO/CAvSppAtIQKDSuUYvU3xfazNR5t4k3u7u/ZCwn4Je7YNa5/L8krGX9Si8Vn6VBF6lSifv2N/iflk3od5Kwt4VuQU5LyC8N1P5IwmSFJ7Rjk+cSycvlSWmEO7rH1/VOknLCRyX3d/J0uSIKfF0TpPXRYzPb93FbVK+a+RDMC1aO3CGibZKl8Lt/RLnE1lwx5DKb/JE17ey+7f9zyZ/yHZBrCX6QicwAd/45vY68PfGwAAAT/QZpAFfBcqyzBCEVwD/DB+GX+3h5dZr+OmtMMeYmXmBXwQcMs3CbX8wguYGhC/4E19pcgrDAY5//gn+XLgdQzLESszDIbkGMDkpVu42Cd+wp9l9AlL4rWhc/3F/DiT2ozRVnvVunFuOZiljdH9OduMzwjOOk8v2R6vn2UKP16h3MOkF2QZomVeuPsfIV63DP39J54JuU+5wIpNDrtEx7hDe756F7/lhPu5f5f+sVzyvuFfDnDVz6BJjYcEJ2dQ2BC9UV1Nt+4IDCR5zgIX1MB//P7b0IuxAQ7+0A5efKxvXVBtlY2ETvLzq827q+dwVSj13/37jfrfW20X1NHd+P5nQ7iXovR6gwSvQlkH/wC3MVn94oKVj8Y3H5f/saV6Xg7fPwAD1Uq9jb+9PeCC2Bt6VdRpl5l6HX9Ufq2PFqav9pN0EPpr4XuVE5Xfb2BlBLmT5pfMgP7VO6j9tCcCX8d7znR/5YdYiwDJ0f3vJ6aWddmhtyf6hCIgBX3U/b35WP7uXuQMn3oqVtIJx6XuPd3+U7x3CnMJv7FBC3uc7uPHGt3GeJ5iP5EMHavZsRU9FrjYlp9gLed/LxEiKd7vjFzisJE+l3uENJ4oItyy+I2vzkvhMsykZDZo73d9+teEyXvhqSIVYUqvVoFECzY5Z5/Sl7HgHVTJa2F/gj7jJ4YBMv75WCk04m/dj8EuieSfb2rC+lun4JelsOKbKLw3b/jKf7Gly2YfSpcIge6CMjGJzzI4Vw9d4LBXpj2SS4fX+X97sKbjeHrnHyRIfj7F7cEcc6gBI91f4Qy/IDxrrmSfnbf/91nmKPuv3qnceQiqZONauryNzbosSc8O9cBvryXMgj3SfVZb4ITOQrV9tawVkcu1i+SXAfbKw/dFvNO1PL/vRRrosI+CYVP0/kz5+CK7/aUrPWDpwUXkC6TmPQ0npB2LgqMCF7GW7WN+Fs7d41mBbw8hpMNt9CRNzDuIG4zn+qbYumrJC29+PFdZIrX8ntpV/pvde2/r3TuAi/wr7h/rFmmmEPbxt2/gOpGTR5s2b+tH7a1QJN79CPQwj1VP/ezvdflw90v1KJP/uwTCJBqHJYmDm1mkycmviPzLr6wRlP//xUt9Fpwlfd9vZ4Iu7mbbStjvvIcPuk+j1nv0xW3W99KQl7hEv9xVgr8/3drfvwWXLHN9d72/6MgVnW+Xfd666s/rROrfybXIuE2UPqY9/vLD4Q8E5L3Y3Drh61l4y73e1N9K5+y5PWi/ZSiSmt9NijXvd32dM6d9uXYIyUcunsnrRpPgkvfXY36+5Cu/L769eEN75Nu/3d9+8IeCIiF1H7U/Ezb3WvRdX94JO7ud/ZECQkDtlz+76fqz3eEfDWViMHFfh22nl/3UFJNz7t4rd3xaxMsSUVxLBy5vrIQsEAl7l77v3TLTP/2L6pSXu/fk9OzPCHCd7niObWlr9NYi6nX9k9CGCLd5wd16hMl7gUXg457fq4XmNgbhLUJ4Sp+h8q8uEdmdp8bpXZWFBDu/NK5DV3b3fJ60XZl7UhTSDXeYL93e9D107/xHycn9flRCVjIg5P1pVUMkBLt+K9HjQaPQytr/wotcRe7jJxv37YrLy/fW/JCAk9WmUuve75Prl/VKuV9CfYn1k7lrrd38nrWIfFMmXP2cvHa+FdJGOKs0pMuPeXlOu+8vLHvUqeI17Pd4X9GOlZr1RQXQAAABS5BmmAV8FfmFGEQ3oGGX/Nkk1F8L93zWXFy9P69/xfKNtO1Dk64MeYmMIb8l/9wxCbq4FNN/GytRUoNDUP4Ogza/yblpL+/YUlBV7cORmcw0zIz5/++1SRWuhSD1jYvf3DEM9gJf7RrP/cpXf+H/8yfbdlu4L6W5OjFYS0HoItX2zxvl/gh6qnH77ZTfyluHnW4U8xtJ69sEBMBI+s3bgTUb9AFDusx1dgRoeBbsAmP9/9dY7jF1+3qUDxU1f4kNqZaRcMtq/tjY3rhCe5+haFoIfh+4XMK1/FKXgr0D2yn7hBg+1kEwjL7vYI/17QV35t4AZqqmf37+Wbb/3CJThuOkiBSh06XswXEU+mnLHyrdy773KF9YTgfI/68+tN9ue5LuU17EoTu0baMrV1K6bBgUIv1eWLaJ8rrX5fk3wTUKPd9hHbyHxTflvQh+gS8OG4e5a/xzr+gWECdNSI7lB4wPGTQO1FtloxJETInLzprpxuV9SGbVYGz57NQfeW5lDVQaAaxm/s5ux9WQe1LhrBpJerELQleKx3/882v557szpVRq5nPrYv9+mN5PrJ6H5zHNtD85SReBft5Wiv7rmVDP/scGJn1l2N/k9JIrroYUJMN9+3muVG74VcXFxYD1kfYM7bS2mHvG8rLIpwkcRPbW9Yeln/5PpJP3NyFH5KnusFeSRd25UB5ctR29xF7420+T7vOtwiS99ShSYDwCHfu4rpsbbci/Flgo3AJ9DF7/h8k3VuF3W5G8reLUGl7lFQ7fn9eoI7kXtwRPQnuFBT3ve97u+sv2u4Rp97y97ToBf56K/uCOVD2CEMeb6CncJe4n5+ik26SI54Rd+w9sn1e6i2MEjb+3bzzGUfzN7d5PX/wmZyTzl+djY2znh2ki2gXG5alcZ/2T9fXHkfLYLyIPcnMHcdKUYT9Fqi/+2CKUL3O8JoJe593jSi81zrzFJgbuggbK6Ih2Ymceo8P58IGzzLuGxI26POka4z3/xV63OXb2/2RG+38pTbMGyL3tJrW07LBDhjffc9xpBve1S7k2L/gQ+e55QiFkS/XCW3HOd/0eLPe+5Y/BTe8z9DfeoR8FBibfoauET+ssrfJ9uT7gmyD8wXd5Tc0+4Jy3I8fonexH7Oj3sQa5Osn7Gxd33a7rPW3WKz/bf04Svu9/da9X4A5mAt11kv27/9KEcsFNMQPdXk+73vWRL369l/8MyHc229qMspdx02a2i/aM6+y0I7r62fLDJ7VC0+VGI5dvsauyIRe999UEynh59dJPpQg+1BaZ75/kvJJd79sOCX31qqfy/3qQh//sr0X4Q3j/7tmkyevakiLvfS+CO7z8oOvxEJ7t3fpLS1kqMvfBE2LZ4y6N3hxoulbUqZPuv3Bbz+dCeGehHw4SVifv/UkK8QOHavULOLnznefjS3Rj6I/7Khf7Fbu0kUzrxnvk97GrDAJ560x1lFedi9xXk+tT+xsqM1Or9lJVy53PttrWuulJu/2CEz712Lgruc3hSA/3GWNsKX3TlPu/y3v8kIeQkbuf4IzHQ3SfywTFe7u73+95V1uQTq/XXv76p/zesUTdw8wjt/u7lD9hLx+8vxnPssZxHpixEbnf5oy/eblEu/VHXn6q1ul6ae97yPRu8hYTX4gS7vZsjf4i7tS+1v8ccuxXL7uf+6UnaXKYuT7zNd+SyPf33X6Vo0ZZ13P7vd/v3/Cyy9iHafyIu27695v5ogXN/Sfkmufy5qh933l7u+GvITkxeILK3N+hMfD/xcAAAVUQZqAFfBZ5hSu9eYz5Y/MV5GYY8OZwKOhUITawoKnji/L/7hElYcW4mHQIH/kcWEXL3r3CkCd23jv3cxJ+Y6HtFVudeyg6LCBQn8wjaZ5IB6s1fk/b8Tsbua86Hdo+CIflTPPYF8EV8bZQHcmhn7xLeregWkOLb9zKSp+Qs7B4eix88Xz6/NG8v/JgwlvmnDVuYwS1u6f/Lu8KeYm4QsPcl/d8EBCUecsAEX2aClC+VYoxWQea3YxP3o7a2CWqwQX6xen6ftYL+jRBNtHxnO+P3K69dDhB+i5iC0Of3Re8NM83TbYq8Am/qcedkfWULXvpwUl4Q/tE6riVR076zwwZPPm6S3L5ecG1siHTCTWcL9r2gAn977/65uevf8f4karfOLTh+HIuvduLlhO7OyilmSWssEmdM0tKHwnyt5evw2fNqS4r+Ey/3tjRUdkseZrGFUeejGZy98duH1U4Txzs94b7BTE6VWXBwkZlcwNJT2MI5ems/naDODxHrfY9Of+t1pb6VDISmn87dIzehxDCdiTu7XiEby75zH8vv3hQsVvlzL/YHsE3D8H1Lkbve0htKX9dY3Po+MBpTOX6vxk60gCnLOHcq/ODg4uLOY5L9e4RKkOLcKmBz7uv0tLfCftvnSGCkn9/jZeQbGta9xcdV6+w5c14s17vC8HoInXH3uO4yYJw07xvOukHbFyabULc0f8A9tZPym9fWH+73sBHt9Tz+OrxpE/CXgnMaQUbDv58qP/ffuN3XZ0BbPe/gSP3vj2n5UD/UcW7jGM/W1jf+ysaX8cD/x8o+AK92KLGcvcCrTS430npJl3nh3uOmNifWGGYj3H/BAKnOW8EuNFLI/yfpXZ9De2cLyEqhqIq4OIcxKxJMghcbztpxzlUFQfL++o4uHoFXvh5sPRA9+4ghxwsAK++f3f4koRul/wR/HjtO8SuiobMvY7q1m9y9mPDdTfuREBGvv0rKZevdqdjDv55/TQnYwR6RXVqw06wV+cy1reuHXkhpe2kt2B/Kv2pehp6EvHCJ47Ipa3609YIr6PGTAh/X0+27cFkegO7eHJOloXoEP4838hVXG8ICKxC/l/3qNPH65zaA9wycZOUWyt5TjgXd8uj+sLU2+7zLw3LyYtfLd6sXElffIvk9pur0ngLVdvvto6oJiH7uceNDrNcok6/vfhDzacvS4RLKR95PtfE/toP3cBLXd3+zcrHnOC32dTw/il9glwjwHyQH93fXTQJd75Q279dUSLK992PpIEEvfe7yqzV1rNP9Yy5L933IFvn9pZF6sz7hHkCma43Sb+rr3Xu/kgjuV/ltsfVHc2pFa66Pdm93rl/93u7rxGxO979SyPQ+X9et7vCPijT+7uV/flKEZ87a976RTCXonpLS6ryXe+8r63+gR7vLZfr068J8vu9mEvFaV7xY/on/Kxh8/d33uzmXf+zrJLqxAi9336Ru73v/lK+8n1WVJWaGr6fqjtG1q8qBBl+II/yB3eLhD7nv9iSb14R8mViOr8Sbf5//ZSvd9J9OIO73Ov+vqutFShr6L6JJfTtPIXqjGHafk/a9eEvCeHVrOnvXbjjXtvq4QvHD3d5+2q+urL8Z22WT5PVCju/e9LmQ6Nl8Z+/jge7iB4JvnvP/hTURP/LIBBqhv35WKLH6PissOT1pSIiwUHPC5Iu3074i5f+R6SLrJ6aSyLhPniw3FfxEI7nQbv3SprooJrUhN9xWLxC7+wmKtvdby/5PZPT03UcxQvl+RqKX8v8n6ReUkg/y9XsGchD8f+T6Un9lKk8dUM4ixCsK1X7vcuc0Qe6rUuCyAAAAUsQZqgFfBV5jcfnfmNaS/F5qaST5f/c2W3/mIzyNl/94Y8284vL/7hHe5BONRzYQ8bsbCplYwZn5L/uVhTlGVSyh9XyYcGu1JIYgWrp33FZ8fLl/cIXvcrt7mQSfpbl4vDyJado5aXr2sv05Nhgr3LEjT16cf4S/TfCd45U0/ZrCvmJhq8d37jSbIIrOwXFTa1ldmfGzM3W/phPx5IARSnydR3+2JX0W0T8Tw2Zmr/jDG3J1Tz+t3G8a7F9tC+3aAJ/b+b/IOkbSSMUu69fnCtarfAfc8w/kbHLh04+ucvH9Lv3Du5xS2JTkSfYX0oqnIycd61jif/7jCh1w7KKfGeng31cHuMJcO09EQJB+T7r8RdMJeebkD5zw/dUp4cnBpwb9Puv1SeCnw0ksasxaFkifltJH4iUTvvvJ+rX6pXL79f5Tp5RIJv3FDj3LFYrCy0FvDEOnrdxtH3QZbBMH1bALaSUP3frO6cu0woiIa/Vayf4ed8K9EvSs/Y3VsrCR7ztUpXvn/L6b9ifw0pjD9i/0lhvOu/wgVP2LmDJjyfW08oFvnLEC2iwS+a6WTd4YpVKFLqio7k9VzXfauTuqSFXd972rbZhGMIYM9UUtSpoFcOoiW5dzB9im2vqakwBq1TjuzpvtsJc/vKuGpOQl4KCXlg11bfqFN5VJ+yT8WzZeqZd3vrL6dvQIIzJ/DEkn8rQRI6lZeG+Z9/h2YJz2QtkFCSRJYKQZH+n2+0+sDWnbpaHhDccfxt60HRH3NJzK0WZsUH9/hb45SCN6Lb3MX08QEXcnP6xyPXOu91uOKROlYMb5gLzx46vOx5MEvzxUbZ675pXSuJKZj4dgqvZ/TWX/fCliHz/x+AUesqmYS4873Bf7uYX4eELRrTS/7gDV42IWu+bNxsqGNYadx9fwl6sVRZL+4b7K+nBJjTPwZP2/GvG9C1OhW8vDmyBXb6ouQQfNq/W7YDSH8vpR/jbz33liZJT7hvv2v493oi9why/dPL39/qCMsgmNn334JcfTPMO8G3lPv0VArM9gp43U9ZjfrBMdztfff/69CPghI8/39oVe97+2Ehs+42ur9yfba30CUZPt+a8Q0T+re2reoKTkdOp2T2KeNujy72WsJ3L0Fuu8tQnve99ngpnTTILvX77e4+O62A3w7SJdv7lI9/oos/n8I+C4ZdJ3vjd6O51rd+Lnp7v7s+79PrQqvNXuveq6d8J3vfe0lUWW7W96SVN6K4S7BYR93vu+XfSSP0XL76vaVP1rBpcnrNd/WIpXuefpTXf1m3uEfBPL+pWLwdENuhUqGHdI/t6V2Tvcsd1QQmG7y71304VlvlJS461dP9ZPPknqr+pdDcubFom7vvBEa6LWT18nUZu7veIRl9X08OCPH340vpoQfM/eY92x9z/u93P3sI+CI06jc/CRLy98bxL6bkWCstx+ot3nife7ZPVT/Wq/I2d96TxvTXku/SmYgnaXk5PTSy7TKTlyT13X4S8Pdyyz+OrfpwcN/dcs+Uow3NKmnhbvxX2oymUSntcjNc29NCYnk9WlK/005/X5fTLk9X3cnf4IST8g3eUKeQpYApXeX+/BQUtJdNlsr2tJKCpISUvY78Z7iFrvIaf9ZFiSmr0ZOl7Evb4iKp76b1tSEu/elwr4IoYu0SpaXkFl9dJxBmSkNU5ba1KLskR/5Ny/2kTXhITPPtKklqSSzy8MeGsmZEpiO/9RHPk9XSJf6glqPabUckr3x0p5IZobL+x8m2/Jfd38PfFwAABQNBmsAV8FflGZQqktzdXXi+bmju4Y83LgQ3aivcYTRj8rqHTAQIGKadvXxH5Kqocd4Ol/LvClO5UWFzxofLhnT4VfMAo3p4nj/dkWf+Czd5cKEfhpfjb0XqT0nLotMN5wOFkIQgRZ4IU2lb/4QLlsnzirvP9e4rnxKYLP1WWEepSyQ5Gv3VcK+Yk9ZHS/u3ggIJH0F34OAYq6tt/+B3DUtk6qWU0Cb66h6kJDitfSsPzYSOG1bVMdZrd+0M3hnVp14/EjZACf9adPsJJPUfCNvlFpQ3ZVuM8RaL0rtM8YWCT6wWc3tfY2Xppc2ideLdi3CcYCVF/9y/dAEt8k/afLLGaI+W7vZwm4KiuwvFRoK9YNBbT3VPQ7dmeUTqa4Jfj889K+Kvu2GhF3v1RbhbkgUMx39l7/2peEM/fcs5Ul3etcZ3bzyfdG5f2pVve4U8IiC1IXyjw1BL/NIP5uf0tzxAZavdPkv/djcf2d1cN/HZK24Lg0EYhmq5HaxWz1md9eFYzvZVuPJ6C6Cz7WhdoH5JPRtGvVyvC9PZH2kVuWBI99VUSuai+HrXJGFgu1PwbP/XO3ucrZrIlY/pK6BFlTjbYVPwgXa4cd3neUG6S3MS99e1r1q5iZUwh4VnW+MjAtoK97neBhUS7hNCPHlbteoK+HYnj8P2R3ulQS8E5LlYt6HfL9N24Ksi0Movg33Zx9937J/RaSRWDDd/cHGLrw3Sn6cO2uQtuSUEfZ+NCNVzhBf93b7N/UKJ476cKcFfkgEljgYzK2IEmqkII2h/nnzW9h4CBWuRe60E6h3LST2MKe5pd0vMPvfZV/GBPRd3jWN0QR7ErRaGEBn7rv7d3diOfUPTpf+1zXXx30vmU6aUucZc0fVluNQnd8JRq14bWJKfqquhpkbtTMUOhTflZS+03iQNHqP/YTdUDSOreCF+cM+sETfuvDvcaKghTvaReG9XP9Wb3y+TzYQF82Xc8E72YRfqC0Ytb0P3VdHYrzPj6f9OG/kuCSuv9ngqJiH0j8GH/6rZx4eTAY1m+7ylIDovwdGPT+/Y/75PVI/6Nvek88FW0PUscpDBNUPf5F+2Nryrr2wief3d33l4RfaYIzOZr/hfQn6Xe8OoXVc3eCUpgW/ZhLY7dkoxkOv1q2T7/3Rf95d3IfulfxEEXn7/ha58eWWPF2a/3z6/e1r29rhJ+WCM1N033qqO/drc7vdhZnL1avpPErql7a7q7o6eN40Xyx8S/9NX3/l9/2d4zTCHhAVy9NsNu+/URrI138v/eUTnZfRl39F934yIhv/9T/1vO/qiffSlJTfV42rwn4JPGaf/RH+ry9vrk/Xy9CzCyetZq/r3gkM9/baxEZd7u+YF7x05eT61XoR8hbfHafBGYr5n2HwVl3d3d3uWKVfKd7qdCeqXBFd8gtfkny+6cvL7qS+v/fJ6e+I/RUCzxXcvwhLG+dr2NljzXW5Y7CioSL+JH+CrDLrNHxXhE628gPsICnd3t7dJ5nonpJJ8rRRO71uTb8nron126yAitnH3YMgGtqThAjVB3MD1tve79pwmtcRfdxrsn/l8mX9NDSHPXfYnvV0JILyEE83u+kk5F6ULYiEySvy0StV/pJtIFIlKWHDsuu26VFa3l7acnr//qFCtO8xF8uW3nyCMzcFNtSIiBTueW5lpXssKsn41TJ9L/uWKXDS/ZMH+JcREdVyZfw98XAAABNtBmuAV8FfmGVJnXuLjL6+Yq9l8XOXxPuHdhXuYmXH+LLmwZtR2JFDPhEiRwYUizSD0Iu96MftII/y4XzleUNHcdCda/89uknteoRCPgcO/LCWfn+r8n23Z7uCORpGT3pV8Ze934CD3rfR8ab7xvyylLVFrL770a7rl9/Ihc5d1q+Fn7hEc7uCR2rPuATetvj3hxbQDG++x8vt/gmKc30pL8k/OYNbhqR/rC3VdP7hLSU/vdk9c//oshamDut9fl3PmE/Ns49dl/vbGG4YFcCWwSQaxnXTMe/TtmVXINns4dl97acbRtg0j9L8WqWypwuhAd7bWuq+XBqxelo9QY/+L2tLuZFsUHh1Ov/whtLnW1wnHpe/HB7VAGPeebO6f074S6eLXTlYKijQu3a8OOCsZDzudcMIfm96Z0nnhrD9pSL8sy/H18vv+DA9ln/d2/1L6vG2Qi7P1lxsSBY6VZULJl74ZRb8KVgeb1D5C7OwGPEDIUeGT81O+Cfa5G9YRcq6/L7V1YfK98QhFoNNlkqXJXGv4WUjCb/BEMe3DSA6r83jq6Kwl5/vGeMnpK/mQu2QLlKp1FKF8i23EiT1mHqdJw/WpYjOuGW/rxW51/8TPX4agtlRCfW5V4JBC4bUjkH7p0iNwl4KS3L/J9t9zXuCeTD/e+t+5MD/qFRClIf3bTc8Z+pqeHxl9+Xx8fEjHS6/eCojJD3jR66n3tpj8cejA9hjpdvFLBMUMqF9yvIv5Ne+qJLNnHQ9K29jfQmWYLzL9jZStSy7SCmRZ+7ve7ornb7CJpJFo/w/boiAOpBL4PdnuCbe4NrvVv/2+sOCeaOF7r4RXJaEXHxeVqxpX8T7wQy9Gul7te4wScL30zvO3zBrh0i38v6fIINk3C+7qsEnjZ5fr7dlGe3N3QiWCfMRKxvezVdiYJfr4QtQbjWi2kZk+3uto19+mII97t79cqJCHgmJPsQsS/t7YJ73vfl6lE8u266sa1y737hIXLJ939eT6yfXt9NZEhTsn136u++zrosEW8uHT8Zd7vu7+79pnKxuEPFCrz4+mh+xnP585/0nd0XwmJTvyigaST9a1+v1BHy45BZf/l36vjBK5Prrdst3f8L7pOGEOQu37pn/9USF17XThHw8Wpmcka4XcW/F0WH5+svpP4IjKOq+35RJYQqNz2Jfbgu3u73SKT129clz497GsIy5GVt7vmtq7dr6PcsN9fuIJu7v6aFwJtZot7T9wls/Xuju6IsSdoJH+felak5CO7wj5t71t0JEZdt72f3vjC3vd5fzw3enr7yndLq25Xum+32++6/r8R272YqN/4s2qZh8DX0y/kvTG53CXhPL/hD/HSGGfu+XXe4ftr291OVArF3uSX3vOmnnNcufikWUq99CF19nu98ntagtiv2Wa8/dF/pTRxncwL7u5WL9bHk/hMvrfkFQJWvp7esLpJixdb3eFvXqyDj3e8buSK0ko4VbE90M/X1lu7+pL37wSXe6ZU0kMYLruXd0j/SLqymkOxnCvkMXVOz39Q6Tiy6G+lo5eR2SywBLUu/0+4KNm4YWsawamTdakO7on3gjpaad1fjvFad3mzk+q7UaIBVu73drcODwtGtSQReMqRMn7k/wx5Id2CD3y/v7rG7/XyRGQ2XMf74LIAAAFCkGbABXwWP3CIrYMdakAR5vaROly+rG1ieGJrDeWeJewZS0a6m314u8x3e/y8YJshfzGsUEezelXuM5jwIvxtKlk2+7AINeDf1BY9djvr1K9mPL5LhaX9/COd7vJ3DbCVu9APiob2VjPcoHZt298vtR8dFH1sntNO7Ti5NDcXRffb0HsTBfM37hLw6aHbaU+/VdBEuTy1T6NO/LBhve09OP5cal/3ot7v8tpRuNwp5hWaIfwfL/bthEgXFe7ARdfgVn6uMfT0RAQ80j+W8pf7psaXXEulWtlwHwTny042Fj+v8HCmq/pItodcDb1vXYC9lMnmfIu9JVjr8DvflFB+ram9Jfk9pJ7/J6pWv9HghxlP2n3TnQIylbzozXl4dzaheCfijNJt6LFkCSs8QHF1cv1PYUI6YhpjZFVj35QdvWqL6h3rs0m+y8n6N4uOT/QUw33h/cIFyFMz8BerDkZAAg+/m/6+Tyu0vjJcv/wgW0W1X+GWAmA85zet8Vj12yPMv1Ze5Q425bhLu5t/xRnZeHpaSOkSe1YlF+CsxB56UA7VnpNAb3LeZsfL79Yst3DbZLrRJ4S8FhOViVftlhs3tUFLvZWavpAhXsJi9bZmz9Fvt+X22hLLBXv3bIS8oqCX5nhv1h+vLcfnE32Mgq36id4j34SjF4x6v9i2CznTIXRtUTy/MIQZXfmrDeig8N/IcdWT+uduJhhLs/Tr/3/uMLcbFaw9MpD6X+3v/yP2NhaCQXJv4ufDBQ9H+4/q3Fd3d721RUPuNO5B/1Lq5bLBEvnzm8npJa9hQ2Pml8wFjol/jE2PeYxhCv0PINKqYIaLo371HiEv+V8Jar3WCfIuxv9mrl5GpOPryIX3UIb0pnR+3CZvMozKPmSs34Ji7pZSJlkEt821PwRFe4VYzDvFml+1S2mJlhM+7vft9N8it1gsJQXO29AQ7jLU2w1YJtt3CX5Xb9v9H1CPguNnzmZOz4JLbnGt91rd+YYzkb+Hxec2+4WfXw6nzTp1s/r3BCKPFOk5795PP7af6xBdzk5L7WhHyNX9oJ/D6LoPd+vpGu7/lEn/CHgsHJz3vn//4kqekfva7mNCS5f/tHTrR4mfVgZ+58jM0qJJffmo0GT1rJS0WLeuJ3u+/dYPUI70kN9910rD5T4zTCBftdsFIhOma7nZ3vfeyFgltxU3kOOxk+v9xYk/n+8oL0yiiO+73vIt8K68s8Jf0f1/k2bvfThC73vc8e+iyd3rkc175f/x2793e/7E3OvCHhERCf/342g+nHafRHyfVdE/V9Zi3v0xZbvu7qxP3U6Hfr8STka93tsSlMQNu/YB35fTCIlzSvn73vXyQj4ey+iih4R04T/xnEYhFeUuHfv9CGPLCG43p3d3v1lE7v2lSh9U5V/qbeirtQgfd523b37evkC/Lm3bf6nPtcn0ltPQSNlh5f0h0BNr5L9xe8/3eEvD2GnDvZhdMbUQ2vf7IZ64cv7BYIc/8Ji9La2BtQN9CImT1zJTKwRiyf05tS1Ccz++/JLP99miBJaflpJ6qSbieT7Wt/Vki35JyP9OmbbvKkYUjJ6VXkZodJDTODDT+992p0lu3/7HmS7hPyDnAH1tyWeT6+P97vl99FzHe/bffu0xPvem9JRMkEe70qk9PIlLJe9wzT9fWp01ub6XkbKf/8L+GrSWnXh+/z/4jNRvJhXvyfX//ujRN/uMM/v4e+KgAAABQ1BmyAV8FfixnD+UWx0j+X/3L1el7mJd6euUup8C/mJw41SX/3CPHSyjwk/6LNC9gkbLVb0HcbD490zL0Xmj1zWhZMuNdRnx87xcf0Zfy/5+Cm7+feN41JDX4ztkDzSUQcWsgxgMm2F/d6TLPBb4I/06hxutKpfy1tl7WstECEr5ru7GMtC/wnvJjVYWL+9uCAcK7CrxIlyK3KFzKwZ0/yY3r61mKrMYb5mH994JvVwjqY4LlSFF9kG19houC3sAgXu/dLgR/s+//cf8haUdzh5qaTxFly+3p4S5Hz9g/5ef6cT9OvW1Chf73CmHP9bIarthr6nI+c2eGmf9QQvsbxvLzD2h6q9GRsn200d6jTPQ3N5dqqN2xSwSf4akrLcT+84KiYWwJW5u1iYXe//uMKkcldyLse7tLtYxJPGOBB+HeXzaR9BOY0ct2EW3i1kzIKyerV0d/RbLz3SefSoss3B2GivvUsEwg4XuUpD6XlASHXM4I7svv9AoKRBD3nB+4J9j372coS8EZJpXdv8K7uHZP7h2CjvHWHvv9K7gu+3ZYS3OOaV3D9LItY/bftoIzrDb9I6P+YYH3+Dh3ub+nPwQdw8RX8qwAtOuQ9PeHJQG789tDdflUcI0Wkqwep5/SbuJkP3whd8b/35fbTuILMGoZlc3O83afrUSrNcvDaW1YGd9eaOxH+4owZcXwBN7+R/M0+jzZwx5s5ATVZuViIekji2nhk/XzrMLw33+EfBcIcZnb9TkbgV3xyvLvorFa0f9gaebyelXkTmz/3jSbsHJq43gig8F7+63jJf3QQfZro9DegE49E7t0juZn0+KQ0q+I/ck7PZQ0Ib8sDzwN1Fv1w/2X8nvVW5YKyjr8+9n4bbyVPwYGg20a2IodnDxhSWmTc9Vbyer+NuED3d1s52V9zJulJ6Slllmi8tvpP6UKYzXxK/85jfH9zlntCBPs5JF6daSD5tJM4K746uvadhVwN3S+1zQVlhC6QevS9d/cDnd3ecyevXqYpu+EfBMII1sj1y63PG47gou+dfy8swtnCHlP7tPvL4xd4eKE/vskaHp2l/wZ0PR28NKycy7NzYfafwSjrcowQahbrsL2lF57L+298vd79xJXc43hH/T/UJY+4Nno/8M73U3T/+CO+7eojWlexSnlqP2vlvfL/l4fvfz5e6/dXCS3wgS7vzcufSpiYSPCy5Ls8OkTwdpXu4Toymoe+9bWJGvs4fu8/xApouTH5Gv6PEGjgn9e8vrwmV7932Lsm5faZ0eWVN36xhX3u13e79IgSy/d7+hJz5Q4zT4Q9DG9wT7v3dz8glq9WP936j9G5+729mM45ffVXxvfo92BRgVvJ67+uT7/8Fm96T7vOmsnBIR73env7hEv8nclCPVlLu3ponKSf+CPd5hZf/o3js5o+qKhIh3xnFXRPr6TWElnCeJk8uXzpPib3d3d2qxurH+utwRDX1RSuTutoxnIP/WEvHlYbf4/jb/eU6gsM7Y/S45t9aFxsrcgxm+2xwu6bu7txO+UWidKImvuCc2r0/Kd75P61zw0TGt6+0e/ubmi3VO5u5q9QQ09xqD8EZJjcq6Tf44kAna7SgYQIeh8uUdvH6PYUf4yN1fjd32rR/fL8E5N07rb1QtF5eXLr67GwREl+UGvSe69if0JK++rVeZip/uHbJf5MLLbwmIu7yZ3q+tpKnrQIT7uRPMyFS3tVckVu7gSIhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gYhS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeBiFLS0tLS0tLS0tLS0tLS0tLwhEUUAFFABRv/xClpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl3gghS0tLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vCERRQAUUAFG//EKWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaXeCCFLS0tLS0tLS0tLS0tLS0tLS8IRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4IIUtLS0tLS0tLS0tLS0tLS0tLwAADdCZYiCAV8mKAAOKMnJycnJycnJycnJycnJycnJycnJycnJycnJycnJ11111111111///wXeAAmFEcIGm63wChQOU1oQEeEj84Ba2lPP/u7vw6IgATyCOF2cOKFpqPMANIgQwqMkPMjTYR88zMDUXZnDIT/2wMBVFRFyQ8o8AmLIC7aUBdUP35c9jxADMgEdpYBJkHUp2iKlZXx94+K8ARMxEOpH4OpLDpP8aQdXIO5Ma+G8bVvsdADZ7iv8LXiuvHTinnkOOSBqz8zMW1xGr/eFB8gIenZ4C5xzag4f/q41UV6poBZeFcZqiuRdDHjGTxnGOzAUtE/G5RrdEamCJMVoVuaNj5YBeBUMdlkzL6n4J6JOpRpzx3/+qKSk74CvjuAV6HcQVTmGubuUZ/6mc1NVdYHpiT8oycn5Q4VL+vkx059tzV9dbuJuoL8F4sWgiDljbTNEnc/O1Vo5ZuG7DhMLZqSekbVNZRCesGjSHjpt2av/VfVWHShCJT6W7sJpWbFyK5XyVEIJNEBu8UFh2PBz8dUdE//CW7gt/rrrrrrrrrrrrrrrrrrrrr//ycnhAZwAIZJMa9CI7pwAEwBGoBU/XeoAY4AmIXwADqQIUT+vhLxqtXTgZsAzKLikgD3Q9pN3Bbh02FpyhbZRrGreUxjALt4KbEEAiIfgTGbDQU+GvbADbWbIgko8BIfdfJ3c9y3YicADsgIKL3PR4gJqVk4ELdrEyCiTdPSZzjT/vTzDHw0mP2gCjgVAVgHiDIO6tBZMg4R7nmZsofTAMYE/9OA7MDFLquduiDdNt0REgRpiGNQB0y/zIB/59OImSEtPGHr0rqG59X7+1z9yfQ/l4MPmiivtxCMZypsVLCj46Og3AoGAATwOokmpYUdr1mlHfgCSO6I+bdsc2+1H3bNsPoNLRhUZJciNTXMNTAGvV03xtdnO324BZ5/b7tUrNgF2YiC3j8g/D/WRdIWi+0D6yJFaQa4dl+KoUnp+Hjf/bRrZKDgOx88pXe/QSvH3bIHTk4nkBJW/q23PArASAAUgcDXr/jGkv8uyNxi6+BA2l7s4nGzgokTK03J+aULFZoz7QGHZyon1Rub5723yDfCeQ7UR7UEOt7H5LerG2Zd+E7pTxNTV1mzfUc8VIbxQz3zBW3ghoQrWNsX6l+HuCtcG66xcXu4i7iKrhiXeUYz5NBpvihuKrC4crxvhE/nJh6CI76KY/dKpyOgOBo35EA9riQR/jEOPVFRm9pj/arfk3q7cbY+AXOjmcOSiXkEXuhbYuYq1BwmQr1lUz4e0O8p/uJNBEnZyJ49z4lUXl2OrFZaiNOmuiogCxpc/M5IZohALHsixY5nBUjPgMeq98hP+9h6bFNY6m38vEV1D2ezJGW37iNb+xX0Cw2AZbszCKEiDz9k8wF/SvXeoRUJPQSUsBbJ/M2EctiQCoThulKkyOyQS5Gm9U6g+G6L3yhPNSnNuZjoTY3XQ2Qh4lus91duGb4eM4zmLqV30xWIi61w9c0/jznlx0f4KpPiyUMXPo61A6n1LUG/5j0DtTi3isJCyygaQHAzk4xmsa5TErfpXvqqzqZq1lVORv1pdMj3RiHwyHzZUGsBMkPknX4tbLTIOeh4+knsHsDTZmDQHGEsaHIwrhbjRI63//mfnYLYOrJpakG5lgS0gBeVFd8vqJrrrrrrrrrrrrrrrrrr//yIsIDuAA4UcAX0ggxmBqACY1OG4ADsIazftaQoEMDUStHwALiCKCxNcO6qf+7PNtj+KXW0AMBg3xsN7XFvdv8FGQ/gppwQTikBAAhjBcBmM1AT1NgkqHbFUAgsfZgQA3qZ5EY/pALSm+RE272AAFWyAAIqYDeBHR8WWkzmzYNb7BIYiv+028+oLIzfsjUIBctGX7//8Wj4gGEg4/JYOP7BgmYRXp0Fpoj0T5JWkefoEPq0Y+/WLOQF2b8Lz/NhSzNzIjL393AVgdr1QiJ+9+JXLyKfgMTARimyMhFBGoEkGABEJhIABhxid77BG1YPkM+0FxIDHKAll6nukFrTdG3KDKX0TwC5yOj1nPs5RKIMws5F4Y6yBP+3q+KWyK9b102HjAk8Ul2w0lPOEPNb/Ogz9RMN5ePqWzG2XqIoBqSHe6Toq58df1wUPXF7jUJTU9Cf6mWzz3ie9v6MUogABARqx4Bn5pO6m2B3OfYuhr3b2EAbECEo3vOCqdarPkfyhwAnWfZc8YybePzm0uPXNRV7iF4OWVRVfPWyYycl8HPeUaUv+4mnfyvlfO9ztTdhAVB5sIlUL3WAhkIbST/D/iSImr7RlKdPrgKvZmwUMgR1pPdJTDi730nP+YBQ3YqUyscELYkVrnG/fH9UCORgsngLto8EmF0bIvCxIofzyl86MzmrELoAGrkvBjBq+ZeFI9cZlGCe03ut09sU7KW/qxHnKcHujV1v4NOTX1nrtNV7vo7X++8dsKd/wGamTPCd/5zbOn06nrIg4VOvpkyj7FF7/f1MABV4FjhR516wl0MGDNw0WrWu06A8I27Yxu0MooE6hcol600dAa7RJM3bWVVEwdFtHGzwRZJgPd3eqiPdALeYaM64hl/277Z5o6EkrOX5jmIbpM05oh1UPSmr58iPLcFQ3D+AbLKhT2gdCdI21eBiGaIEfhMnIP6rseOSY/TafXcXVmgTirqRrx/mGpCUoEDH+701tEDrz4bz65mJvvhS2rGUb3Dir9/sLstPPkngjrpGyPz4Mcp7nX5uFmnmL0MABoInMlZT88CXeXhMugJWYMJIS+/2PB3jWaYIpUde9zCDkRxd3d8yuyOZj2ZPZVM2g6Ce7enV5gvYO4AcT2kVWJ970+4X/tu9ITZD2sF/u9ao22MjXjZRSsGe4uJDfDkHA6J6id7y1Vagx8lcBlqKGj4ehObUfUr0Kswk8e5oIqiCZuVWDrtdAFbIjPWAp9XQqzdFCKHJGbiHeZ8sQeTSuVylVqyhmthde/7euSYtW3V5kZGCvnHAhO0GwvPwScQVJvbBqwCBiiuxZvEHwOWTW8p7F0VLmpzZSWgvrmJ2gydnb5TOXMwkpt2gWUIv9sACbV2/cj+XCTQ/91UKwX2yXV1JjO9J2/N5h8lBg5ZW/R2+Plyy9Z12UTmkBV2dEWbXwwnxobileWik0utGMwYpP9XVo05voiEct0mVN6DVfUl7ENHWVwRMIcBciT45y58/qac7H88NQEQ3W/KbBC/mK+hOn7o4YQmRBU3g1Ix472ouuuuuuuuuuuuuuuuv//s2HBnAAzb3ym59QEBw2W1nMmh6E7D8yAA7kJS/Nv/f+CQhycoB4JD7pBrH8EATnMEACI5/AJ8hHBftuADsUzjGIKO/WJmZrnYHcByAAdEm4pfkDtMtrHCQKEiH4cTJtFT94HVCHVKukWJx18MIj0WAAf5T4rfGH/2+eCGbTf7djk3PIkwN/396/2uKWICAGvWNgAgGj6lfTVBm58LMbbaJ/Al+trjH92Cao9p53NGBTkpG7AGKZUygnCX/uhABCGPCAAQChag0mOqrZolSo/9oE3P497gUrMRmMnbN8cw6LkRixVfFatMC2dLtuSS6pZgB4ZBfvPa5NhpxlXdIrY3ttb+etHCKoJybLKJWQk7qAprokWcbgZOjENqynt7AKowXfzow4xMXX/SsuNY0rnqXY+bJw9P+md1BL/n21NS/wfyO5A/v9SYJikB/dIp09c+NI1iBf/jdDGBouXHxYA6TgQ7f4EoYOEjMme37/O+eZgZY9l0E1XvezjmSZEFQ/sFFYTKbFNoqY5eWTmQS89FZC/6S5gz/9ZkH71dy2t7/YcVMbO4aSHu86/jWrN/g/prPrpFTeBDF//D+CRgy/6tOsgi1qsWv9xomG3FVyaMPd//+LZ7QCYLOLlLCpBlqHmjffsyynAcovpvMZdTafAsIRLY2k0/zaBF7ygZkAoKw6czu0gqmCU+xZTqifcFBksMVI1flqHfj5Gfc+syQlJsisHf+6m2eEIhf/fJRF+Xgvb2a9Be59HbTvq8ileOl6YfHBYD8X2kptQJpiv/dJjj7gc25Y8qq5erpY8Xv6S+pmc79A2QuQvwvKb31rmC8l8fZxZhe0uH/wNZoUMqpCVWaxbEw/I+e45wk4R6d0zWsBYEEkMZ3nt5lLwJ1PQbumRnX32i2/XsLRlwnHmnMJW6/VlVtGEf7dFGI9Q7maEkkNC4TCeyEhr21fk7wz/eq7smfUJR+TpkXwjJ+W/tYUwwgKAFeA4x1zSuawtoNqY/o//nMc7isRI+miMfiWco5hYnzlvWsuABhFXmQbNQw/Ceawh7g70J7XUbLQi6v9gJ6My9YKREEs7f0Nbp6eIc9DyEYjPuLvxU19uLz5f7okye/7bTl22Xc+N/gXzz6BcmAI0hqsEzK/jEnD1lwWcE1Jxxsee/xKHjBxQbg1jWKgRpu2H6qDhN3e2wOPj+n5WdGukImWamk7ol+TmnNSyT4ewETvX3aDOii/PrnnGu1I6thcPWUvYsXmZyq7M2/Jv3FcBkaiZCxgMKrDBS+tfyqAIXCPmy7uxANZ/9YpR/QNZcVD2ks/UTZN9vOWsbzyRdICN5ZWX8PAkFBx8vNfbq+TuKmaD6V7W/J0u4B+h5s5q7NIPSEa9XfQ3gEJz4TuxSt3PwKlhlI+4uhn3WIpKMZSbPvBiF0BgGUq/KudQT4KXZlctAcf8j0AmU+ZJrvU94uJt9X08rF3954awG/Zh5MZnTd21knA1HkQ33kTqqX8crBoDz9SFJ5aaMPko+GzYA7HZMhM/Tw/Lo735yruzkeArIsUOCLGyiIwEEFErhPS7sS+LVjICI5sW7QcfO0YPscFs31RDoBICKyB5QZXa5kFl/E1Kbf7rwfql2tZxpQc/k2hI0BvX4e/Efe7X0fG2BBK44g1bQCuN/suRrWFjfn6PA8gmPUdn4vUB0QBBdEFzzaXxTqNFU0zKBZQNutheuqV/6U+wVDUq5qxt9T2uuuuuuuuuuuuuuuv9rbW/HcABmwTbnDrMFYXdWAtGd0qAbJs9n/hrbiOKgP+hfv5ThXkTCprlABnJSIBnUP398AERelNpzihMb1CgDrrJE/5cADgi6B6YWWNy0LCCyzwIR0IGQg6CRzYlBLb2QTFTA/bJgkoR42jsoI0vfgH7N8gC9jtP22zAcJM4H5D/FyOvNsCMxZDRZn8/P8bU9k/1RWOn5SW//NEYAvEqB8G3ikyxX3Hr5/JKxetpB1A8JLDakd3x7qjppLPPgYEWQICxR6nRt7+VvORx33nV0VCUzuJOZe3N8+PgNCZGPca+mQmnudXq7Sr+xi9dtGuSEyu8D/vDrQot7+q2Y/b0AANfeqpLjGojvvwYhOq44P/+wGgpZWOO04AaiDVgJqwhZEyVwElsGdF/9Qov4BvmPN553w54Rybz1UUonIVZsn0biHwuwVm+dan6fC3fsmPiyOMkwgyT/2v1uT2T/Ldm0iC8u+2NGD3hghFhNPaXDVKOrmc2AjYtqcXeYr3kcegBC1OWJfGa3Ro8zDKY0GIrAl3D4p8io05NzY+qrTzYqAAem9/GbMApgScRgw4IA7qEPoFXyCb5RIfBtwyCokj7voois+MDODHwgfPHE0xKWf1m18UhfvonNUBQnJwN9xnRkC/6Tz34h1pnIxw5paw4sfcMuEHqf+3qt2DJqzfEDrDWoNL7aRadHgoR/c6ID+MvSD/X/gUqkgFCU6+0kBFKcOsyXGJW8Y/TlnKPMFB9SyCgFc28vlJAyG6zIQ6phUImNYmEwHZtoMT9dsOWYOMgSpbeB4aBQZP5T4M2Prfo7beASf7K/udV70pfYqObNyoH9ZVfSuaOUxVwVzUn4bNoLqabC/dalHs4ON5/ujWLn9zYev/7amH1SUzf4H6Cn2gA7WP8pkeuriExQRqS9lpYQ5z/VFVpFf//z0ZbAndw7D+QPLaE0OmxSDyocvIpq3cj9w4EQ45y7tzuec5543eJaw2fLrSSSTeGJQBDQHtm35eX7wMnbSoBffoFg7ZgE3tMmUBQCvf129ZiB1BDe2DWQ5GgH+97IpTEodriw4hSa9XtDYU61L25gZSd/Wu/6iS8BJ8HlJzvXsL8Xsl3YCztGZPXgiAe4gFWAdx9yI7xlD6WZ3X2lyA13f7we358HdxYxNjIaqayfy0SBCkUkkbsjb/e/hlKLmMQuCjG073YeyOeq9avq9L8UwZ98j3/+eUi3rK9aQIOKGxH29o84rlC6WZX53oG8MiFovWx5Jxuz+wbDoNguxhMtg3MPzDjwChCXo3kYiwTzzXA+FCt7PTr9+EB8nNiphxRohJUf43i3J3Wrnj7ckaKc8DXCLVfXJv+K1UUMMLyf2mq41PfIAHzu3iuAVeO9MBpFOQkfbSy4Fh62dRL4D0F5uqrGTQ7/UEMYC4uZu5Pr0kBM/WQQXJtYz+ArqF/HpYf2/k8O28JTKuvxOL1Jlti2ogmeuXNf9UW8YwVGTj7D0soRdyr94obEGWTtWET/Se9/79EfbEfkXf/+/mBnFylEJELc0tD8fQpOJQYiffhodiFRH1PexdKRjsDc6ZqTGw8Ww36TbMT9BbsSURHuiqhJsZdb0Wokp/9+hAu1x/wUlhWn3QoFvNz+0mH4hixxG/fa0nBJDBmZqMrBymKUIv3pRDqDJtJ7a5dDQdLkI8xTPZZOl03hxDGWhF5j7BtEwWORB562GzMhRMYBx6l/1lkG2oBUac/+60pHD1JGedTjQbOBZrQOLggAY5AgzFm5M6owjareZO/QDIDXMJyQ//WV/CT3VI4hz00aN3oxA1CNgt43zTLjgNzOEdVnAMmh6GTTdYyVCp32qO1SfFcZMsfpxqqv/33UH5yuZkzQ0w/5qN32dixcuBBdhAnggQUpXlPY7es28a/uzlFio4+5R7ogHpBAT48XwDZw2tMK1wpoueQVSNmwzCs/CQWOBjmwkgz/DfynHJ4Jia6666666666666///hBdgke7QAebACRO4BwkWXSJyl0AcJSfAjKzz3AHaGuJnrWws1oW4QAk0GyqSEorW8wIv/t+8kVmICMBFDCtRfGBBCCAut/K0dCMzEkKouHidQS1zJeSgEl5AhIN5SAR7EBBM/zNxhneH5mSfAqDYAFaeHHYWQfDUMBGKhkAYLggPq+d9dI6xLQSI1z4CiB57sLEgCJY1iRQsRBCx9h84cMXcLYQNUoSDkw5rSe8kArC2e3P6rSIT48GvvTTXNsgzCypRdXWluzODwrX+sh25bHMJv9p/+XZYJJQqe6bj+9L6w2TC0qtS1LwAmYH+ryHzglzS/bM7lb0lZHEkDqoh9iUw8UemQFvqakeOL9+r+WmsJx6zrufAmJjoR+Jb0Z2YAfUSCLl5f8IvF5sz6N/gsUiAXyGxJRCtJkV//z+UpnjN7Ha2rTIygzH5F7/049Wuv8JDrW0SytbZhuXCKZn9V1P6zAgAIZRAQJ+4Z3bNsz+Nz9P2K/LdnaNSpgO4ebOZ2aT60QhJIUvT0VW4wux4EXVSe3gl/svCYmDniXYDjv21BzFGnNQz7/1mE9FE9aRsev+Wrx6Be3JHRl4AnQmQzwVaWnqHV1q9DN+y9tlwvGb33QFzYVGnyfTB37nIF8qBaO8k8z/tuP2wAnJwKJ/bLBJfUBSymw3feKFjR3C6NorFyAQAVIcIBjkfrfWWOVU1kYGYIO+NI11L2osuG4QEdzQjwCavQXTz7dm+kAit5ialNScceH0mFFQWE6q7AM9jUpobf/z+8evFxL3tsazQCiDXQ275JlDfyIIScis3YsSMzyn/v/000Aw/+c5Dy0LosCIY6AOSFttGP9HvuzfSLLyXsEe3I72qRwYUZBiaY2ckYZ7wse2bLgWPo9EyUxIFmsRs4XOCs2Tw0Lmbk79PVr801vwgHqMBZoMjP9Xn3Fzpu6mFS9UUrSZ0fA3RdAM+MY7gHz7GbFjd7/vUqWTZ45wQpYf3u3hEtJfXNTcRf3XIIyUUnQ0BAfJFTElnRHVLTBbAHWOMA/WQoOtqsg6+sfkYACvf9aUh75uGYfXx9627yGSVTrh+koM6YVIQv9aeFL5y053M3VvAplVUq1Raaw3OiK217Zs3AUEgj33ccLzSkEmFJakkEWa2CQMEDQoRwQdHsiAh/CuOdjB9x/5DhkgHrY5ubGP55PGM2YsmDr+FreSjIDSJqhc5NWJrAnkT/PDM0qYMD0i5flwKFj8mEuk1YQroGWvf/ZuI5JlIGm9t8WwJi0VN3/vw3wvhb5v6Qz/KL3SFoKiOfhnv+50+KaAbv4MkoM5VOhyRtTKVcCcaCeNXdGp/4F7Mo1J3RUNYgquA8okStWJ9RnLKN5G65ahtZSNtQpYwIpkTRG6ELvHaqdoqnDdwVE0oFPABsHdkaJ1cqf/twWMNe8fHCBzL3yvOwYtDQYVpZsBWJLkKKuPG2FxdInDswxgA5lCUJTSzez+UrESToAQ43cCOEbKBb/Cp0swqvpiAwri1/1yhOuIL6RyPtZBLmjpiYeP74FvAZpqb275yd61rI/Wgbj8yZPDjug3egqAy85mEUtLFXfaB2GDWgIfesj7vebyGHXG6Qh5tYxz/MpImUBNiYkza21jeWKNsRay4L5y5VMq3VpnBlwve/2O0iefvbeu3Ri0ZHLas1vo/n6u2YQS326fGtrI3cjb4u0JvMqbBPtyMMzHUJ/Ob+abd/CD1zzMoPQ7SLJAL+0AFcB88AYvtM3voweRf/vBgoyCv/su7jAQ13ASOf6+4CTj+t1WmynQJkYExJanbBNzas82kvU52sQ+0khDUZont5b6QqBlZfe+9exkFTEjYyS1IF9V33no0AyS/bpgv8pyHjGF//jcgA/k43UR0KlMn7RfU0giTEEjRTKtMnuXt6O1Gx3rnXAQXNZvbfiY69oAiWb4eAgvDE3sqoQ8hCVTYF2VrxhnhkaWnWLRuK70rNWLN4yMyXUNCANGn8lDh/LAxHnDzvOeOY///o/v11111111111111///sODuOacGOHPOhsi3+gXAHZYGDCelSYxv+hDYAQYDa9kS+/QcFaYKE5ygxq8FYMwQkBC0djTQ0MoVNNBNBB6CgNgI7v+zfvfYBNjAcP0cCfMnIgMPHabaFU8qUIPEyhT+XsBN4b8ZKZH3moItJOSuGoeOZab2YThIOESHYcj9xkCP1Ksd7X5jIWwAuwuJ1qJxZDfyy9Ti39nvU4b8K7IBp9marp3kSfC3RArAzZY9uu+e/9qBnydajlLwlP5p8cRjJFwGLA8R9rqBdO7/gyopokKJu4rv+fo2BT3amCAdZymjUhQtCiin/F31lcwhrh7wXVzpP963voBhG7mWNPHi6yZcwBjP+2vn4H4RY7/cModGjW/safgmIaGECKDQKgMw+IEmaQ//4trhRZTUkJ0zQLwZ6vuonxZpHIKj4mHelpX5sn0gszM4dl6ziHryC7kwpfX+bNRIAtX1mmjEqHYxSBhl9T9rpv+gFHnpYiKuLKM8AKIiUorsTPAOAAZ4YDBgV36x1Rv2OMk+gsfBFgsGEnkKaio0lKmQIJ5YeP0CejpiJqjmUnq5Y7cBZTGFl1hm/3TsgyX0I1ru79+eqe6f+1awvR/WlJGf4tU28qmNhxsUqY1JJ4AnleHfiv/CHUBCFmdR3jxM3iXz+0Xjn8ICYlCEMuNocoIF/HhcbB1K00lv+yKJi6EOAsGVF95asDpy8beE1ivs6rOciDv+qEfgE1O53F/gYldbQcacJtA3W22/DkGuxRqwoQwBoqlet06m7KMryl1kZNHgpnBOGob2Wvo/q6CiO0FomW1xBtcrwqmBi8o//3ttPAac6OU9keuoWt3wJoliaq89uftXBjz/yG9vuef3/kS7/lzthSprmnnSdN3uvOZiY47s/glnAahkiql39vujMW/U1dreNiNaixeDqZwOJM/mXsF/BH3pKkSraU01Hpa6jlmNXDT/r2y8sZ9/nKXDnl4fPO2Vbfw2uv7ZZmhS8ytyA5MrZdkqTuzqY9dcRHVu6p++F7nKijcD5fGOpq8TPziLvwMgqGME6NRdJC+XRVOrrNs10mTuuRYqIEboTMRIKeKSOQC/aeFqACxtPht5i0S/W+dAk/3MnXTZq6adcFzLKavQ0t6xRY69AkGu4G5lsnUY9qb+to9NAv59hlVkCizAdPro6tw9p7cf8v82cjdkRwFBVJOu9p8dqiMln6yBjBmMZ5ATCp+/95HaEioGLlmEdvjLBAHduk4MMGOxJ1T9VNw14Gt0tCv1rVNfdgmg675u8NRsvTZB8qy2Kb30k9I5pk9eGE3+62wnlMoq2Rv2LaIKM2QeD2ESNnEkZXv/xMZrAxWkp5+C/DdZTn5jKArH1cEzW6C0yZHk//1jK2lIV4jqsuc6dub7Mnor4uK+9PVtevA6AYoEARhNi3D4/A/5ywdkPYTjJVEu5zjdvAiijwagd5fQVtuyydQlTAJXIABARC3LOPISZZy6Vom7qjK+rB7+x2oHECQZIDkl8Wsscj1lTguNsNmh0gm16UTLxpwIAu8H0+H/ceigD4799vfhx+m79E5tzlRzyoLSBxC2kP4MpV3IWrQf//2GxY09khfe9oCOb/mPxmkZJ/ZDj9zUtddddddddddddf//7DgQh5QBnfhkR6LTxY8gLcpQ7HoeC4jO4l8eEVLkr0t5r5f9GZyLodSkRvrI+IOqKdH1Rw1MbLBi9Xng8tHVWKTXn+zzlAHyl2UCWJAZdP90ifil/bREJDCg3coUVOUUqb3m0kgA6jrVq43lNh63zMq+DPfEIUgl61AaKAhEsrN1T6q6ihgilBhygQtf22iBedgzedevsXot0i3lLOqk8tLVlexthZurnhYJnyX3HmQMLQqpiH3bNx1ZkZjAXMVWYKsBfkeSYT3F9ro9NMg9S45ZnsHcYdGxFfG+qPCr/rNY90I6uHtyB8vaeFJpobcrU54kSMxbjt1pWmcVDd3cYzNbETS7w6YmYW6OPRSf/+SpZjgvNcjiMqct+I0VMNe3hQNjC+6R7JD1xm88iBjAMLs8Mg2Dk5e5DPRN5fo8KYEsJQorJ8lpkKrjtll1AoOgzmakqjGd3mHBwUYeNAGl8nZAdLyf23m5b8AgE3Ik8EfUlgAf480IJv9MW48lNcgjfdVFBKBqrHw3fRX1msAwub6QzpkKgGVtHWMa3ybTqn6NphixbadRJSjrZEJIyD4pKPjtbv6gishI7vRA5mNrGtGP5b7ZvSO6yPrD1EbnaL01PAaT2pduITuW9mFllMvlnP9DxMQtr1ar9rU2ZzSQnvBXHtOwn65YcyQTJ7h2NmzeimSXIiZ9nYVd+WPZwfS2ihFs0IibRrzcOl0QOU+qK8B0lRYCUbPGPn3bUbmn/K9c4GHn5W3calEgAMK0xIJnCGFjRgDYtqIbaJ/XAwWQjuT3PO+8+zQNxhlnOalX6xMSm5uBkJivMdzu/pKofhHRjvJiT/rfoHO9+ZeqWxoHg4P9by19642jlZN/NzSf/K+xs9nyac0g0aQ1DFkZWk5mtXGRpih4IjgaFcAMPXT3xpEs1ap6s8N45IwEUKbg9wo2jrLuQpIaHD6f/3x2E60H8D0Ie3aHy4JZARYYEAdowx5+oGtooz7iIbI7Lmb5QJlkNOBPJNshIt+/tofjRu/mSw9eJYYRphQ9vjUKFBCUw+Q9UxSkJm2Rvf2sG9MmJHC+Mxccf0OIbwTuNQKOuAMg0PZ1ttTRet232HMzxsbVHNBqTRBSs6ySxMr1ZvE2Yz4GVWL7fLvH2ltO0e0oQOzsaS7BuuXSxy2G3n0VU85qvgVS1WU0+MbDeoNcijv/bgUIBAOMhuW42Bu0gm2fPYXYgbRgIKICFCaNtG/Hlp3NNLd6vDpSxmlg/TWRJxB3tRtOV3ZFPfJkdDstIf6oXAJK0n0s+iv//9hst72gSIv55zGe982Qvheomuuuuuuuuuuuuv//6MODITnApwRjTa/56CmwKQDkgMSmdP75AcAX+xwAUFKxcKDtpRuEBQRcVVe/qCEgIUILish4KqCq0AIPQD3ZpoFcTADz39NOlHIqwXFUVTcWQIPbataZLlqIlv2dVZ5g2AkcRY1ybZM7HgN4hbgJLqMo04PSWXj+AowBABuvF6UYz4XrLtv1rpohIbBlqTsVtYXlZ5VIA+OB/XP/2awT6FGV9Y3HiGaBT03STFGZRBElJ54iNKpH79a0RJuYITiCAjng/VK8T1h+123sq92ugxCwTGU7WknX6dnkCS2lPnMRpA5xwN6bxJffeAaRBqc08LOC7RfWhPi12WJyEDMBu60XvPity7051Qd1OTqH0yWNpD9noqjJ2sX9xPA+3DqxYqERpShZb23DyDPRroYO6D5C1j2Q+/LyopM2wg4QFFfI6Axhk0GrX2oEPumXqN84zdbeVxryaiS831vOTh88NBjAyY9oI8lelf3P0EVYRKpuX8NmkBJNk+X0av2BGbPEnkH7xdIo8ZCt5KNvUAwCeBbxr96+tPDq3FfwKdaYqvb4DLELCdKbkr7TXBXMwCboiu5MDE1wWLsfBU9H2txC/vi9zc6tDx2vtLg8t/ne48lFNCbROMFPxEi2/Z21hKtUMNtpc/xK4jRCzHWD+oAQ2mnOJI07KM8T/2dHBXgbpRx4Ft4mAibscVhy557WhSaM26Ehv2CmmbAUwvF9aWfQdU0nhvjLs1J1Py8xv91862YsxXlw33a9RNhkGMgMQEoFiReiA7Uaneu8hueNSn7kaPcCQbluurkQkinBf+G7+SQGdMsVY8GW0lJ5sw6m4MA+eAW4+X/uzfd72SXWYHLKzCP7S4xKmJYDrK/DAwthEFtW9gp2AoSJ+GL2cZzRQrfaaOiolUcLiEJMYrybduyWCbeZZeCZWgrJ74Rw9ljBDzkqOJ/pmvzKJF13Y0hmB27DeJXobwDuyaWAMyjjCmcc7TZltbBIKBYBGfgrVBQfYzKqMFXW8bpwrghIyG6somz9PmUg/YAVb2S8H+X6wWeAlqfh2K3D5pZ84P97K9x/k5vp9sMWXn0gN7pjDBvEnFvOg7P/WVEbEgS1GFfqml7n7TgxHDbcNiWoAh25n05tdgkFna9yO61sl2FYQ5pwOiN34mW+wivPBWidTLkQy8dVY/sgPAbkc2+W2NSHpp8tZybDUewEKBZTqmTm3Id/RtX9LOwsoMwC4hJiJ+B1dj9t5n/v1v8ui9PhV0Wdu8nVGTcDug34YVs5lHs2DVR+aNYwk3Htl9/lqnehuV1MJaAI59eGoW4e/7otgwYQIBB9vlIRWE2jspNvzk3aQyLm2KCcQykrqRVc6e1PP5vIlsxTxVXdRT7f2t9hsSR1EHey8ZKTPpTyV9zKruZIRi5qeuuuuuuuuuuuuv+n/oFnCU0ZvPgLAJiXzj383oPeiJ0RKfcdwEwMuLWHM9Tg6CxEOSAa1pwAAgAiK1bEACWn5ha/8aDQdTbTMVwfdOHnjcrj+qAWPACuYHgwr3gPGv2eUcuFwe6UtJlua7mJAi1TUR7Qcs9fiUHU8A4JPLCADgFkIAHg0nFiw4e24MB2YAX2YVQqkE9Gz1xWEoUVwATVmboGWQKJoS4XgfMP+vb5M89pe6Zuexd/cFCEMz/3vUTR5OlCPXPTXyL63uITYdTV8jodR4iGvV36zvbnCRMV2ChGX+HiqCE+zwe1dAcUgujtqDd02Y31mzHhb+r1tHJaM0dye7GfLkZ63h/BPUz0LK/38/afS9NzIYeXrLt/URAwAICPXySiZ/+R9Ab9kWfpOHsJiQ7Qy2HFX2/WCiqgNhwiyg527voyUFYBhMU4Vec8aZoufhcSVxYyoguVb+xtd43yk/PG+AqkN1bRDBj2swczzfkTtNuzdg+59v4arBdN2aeruZtnMSCGJF+Aqe+h3F1Id9AhKAgieN+kd/PPM1iuF2Q5gq+AZwBwCFk8TROmKCESP6KIw7ZGPYOY3Xm8N4WIED55FBqNFk57gxoHLiSFXXh505wu8AIn9pIApvMDYDVed2gcVzAGPKR4xeH+GZg3Q3lBkN6GJTgujCdiv/njqTLZ75zU3dvjt9xIHNu5OeMisYe/DYQWeivUjdLxVnMmWArIIRzq86NJAOwKmcwHhIrmJg621NsWdYMbjralLrnuZKmUAPKmC3XKELMw6SMMvTuYNwuvPcUSk7mbgpOJmTCwLbTwK9YUlA8kDY0P6c0dmXrxMkcTXH+iEknuJ0c+tu9Elkeve2lJ+2JjCwfMsnf9DMS2KlPDKpHknv4Xyzv0BPoeAy+X8MeRuM1ZhhtHeLAoq4K8IE5O4wY5tyPsu7UuCXoBBL5q8A3geAxdvE5TZB4VaPZhvzrVav2hrPvDiPAp39CnVnrUyV5+wuoGGS9iqHsN99bc97hDCyTi8borq1NoX3HWO2ERV0eGzzUJwl+e4ZHwn4nnMaunYwoN0UXDkmje+KZXqP+wtLfW3r6Evoq61jU9C0te2Aihxl37PntrlcD/oAr0A7vBpSVTsbyMiCplOt/vPSJEHuc8oJMT76uvjBwCkwUAewBl8oPBKaIOpyqBLyb/JOlYsFRroZOLG/EgyX448hNr5byqtv9r/+9NqjD/DeA3Vu7/aL++NNhqMhKuHBGyga694273EzPSePjfdYEhtA5bjT4H2/JnAWsVmFgk00ueR3rllJ1vBXH9yWsW6150zXGiqhiG8cmHMmEkjs2tvOsYU/MczS5Lkle5IuuuuuuuuuFsBEraQdX8Mj75Y47yX8I8bTZjbHU6Z4ESBXAq4sFHvxoIAwJZLGeAdljxoBlwwtZmMy/Qn8XXXXXmlPnpTHBDwM4AMTr3UBqXekr/0BZirxGKMm8b3xHDUt3+aCLKyrPDwMEIWzJm4JZqP/wBgLGEeTDWo2VQvpFPvaV7Aox2BACYMaAx7E7D9vTeL28BEvG1YKM2kNihqPLYex4Bd9AjxxjJolE/9zDqZusnvl8+w1N5sCWiCkw+ZaXXcuf6JAiFkTPTKH24y2AkGcgxHcaynSxW6j/8kKaVX47+tbHz5gAgb/+yUUAqIpAoPv/pCeyO/uebTo/jALM8rab/JnxR/XQgg4f8FrKXjnYBcPVQlhAPqkiN8it4mCihzRyT9DjEqWN8Yd5j3+6Zvo/AYe9sBY0Y83Wfr4KKYju7uS2fy/0hA2KE7ev6X09FuAursa3QFs+qCOuTqZe1bxjuErG7cdD5e+ro/vR+dg7nMisAgIDyHLxpT4MG7P0ccLzIEqqWdFxoPP4bNAqPMctSx5RtoXYP7NYuZQU0lbZbc1/R0+xD+0dbrq7gwqYh1D4E8Cd9E3+RhhSRjo3+l30nva7ughONrDx454XaNdM4bcY5t+zzBRYBOYICwXCHZa0tnY1ExFynsc/rceC/7EvE+fcsNkI0tX7rngEw/DTsvmjJtEc4ugUVZB/0eWlafsuoC5koq/aVDjVe0o9yd2O39257xflp/zx0GenOxaO/Tv/iQmAqI1y8nrowDc2bmckI3i90umbXs1Qadb/XA74LNfpm2KEKqzre4uPWP2XyXFN0sX08uifnZ2C2OldKI030uu5R9PQw+eOuhOll4dDjUf4aYx37XB8iImJ3Z5Jv6msjAkgQA1eglOzfNXX8fMy//XghWtgE9OnuAbDbUHmC6K/N5OBfviDibWMifKO9so7mB+BVxxJEj1PNVVRw1SULqoMLXafsbnzy8C+2Me/jBoZbH/fW20wl6qzfy3DawWYYyFTDF7WdaD4t7+qY+uuuuuuuuuPUMZaI7//rrrrr/NJ/6BYKPTwAiYoKZ0KB9KYigT/5A5QzRDMUiLDWCBIUgQBOY8YuI8MH5fhXtBjSSdy/0+aKmiOQhhK7MGygzUUZfSsn4EQsCk2OwnqecAloAQsnBMLaVgMvt2xkcxddTg37+MXrttCgVIyDMVRSXXsi+jYwlDIdyJ6NwHgJfQVAOQQABrB3BgAGxjwU8y8eIfSYzBHaMF1dTIyewy2nJHZPRpgvmCkg4Ij5eBFVfQFTSJgzmkepLsAeQb/3Wf5/u65QJ1Fw5yn4in/ioGcQpwziYHTEEohhYdkZ5/xj9K7eSsqaYUOkMWqr+PqincQe3cVSmsD3ZB5F1d7viiiIbFNYSdw2w35vDbjYWbbtnjWjtT8UGjunXT+Z7VsRqvVqZclbK6u/S1F0GbG5GS2TtWsnarovwrhd8rgroYxRqy9xH739uZMIAzg8n9ZH1kR1mFA6F3n1kuX37YFJjXA8kija2e68Bv9/WihVlUIRDDQ047GXwbeRPt8S04Id0x6XJvuHNeB6KRmFR68Tsp6pfk/LzL7nr14EMynmj754pmaUU07/ZuWP9ucDWEpZyYk7KwGPtO6sbau6Oi/xBP8lYsIdtrFNo/a9P1zYqlhIfDjMkyatPvwhxAmANZMUblqh1/N85fdKtm7TcpNS8zE6xVn3pyINymyA0sIEAmvpgMlrbeMEX59ZGcgmp4mIKOPOsZ8ZqDj8DRe/omeneCTaDGFyycjj64gBT9ubRUsL/BKn4K4wr46N/eoxQ0gLN8943hHZW5zGns7/+NayCPtgh0y9+tj/NLC5tSb4os+wtqnFuLoTS+wlBFC5xt9hsnUEz1Y+MsZl0J6QHd45//4KxomNHbeHAdCJEX+nff1LXXXXXXXXXXXXXXXXz8/z0OHeMe7m/rP6ErFAkSlEEh3S3/6bTIDt4DMzF4rdPL//OBk4kytkedNw5/EzCWFJdd86GEAAVBDxAQABgDincvBcj2KjFibVdbaX72Bs8xTCwSRlhCbtbQaaEiZ/fjUOxKADNyPF1AlAc3qNUlw6YDajbcQd/XX3AoGUbCXOiQpBf+WCDLcuUMSKvsmrVaDH9DjHzqCAAl4AgQAI1A9YCyQWNxQXi4R4vFivTAL5iw9hdvGkgtEkdHAH0rUqdutKuQ7AEEiVfbxbm3xD02tTkdYbphETleOr1PP5ZfK9fMwoxohIA4lK1atdEgtmN4Ybu8om4VVUbH9YRDPFt5hRktM2sOipsCyE87/a/QIYGhFHDrKsnp6B0hyl5Lrb53zVDuWdphF/TX+H1WZ4QhtaRzRV8HOLayXQHmXTT6dZ6YByySbYzdE8wisGobviJme+enKCQUcLKamm2C/shTiIKzEEonOdTubU5gbkZBFCeUOMEZOKWqbJnT4kgyWXKYXbRgue2AF9qzHxOeWYNks1efgyLwOPCrZM7ycRjvIVcmeQ5s5ZWTJ7IxratIK/eMchrTKr0tLpJYbYhPyzLTiLpCUJBvp+7ipm7g5OQj9WPdBCRHftZ3O7UrzEN5Ted6qSBW1YYoHdps6P2aewvvkJvOtQAUyfxG2Q5MffBZnXCxLBYogeepTsD2Fm1Yqnv3bfqaO3QNCw9C7UeEiMHC7KTvB65SXNtwcf///grLDH3XLGdf+EJmYVJMS7+TvfU9dddddddddddddddddL19V/0GhngFRTQ26HQhWh6wAuLAM7HBOAtI16vLfwHQQ/ceBOBgKK+WTnPPyhMWwIAAiEJICAAKAphvgIgCtBmo359tb5sD28G0oCun6/+uqoqxqvkOEp5zQatRoBKGeaO2S75vQ+GiSnwtHSIoDVRy4GWNKgvKWgaIALkHEXjFpWlyG7/5F2fWk6iuLd//z4ANFodWRTsjw9/miBJ0A8s2FGkV/3ggnOAYIARnA0UtrNezKuMzBoIkugw1gijafgwlzgl6zAV4KXWCcKqWBqy+gMR/L9rVTSvVck2Odfj5+lK15lbqyDdtxzwsrymnZfkDyfgd5p3uEksLjMXZPSBVhcjfxVslsUQjwVQAw9Uq++30ozUfstreqsNZAF3+36DlRTAdXFxI+1+giaC7IX2XRajxUeRAKRzHZAK52F0yEMa4+7Q8CDC2d9BawRU+W3HjGPVxMMl3PWA2w9KbD1ufoIXUwRq3LekQLfnK1wo6WmXTiG21b5NZ2cukGLOoxg8tkzOan9UUQW8ff0qtSfvquqcBidukSuJPUiX1OkLn/s1+B2+LmIMQ9z+rFi15pblS1feL9Qc7E8vips6d0bUrjiCF1SssqZDKp1lkbkaVNFfoIJ1N8JIgx9YsbOVNzeMtN3DWYO7uAE2Z3Du6PPBM3Lc/7TAks8WTb99WAff+7KRGvKuOjfODE4GWh5iiqlHPOpu/vypkX/8NFbPx99R3TBuM8kzK2++98JD5QqSB8SH/Bq++p6666666666666666666649f/0//rj1/0GhmmfGQHpUH41/2IAmd2DmP4EaVf/8/wIsRCBl7dKG23TR0FCCyUe8f1RkHrjkMUmAZXcGC9S2aVWH++CBBPMCAICqGgN+YhtRsJ6YArPP8BIqCKHQXUM0cVgPVvP//H/jqE9MlBy7kCI5zACuig1QKZM/956QQU4xd54DwhHe7CX05JAn/GJ0kZ/5QZhAgLq2+BCLLBXFJcZfmAELZ4YLarv6/wcAigUGgqCR2zFTeyGS9g3SGaXvIkwLzz4Uzp5U8df8mzbQruUF6jYIICwIkWCEC4iKPlr1QXMCYPSHHkeCebtBDomU+M6Q39EABAV0BteBx83+OQZGA7aR85SQGPicflg6DC7BRqdipfQpQIV2cZCGeWF0d5dI//r6nxTHFTJRnjvxyIF4Gh1Ds6Aug46/DoIiYMviS/A8BAn4cniX5/iWoMMihqHctZMOd6i7v1dXvv0NKAz9xc81DgnkCfkHgS6V51fWbZdAmwCSS0eYmExQcKvBX2RQFwKwfvSORCF2/CtDBYEKtKA71WxhrQ1QrC35x2YO7uH/QaikOdHVkh0LsZtsI2u+P+BgY4H6Ybh0PeP+J//JE+uuuuuuuuuuuuuuuuuuuuuuuulrrrrrrrrrrrrrrrrwAAAedBmjgK+Cwvv7ixSrrmsOF/9yiLYZQCbwbL+X5PLkv++PLeWz+Kae+4vA2sXR/L+4sk4egq4szijA3/L77Vd/lKnJCFnuERAo3cJJzC5GQCi6Z+/G8Ltfkziu1zxM2de8ZHadL8mDp+x1uowqw8lOVZeAmM2YHfz4ekvy+xIMz9wnfeRKUyJRQUwFIvu9tMiZL9yFfcKF/vbCIoVhD8x9bwq5XaW4wmxfar2bG0nnFZiMcdoapmzOSGikHn8PBc2Up3CBTp4aRd+2wh4pHBdzG5r683KFxCEbdoWSAe6zwVFX3CRYMCvcJFfeVXdhN/312UZgYy+peUsroYEXd317Lebd3Oy+GVZ5CcVFdOWT/xRA7R6SeSy5PeVcl5LlKC9iX/Cm8v/uKgSete/wsjMjIq8J24dvl9eue7F3fdPC22c/vXN3fxF33v827v6mSupKn4s0CNVDE97qwEPu2X/QsSldF9X+96VMIZl999z8J8SZ7u7u/iyvq7931yF3d0nzS+CO74tuqoqfQRNw2ko4S8RpYPuC+FHpmIE1x/3b6RT4rfxOX97ovrvy630yVSAhJovrCfJhp72Vmvbu+vX3Vdfl8vJUx3chPl+9Kib3yOGK733k9S12cv0n5O5+GpIjVKtVr+TBZAAAAC1EGaVAK+CvzDsclfyld9bw0vcWIAxwdBbMV3D0DvZydfi7zfD77eUmE/sMeOKO+8iAwjfiM/bm/8oQ2pw60EBvzzvS+metG4sh/yhHBjg9e9vlLtFD/yw95uZZBZe4RHbgTBLfxwrd3e26oS2SlxSeTfbl5T7vup8hfCp/RtaH+dyMe4+Fiere7jxTu4R88QO0ouCOGQxJZ8inOyiV+ji6PZ+jb7lL9PVhCQNPvH1Z+CT6lWraNu8Ju8f4zh3u99G/YsmMtreWOcn4u/zlhqHtMH/nki0XttUYjljIOWPwm7Uh3v1Ex2nk8N33pal9G4LDGBEsQI96N8pbxlwd/9PE8F0g/bUGzcsz4+e+Z7VxOfskvr3s37IcPjvP7NzEwjdHnLKv7SeEn4nzfUKh5hfnC8687qKtjcdLn1sZmHD7R9kFm/332jOUx8lNdP+Tw1tJYNvNCPgu8Z4fp3reUuEb3ve93tzLZb5PB6/lYVP/n7777IOLs+ZPXS8T4dRdK/PPsVWV3wiuXFmvd8OO5oMrHT+7d0O72/JMfd7P2LLz95PXJ1qfQ6WT73/apOx6Ez/7tBHyZBLz8iEXr3qT7/wkR7z+f8ViTveJfFG9ORCYYv88IwCfd5P1L09F0bUh+OjcVak9t77Lap97N6nFmgicz9l5p/2E3/Ttr/ZRMb1Nf+T1r8Tu99wkkgvKMl2FW5ebiywn48o/cVvaWvFfffOxN77yeGpk9JTVUg5QkTcET6nxwkefJPHNM951FnPCXj816XF30ZjP07CrOWRrywiRxvtItue+wyo2W3sXKd4kYPYl98T2Ejo63f3ovyfPb2bjnvPk8HpTRwqsCOgaJfwvH3qEOPbwoXyfwSy/3G8hXadVy7Ig7r9Gi+SEhpFn3u+9GV+bzm6T2kqEe9z/+XKMihdPsU6J9xNlsaCd6HrvSpuT8XSwgLu+93e8n2uSTDXkJu4LoAAALzQZpvSkAr4LdyjkpKaSy978MebwBK9Ux5L/9hEmWxlZzI31f/tbhzooR0/t9xkPv33z5SIs7GlbezBmvx5Yz1tqbHMvuX1Wf0Xp8ntxfOaIj5c7ufH/lreFX7YLBwfB7uwIV9x0/c1sGc9RYjXZ7RHWX2t8E3vwphhVi7RjmDkGkjtwkXgEx6zX/a85btTSTOGZxTk1R9rqi73v8p33Cb9sKCuXiR4rLGfJD5YPtuFuwg0OabJ9J0d2owjdOwyt4cVYa5z/+Zd+rVxJSw25Fzmo81pJ83je5PVd38v3tuytRgutKsXonu2e7WbpBnuZPSSPV/J6qltOCMkq5/9CXgny98/vvrfHEu3d+BNv/OjdPJ7f7bmLxmXSW4RNnDwy+cGHnTzoX3TROcnpJP7iy8F+LkjdX5x/TibyD8wBDkLXyk9t69TZkL3pwhuYMuiiSweyA4gPLHDafKknwlD737vH8KZe0txMIR6gN2uq9J/vX+vrFx8eHQdAj/m9tufrc9OqF7t+nNwwJEQldq3JEzsTlZBofp99ZS7uEexJHcvt7vXllvfq+n6/p+/F0+R3urDRfT+2uEJdAh8M3J7f4q97vqvSTv7iQru48Gh/of67SXe2l9/ihj3vve+S93CPirSe5nnuu/cYR23vd7bPu7it7rvaX1VlF5RZ6S/r6+yeq7XTdKUkwavbr65FhDyZWLrS0l102Y7y8v19UUi68Z3Wpt3ye2vWb0/eLEQRL5xvj4qvSZUpRKKen+Wa5++EvEinus//iSiu7blWe7rSyeklv10uT1rTfY1z/0N9Y4ic9bvkj6WhXv6LNd+2toUTd3d/UEN10iCRf38VFbu80u9cEhj5e59iROXZ1p2vpdJU/119LT6T6Syeq7qo65xwOJ9CVE+R7L9OrOFPEEjZ/8Jf1dLbKEyZL39VpaRiiXvvHeq+vr3rW9MlTihdfszu711+SEj7ve+vS4j+CQvNacdSElxy+9yQx5CH3pf+IkK0aGCyAAAAP/QZqPSkAr4LPFjBuXT8178IdJ7tca/YZL/7hI3DN9Mrgm8N3y/l3YzZ7no+wpC4JX5zOw6i9jgy0k6db4wpceGcryZd3e7yfaTZZ9BDu7RThWG8fOF71XlJuMkjlhA97pvufX5PV1z/V5Ysjvu/8pXrCr9wQBIVu5b3H5w9NWRPsqI//+CndfuzAvdx9o/RmHZfaaXIXh02zbZbhMr8CT+fseNr3tvp3g51Mnttru7v3RPTSLotrv7qFPCJnw1cE4fuG+f7DGiT6SbO7bChB3RPOf26NDEhRK1IcS/s5Qyl9il7hQro6U/dCmJGLUet9sD/pq6J6ST5blJSknXuCgrw7JFx+gIdC7nuoS8FnTfl9zof2/bCN7cAj/+bppH99pvpxdzFfjMuX91sIEDvv4YdPiO88MohfgAY8v+3hErzFs6mPIHW7UH8soTUCBdLz/+hrEc/5L00fTnae9UX+CrHaeCQi9zoCP1Xrvl/wiQvK/705qAiekf/wVlSQ5nN+XK7/L+9+G1LzCcfphHwQiLx+lfZJ/He20m8ntpVlbh2I2pC192ZPPbM4a9OqhlbT/6FxhH3nNGOQicXckMgOoou88l5b0uKQQ4+9ArSX9uwnOvOs9JbW0mvrKWUL3XkiIaj9lQJL3AjW/d/baykBNLy8FcTs0WUKDEv93n/298pXd4R8EhOVh932Kve9+8wngj+df9NEGBF5l3mA1reEBbtFYmjJP+GXvZ7EQQZofdsS2/uu/dY9U0hNIQaYNvqxT6oPJ6SRf69tyr16RRp/wh4odmfEqW7fcE2TvHu75PdZK9CS3V5P198leJfevcSNymLpb7+37+gimMd/uTCZxgvfXr86etwj4SvL94zjuFCO7u97u7vivXQkpRL2+vr9e2/EfwhlC7qBKe+f9tiHT3v3MRx08/Re0l8nq665vL681jXnYhDyCoT/R+SMJM69zd95WM37bEEcok/ivTtkenrKWr0qsewka73Fb/lyD79O/37fJ0u4szgRfT/f+uBLiLOv9uxh3fu7SaFRw933vJ9ve5Jd3cJeJEFYjdzt4yY6jCu7vOHXxW4rd79C3k9ctpSarCAt3eP990XqvcxMv23Xq4nKV826FkEXvv79t+KEPLzBp7ZPTapDr8ve/7vCXk5e9/YJyPFbnTeOPaVS/+jlEnye/J7tnL19lbu/xiui+60mpIIt7otV2R2FCSv2R3L4Q8GI3dvSsKF/XxENPNj7dzlerMQRsc9/+0UXulT2XVvp3ERWPq+rP4hbbtdtuka9/qFn5GKEWj+G5c67ctK70mJ1rclCTp6u+9OSviInjfdl+Gc0RVrsly/v4iTPDH96+r6YtGv7KZEjPPBZAAAAC7EGaoBXwV+LHampDcZhiyK3BHZrpRRf98XtLzjZHBjzeCJtlr4wmXEqkn4Bj3V+vU1gf/itaIwBjuSm/3ur3Lvjlv/ceUZyi3u9bOlbniybudj7V3HHy5l3uvYmOJLvNvxlHXvvk4U8wrhRMq9sKE553pXAuyT+cEj35f4xNjkR1tMrOwU+/ceqmoP2hwaI7wplk90t1aIXjt6qewmXNMPxtq7/FaTyL+n/ov3Cet7u/s/J9/lyfl4b6WE39AsMJcUicdgzdz2O5WtkQLlS1+NI+AaFwr7ilRXxwKOTzqG+osXhkjeHr3tYQuc/00WeEC3t3uFDE7aMvh9ll3lJaopQ3f//SeJek3q000Vd4REXvDsk/b1l8vVIExb3w5tH0JeCS9jm+svBTFGWxW6ursnZVU6dD1+4wf4BhrtWz63yfreXhgmYfCOpVMD77HjtzTcIfBU4a/TYRLnJlK3lUDsGcC2rJ1vKkrodxl9+fbRkFu/3fv7xM2Hv52N9Jt3BPy8hKF5Z+tNZIRIH0XI/eZ02kH27aZWWCYppxwhrw4u833XuP2IS8FfL77ZMefb9xV73u96ZUIEx4H3631hAUkiX+9mVnU/2E7zPbPFE8r3eRgT7xXyj33RLia/HoeNcKt3g1a29+/v23vpv0fsnttaTmmvcN20tNoqCGXmXsZhTbhXj73fb/CPgqp35PJ+2vLLu/tfYoxv8fjvMvTWeJFvYIePyfyCCBobbP8ULve92T26/J23r4p6vEkRItt0STe4SfagkM+7XWQt3/Y3d1R+9+r9t7erEFSMIfe6V/bhEv8u4gQ97v7Fumu/QnKT2/ya2poh3rCXlEu4r7F8v199jXVjdE9ctdaJ6164s0085o6q4V8E17u7u7nd1k3rWt6wl5Ch9yfvtIEhnu9b7+i4jSRehVcu4FWQsL/SZTt3fYn13vS1Je9+sLLfCdxO3tote7sk3EOFzTfSW/J6JR06+I6JR3DY7/ZS9eBHgAAAOXQZrAFfBX5R27OX/z6y4aL/7hEksh9iszhD3+YI/jSqD/OleGVPfRY2EY5s/T/46XOcq4b9wFhlJ+uEHhZD+mjtxHRFH13pPc199OCi7puQD3hqlGr3CZ3vdrl/3r9ky5Cz9xorjKiRwfZP5tV66m45sdMrksQ5VHzEJO+L9Mn/jfQWeIRojWofcNXlWLgbUHhsSgzZ5+v5GjPTut07vOwoW7I06l6RINp+HsCc/Hf++Vl6mh/Om2vHb4WbCvL/OvSW5ZV/11r7UqcTu+7vX0Eis97u97TYQu93MPuv533Cj9sKDOXhcVstiuR/buQaYyfdZ3lggpi2WULiBn/vw8kdTSMppd/uExWmCB8O6mh9xdN2riyhPxre4Mfx5fhHfozL32zEpgik4vu8cPtrUvsTN3OJbUWZ4LqWpY5jH3g+En717ZuH4ubCe2Ou7u9lCO73qqPE+W5I/4UoJP/3S4UeRuhwyhJgT1flmn/k/b8aVIElnBO+sP2pvk/b3ehPmi2FmU8t+j+vyeq6te8jBFTpZil+yfek+pcqIyfL90fwj4Jixm5svvv6ip/e+90VTZPS52lwhQq14/UxpV6Y+u2dbeKNCVV1Zf1cQCDb8zovJ7afkuEMN14Ses4NCBq67Mrbuq8npb5K5P7I9xui+u76WqBEdzLeW+sMZxa8wbcgF+F4bun/TQJuCM+dSMy82tPd7iLsB3W1XNVPCPhTjtO9/Cjc8vevon21brlhrKzfQRcnvqt0CEnpZfrvVF9dWLe0ipsnZFvTe8uoR8ftyck1oH/Rqz/LBTe5nvu979CS9JBIXL999OjPqqWrPqj+/yd7rrkeEezXn43S/oWS+77qxPvu36+vvHiX3d931TRt36Izb3CPRNv63xxMZRLdufwd/opXu8n6TufZd7rUTve95PexbXeSjrdVhMl5eH2awX1r3UkJeCYjy/e75Pr5SPZ3f2UpCk/3JTvox5M9r0f1+169lgkETMp2W2viZUW7TvCXipWLhl6arard94ILhVV74fHJhpxJZI7Xlh7wzH8nt69Q6JPz/dGrEo9bUhry9/Tm7vzas5Npddle2i09JrIMJdzj533YeBuHpbv7+qhPxHG0H1ldWi1p7NKvPOd/pbOyiR/u0tlkLuSvE9NSl9fXqzayfb/aJ80LLSxwh3FbvFbdK/NL3fk9KhJE2mtEu/bSyG7veMbW/4aWfieN6Xnz5t/D3xcAAAA75Bmu9KQCvgr8FA7UmakoZPyzYbM3+bw8/Ly3jb7dgY828OD0v/tgrJy8hammO+M8KwPq1f0ZRa+hcOI9ofKTcwLQtn9x5XsINM3bmsgWziVrTRZ4Txn3PeU3VbmIEfHrFkqD1cn6TTrbPN3r3GSb+XLco6/EXJf9k4WL/e4IBwXV7sES8mUZuOjFrL6gWuy9G1/zDkcNEbK+63cZuIj8KndgpHsf8hdG1WLOElUxqPcv2/hE7Gk2HVbbaGQp1UyLfh+KvtS1/jZuYXI1tPWNC3ymei7aOYsSZg2o0k+vyenTkfmnHL9taT8JcOJP9jfu9yXu8n91Tta9vXOpS47N2E/Cg7mldjCdx+fckeyR7VwbmT3srV3GEeCSLqPULpTrHYz7TKv+T6STOXcFZc+P49/ah+HluWMlLtsWUd3r4j3e+urcEmNiQ210dBEz74L89FS3cV5P1f827KEvFcuO79/ir73ft9dK+JIgv/f2YXytNDxLuHUtU9nLEqkA+/Li2HAEnbbNH/Z7tXx3Dpg4hO+O+8BqvGft/Q2O5geYltnKGL367bZ0oJ7u0lu/d2ConWVbTL4n/sGC7bRn9k9V6fdzxfCb1ceW6d73v65PSoXfdkBD4+sYxtav6xkbEr0vNnHWvxuIKB3CV74K5j/TcnNOS0tD/diy/fscDj9Xgj47P4Oh/8p5Y3pTrLTd+6Maejv3lgpvQihpj3YEJ6n+3+yzd8I+rb9xV3e513+5irmX1mFXvdip4k73hmnOZ9+R1fgiIwHB1Pl2Oz6rpe1r7JQkur9tlS+lCPk031ltrbq0JIn1k9pOj+0S+T6ci/6Ev3Vzau9ULXe95ck9tt8tRRXvu+kj6FZfe9wj5iF7fXTYKbu/Td3Izs+ylTvV13kLu+qp/1vtITViHvtckpeX39BLe93L/EQh5Kp/SHUO/Rsfl7eeEv9LoW9p1ZRuf5PbT6E/rqhsgrkjpLp+lC5LlvDcCU1VHfrx//6oswWDbj+6sqhLwSGyxu3XYKzuh0HDT3QqGW9hrElyLyT/Edtaby5eT1XX+vpevr60UqdEgkM4aT4nK2W8jhLwRSsSRot35YJ7xWG3nMX4+nVZHgmPfJFCrHe8l0JXaiu5FyKd34rVL451yAny7d9ypk/WVIqJIQfK3eLQqvxBA6ugM273f5RO76E/T6yd3dtCL3vfJ706KckgKqek1e7vHb1hXwRYa03Ftl8n8hty5S3i5JZLlshfk+tJf6dHIm08q8n16VnFusM5IgxrNTNRcnw98ZAAAD+UGbABXwWeLGLvWz+Xi1Xi65tPmOGfBYbD11734CPc6KDfGVLBL9YM8i/eFmFl/bfBZfJu0R9z16KWtWxiI1PLcEkwOkQyprLwUS95DpbwUtvXEleXmb/i/HUa3hT8faxxo1sfrZruFfMTgm2/Iv3BAQUYVQoQqyNpDy3Bt0lPnWYbgmzxYT72NtgZXfnZ/ssPrIfvW/430sbQIMb+zbmJB+Xns5xwkiE39/DyBUycCXc5QaHi0g3/RNuqh8VHFb6+//3Ch+pUJfakAiZ69gha03oz1LL8IYS7Uf/9XGQdSW0dfwGRMvu/jcQbYAE4xqZbhPcFfYyjnNK89Qv5gjtEMXJeDPCG77p//BThnvJy4a3zGrtTQ0LPVtraNhHlB0WPgJdXrv/n52Wt/cZeke2PDeaIlybvrfBPZ327nt74/OUf5/u7HpodpPd7z4ES+q3Ex8vJBwm/bBYMEj+xKku0XH433Ybt7yumjssaQ1t3hZt+R8zYz72tHiAFamnabVOQNNDQyHS8vqPb/xhbKHn4twyoKfHuoEp4dcqs28Kt6PJ759t4wq1owgtsnaVFhPaD9tNA18X9OJKk423/k9NsrF09+HyWz6vr83Gznpo8vJ+lr0a79plnjDaJJj274750HddPn8HJ+3XqE93c8Hwm/xk8Pe7y/uk9r7JTlO6vFsxLZQt1aWYpXwQmzcc8n377+KWvfsbrJ+hVCkmFH3I7hZNz9Wp4JL3tCXgh3n3qr03tiaGGPeRf2+2991idW90WbefbT1BHj0B/Qj5j05f8FE2vEkuk+9Swle/bXv36aySw6U3bQd7chASaW95lxn79J994qvY1dqEj3u98n7WTuCUk0+HIdcdOPuvJ6pE7VQj5iDdz9+iglK7bt7p/q67fvqxdFc7+jPpIJXP583eqI9336u968vH6YQfzlp71lsgJiO7u7u+u0ynNd+ie/yTFvdZPa9/RZb3hTy5f1+OIx3d3d077p7ElNV7vEOdp+WExp847j0kqy2MfLTZbqvb4pD8v93d+qs/oneXhMz5ZAD+Sfscq61GHe5fFdzs9y+2/CXiRhffPCnJ9vXC9guO8vd7vhk9/JapFMnRS0T17JrR062r3p6vq3Kg32chba/xe93f6ihG7vCiEi5fdZN73CXip4TtH6Vs560UsZdfezPe4rdwh5aK7LvoyKVHt9d9iS7fTKXbdV9Sk3fxEudvr8lEMm+rHXwR7Pho38H/7v9/UJl8lPx+PmHgfjfnr73UcZxnBbu73v5ZTvt6KuifN16qcWT1sT8ul5JJQm/yOFl+EyO4rGZRj9/IhN993v/WpPuQ97yenXm1pIqondwzqIl1xnLRagvgAAABClBmyAV8FfmHTYcuwjfubNfX+8vMR9/iyvmzmkGPN3HPy/+4ICcabZRlMzQCb+L1rvdwce9/3Gx/J8IF93bx96LnyncpyNtKiJ9bIO305x4/04mlGDPi+eVNu5oc3hS9tOeaHH24I9KCY9bQyhvfxePL16iSYZe+TYWL/e2ERAo18jpnZXjZwhPu7v7T7IdbWr69xn8D9f5NHp5B2XmgXQqXBfb/IZT5iz0c4Pd+4wTto8XVB/XimtTrX4Ysu8CR9LYF7yy6kAg2cv6+JJyLUz/MB38Zx3LKGTBI4N5f8uxpSCIWhy6Z0f5cBJoTur7/o3WqUsI7laD3slgN2dXMe61+j3TR+CyP+vdbr+M3dnvx2z+P+tXvPj9whve8EtyP9v8vHZjCfgnGOc3D6XRLJJZbm67Tr3GkdqKK0CX9g3R0ySSXXpCWc+/GsBX+uT0dhf0olkhIt8v272MLjx1bGVlXaveFnhC3I+PBTQcz4RK3gj8Nz3fexAt+/yzEbB+kv9YJON56O9l6fES3f+MM/3xsWG5R4SjaAXuV4n+EQbVq3oEF77Z5P7zLkVa4z1+EvBZd/l5EJHvfWZTf4LPGXR8dV9Pr2whL8O3D34+D8Z8v034s3NkNoKWRmhLpP6tujoOHnNht2qeJaXiL/k9Jorc+i4FGxvr23qiee6Sdx+Re4bZ1/K15fJXfv6LBF58fJ6pVdYq5VGcLzG5p+kmlZLiovmGklbhHwRiC/iid37ir3vfsS8nttvW4MMBN67/YxbrN5rUO3C/8nttpl1hEmvfPhF5r/KCxOO8FfISDSg0cNODY+8ITvG/RdtRp3aEdrHd1k9tLyfTlivZfagnLDbDD77xbbpTE4dfWm7T24TufJP3f7R+hHwQk5/bf4vur338hRqpe2+8EYokIQvlPQQduCKGvQPe84F17dbKY8H7OwTq31jLoji7drxTonu/9L+En+CrWbvu99vSq3VvGKt8nv/69K1fhJ/iu7z4/2+jogt71u7v9P06XTXY1CfPz/9HRjXvJ6SX0mK7bmX/WPK7u7ve8vl8i/hDx+94wk9Vb/dOkSXp8v+YuOve9+S/fdZYTHnj30tfVhMc8/3vL9Ot9F9ZeSJ19L1gqJed77jQeX38ZqjNQln+QqUg98lwksSE8FetVG6eZ/FlvMOJe979Oq9rv2N9fmX39dKL3u8bDjd64k+mXp27hLwvGTH5e+b/npGuv7FGTvcsnLfxJ61IvjP5hhU1y+nqu2ilwmXdyqNeM9eXyeiexfv6b0tGjDHxjp7RwPbfy+7ny7v04UX4TJe71XdNnhES7+7N99yd/S6fIu/rN5cpaESXe/WF8soh7vJ9VfuYXkh15Pq8rt+lN5ck9f/Jul+ynbeGclkF1cZaZPXU8REXRwf4lxEhQoxC/ov7ncJPBXAAAAQKQZtAFfBX5h04CWfhu0NNGV/U9rzE3Iq+Ut3hjzdQR7xL/7jScuaRTtxHrTnUXi7svotP496X/tsFfLBmpKGyLsjOOwQ77I2X3dNwndLOg3k92xLd8VLDhgg2DtLavoFlF3L3lAw5r8qZPfP/ZyS55VWJgn7Tz5cU2T9fJkFky5nz+U6UVuFX7gsCQXV7t5f3c168sKe8I7fZZRRhm3LQWvEhG66To1ORcW/aGCdi+wNfDjzrwygjG0jqH3WVNiSYenYYBDZcyX56ev3ZR84K7H99a5si/33rm5IQp4w3HaOEbhQfyn6Z9PNyEenbRsFGcfpJ7La1ezO+a2ysKEesETowtHokogg0k+1Ny/JZtqkehTId0wcf8esaK9sfhAue1jbqIrRZRZP7pM3IXOVqsrChTIGlJEfLBK8JnGRFpcN7a9gtk+qr2wh8cTSgvVFeEYNv9Ym+MZP0XbJOo9fQve/P5PpeQpfopRd39DMP102Cwwdz/QJNrBSjYiWbctYfNfOkW+/xd4TrBCtYRWXmcJvpQVa5+0/hPw/JrUve4Of60mh3CLzDe953psS3FiOHsPQQ6nX3NXqzabdwifjCKYf1KRv/d7Gy4qZym/F4B/2O2fh9N39tEzKzrPurrH5XPjk7/PnXl//on1W/Q2H69SCv57SvrvAzYCb1HHv//ODyta/LG9P9V/5hOfwj48RlWcv88a0X/PvJ9r5VslIID0NKZop4pAn7zUOzZ3K9/whM+tEpBLWokXQ2AyO4x8pguOA79bexb301l6a9Cu7ox6O9XWTD6Xl37CZrd/DmnqlKhPkUJHK/vrR6hHoExNN33/VX2JLp6FEE8YJetg9HER/BPRy82Br46596r01fpp/VVs93eT0lL3SFEobW97vsqX+/wRXfaEV8UYzp98rSL/v6J6kMV93Z6sd691WT00sq3Je/ZYTu/u+8EN7+2pevflnX4QL88Tzm233yOJpjeHf47hj3Xk93Fy8xe2+iNle/WTu+n8z7LRO3iHX1CXYmdLPDJKf3v1RLCZM+yyDnf4xdZd76X12mXSKdOjQTmd7x+x8OxMtDj5hH6oJnz7Y2y/CXoV2/bCZXeWWKyFLqilZXlX35NPyWUbSP1RZ4jcv3f1W08t589e00q7Iwia7ve7uBF7J37+J5PUZ/CXRJfvL+Z5UCgmXtu7+9fXL+q/7CL66cVlju/q/Efkonr76glFcs9Ul5edIU8RhVub5JZfsZV2Ry491q2US9exurL005I/NteRs13+LKTf5aX5ChCifd/uOtHwgR9y5duZ2F8kLajDNEb23RO+7vpfCXL5dXtyyasbZy/0+EOT36+g6V3v5cks5LY+pLb614Y8QaSHPm/Z0tJfD3xkAAAAPbQZtgFfBX4sdtj2nEQ2vLSaJnL+TubeYteeWXLKUeDJf/bBYRSjzzG48JruRo9PraaPZk9wVzda/yHJx+5h0JeDFfO2lbcEm8guUW/c0fB/14n3XuCrjdPcczDMsn+LLTn7PGcT3voifWlwr5icEecui/bBAQLgKpxoRg4R/x5UJvslK2W47qZ7JeziLpK/k8ifsbovPz1jn3y+2/QOPNEt9OZsezXizBzsFha3mAaYEb5w1xrScztzvD50ozp4t5RmULQQfZjVlkki6L/k9u2jsrD92YIlQvxcSwl5f97l1sk4Px8SChlRvjv/1TdDOU+Yr5orJ1pNdKYHg+7X6osEXhthUoOlyeq1VuCfem95U9ode7u73uAl/axWvl4GeNGE37YLBgo+33lt3s5b2+mg8S1564qzgSPEXd91BuJXtXGB/9jef/RYQK498GZTmEhtW44Xy550uRIuWrIhby+/fpOxfdkierVEp+4KCcGsJEOO37LtQvcFfdvgEv63/V+sgFxQl4cvDbi0r/J3vrBPe3d5/t7e18MG59zvAM1sXnXjnf8aV4fZzuUn5lmM+bhpTEmxL45c+/ovWBfvd9G1R+T6aLKt/x860COts1i5StblHG+62gt8iCOiUCPdL1PaoUfRP5P1/4T36cdgjeU6bGd8/wn4e/3sawVZy19XZuGEJbuvtguIf0mfJ9anvhA3U4bHRet3zrbItOI9Pps99tVSE9+TGBP91lQoljvcpKF8IeVcJM626O6bDmP0/a1wk9fpLu9fbvCLDsPtoazyTqXhvk9ff2U+ffk+6FdS++rFoVMy+d3vT+Zd+kJhDyGzS8sEZXu9Vpd4Kiu/OWffd6tey9Nvgh3f/aSSl3d7W39XGmj93iSAj3Ty25JWuoR8JZ96af4zbu7y+73bj3urPKW795L395dTd3a6x13d3fve397u4SfkiIIvp+eQW/fxXfRUJLSdxL9+/J+vLv0dkI7+qJ3fWXPl6VRHS7VWNQq73AdncFKc+v1hHOm+jYW4O3297fyFp3CXhMVz/Dr3flmKfL6TKhrRzi1REnpLS3iNk7uqP3pvaWiTE1elJMxF93n8JeK3gl/2rV8v3eeCQghw+iu601uY+f5Pab/8n9flZimI33kvVjdvW/RB5Hw2hbA0hR/eRcu/ezj+Xwn0YkudZS4LjQCR+W+JtLurPlumnqil7J21mRBJ/dlyWW962T3ppiiByI/887wuXydJIKkpJJ4o6U+b5v1F93xa0+vfrc3WuKPu+TPJJvO77LD7k4ZL5F+IMaBJcejBXcvki/vGS/7LGn5wWQAAADskGbj0pAK+Cwv/nlGDUpdp3fuiJV8XybmH7hrw/41GH004zxALdzH76iG2fq+w0LjPDfMahz+H7/PXuN4zct3RhlnXdO05i5Sq4I2pERFwl1oZ/rLx22B8q6PmqZ9V2UFC702W4Qjld7mq6WBdZDMDBvTuqLLBNkjlKcCOrNsW1XiC4yxfs49fb7y8SRw4P20nws/cFgoVu+9ytsmEHKnpZff3CHvsJHprSQUIwNe+XcnpUd+7Pby7F92TC3XVOyoLKnfm7SE0uve/CnggJIFj9AklpjuT/UBG/8FbmqoXPLap72ZVvrbKxpmMPzjVr3YG/0kN9Dcnx8C7fr0gE7q0P7A9uxKfO/L7P408b/z9V54vMnOvo34w8OzutnPTSUf/6Z7rzfmvmECK5I+fo9OUUP/wXTw/y1lBtMWzxxXCL3Y7c42SsbIWsDd73mtzZiVe4m7lTyFN7aPLNs41Mc0sFdJfe028FWHp8vlp8OT6CoWlXpAgMZ0PpE7qIJeORq9aT3lfnKsf/J9db4LON3t3E65zbQopSLAvo9bp+EJv6BOZ/aSe1KmtLe5R/7YRN935suOL1dnRiw3BQvpN8X4ev842UX93ppzroTu3EejNeustYu/ln2VTCXid7z9+/lJe/Qn07wJvyzKW6+nN3Ds7dJbiScbcWlqbBPDF+HdaTd+t91VtsTu+8xHft9xORd2BMpevv+EfBRn2ll3VXoTXakNx1bS8FPIOp2+vLT2MGHTtu7twRRx3aIf2T7yd/vcc39LcvoT7cmET63vd7dKaEn5Wa92Puis7S6cEVxouX79clll7vv2mfghjTX/dFIvd4Kcvu773eoRfrvtMfvc8Pvf8UV3ve+l7NrJ8nv7+62ndeSE7u+9+sJeIn6kQcuU/gmLn8S+K3T9XVlfdOWvpvsJW6Xcb/v6EoVve5/2ktAin73Omu3BIZIBBq69b7m3LxO7vjZQr1+MPLz/d3e7um5fYS6Eip/y/v8xW3L+10x55/d37v0J6TLI90aWW9+/qxBbA59uct7EPa0vdlvftTCCwhtJC5PrdikldxlAQfsJeSmHFbe79QWEd8u/cV7/iTuieFEUmYXPQ1TrCe95v13Qsr3acdKD99Ncu1whNV769tkLqVOn+UYR3vbpu73c/f6wn4jL/j531+CY127rpd2WUS8KPutV7/JyftvnkqYWk/ZTNf2iG3nutySEe/Swut8EZn3qTwmJz55pckTN/NHDPkl5a/JJWtZIgue5bPeCyAAAAPTQZugFfBX4JB2PvzGKfi8gvVWfJ9JCe+XMCyS699ZOLjfPztB1+sJBjzZJOpZV7hIlSSXEsCD4ZuBvndApfy7whvcqAxEfL1E7vZP8Vztd5XZf980DpYJN3+EL78qgNQV+vQUCl/9Qqe9+SjH1pvyx8mmlhmtza6PORpfEkNv0b/KccmdK1puBV/YRCAHcNTNcse/4yt/tNC71xL0N8E9c5i9lRiNrX/91ZGzVLPLUc1OpLf7g5oYXGp7UbPEL79xx+7D7zn+4F+BPTz9idJO4TJgBjal/m+TfT+/ToqVVq77G9f9FkvvJ+v+XLkdUJl/vcImFY+oZdFgS6PGZ78Uh4VLcv93GGt+dLdaCN7KHb0P2s9soP6JQW3hruFDqr/9LsIlSlLcjryAwXnRO6o269ExE0t4ZduW0yysTHdP73j7Unv5PlLlUkDV+2I8MpJD/e6p/zZE7sn9aW0Cw0+GDXwW4cs5a8Pf5P16WwUXcI1grGROcMzqhLwWUybfl4zVv177+wpffdhtJh7z794bSunhvqxkh3TbzG9u+Q1dIlBMH76rPCJkLklw7ltI6E37TYeKe9a2uHn7nFRL7Hv3XsKWXH9Hr/aeNYLIJ9v2v9Mo/wBDX9m3m0v8u8UWG/FGMQ+7+8nq2/cgkl3uf9uN1tZJKfqFLoGahlJF6vlYMrS+0q34+EEdeGFjJ6q31Qd+HxF0GVt77kH5vX9XzzsRqsbpvJT8ws/tQmssb0nnKICVqGVdG7H/Z5inL5QZvFmpw/c8ovyx0SUXng+2u9m3Xei93kwzH9Oa6T9ZTUw90u1T4R8SXY3u/5sOPfRC91vFaBNw0YH94ZRZiu3694y32/RPf10qJ2W9wkX98sxLv00jsp3pkuX/EJxuq+qL60S2lzUWXfqsQgzDZnd9R/v/p1120S73CT/F03b3fat+qbcJHe+xv9Iz9jZjvfv7Ut399Xl7vbVYIyXvXkYu97nXwmsvHXPAV3d8E/04v8vq6bSEksvpbra9SEu/q7vdeJ3one6J9JNbuCcmka4w27c/LQEu0w176qEfJe/4Lsi+XVFet+oru739Snd0+ivqux7Ld3pe7LuSMv7pqJobv0nk/X5PL52S1gk3eUH4JBGPFAWyp5ImWM7M6e4TL+bKdiyZtu7v1KcPIchmFB6dLuXe9LeLKWO+WPVU/9qde/c297yfL+lkhIit16GUNe7hVfglNYagq3vu9b7cFAlp/L5gb9PsparNveX9XyFe+ki5FHbetdEwst8Fwq4rdEeHFutoTvcubvr6+v1YmqPVZJc0fyQI8AAABBVBm89KQCvgr8WO2hpoljLN+XNi78XxnPfaDPgoJcEecliDcWMqrlBEv364UpvdsJwWWer+7QfSaHDeywndLyuXeGOWEA2p/48uwI0eHcj/pRPBJKgzBdKEnr/dFPe/wjvISSdLLgIm15v69qFfMbcEZWxivcb4w/zwOqUkgCRfT4C45Ft8WAn3u4I1YNt3rx2Cb3n0Em3xhei4Jr6gz+zLEqOc4l2xpMZIX5DbPMn733qmUNKwY2nPwJptinQzB3ZAlH/Q7SxFdRma13d+1ufO1pr6uHiz/uNEw/D2yB8vNkC/blHCPdj9iVtcDb7rbQftZ4TumZ3F2BXEHM145M6KE/pNNxzhvEP402xcw+a7dP7YT+vhuF7T1soMX/HA+Od0P3P7NP+jx3dx0KHvhpJWwcbDd9p5Y4sP72n43V7p94d27z5cM44aDcDrtScWpZa/pySQYUHoy44RejWjv1L+qyoZvdxRvzRb9736QQl0djPP5AQu5kO99q+CPhpczlSEy/3tihgXau7CLzcvbjr5vdxhL2u+9K3juSOqt9VDJtX5fQxdYy5dy/T3gqLDfhtP34yVv+eQdX0ee/t94Ivcl92WUss5M3+SSZf03v39+T0s/NsWaXGLIdoiDtfQK954P1FDRe7QcneeEvBJd3+31QJ725bP/h726Ecsq4SP/RhGPsTLhSSf19jWJLnnKHz5sC/5adLfvS12Je/k7tkyEh4Ycv29OJI4u05Yml96uCOHhOzbl2bB+xvDjpBHwsKuO0rb/v8kJY1p6aPHseTlXI2xp4/76Whs3hHxPNLikH5QUQaGXCiZ83C9uQGH3eDLykfX9flOV7uK+z+hfrBHWP917Lt7bBbkH37n/2mypw+bw33xXMQZF3eqw05X8v+VKY7eoS8Tc/929fX+id+ScqsFs+Sn7L7rv9TFcvuEehRs9EhD9zxvVwmUrH0r9aLs6/y73v1BHd7vT1fsvSa/WYt77fbW4Tve9/ahF/mNd/bH5civN979aLqt1aSi+6N3fmhMr33vaZ15ParaJ9p/CPhOxub0nvvbUExnuXH7vk/opMkbHCXvpveWHZ+T9ZMRdlJ+l3lEbvovSZYiYr37spH3TXsEhIEupFf/u3Xtv0Y7HI+EvQrt+2i/ye3+/3mE7uktkT3fvVL37o0X7I95taelBIanBfi6Gdvfd6AnCXgiKSIl8If6JfbChLd2bVt7n7jvu2NbtFv9md/eExKbvz/vVP35PbvNv1f9yfKUm716ICKUuyQQccTXkF0nCj27BKaAkdPf/j/xu/qXddF9fXk9bySfa9IWnNOUhvckK6QjPm7d+SFTWTks7kyd7HaX7/8X3fd9Esu70l/teZsrel9+Mxoa8QStHNS9KTvjLJ182CyAIRFFABRQAUb/8QpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWlpd4GIUtLS0tLS0tLS0tLS0tLS0vAAABE5Bm+AV8FfmHS2a1DL5Y9lw3+Ls6kJDTR+T+/LLL3KU+Xc7n2GPNnHY/cl/9wiQ+48XxAuU1YAm9T+f0Plng+vwxGI4tqA3hP77qZjfsMLYXKDA/2n9wYTXyW+TSpRbX9nhrJuVO4zd+k/C9OaWYkVRAKL1jWvye6YmXaQk9Mr+reqrGXpywonJb52LHQeu9vKR7OFn7YRIFwBUtlj0pYOQm/U+GZf/cbKaDBEZk1OLzDo+yWyCF6Ul/bKI+UfJD3fRfWwJZfv3ElKw+yohx72UZ1+FCx4cg7IGzeP7lwSf1mMQetwQokVUrELHT7fiZEsHbL+/hAhE3AMjvVbK/3VmJhZP54ZS9ZP6/LCdJvhhEp/3j5O/wWlMChilgXd9Pk/Wrasf5TTRrw4X5ff4Quxn/b33Wvxd7mDWURf4I9skrF1Cb9wiMEvd3mZTU6E6xfy3fxpEKzC/QxnaWVIHfEjXaKghA96XAieAr7nWJwkp972NK7UeqyeRa5EaxvaZUON162t1Fgl5oMPbsJGJxnOvjnPlporGFRd3BVHiv8/hKsCYc9Z6fxdwaG1J8H7mS9pPvn6te5Z1H+bw7vvZ0CM11R21jt74KOAk967vbjdnaEvW+/cFl8Ivvw5o6LfgyvXBdLJn1zjLR3dYTNk13+ErUGPLwfBN58I/puRvi6xheZc9MdLTKT7a2Off16rcE9rfcpHKsvW6kLaDjlPRUCUixo8sWQeFMa9yFxSRKgr9kXcfaFin1JcLs3e8EJeCHI+7tcuS43j6X8Kk4Oy5+H9YfUr/2NYKy17vuGL7W3kXj+SCszc3G12h92hLD1z2C+qrFn5dd763IF3lUaoEm766cFJXe7njtB5Jh77VEeMgRP7ef5V4dQYjWW7veHfVrhAR4P14dSIc00CLufvrhHwQltz91aaPVUeCURhLp76nv11QJaV610I5ed/dfWTlX6+shTlzxvs/at4LdQLQ6feWkHYkjr8/y/7XCT9oMy9vV1r6sf6rr7L8jZcve9Jfct3eiem0pfmJd/lWtuVa9CWWCjd33fD3Rcu01sd4JNt9aSt6sapLzbvWTl//YsRZ4R0woMckXve7u7xXdvSKJe/dCpyTcHb8RDXFLsb7yk3ff31dutPJ7b6djIJCVvn01mr37O94SWcfizS/e+6ksF282n815mT1TLt3KJalX7XJ7d1jpFuxy/GeixRY3Ody/19/cilBtOsFgjd+WjvYYmmYWT3/rE9xlToL4TL5lq4y98rD0j9vd3e0kiotycN5uu3Hm8n6yWfiz3fD0GJ+xsvnx1r39/ZJe71TYiII+7vZ17CnQjL/njrLwSksfnHbnL3ra2kQTu+0XuX2+/v6NyencUpiohLvhcvkv5Dbv1BHxW6Vcy0lqU93fSXZCAmlvS3NpbeKlJ9QxkiMRzNspHL6m+I0i4W2dG1L7if6zcQc8x315NvPkTctdXWZRcFcAAAExEGaD0pAK+CvxY7ITWpQVD+LzZmsm682azmOGi/+WESTglJ7JAOQuBbeil/e9VDfcIQu/Sbdqdvz2UksddAsM713CmXDBbeidgm9I+c9Ew324+99zwsBD9S8X+oL1J8n6+JbhDl69syBoqdze+zz515WHOWy5BrLf/MRy/Cvm8BZ7ZOl/9wiS8MjaJehbwPTF7caM+C7asvG0wh8fdXwdkAs98SBN66ttouEwukDOlo8mGS7fsa2rly9fxqPqugqMLWpM0sO5q/8v2/Y08jb/RFX8LsXm0PbgWht2HX0rYTDCO7BQXzwvKekvohFqbRyrfL721jCBBxzrjI/rrE3vnye8wpusaXdqsgaXjRmBGls8ZdvaGMCbU93xw+syjzXsLrGHcnrddngpux+9NWe/eUVLat8F2zE+SWRvUIbhqHL94bdEP8X9oaUZmritDbLsVlbMKO9G+XHvf3+M2+e354QksnoZ87+pbf5eH+KDCb9sE44Lgqezx7+9uPokt+4wl2nGIeLiU/BNi2pdvZYSVuQ5Q7TUPem4ws3EOw1sFb6K2H1/s+sC8Jajxtk+0t6TwZsaCHxvb+EClb+GJfXoV8m+cfqrFssgCLDQO7pyXv3Xf79CYnu7v0kmoKDYYuib7yD+GT9X/Cnd72uxhF/nbG8JeG/Lnfk559/ir73cbi33BVdaIjrDb/nez6D5KdLQ/juHcifon5nhMeN++6twxKF9NgXBZEgNb66RJJ4fouT1S93CHmq/IPjYYXnXA70N9eqL6vMe96szsKEqx/R0aEZ69I3O33C1ODJ6S6TiIJtTiFvXDokhe/5Rd7hPlIQYI/6Fw0KMaDmNa+dkN9z/J9dW+CsuBf5JkH07HEU1pPJGGmBYSedWmRdH/nN28ONdRYbET/2SUuFr7+K9lgh2V9evqCM8gXb6/BLc7PK/1u86CIh7uNea69SDl+38p92wiX9U3HTMv7aatqn8EV19dlaLXTe0qcWIgjfU0m3by3/MgXFD0XF/dx19Vp9xNKwuAtXf/N3en93di/FXd976Gkckf6EXfdijYmk+8Ed73N7H8IL4hiHr77o/Q1hqT7FXffeX970R/LILll96f0C4Re93fEyfVf44t3d93u9rd9lVeJvFcV3hHbGEP3cVvdtz49237de6l9oEV9/3Q319nq+k60U6Zf99E13govfP/+mloE/d3e/a9YR1BNe4+f1dzb+0xJJX1WNKbxgqGlTVES9fjvf9E7pUmJuCQjmBN8vJCBXcbNDcCHdK+zt/3bv9G23hJ9zmI99/guu+0Glj/T5PS+mmhwufKFyzeMof7abG/wlKhNm8vf73P/qI7ozS5zd95L6K76Nd3eT97tqZlw0P3a3f4fFPve/CDgcwvD1F9apO8eB6wl0TP5fyII5q777vJ775JIJuMrj2whA+8kGNThfrv/emEIk73cw7fJ7tFl5PZ+q2he77vrchDb35OtSRBCw8/8JnDhfThPxUbxsQZp8v9kkglJa2oV2hQfB25dtSlcpzVvruTdtL3eT22v/tLtNFPa+gjj4fe7y47/7m22PhZZOExDo3cZlb8+xcuOKxWnc9ORdqCM93nToqBdffdIdu1Cd793vESerZw4OX8CPAAAEmUGaIBXwV+UdxuI+GJb1rZr+ft3v1uLlx+4367/LzwZhgv/2N5ojE9GGV9TxWSPXkIEra6YGIWi+vbv/+HoMVfdN7jP/uNgVf3jvXXCL7Pg1NOBgZqpXvv/+k1H98buCJPv4ZFfPh/EHlfPWNlU1R8bz0e59pNots35x95++kD6q/whz6f3hJ2qbwRP6xEuah6z/5HdmrzwuR92n4vDX3/FneN1Twif2S+pZWSHMlh1JFv+aa/oIEd31zRts3/KcJen9wqX/ywiMhN3853CllGbgI32tZsvI/4juN/4I8y8Py2wwiJNwyDS4O6wRp7z5Zv40cSrXcLskXxaW8Of8fxL774QLoJ+0g8+PYPOm4INmZ/+4QJ4JL6hg7YQ9Z0HmbvxRXjk5TrR7+bL+qtr3eruFH7gsECXu4CbX33RywH3AC/q//4srvnxtb+woQwPr8eaRh6mzEx9beSuouj78x16g5Cqdrnvur1U5e40sheYZWcbVXpn35d3Bryeq/O+K/UkXffgxGo5oLbNBK3EKakmL/40rjpk6nV9Lwg9NoqFcpysjHxJFcS9phtMwZ1oVMpyNf/4znTQXu8qbmX/Ht3J7mbdXY1j74+cMgGlPfd8v7qV/gr7vhP/EUuOrzHb6CN9ykb/vf2buYNWp00CwzvyDMaoWszZrpHMdv6BRd88A3J7xu9btCXgkJY3mt+4Jb737bevwR5N52u2wVcxS2aes+3lHwYK95b2sXIuQ4GiLo3x2lP4zx8vs373jOzprJFm0oO/grO8g/dXjLyp9hdZ7NhmX1ePbG77CXQE8H55FT70nBT42+/cdGrfmnHAS8O73n/cZpyiuy/f4q6K76qisEREUPB8FnaTzxhYyBt/d03YEUkZv7ca3Emu4ZlYa+dREb48zuJO95R9oZRKdNZ/uCup/yr6e7D4I9397ghLu57alTjM8XuG4NZsdRO8aKjdNCZYJxDzqHCQUXmfue5j8vCf1uQ27/BUJM3Of/hPx3nYGuWPXR4IZWP3WPsh9fuYM939V7rEXfZlo7ztU/k+vXkBJe+UI+YVFX3+Ezm3c/6sbyfvb+Ez3d3fS9+s3d9n7zItSCon1k30yOQPeqXulBFlX6hEv/WL0r07/Hle7u25u9ZYP3cqnTuXrVjyFe79Zr76+37p3e8v6qvYmbd32RE7veuCG99QlzgpiHmfEvo78OYJuZbdyYk5caef37ySh9+lJFE6yEffkm7vrd9dYRLu2u3Ifva9dHRd32+KihF7UJPh+mWm6qCSv71wge93vO+nCS2X9x978S4GV2SL4X79PyDC6urHkiVWn6HXWnal2W7/Xr2spvuI3fy+nOlJe78UU276a2t/ijXfcoWHdf467u7z/d3CXkw66zp5dhHd4WTLeTDHKqwD+nIeX+sXfV9+bWTrd9/JpK5Hd+T15ssiCJCjdkPN970r+sKF+XvBOQO1O+azZBuvCYksfd+y68pd3tROuirapk+T5MLZI8VueBGrdmbb8kIUtJ7vlzyeyyn3eT7W33ye2m9J0736khrJEZSRnamp1ieCHy5M3iLPnyvh74yAAAAS7QZpAFfBX4JB2aiphl/3PLJM88d983d15sfZ5DPi7wh8bAR2l6deveunSrKr2xksr+HxfEHpP1rOT089WV8bwO1GjSSLUrthT2WBmhTObGB5IiqKTSCqAg57RWf9F+vaBBfSnO8OxYEBVu/Z14tmb7uF/j+nLFk3c7BXhHgZUoqMLr2eMPcgnvfhY3+7ln8XerXw9KC5fyXLFkvd3uFl9gsEXuK3P7bwzR0x5Y226HzSLeYmzV70mBO7mEghGkllCyKjcv8O4OYu0Q4Rd4UnX4x36CBdi2NvBieecJ8dOFtCZUCalkz3cCW6gYV7Wh5yp1jskdzOUKySzFaSXe76SrCN4+eOG28f0lf1k3vVe828v+V5dySwn4o0OrgDqyjZ/P3ujeB9vqThP+0a/BKrnV7eNIrp9nHxsSkrVGQ6k6fR51QKJhqH9QvyllFq7KWF6+xpR26e5nSlPo/xE2z0w3eI5eh1WwXcmDX/SWWc9YGNCy8r066cENMmk6f77ocXax0VpnlOLXi9BfFV8FFN4J9ib6aRx4eHakr5N38m0qJgqJe5fSPW6Ib/D6IRYZMj2orDLSpqGPEPsDdpbg3TEVJTtcszLYec2kEvBQSXzRe2UpI7/BVI/v/uQNu9K/2T6rdpwW3t8n+yekr+RBet785LA6n6vL/HPWruNrij9E7NAxmgen5nThB0c070K4O4VoOLP/r8LfnXAV9dz5vfcio+zPlTiWDfy/7uCIuNR5vr/y96r7EkyemmWVU4UIUsRVKH/WhkbuQfG8dy8o/2q1G+07sg5Lu+5jj2VbITvW/uGpPv9VkhMbzSB1+FnGEfKOLz7dbfThoiS+PonX6xB61cJlmGM/90fx5sfFpRSR2MH3hiJaSIH0xNhH5TqaWQ2j9S+k2Nu8++y17srKWORfyNe3WWCMQW079uuJhLnElu93vrKe9/fR2YUcLb6wSncYEj2j65/fte7Jdi9t7mov6IV7uEfBIaf3i8sYd3d8jdv3Kx2Lf5ivd+l2LYQz3u+73t8sSLe97+6EHSn6lvdmkrWIJCHznt3fZYIu79t0uEfV9/QUKfZ+Xvuf7purn57X4m95I76OgUld/N9p1MiHVhMjvd7+MVeCG95al7k7urKwjeGly7fdevvJ7aXSqK7l70T7VWnd7hHx1W9x6nk/WW2M27j+J/dvb3d3l982cce3P6Vqln2Xb1c3jphb9jYgj3vvo8tt3yfq6+W+8vq9L6RPJetmvfShIs/eyYcf5PXW7oQW95fcJeYQfy/fuCS98tfZRe4bv30eKyv8tH197z4ZV20iqXe9Un6pP9IhXvVKCcVd94O6gqZP6bUnBNxwWt7pz0JeQsJeer8+ECcuTwxmqUzm2T68hfE2qs+nBKeUcRnYWzva5Ikrvvf192Ld/XuifsbZn3vll300ESDZhgPgm5n58a7hTyT/PgY9fpAqNjfE3XlaB7393d3Km7JxQl3P+96uVLzf9J0Jlu18nV4kmq6Gb3lApYXTfu/kwr5LlybfhA0OL7fPnMbt35Ym+U+8X/yaXkmEu96W8niHPsp/rhfyEjuX+I43E9ZOHNNKs3ES23PIXar8EvIRHeLDmcVvJIWP1HL4LIAAARbQZpgFfBbuUdNhf17l4xV/F+G/V3DK9wQah7eVr1Ag+D3p2qeO/4dSwi8YScN2YhxJag/cIQnWvpvbAaE2wtj4Onw/dzJ7u0P652V95fk9q37LBRPC8o4zOL97F0WrqYkNQ5R4V35UFzllbGQnPFbf7ZDd71cE2753RX+II1fhq+LCte/sFhguAKk6p7neGhchT1AlersF7X42RfvzHhfGraJvP2I/y/NAJzy7dxbIfunL3LptKW4bAtdRm+rDa54qgtMxc2n5PbSO9XGFurG49UXnxhPfcAn3U+H86Bxoe5H+n5Pe3p8PUxJSaPANN7tFg7DShD5bUlbS8odS+WIdwn3/XTi5wehLRWokmpB9EwS5Pvc/yR2G9D7gh7TSK9NHlha8qC7edJev9Xta+gnktFvH6v5eGUp2MJv7BOKFYJH0k+vd4y62nZfa6bGG3/xC6xqe+qgiCrJFwxKN8nqpbvQKS4iq0NOeP3uUQnfL6IXyMdd4Iud9hosm4+d6p+l68n67fgkICfyxl1j++9oIy97u/lhPwl5pkN9eWEeOE5y+aK4rvv7jcIocw0uEb/ItzO34bhuXqslP/BVhCpn21Y8oloPAvWsKLL/vgow6S8oRqBgllxY8bP1q2xrGW8wVmGhw7zCQdofVFi3Fr+Bf0djOvQVq/BYXc6RFLb3e78OseSYlO+7T8NxL6sk+1pKCmH21UeW7i5RzjZkYNR3PwR0INu3enDhLw7l6Vv37otfHEHay+sqrXgnMZcKtZ0KLo6JJxly8uN76R+3xiCJJ1kvLEvuBde7669+nrwRiWrvW9a6LBHHammv20tQUEDu3CHXnfdhJF/tr7O+4R9ky667Ym2/d9EoVhr3qsQV37319VVP1ghjgff9t2uEfBEQ/tXay8EJ3P32e0GxIzKqt1yxX14qOVfDUROK/0b9Jf6zbv19nlLd+shHvpcknd733ffx0IP0UEW621lsgworu93mQR4/l4rvu8vrtuUrv/BJbvSLRaIeL1rFFe73SLnUIbv03fe0nrr7wh3RG2vu+8lfsxIcdj38T8kIea9PfajMdroXRu3Otpzf3Lv6BMcRlwy9b/aYNiS9WUjx90emyYz2kqybT9e0qXr6SBER92bVcEO5KNk2X0kdcp3fCSxI/BIIfLr5PXJ1bW4u/2tETE3f0636Uvl+q0uZ9Vk9U+TsOEzwcqf436/kBMVKUfAY+2q3PqRBLyYyY68N3GQkBI4Rl7jwcJKlR3PL2Neki2uxMuH7R9/fk92xM1/pF7fXaQevuYBpkqJB2Jw9w33fVf32Rwn5L3HixrvBSR8LuHBLJ/Nmju6fCJ+T2n5opy+SnIT395SrTe3xkppt9r5oQLd+75/v7JcED5+03/fCxfvNNHiHu+7st+mJ5JXv6lK09KlNzW9LeL3T8c/eI/kjjzMPdzvsiIcM5ESSBJn88iCs/pdI+o2Er45/irz7XWCqcnvx9+nzxbeIwWQAAASQQZqAFfBX4sdm65M/LaNiW/LySO75i4Zy1hgv/lizXNICPlerc+d8c9cv9u4uk4YZuRrgchWkYuZJ9JOXufrG+/yf3+J9iYKNzD3KDxlU0S4qa9wTF3LDkOkthuLx5YJb12+i5S/7TwsvcaIveOxh3hI/vcEg4WwkTxWe/WFm3AaF+Hpj5fLT3G0cP1X4XFxBtRVNyjssnhQLPm4Ba2u5ZZcvQ3YozyPcNH+yaT+I7otYTpbhF3aGEK4rW29ZxaT9XVvGFE3UOP1w+51HmS0EV/vDJ8PCD8/cbCXstHUAG2/Jqt26HbwjbT0Z4eHR6g3CTEVvO0OzWh/9mlA3QjzZ8whYYQvnOlfG7omE7Q8ixE2La0KV8ORbnjrI5cG4h20zVn6p3BTSAI99v/m/V+A/ziQ9pb3RX+M75vMFRsSHmC7m3Bjk3cS37/CGoZXkfxm9xvJ+vsZd3d4r26bzOf7jc+SOd95e5q8ELopeOxaLVX/5Z2zH5rCZf3ywiKFbisfKzhdnBWr4ttYvvChrZrZzoN02sW5Benm+Bmhey5bzJ6pFe7hQs182d3YiOf8SldkSbKJbcgreHYZ6+T9VvcEd5vhh/WqzwgWH28MQ8bxl3uQPGfv9OTckfX0/Ynk/br/J+70koLCLUqFQIqp6cOspoXfEhbWudr7DEoat36Xjufhe4aRGX9cnReE37gpI9Le3ef9a3oT8JdPvcPY99VR3k9KqyXKQoLlC/mRbaY6CTPwXHMDp4Z7+9xVmP19Xfste6XpVfqhBOeVNDVe6doE3PT8PYX2Qp2CE4QbKvvpsXcgoa7gu3CZRwdmlvW+M2afFIKG8ZcOle+pH57Zwo9Ew25RLxK3nLegZ233gjJe+PzQRnyvDCVlFl/2su7/rWtcYZ3vhWW21n7ku/aFHROf3L5eEeikvdLyyiXyO3emsi/BLY0YOD3Zs4MU3WT0s38EQq98xvFJcv/ui1q8b7G9v+/8v/yQj5iXv3R92/xIle+RYzkT0/LeybMv6/BEbd66F+iRZbve/5DPe21RUCEsaEj9aaFrEXd779oRvfdwk/cVe97u9a3BId9SpuvNe79xJKV+f/CO973u7ptVk5Y7W7Nmf9Qlffd/sST+EPIa7f4TJKpgmPr1s699ZW0OFlYvPMws+8V9CWiGTrLIF6e1tdUmeO6T7u730Wby5sbLfotHgku+VPp3u+1ye2nqZ4WNlycDHJfWo/L/+ET7uOTvbn98JeCQRu7ZPqk78EhS5dyi7L83rIe79fW93fm9U93fVqW7feS4RIeHu7d4EfWX2V2RiNzLW94TL+3WO4LuTD/LLOfbv8Xc0XAXoqnL54SYu0ifs/yRJ+awRtFK3Eu8iF5+3d3eT7XEFEu1TDTk2bdy7eQuQhingGq/SxnCnk41m9XWlghM+WIu3EnpbaWX05BaV7dkjfeTy/1vySYB9tkjhcvqn48iRzuXTr3DzmPpnhWTi8r7mvaSJ9J/4sr6n/y+Xk5fPvNGFP7uY/33L9y5epE81yX9lAG27dTthH4ZxEggkDU1qIKfukweSZbwWQAAABOdBmqAV8FfmHZqEy/L9/zbojTr3F3a92jD4Y8Emrs0U/gr81II/G9Jx1lTepfvwl3ZS17Yf46YeaWGhdEhJ/Rf97LJE376N50rS7hKw5NHZvDDmyerZ6n2Ot4USs1qFv5xerehuUWVg3PMgKifazC9Ram1BNrvfV+EyFFuQLSjp2GPk/fXxh8eP7qb+oJdWp932mdnku7/ibBMivRvit1/LeeEKeY3DDhL/5YIMEdZep5pWSKht0vZwWlyKUYbDLF1f7hT9fjVABKvUalWJx08dDOaeYu11Q9mjd1rwn/+pPrY4t87HWBukz3ix/usreuo3b/D+iqye0p6WoU+6NnVrfe8SrPnr4x5I4E3s+T+ilVm4yMix24BIkffELvAw8HUo21pnDNJgG8ivuEcsSwTM1DOz1AlZgc48cM/w6UeYsFPpoal6eRGeRq/SVGzN85pO9a4z8bVHvDWiQ8x+pnU8WwmeEbYSfvudY7cNwXRvTDy/Bb4Py71vh/XmMBcaDV+nH6llfxnpl9rrEEu/P8v/2U8yAC70d39TCb+wTjguq+pCdPBteMW0kHcP7re02FDWmXSsXEH/+ahd/M2d/nvm3/HbGnW01X4K8LPltuGc6DIjNTZ/Krzl2/BGfDDDu5XiuIhoiF937oqfTed9YLcV5CxC747aVTPJ7pl204LDXe8oWcNy3TB1/qx76oI5YvY7jwykU9VWz1YjcI+Qi03+a42KG0rJ+k6lbghxspSTtJFbrn3vh/kkrSU7z68LSBu+zTDVuP5P2txrLLWYF9FgrKRO5cKC97nEP2tX/BHzpnbVerapMqBKRncxN74X2504JtmbQdYCNXhgKOi8QUod3ndKG9YXEGNkWyoSk6HLBbN3/3iSvFL28PYep8UhRsM2vaHkUu3hb4/mpLUUJ3fGTFXcERHlXzdjUCgsfZF7SV6eqq1vk+1bOSkimLIfY++ThHxxW7G63y99VlO9+7ye//gkFHC8gdfD8EIkw2n+VR4vu7v/BT2GNnh3ujornqfThCVj7vTkul0gWXHW/3ve+WT1rfx2543vt72kW4J8vp3d3a6bKJe8I0SEBjnx7csHt9+TNR/VdfYt+nWpRZWOEcyGQuwD5G2K7puHawKvvXS/Yt12Et73v3usvab5m73yfW71iZiz77yf0MvlT3d5Pq8pJBPJ70f6m3uEtsKEdXTauiJU93yBZ3u3aWUTdD21nknNgk2btjX0uZDtvu/cPwaXk+qy5bBP5dlYs0qvf9wVXQ72iHOehR60/qJlEe401u9qeoiVA+WjsmvfYuOI97nhD6N35Pvl3UcTT0Jx0hdiGnbk4LBNy7u/Az/PQl2CcU3P77vvzwiV0T09d0t0Qr36cvmjsXJu/dPzx+ykN66+zrd1ghnzim7/JBII4yhJ3f5bN43CRfr8hYaHD+gkS3XnhJ7SV/hC97n4rTJX7t9pdGeT+xnWyicr6r7TNyb0IqmPL2pXQSICb9eaBtyzcd3fksSglwvhPyCDwzs32WKMUUdb258Sp/Fi8t7yv2LXkKST9NJfdDOE7aelvXTkonvyII4wJ/cvueGh+rpulfCvSptaWCEjoncwipXu0Czmc6V3YjCV050eyxvvfr1ku/J66RGtAuK+aSO6aNfUlI+M8M5JCGpfxEJ8mT56/fLj8R+oe+NgAAAFIkGawBXwV+CQdHHIbJeUVeWek15f/cX45Bdv8XkLmG/l/bhgv/uGMthm64AxPSrbXhDKJv7WLeNf/uFITp4rzw3fISr8FVflPDolI4q5FT7h25pBG8m5YbNF7XFpUpVwUimyIf5f38Xvc4hAC9U3bvrl7p9wmTdvLgdg2XxxXc4vFd9JHcf7PLfbk/f0s1k0ihZfYRNH5x20Evd3KZeWz5L5brY28g1terfaPaTH9XDj23tfGBA08+9q29Q1EH8LVCrFbieqGHf3CJgIb/KshyVHkmMOOdY/r6GFr6bhHjyFrAtdbiKwaXo5ZPlx4wWPIgvy68IZQExL/cbSMNzQgT1qmf1UorUesEIRxgn8MxWWglH/zLNNGx1E4/cKYe3ZhbUbgzTD/C2Vu5bVwxcbsm28jfjLkDWN4EN+B93n/fn+OA5vyfVZb5p0CCd+SCbh6T+N4747gXjJ62VeeK7vu3e+H7orl974CIa44VL12w1H8v/WW42r2Ey/vlhEUKN3fjKn6vxbV+l96dxhD9qhIvsQpFg7tRl/5n7M/HS3Bc3ZpG2i0npKV7pB2Tt8fdXoEW68L9tOrZI9EzhdQj+a/h3Uvyeu/qLLhK4uXu+iwSyTyx+WJ0k3ye9NVZxJDu96vOgWCO8MpP3pvYOhl0oUvcI33zaGETuYP7hLzSa5+mev8FVNGta93n7yNPjgaVNd4U73DS1BvQ72dnueF0xe5U6wRR4HNfR098v/bgpJ5w1MP90MxFoXlAMGk3FsJdyIRqTUvvrboExz1e4I+3/Xe52Nj8k7MyZF1tObnXq/6LJ3fXvI2hxONifkvPLYQ/+oK+2g7GbJZ3QbiInd4S8uja/h5Re+EfCmXxk4P7Iv/fy/1rJ9+MDAIt797bvv0hRoZlIFUT+FrY6UtwTlwn8Ct4eTJH1t/Chso5eXTJx4RfMDGd7ttRefx9K7o8Ex0bfNuF6MMLqe8v4wTG1JFei/UJFfc+fYlE3fvBPcPLke9zBWtNFQiHRFvfEp65ytUrcz//bKd3l87C8IdAkDFXv+bn38p7zhpJ9wTinc4/hys8h5hdLghE1pan/1ghEKyhVgy+/m1bfSlPu/EMEWSV48J+ExDv3l/LBaer7sfXR2xO7qjxWRTHYV9LqzGu/V27+ujd3vto13fL9Lp/IxOfwi/x+eL7u9z976ibbfd+ryetWT/RV1myEZRBk9Ur/6P63Iez/4S3enf8FF7u75Qfu+/x3Td7u5e+EfeRmLdZePIWDuJWHYW4JHxdntY33RRLmMvCl90ndUj6lJuf1vijvu9305SFr/NFTC28x9+6/J/Rvn9CUSlv60peONTAKPeZ5e734iZf48Td+7u7nwJLOE/ycnqk322O3DsJTrG0bHbjJzontUL7xYnctN9yVfShO1Olu/pVPZPruXVmd3eT+nTSoSW7hDf9Hb5NhMvsvUokmK7u73UiRbjOR+nSSyev2qlFoc/02NpgjM95k7UQV9M9fvfKd2vY/1szu/pgkt6aXq1Lb6chHlb9wnkhrwwv4yy+39d44jv9txyczvR09pu6yoIC3A0sS3uV3cvu09RkfEa25vd1vXY/o12973vv7wiSX966byeml5JIIfFZUyeqVaWCEr2nTm/wnvd3fyOFfREuT601VQ93fDA0K2NomG78X22380P8K2VpFs/xnLY+nN9NfughNIUV/S78RL0iEuaTnjDOSFZzKzLyPDL06+O+/xGyCuOedT3ur4yyi4i5/wWQAABQJBmuAV8FfgoGZsCNqLWOSkYqX8nbFy2HZ6jDUeT5q7K/4vObO1hpcI1F4vd+aCNdwuX/3CJLRNQJvZy5rRl3Lt2/X4Qzs3KRKDGg68Ilks7hG/MBgR7Nyia6PhwFucPsEpfL/FXalonY4Cf9r84k/TcrLLG5x4ffG2TJ7F/0CqQHo0WYRT/TCj2xMlpek0RKF37ggIKDEMA+PXuX2QXX4SKaW7v/wpX5bgkQvc3jG6puOOYVHcsL4pJaMMBd36/+7Vb90rW+bD9vS1qNllxAQNEFPB0ebW+1ytrexlgQW6qBZsMb/7ALeD/OLS6YSLn75fdusOxyj08EQ2PaV34bkjpLCVnUaRP/VWVDCuNtmpmfzOw7TfMmiP/p4N4B9yG4dDKGYGqS8P35Yc8uN47j+n+T7Ul/NOedd2m4JeMhacqd7lTeW/uKxn/eNByv5S5xEI9IE39ihgfFoM7KcC2Ci/NWCvtwJv6yV7TjSZ5GfsOmZUNfVSt2Ggy5sPfTXeaIjbou+iNsOWqTqfvzavW9jZN5/HtW1OEZ9PzN6rZCTd+1j+dUc/anLdAH7md4dq1JH9P/jTq+sJfffWtRrDcF0T+cE/ZvuhJ/M1lFuKpZ2MSy+beNK3a7w/fKEPgGjQ62/Haikrphzc357Jq5b1gs8En1gkzl++nHBKHbSeJghLwo5JVrD/mvZLkmdGp+v+sFkiB+cHB+95vunXV3fk90z6KqBYIzB10aSDvd3B4En0/Piye7TNKfjfdGdjCHOXdFsIRC9wm9of+5tcOziUPwm/bFEft3c03tOqKwWzmx26W7DrK7tk/S23opLcA/+D53YtlzBQw/T4yCMps/+97lx5KT131Ve9Ipd39kysUrVszBKaa/RBT6304MMupskaN67h378J+tfglKSXldLrG66IKhnmWL2tkQKzvO++TERnTKF7ENxnLJ7ddZGKEY5U0WcFOVfuU4R4aT1DYYgof2V3vV42KM73mDt9H/hM7vd9+vuETXBN5KZ6Rg8U73bivv5zHz+E/8v1FG5xl71+CETCRgPFfpEqPJN/rRH17gi0n9pS0uqBd3e94tKpmvZf9ZoSywV3d58u7t7kzTxOqL1T4ISu+zTn2vb61nZf6fNL7wlkjCvhBpoJvXn7y7d7v23u/2Yu77Gs0/10JkNd/fJ9pvvZj8cbJPVS91MR39YjKGofZgO/6Snt1lit7u95PaaFy63d8J7Yy7uXp7vbu7vl9JIllBCdu8oujoE8ien4/IZOkykffWY777EyEfXmn9l8/+T1XdV61RSV2onL7nf9pdAhstz3SYw2CFoz3733d3R9qlwkuXBJGadTuZPryVVWPEevzPu1c6xY1q7rvr6+n60Q4vUUa76yneZuXyqIS8Mnd9z8WX8FZB+nc+BItL5NLGo1ujtMP2p/J3R2WBLclnOviF31w3nz+mjtsJC47lfhGb92CY0V+Xu2ZH03yCyjPJ+pag1G/yetq/s7pevSyCEJNny9PWMdqndd1rTkBUTdz7eCDmnh5d56FPJL31gKf7061xxr1Fu7xoH8Puo7c35PvZZFsPjUDle51ow01G9pjSr5ttyfeKJpWP2ivy5Kdt6W9933l3JH9+0Cgma3n72ct6da7Ut3S8mFfBFivTZfJ/OTXPqfqhHv/CZc+ea5fNfzT/15mUuePZTQxkkIPNH3miCnlnH0UG6/7IeyZgrgAAAE4EGbABXwV+UZzZ8XUyDTVfxcaaym42dufF5dRz2GfFkcZ9AM36lsu+fbCt27QMruMhOul97Moo2gR/Hd1xhC84n/CXCf67wVW6olUFVyoMZ5hQ95YTqr63SLSDxKdI5cJdXlgq8bo7+GpoOBTHgz4VFUn7v6icR/6VJu8vBgV3l+Msd/yyv8IVQIJuzNzaW72ws/sEAgLip77uROXkrRMPIXR2Uv55B7sVtlQUiUA1u4i6fBXq2/1JEFg/fzVdEn+fb+/JaqThdmZDq3uTo+f5xcjnr52ydS/27jNB+EClSy/0T2j43u84n5QKdIxOBvcFMefFO4RIjqePSwU+xGRtHlkjYbBRM6rLFlSH7Buw4wWaYb7rwpKZjRXw/tm5EO+hi6/6kWOmT20nXwluQ7u+38FWd5xNE7iQkfc+xleBzJq3Kg/svfbm75WH673+q3NwReOpfL/vlLKfIaJFICb9xQ4S4KxW4rGyQJetq9hQj7EHZrqDLI/G/0upcJS0W3r8KQxXadnkH++F5gcTYiUddxixnE6+S3al4fLieeKDc4t35S4E+s93dT/1i/Jw86MhPr/0JTmryoFgq39jcgRILmnzP9+Lw72VIlK3wwpoJeCTPxm5uL9ofnx8txvl1pQi0k24LJFIhXZrt+7fHy57KCZZP6/OwU/DKl9Aivql0QKsxzu50djecsk9ythuHfVHBPufTAkv+P32Tmn+urgffhulPGdGV7jecuOpIzGtMbtuiM9i6EPHd/+vfeHeFtzyvfY1gmn/ge8u6CX+7nR+rz1i/MW7uiekku+FCZmnDG2zXil78NJG6b6fZPas6aTwU+cPvxlNuwRZS+Q1OPMHTtPEkKUXLxxcI+CMIXtPTXbRWGxMgqGcjvDcnpN9PghOxT3URbL8FhjqLAi9F/vl8xXyLyhSWtcpcYNYPT5PbX8VNh5bH3gjLA589Va9tdkITOwsvze4o42u/5fhLyEe/abOr+2lfJ6pErehUqrVt5K9uYr39oTffl+nyfTd5+CIsfY//ZU71IQ8G/e9mQJb33vKEfMIl/2xx3L7u25vc/9X39UCE97zGmyldkvfXVv2oTv3vfbaud10Rrz9Ka7vCPgmK6W27elUn1/LQUvd3ity9qDt9usSW93bnhJ6rrr1XX1mK7N305SXvb47rp3UNRddgX0vWXd9ZNit3vp6xW7vd38VCL9seSIe/hPz1mK8q7e6kyid35kW+/pkMnd6zyxQkr7vDfx4VVkkYIzNp507yzZ7fo18/6YR7vy9Df1rib3NB7/u7v04QNcCN6f+/7vSRu96s8KLc7qitidt93t1GRJXPktvu9t6WkxP1Wb2cgJ7zwmM7opNVk/XonKIO97hLyFm3+CEh/CH+ml9QjJPKySQ75329fMd5Tf0HBsFWpbce8n9MJiMtOX018QLuSWfGvblI+8nroIiKqa735Ize73mz3u+vUt0rUn29FuRgtM+FVI03KnZsJ+Sh4fXM13iDPdvauE77bL5PiWCkb2r7la/ZcFNryJyBrvsX8R6f1ZHzoPqn0+SI3vu+jYV8EW90u/sFRG5obpOXHbCXlTo7FxW5bFeWHX5Iorvzt6SpMEnms6fj82973vyRReTS5/3jC/8L+QhT5t+SIyUk2ozp1/5Igr5z5S728nw98VAAABFRBmyAV8FfixmN5uzE6DhC+Eea82RdE7/l3KtNeXqtoXVqWh8H/Xhkv/lhEkw8PzbgHRJgR5e+ql2pLPL/bti9odiDDg8742+WErVGYLmFKNt3V5f4vx/uwS7GppP11LcE3HcnHby3XpfwgVVSvd+G63L/5Yy5slJY1O5HbZEdqd2Fi/+WMNuO0hF5sX2K3zms13XWPRD1+424vbwVmGWdTZ8u3xM1QmRCH9D5iE1uU6zdry59WvkdpGhQj7Tic2LZE+/txSqD9LBTQwzhJ+0p2KVV7jatB43eMaWK0W3xy3zdkMj3/z7QOUu+YmW2iPHLKm28bT4x8MCa3Cm+gw6ktOGZza1GnH1g+D+5F1Qo4mP9YI5h84h5U3upt3Mdqs8J9zA8fmHgX9wRb18qIN/m5f7QKJb9t3Sv5T3PBxsJl/vbCI4VuKwkcEjOTRWrcrVkLlgpJYTOKYErdnrmF5eOJBkzL6ffuPqetb71sKYH/xL8k/czw9hPvPwEL3L/E/MGY9eqLqW8s9dFlLjQZnC1LSifNclOnX9fWTu8nq3q1mEtPeT0m96TBOKvefAk55oIisZPVb6oFGOH9Ikxc2SC6WfoS8E/nYu9hf4LL7923vrX2Myc7nDpI5A6ZuPFz+6vBFDC/8tC12/pCzXgtesbdn/goLEKmAUvm7/b92y/+4I4ITZN/+OxvvBDHO/3X5n+CLROTHhPoq37gpgLv5yLvv91D+8TYYkrOJ9flF47vCPghFcQwaqLRUzu8bIMgmdLbi0JWmdjWNOilf7ySi/e7TysYZUbUPre/+T3bp22wiQooV5wy0f0awJnsfPxquD+b4UKPij215ir5Jlv1ByNbi3Yq5ZPbv3cQV5a313ihG5zyfeT163wSCdyh2tfgt7uxM8fdW5+C2cM73fW0ipQkZqXgzO80e1XZ97hF8QmCEiqLpt7rVbYJD5ZW2+KQJxHDI334sCcCzY/rdk+3v8ERR1y++LvBKSAY26qy3fvpx/bokglyC+vBIZ367FqTYuEPMaOb8v9VgjPbLi6EurcEJ73ll9qif9lDDL/1dL6CQhz/y++gRZ/100Xd30bCHr3qCLu//RFrtPt60+x6hfyCWnfL+V7jyTv7n9nHLd69WJjCn/oktyX6XWIiZDvX/dOXpc0Egi7unG18EXnmkXovn/KyZY271sYbDfnm7hqL23nzIHdXYXx/0MEnjvd30O7R+FHn8I+Q10/xRsvV3v8EZ1l2jvZ7Ljb19Nmk/ulPyfSuX4J8n7lzTqu8h2iikN82q66yGe+k8vf6nuqCPd7vc+nVuVXSKoJsbo7fjOHrTMKLEzsWR7glrWv0a2GCfViyH79eT0l+zSkK3tp7RKb/S9Pf4JyO3l93eZPViUUy+E/II4eX23+KJlAz2FH129KmShKfS77Veq9OWZUvN6kcYX4XXvVif2CXLj84s+RNdm+bDOoieHbXRPrfVX3fms5U9wWQAAAErUGbQBXwWeERW5iQQvAOY8sy/0ZKl5b3hkv/2EdQ7FiIL0Y0WZh4y9pcW0pl/t2hthI2a/5rXDt7ZC9KoxF+wXcG5eGGyfL+/hGgsZYlYdT77vduJhC/SPricC5ZEcwCyerll+K1u+HIszJ+l58gm+nqxe3fuMLd973DEsF0ntRkH/iZqXF8NZR/hZ/YoYJHHd3uX9oKbrgi3JrnNClw6hSDP+6aRQQ/Pj88wXEj2X3yX3CFb72SuE835fPznRiBpNOWQt517X6Sl78qQi/L16gou+jfTMKP3CJBI8UYRT7CT+VnPAt1bbD3pv8rexkNokCH9ZtJ+nNWuBc7dTGPHYyQqUpq/hZueTLqPi3SDbFh/uNhXryNaemKynyt/VPzh0xZ2Uiy1Yzv51IWtXpC2w9XOu1dG1b+2zdAQ+S/CWWBXdWucI14CC8inZs8dPWMESfjuVdYLXraPtWSN/Be4ek+q37GlMVFrXQjkoelsdhtAOE6UQ+j9jIHCeOtwW5aUbwjiuX2nZ2N27hvC3OvgE41XcnuVH9Tc/d1h7Wf9/lu5TnL+VuuX9XwRy6ccqe9kGF3dz98N3+70b3yfpvllQLBT3MGuPtgblMEHc5NI458WX623BZw6l5b9IPotJhPYdIND+rFQm/wVEjePaiGfd3Yu19D+VwfokwiyPPeYz8KePr1wPSmnn9wc8IvQ3uKMcvT4Zi23qgkVg3jwtOE3vupZL9YI4QeO/lUa99a619GPe+knu+T0ktNugRGfY31k/uqzwQ/LnflF5PCL9HCwqTe7u91ZVkhViyou56giFQh9d3x9/wsWUs/1PGYB118MUn/4ICCxD5cIdw42Wel3prDlp5/4kr3IRu/y2ATWH+vJJF/2C0ru8Nsn+7EoJby0l6F6wlMIbu7vtOsImyLlIY1/vQ33yYsTiu8S+EfBCRDlZz/gsvfe739/td9aqCIhdnzL9HevElLHd3fo9CH6wTlMHcw+99duW78npNfXpclfC1XBJd/oRf6NLywgd3dxv12eQycO3+UpNvzoSUEmleObXdU4S/+qInVfsTJJ/v1oQ/m93k9pvqSiR9w99OJ51F0+98EO7vPCT/BWWfzsu2LpXnFbf296rZY3j3voz+WU73369fX17a8hL31gm7vd3c2qr8kIvtR+pX2fvYrdxta7lECXvbZ32LZ6+b6onql4IZTguEnklZ06iy3WXql2KPn16SrE0933vNaJeK/J2uSCox547l7uTPOtl/uiMonl4T8Qa79XvpMKFw8vi3d9gneWw23/d0UPr0HO1zRF3d8/+hh3iu7u598u8n6d542Ql76x93zte73+hG93vvbTyevebRiblDfUTe+5NsJl/XxxHvnt+fx1fQKO4S9LTXMaUZPtqxe/qxZad93k/t1UbKV91Yv2b5NPk/ThPUhHe/sYR7u3CBK393d5cGfXVK/Ytd/f30qy/owl0sLZEqKsv2JXgn3CzVa2KQ/7NkTJpq7CHG1gfaVlxK3DS4Qm8+Upv0oIyk3UraaE6CN3d3Lj93Dj3IxM19336TOWRz8/DHgiEJJd29O/gl3JhNVktGl/Jw8y39lPeSav4e+LgAABH9Bm29KQCvgr8wzPM5b4vNj+79y63XlrJkMeiC6/CO0lXAI/qcTS1OK9BJ65cvcsFcF1qbpcw3gKFM9twwbTHR4nmLnHzg438/zR3b/k/pXzzYdIIH5P6fzy95/3LwWwKj19DC1XWs37uj7h67uGl8WpDAfFZZa4bmP/yy7NsKl/8sEBo2MhUpl3YXhN0l66gnrdJj8l15Y2jnLaRuk/91O8fbH5+vR3eswGvbZbTAATGfmR/xKW2Ta7eNWtOunA+tV/NO19/eeht5y0eZR95blx5S2zpaTtsbUk7yDvOcGgg89di/jBDra8Wo9XoZ3fXBbDu2d29Tuvye7fvjOMYpGK53jc+7aAs4u/BJ6YttZtb/psvCN94EWrr+/0htCHYscn01+WEplyL7S9ocyek11uLlY8zp1qSvBDywGrUoawT5NyXTlTpIKdO7n8sGrdG/hJ57Oy2X/awRnc0nJUrEWQm/sUOEuO7iH1uHkJmX206cFJCinTzo5VjsWOvfYmvhfQKbd5qXdv169tRoGJMumeD8OL8QCg+vo7/L6TGoWFWF0W/El03FqLQMdt+CHc17U91Z3vpvcvlYuzfWauvcWIzJnShIuf7UfaPf5PVWttwUZQ0Jn5+1PfF6Ruwgk7Tbb8704R8EhMvuL/J3fuaCNeYD/+COa7d/sxM4WeqsbRW6GwTRwPnwHMs13yoj15PdJZPqzk3btAlxtwfxs/66cZzr/gyVpeoloYf7u4S8Ecvpt3fQIsv+yfutrEIrGqcbIIgx4mOwb2WFqwSv5XJr4QY1+T1vFLLCNBHYDMsQma2nlVsFEkGtcSCx27xpO3CHePq5Zov0Tw9lzskou5Ke8goNa9v6EoFB1MVLrK5g+VZ1dLW3yoSaXvzZ3rkEvQ4R6BGQrKe1VfTe1XMRzMw/sN4LSgfQav73uw+8FViXou8eci088/92bsu7N6axPa24Lbt0hbd3Mel0N/u791ZYjILN13fSS4LLv4ru/0I6L5YSIfpYYczX4vv1q5vpe8EUw/W90/ZOt9Fc3+C2GUjkfveUJ+CSu9b8kFfe3Da4Pvfuy1fqT2zH3dpxLykpXvFaNd++0/Ty/vLJ9Kn31lpO8JPtxV3C6Qbt3eT6vlRxMol9vqCKzvRYtC2LM+Yfz/J/UnjXW9aiu7u93ZYV3L3AzYIunWh6iXIfy7T6jyvvTu94SsSEUKn31ZT5/9dJ1ZfqxfL9nsl6HWlYvPnmX00JQuP3vd7t+7yfdaXmJissJPa00yPE93EstB7Ceowjx1brXG7n1FeT6padQUXd+am7bmuJFu/n+nsn8feXvPeG3c/607L9XWp01RE5r32m5EU17/Z2fhPyGNrcZ6tcFxHKxd81xfiRJSt7tlt9le3y/L6E9JcgLue54cqdPp0hk1p/ZMLLJx8Vz4K90j5wl6OVhHpHyXEKO+XH0VEmn6olMpSQv1gpq+a2DzNktStrHJMOVr6mT/8qJ3frDGaCWicH/6pEtI/glpFrq3P6bWJ4VvB7xIl/KbWv+Iu+6+HvjIAAABLFBm4AV8FfixnNmQfHJXzdSy+WwR6J2XZFeGJ87utP/junz1xpf4YL/5YYJeEpxsI6F9y7hJx+PfUy69QV7mH9XJ2cDkLbSSm6LKwt2QalCSK1NG55c03+qdoVe7wDN8PHmfzJ6TZeW4KNTt3XMmmz1CN5/zt3t27TjCuFxU/97v9wJ8ZFUvKUgjCR7ZQwqX/2wWCoceLCKVujVH0zJiq9xvXxoYz3BVP7H7PACEa5h3nslL+x/R/R6vqyFvVx+tbjxB+vjFgifahp7yinGoLybSP+X/dxkEv3z6+JubeeguXv0GZ8IKnxhv/3d07yThH66+rfbQ2vbHE/tHHwEOv3W4/BeaniU+l79GDVUs8bOepB6dzpf8FE4XHxaHk8q1apBseNJ1btmhKQLseCH49cfSb+CorH5+55bnOKZGeMUy/6ThSlk4KZ1aJOYS13fVuN7zR0wy/WWWCnyQjNTYdOvbe+8yb3wh7dx5Datx2Hj8cInv6fwo/cUMEvFYrAIvewx0+tAltm4pf3txsLso0cSalgV9f33hzusw3pHf+SQ+zMtc4f7hIdn+P89ay/t3hQmUiRzLM/WwvIoGqX76ubYib7h1AY+vBHFnyfSVl7iyvwQPmeZ/taoT60NezV+tYMnqnLufVF6r9K1hf42CRSO8DVLzaGBIjPro7NSvy/7VCyW+xdyF7mTrhLwSbpDeMwl/TiCwVYSO1T/3d932kW42vX6kOvuPifrWuB9ho/Vu56/SW4JZYfMNt8vfSXmNk1+SC0t3mAvDcn769yzLGf7GyZ6nv3ghhl1t+qr1XQIpYfL3y/7e6F4dZLVvhHwRRXv79a3lbghLdwxezTPZ4IhEInnt1vtPPC1s0ucLTr+hO5t8Y0/b+CCPiQtuuQXhmJhZDttAq8N0l9PeXI4E744vk9v72xI25wlp72mTmGXllT4qCMqb/un2l0Ccrvn88HfJ96ntpgnEPQ7RF/7asjEhMTunn8I+FyVeP0+9av/wR1rl/VP+FuBpOGXsh+H7Y26Ycff17n1h9xf6/ZX31ojVXpUQkIFd/d6K/wQyaEbjoJ3qMt/gkpunqEtwoR3dsu7lze98HR/Wv+xb32uvVnhmV39eT6r90R1V1RUTL76UEl32hHwRdN+yfWv48rlx2vty28Von1YI+f0+7J8npNiP4KC3fu6UK9QQme9h1Z332J+bvV/oxEr9nRLvMEeRmvfS+Cre93d3dyp+xY+mfhF9lgnGO5/bnUPc/pCRNnlvfo8lmfOT216od1tX3fRvW936U299su7EVHSQWTcsr31dYUEcb7t7M2Z7tuUnCT8oTBPdj+X+8QtpLbEpP1UlG3K+7tFrpdrm01oyrZdpq4ve+5du2sTPB3d3gmwn4TMeFzs2w03Lk9KpVLwnavxCEX7IUXcEA8Z+P1+SLveHF97jvtMvqq6ly+TuIZe709UOM794MnQvvCnk5chxuL+wTkxWYPvbunVamQIRL5ekLxH19VMmkpFNd+9cEfdxXQv4Kp1solPHM8xfKnklvFbrxRWnfzXL6XkmkzWXy1zXnlyf1iPw0XyTfES17JbfJG1XT5HS/avc+3JvkiI1n6pagsgAAAEqkGboBXwV+YZmgvxe0FPsN5bv8vkyvExZC26XVZf/f8We460+bHDBf/sYbi8hY83gkqHgv/pL2q9oFcoKyVJuYBiRvK9wh9DBLzI0xrewYbx/xKvr+fCek3PBJlDDVFqqzcMQyhsXlYqVXElX8MPW57QI+deVPwQF5suQt1UbP3fWrcv+1IEK1msCsdXvu4WL/7hc0J+nEOdvcrC8KOf1l2NjX4uC3K6bEVpnr+e3ZN7Nh8Ey4/VplFNBY/dcx8CWh3/r24YsK6LsV+RSS28cP/jvvuM+ySRJJA9c7+L/YXO+5BV1ulOx4eMh5Kc3K/jayFzHcIrEyNfstbur814TvMp8nrVk+MjOn/ciZJDj9JNkYmlvJ7VNqROWcL71ReFy7pHAyib4CiMQPe3/cdtwG/wZh0SRGWyadCWdL/BES90qr6y/75SxuctgE/FDJfAxLBCOb9vlwuqp8vt7WMnoHUlyB2mOcdo1pnaXEZ/te0SF+Ilf2/tB0k8uWB1v2k3QqE+ofcQhrFudDjlzH1qm4+BZkC0E/oenK2P/+JLqFr/Gd02Jbm5AKafcnlXq8SzHff4RunRYdUXceLe6SzwmTZuPz/eqf3+CQg7yGcWjgJ/wWT5d8N94sC7GGsIYs01iNCb9NaRdTEEumjrwSEsgJO89L8N27hMt58zozl9EQm1dG46FJh/S917dYz0Wzu/XVgiMQfTyWjmuyRtbpDN9tMTvd5l4YwZvy/5fCPgjma96rfo7BFlH7s7FsEpAKrRQ74+RuTn+0+KQrudswVf3hDjERxwRyjDYxPBCbL3t9xI18kiASqqvBaIu8PRejVnnXqaznl+CU93lxqdXNpykoFghih5bw4bckLhD7w+rUjv7vUJnfeXy8I+yHyOmqXu/xVu5gRff8FsEet1QXHxN7AxvvtRGn1srb+Ofxtf7Az2To6BOWf/GYBrsbERwur3M3TX/ir781a/BDmn4b1ZAW3b97+hLL71fvX9ePsTvlfSd3dbkvvsbBFiO/tpOvdAj3f2rqne+93E3dvu8vuq4Ld7ve/pwi+XGFPj0Ny493uNxlwm/Jus5fchbz6i+osttFvd5f10xRHfu/SfZZJK/eTd3pqzRV3cpJ7vJ7Sal6RLu+9pkVOgj7063+Cch+3viXu4r235YkSeTPLHaexb6KUxHnN6p+rG+/tMJ7vvfd6yWO3SZWC4nDOLISd7a5STCSscI+Yl6f0Ivv5RIluvG1naglLd+6mTzEEXv5/a5GpCp6rrcsJbvp33WmqI1LdkW/zdwsrL77Sha7y9u9+4hz4UuwVkTveK3sIF6sdmBu8W+pNLZpDvvVOQ9XXcunxBDbz+9kyEdpX1JCfh477UfMVZCTRD3WN6/61xpNzVYmVsJ7c+zuEf7te6X38uMPcm/rysPndwxf76nIC2O3RKpMuf63Pyhn6KTN176+5ejRl6e7ve73LlpUoK5X/u7vpNcsgRp3lwlxM06X1K4f66Fwp5JHr/EaIsqj4Glc5k/Ui/Hmxlm/3eO6e6UkFl793fFdMOv2ggd93n6Zf+lBbnz3NqKcn9m+vkfbs7k6vDGqEJw5rvpLJZZ/cFkAAABTtBm8AV8FfixmWzZhyXkuX/3Hea93Lbh5l+yxc9T2zclPnryzRIWCb/PDHmzyJOl8KE48vIJvGeKzR9EdF/6iNtec+QXuFIXdPy3Z/dvCAi/o/iN7+6335lZQEBp28ZvD2dtS5uENuYt3/d1F3PmrbaBFdJ3QUqlLoFG4xdV3GneJNYKL3HZt73KBKeh1+Cgu54XdxWPxuQdlx9uw+SbvBquXtaZOFl9hE0hkIXRqwJHHFbryYslREOGu42tsVkHQreeZkHv5aUj24B2tPHbV7q3yI3bD7Y66iMyZo9xq7KPx404EuShxv/jeFeRHXLYd7ofSzHB+J32cMYm8yaty7YYN3v9fZ8tjGA24nNaw6lCI49NRE7dt6rPG3g9OoXfAlM/4sMHEePits8qefdQUP/Opw+4k6QY/VT2MvuSM54484aiwpQ0oamn6yhb+FJ9puJ04iwdYDfCYu/3Cd6B+GMGvL/7qO3RUPvfe93eT9IvugXab5+9pX8pXmMuE37QoUJHisVhCtRWmYUEPfO+3BSQI3mp+G2kfKoUtWywIl3JHL7ur2mhLKxt9GMo+WaH+Puyq8KbEfU+Qs0rcrvskv+t2dxXYNtYcrY1yHpysmn3/caXLTZ+ETnT+tDI9OhidEJgJv14bU38wYb7MN8b+lvCHj4dSHaK3KFN5PS6fwS+RWnw6lujppstsJnyPS1m+68pLs9VlghK747ZPSv1KgWCrBuHLutx6NdSSO39wp33T3ooJfDQ0cpIYSKHhKnbSzPCXokTH4Ip4XftJK/Tir733k9tMW1yoFnafMPO2CRFe807qXh+L8dodeon3J9+2+CbcP7rwQdZ8xQbeTBL5cfr2qxNe8kSJn++/cl9+4IRDsT30rWFKLDvBkXao90fPwyx5uWsPmEn94R8FIjl5/93bx1X1gqOYTcwrcBC1VZ/r3afvguT0235XBMKnmlR+wcRsCV/zTVkiuUccMa87ff8FO7wlu+8fcy+5l/dcw299ieT3v/243gkJIcMc+ryCONpi/wVCb7vvlY9CPiSZWL3qzvf6Fpe3IMh3p1xc61f8RPEoRyAYgW9Hs5wY5n9p9AiI776t2wXFffl8HqOyqX7dOnt0lKv1Hd3u52Hd38kf4Isv319giET999e4UEu43isQ/0opFnuUbne9uJPaevEQTFRvc7PuRO1RIxk9u1pKyFd3yer074IiQ6gxH7r6yFd780Ze9xwT93e7vk9u6r2S79pgku/4rwQ+HpJv7IC6Wl33doR6BPJ/k16+gpvd95WHu92Fpvq7v8UurNd78lnz+vFEu999tC73vftoR3e72/lLysmQeSESfqSV+Ce4rEj7gDYlb4z+VZ6iT3f27vve8vk9LPc3BCTd0oVklmGu9lsaxAznt33VF5PST/OjX31Tvek0JIVAlJuATarXvvw46hx8eJ1xf973FcI+Qmr/BOIveVj6kp52Ya7+vdqpt79MEZLu6dJOJ+k9La/S2SmV3feQuJlwpx8I1/xvTJIP0JeKyR5YfwgYveJfEP3nh6i7n73gb8RNjGlEibZ/5Ri+mwmR33v2LhEr7veUSu+0mcmv9YR3dtz527vtMhnvqsR9vL+tyEI5aPe0mJFHFl/ChfJfLFDL3d7vemWIOsMqHd+0ncifxnv8VqtR282vhVa4i9p7bkNXl4nd7pO6X4Ij7ZeKRk/p/wpywG/X35r7ukdZEaC+1d73GKby/78sdu73dKXP7OfGXXwtkgiEQ3+6QOSCXn/ZsJ0uvyXmb5Igt3vv9w+98nw98TAAAEiEGb4BXwV+LGalvATP0ZDqyv3LhqOd+L5dGY9oN5aqwx5t1y/+4LCLIlWUUhN18Nzi9whBGOyi/pvYRQIZOCCCTz2dN7McF+5shIj+qcrCHd73mbG3BJ6uW5bgjoIpXMNlRVosTXRBmIeOK4AhqtyyzcrL+1VkK98v+7wsX/yxRpSc4dP5+2NnlenrQX/rfmAyyI8pS1JYCVaIKReFoHWudX16d0dZNw3GK7hVSd/xzyLhLwwt4//cbWFrct+ik3bIOIi+Lx8V/R07EV7ri2E4bkHpJJV/Htf4yJtQGF/0Rg0Hn5gG1xpU3cN3YC/xdQxKN+RM6bAL8aU/k6k9iQ0GOk9u0s8Hwrxv24plJhUmOv9OH+J13nhBWOxlkFwR73U8l7/GOvIzSXQzeE3Ua1aNfMuB1/B96T0mk+T6vkaPFXcIn3RVh14oLYOS/9Zef4TftgnGCQcEjgo7FGHUsJyH2V2NvrCkVhlZQanb9zmRkoU/AVlbCoTFNR/sPW0pD6y+1fQ0hzcJKfax/S5J7bj5ApeqRrc9QvhtWk96CxhTVk7J/iywx0ma0f3t/gt5NzkkFFjPwTF55XHrGK4DKVZ/ThC9z5ec0YFvb2t+73CJoT+4dwvl46LF2DDkH+4LLhl7ZAuCb684Sxe/2siX2dEHxZbLxoS9Ey1p4IcvnjW0junZbzn6Lnp37hwlO68PW7+rxbHlx2xvlKlCmVGUf+WXRnfPda/WreuC3h6c/5V3bc6aZIsvvS2N03CVGHIR1E/X3dHw7xwwCW5Sd3slw5NW+SPk9733BFYFc4dY/BHckzhd4bb8QNbeUP7uvEiE5l5Fe/wSl3fjedZPrp/V8npq56mgvNuR93MHVrPD/YIzvfUI+EKbKRmmRne63+TP32/xVwxJN2lbHf4d2Ya4Eu5+M7R+Q6yrMC42WT/3vkIQGp9+2j9r6Rm1Sn07xMInfe93funL3ltQh6tWWELu7uKzwz+9Za5z7ZXu9dtAhIit5E7GskYff3qCe+7vdlL1JhRseT6pO/3rq8I+vb5cJ3EsIzzu7paZZT55VeYl78j6P2lrXi77Pd+10VAkvedPzTL8Il/n9lczEsb5rBOYub3uK3p+0YSZv0VAlwyh1uu0tKGlHi1v3d35PVb2j2MvfZ4ju8+el/EXd77/Fbvd3p0moUNw23fdre39vfJVXpjBbvu8sHu76UJ0pRD36OzHd30spJbv1iNF3vJ9aX5iXvrZRic/rqj+vVKZbrxO3LzrW7hPxJnx07u3+Cju+aw4pJP2LiRL3veDvXe6hO+25A1vrv6J9RBJfdkVThTyHng7ynJGktwteSjKOuK20rvrfCF/G8W5lZS6VR7v6LBSXgJ2u8Oxte7vy+zFOfM6V5S3P5l9Fzc1vXqiCm2keoSz78uXRNApLsMJrci9uQ5reizf4ylTczDLkEe7V+ikdkm8LPzTGcJrZz9a4KMTulpFwgHVU7gil/Sr+lJ2UsC1S7zMRJIUWtZv5MMaiJIq+1azCnJBdMjvJHLTfTa/BdOSjLKnPPTLkvPPBZAAAFM0GaABXwWeGBWXFdY7IlX45b/KTfqry8uHFwx5tX/G83yg2HotFpQl80gQD+MIaKOf1saM/l12s2dw/D49fZ447IEXB0id9ht1NEM5WBU7YdSNYO1M6aUtyE7JaXwQFKPzlrI5wq/h2LA8tPtSmftsTsbObd7V5u4cHZybmJFTFefbTJTk+ny6w3CNh3u/6V4hp/lhLsnkv+Is5bdar8pW5GRk1CvgsES45n5a9zweaWvvJVW5Y3QpuLMfdHBan0EonGsvVVkzaSnYNZb8etkUvZv12rHK7Pi5UcQURIMFrp2xlwgyvS2qppv9O5tU87akP4/2P34PKjBl89cf9jHwLbBdS4Y1uXVW4zC+vEwpFv131AI7X6Re7tnxxdNPQy93OguaUKyn1bfkjCmTsw4/CtFFj8EqqP61/RIoj77Y60r5syxhFyOtcGHNENwbqQXqx91X3BCR3O/Sq/wh8sHq8C3wsPFflOWT1BuWeE/BAM4EguYExwzxIeO+bfVrgv/63t3X728FJHDd+iefFcdpWD+Jc80X3YfseLvO7b/5PVJvdxsNw3q7JFehtJoupXvDVu+ITC2QzBsYhmKDtf5/8SzkWX+svPQEvzY3VAsLmvBZYJkQ3F4+ip1pzXf0r4f3TvfL6u9AkJCAXMBJp/Df0N7u93sihak9u2dz8OOoRTc+Eieq/4JLkaFFX+zXpAihK9V3V2uPt0+N/OvROPPvBLt/82r2myD7tNr5Nt5PaUv1EwxK4Rsj8vCTSckKGQuNyFx9+fe98bBvYdoKFcpb7hxJaZM4b6yd9PJdfh8P3KNPhxkIgp6JWR/W5LfX9OCPMHb1pPPWtuN4sW9736oSR7u79qW4ow7AXJsCi2vh2l/xu6Do5UjUEpU345yrnNkAmH86vP98KtE/f/ixOanGc8Iv8ForWt7sFWUr76de2ni44UHciPtfaFbzhKXr8I9Ccs70rY/V0vgr5Q7MZOjw0l7buROi1k/SJU/6JyfpfVghLl32q0gkW97w62HwSWb7e4JzTBpGgSQNd8vaBOfP7u95QnWYuHbo+7JlRUt1kGIIaf31ghKvY/7r2nl9Obe7J/X7ku+EspRN7uf1HxeY+Yt36OlzlF/3wUb3d3dhXuXd3rvIWyeq+ywVEz4+73fWT3froEFzhd973fWG4/+ix+Myt2P2632+4U93e7u77vaEfWt+ozRrrfcQ49O9u7y/e+S99J42QpZ7v3e/RYnem+SGvrBFGTw287I7dYu+zb33ib2n3fZCI39pPUI+U8VffkQozuf4lodQ/SvgmF4l+4hwmkTtMmOq+m8SJiD3fyw19bFJ3fX2JQu93u29p+a7v3iCvTd39OC4UX4SvP473OmX0yWYmEn5wutvFEF90rb7TJ0JI3Re0vJe/WYl77E9p6fXpcQTv3iGi71xO7ve4T8FUvV423fP3dzg35YLCueO+acpkOLjhn3omFqyxrBGUbufG6fVrkI8vfWC4orvYh0T+l6xIkdjFJcWzeS3XL8kuaF8S7L8ur2nPvyfeIIEdKCcz33FcY27lkFy/d36s8hfxhHMivPSGhJ4uRY9xy2W+lYkfPP8J+CIRG7nI2/x5OPmP3ScZ8912oJinrka83V+9yJ1+Ijil/y/lx5PaTL5c1vXSiiXvul19TFe7y+tLj7v7uQvtbr4V8NYz3KVs3/6ijM3Mdid/4I7xW6RfNz/pdNCeMPufNp325XdnSliru/dE0TVrJ67r7Ez/DOSIFGjkwgqrNhyfX/qlXchVM+oLIAAAF8EGaIBXwV+LGZiZszcc98ubFDJsO4uPkdOg3Lf17/i+MB9kpKjzQ7Ax5t1y/+2ESVHtNc10rtRV+CjRGDRIwMwtkvOmmz2wp5iwQvDu9KCL5tz/KJPkJD07kF65fJ3LLGxLt6d7EwtZnDSycPr8EFrjqJ/J/f4nunShYv/ljdMsuQ+Ht7M/pTLSKkrWtiL2UVsN+sblXvjSH7/wyJ6XtAN2ZY2MCiaijPXjMxUHdd+hPNHqA69qbzyRgO91JdQ+638n29buEL8PwWlOhlfUM/Pq+AvznpPf38NcxIM7taD14ddX6aEssYUz7UHKeKhO6UenVZjvSgp3GCtw1K/NC/z8G8RGe6cIUEn69fnXMg5I/wVTwxoNQ+PB0Tpe9x23VHY69KfO7/oZd70ru7kinxslV+LKXmC93lyEy/3tggEBdpb+Q21fZIGqnjmQP5bAje9lB47hN5jaul9btDYJHN2i/Z0IMf/7fu03txl3NjXnOl1zJ29l7/04FXWvc711z/rwr+60vIMCgJ2VQhvHUrR8y86RBgTczZ13cfxV3GmE+mM/c99FPvsVUgNPKqH+/a6v/Sls0WKsqNHuKHbVle5g6m1hEPWcfmfqLtOk/e7VS1MIf7v938aW4isrLrIv80WMweaHsZOS3QrhbtLiQ/HbxwXiraT6T1/L9u9jIJXx7zcDvd3uXVs23GEYr1zG5PSV9zQoVEijNSeHI1sLPcMTvx9+/fHMVpVJ7bYm/ha8UkJ0L9b6R8Onwxmz/LCFzp+ZE7K4dStGjwh5GCvcW3HQNHEnL+xMEd30pk9prL3Gmu90NvNsbUNpJA6vlNODWztYHs4T7opt7Y2Y9fgptP3sS/zfZcizsZ/UUXHamid4Hv4pBNGavQ36hp17hvaZQr2eEl3gqDD3c9cwb7vacyby8PchdIbp+r+63ds7LIZsMPx6v/kW/L/3jNlz8/UsnkD/HCOQlV17mMYFmPH2OtcaXKPvq+X6FZ0SRTE2o68ZufJ741/l4IPR72C23BHiwfXetaxEtjWTpe+tcUM54GM5RN+4U6tAVQ7+QWxWW8gSpZzgT8kuP7HtwiX9egzvLi/dXXQISjYut/sLU5XESlNyhGHzPPk9NsrFvwSinESi+hofy030KvAk3w9fn/D931H3O0MSS5bH0oxS8d+5XE/7wRiw2tz+WT22/drugTGCN92Wtp1kp+ov19Ir7SegSGAh+w//8wsnqqxboJGPL6cvr1wh0yDNP/Lk9a88wl97/6NIInDcdJunBN1AZ9wXc0mhXP22CKNlo1NgatjeK2CEo8I0UeuiURjb25Cnp9jUE8hKdt77xd3d7v04Jc4Otu9+2mp0TL+u4QhF7eFLvveOsuwk/3Fct26OUtt/Z65zrKVKeGlzb6JtO6fbJ6KF8NYZ73r8d79SE3Xpgove+7/JCFa1l9fx+pOHt3d4dHc3d8oor31p8Uy931hMrs939F9jX9mJNP0dku+ut6yfoluzkBFd/uqhFbePiv3cA7XkiC67tVu7fHCbwbpt/bv+E7WfL31YTu0+SJA70+MiBK358fb9bFJv68nr6v73d89N6VMsWSHurKgoK4zM7BYos/d75jaSzhMWc3X3rehwj4JzZn1w15N+if9Mvdv11WT3SCKF5ZOvyexfvILd31WKJToncv+8nKQbQXcJrrCGflEi2xu1Shl0mS9hHDsFt3u5kHBADfJqk/k9PylzIp77hVuSe7chvel8sby/1pZq+yeuX77KKCU/CB39C/GNC7RcCngi0ZjfLInbf4wkUDHV6Wv9u/VjqityxlzqCsrih+IBtaXe/y7vve7KdO8k7/7KVw2qHz6l3d6/RC2X92kwnmS3NIw7yetSl6jClm+Rve/d5P1S2sXe7MMXT0t+rgiH1ZujuFfRC2T1zEL8PQh7OtTg1W1M0m0N1Vma/P26w8+5f1COnLhbctl8uv/FS++556frvbo0/95KY4obq8WWh3tgwlnf8v9fDGZEIXQjZel+Iz2XLbzEuSymLtTXw98ZAAAAFb0GaQBXwV+LGcda9QfzZ826J/llJkJKQwZ+YhUD68p7jrdhev8ppNsq9wh6lhYThza72yrLw7bvw+y0vkHheOMY/k9Ki3ywSWQnSmlVU6jvLS9wH/l3XvV55ZxSUPr2hh93d3fobyK5C4W9GKJh+CDl5fNu0aNPjibDc8LJmlSf+WNJDq5kPSK4wJlS983x+XJ/8AjxuR7jM9i7+J0tCudum75Xl3v3+t0XcbNKA0ubnnLbwb6zpbB9mjkEv5n0j7/P5QTkvcLJOf9+4+GIMCwFpFFLohM+cB6ye+bGlDX/GF6RsG+obgx9Q2pEsob6Y1DFULpv47g1gMXBeYy/huDddHT+K5n4R8p/5PTzs68E0/c+ai0RG0Kzp5YQnjfOWvRS3L7/mvf8pcNZMJl/9xQzgExut/yHk/ST7/tjJ+E/H1fCTy1HOzls0JlXn2chex0Sv8P8MpIV7hQibRFq5813xIuUB/3mn84yVjodmHq0u16XX4eDxpZLJhw4ui962fhAqzw77AmOsngRUJzE2OB3CU9qNF/L3BGXK50yfi753vmn7hw0NxF7XUl/fVBT3d9+HUmGvdzC9Ve1oS8Em9z9l/vxGMrtXAIr9wpSUsiaM7wPo9DTtgcu+EPnh7xm1Lx1JCW/vkPx3fYXj5cN7bh4mLk1q970D2Y2G3KzvQLnwYzWBfAvDN+D6vFsaVgslYfl1yB3CPk8yM+XYz5fb3ZJ1DF+d6fVu44C5/xuzAtwtoTj8Zom8Ec7pi5icd10frsehvW2qLh/+EcxIsVIeYZRdHzX17guzhNWGbxUQXgbsfiRKe+Pr38F1LdS+p4IVSTTgnFFnuUZuQop5/wVXtpUYT3vTDiXI3MOzn3RsP5l/5lEiY3uaNocK4R8gosKzs69X9LvHmCVrkb3SpaWXN/0z0k+3ELdRhco+93eG4mD8xk5d/7grN5kA0C+qL7kPy924kkzL7f4KRZfZ33zr+ovv7ghNSu/v1gjOX/+T7pzq3FG4+8DP32/hMvoi95fCL9RNJoZpLf/zXv9kK7/IcEYl7ul7ogyYHQl5qu6ORryz/XuCLCfD63wXYuQ7M8Cov5N3fq+7/BLvcby2BrW38EVOm+oRL/8T5YKbu7veP9W/uDv/rysFHdyR+MeT9vH0HtMsb6Pon2/lW5Q1d+q/qioEXduW2nLBHd3shG9a3k2FNq738vd3mN90eY7t3q1PefH+16ku7vrBJu897ql6PL3ekl8n9O6uFN33fe5BZ+CEZPr3ywlffd5PTbe9oExLlRW8vzGrckV3d3vqaEPFFnyt1e+8UZ4rd3d/QkS97mOBhytJJvEXvfWT188ZG9Nid94Zq+x9c/trXVeK7l/DzsVuvr1gsEXaDNuX3e9jeZplE3uEfFXn2fdZfsnw0Z77fV11xKlj9O0uRAqEvd5tk0fnLZekdU2eEKW92eeMsbaFzMWXnoN1d6Sk9LKOTM930uT0kmvxxd32lz/a9F5VG1PwUXd93pj8Zu932tybwy/3p4kk/dvInaB2H8Jl/t7CZJ/CH+h/Nrfxd79oEb+t2tklEnl3DxD2kusssPz6a5G972ltbSUnf4onl939YT8ERZRe5G1+EzXd51+X8tSCQUnhyUGaX587niRNJc4ws3euWB83lRZ8P7pyzRG4+vz/f+P7vudLH/Uy0V6h6UlePrcZsEy6OMZ7QGH5Fp/J/X1iI7C1L6RF7+T0lroxRMqNdy0+RuVr4V9ENeSCIk7bnvsO8vKS7fe7ufpm/1y/Zbqjo1UntKLv4qu+7o68RCuVlO48zX+3WX9af5ML+S7/JEYbWVcPyQFZp8kRCUzUvnSJvfqP1byE3xWW/q+5D4LIAAAE8EGaYBXwV+YZjUqnd54S4R+A+WzkZQTX3LrdL3LcS4O1X8u5g9hgv/lhImoevnY4xO16hSZES3mzDVfRp7+e+QHhHyuTfcibovESNw07lotgOv5bq8sl36t8swlO/J+/6huYCxvliv/D/3J9v0JuED3dzypZZvy/7WJM9ohp7eFa1KJhl/9wTk0in27eQvcbPiwPaK6L3j0Eec5Rt66MqBMUeZ6R1ab8KNbvnPXERiuxegbB5bQe5EYnI+X/exu0rYC/9KrXwxJHJ+d5/YRm/vVDIm2+591J2Yd/w7eMVcfzzWT99RPG2Z+DZ+5QubKnv7WZPBSGHKWhxiyjkRW/uMLjMrXGDnM+3Fw3Le6rKQ8okm7WCWdang2TA9AjGPMv76XJ7rl+C7ug7pyjtOFWmeCLd3KmtcJ1dcZsaVVli8mF95wccfCnihHAdU+cFds9RIxeOuOWMnwCb+egR+xxMbNZ/Wdx96fI2nypt7GMYoNtXad2NU3P7/GmWzTW1d/mv8gtpQQdcrvXjYnWliHbM4TCoesHr1mV/J6SlfkQkurH5xfMHQQq7nSek3Wdvk9JK+nFlw+w3vvrfGJ/r6y3ey0XpaJMapHOS16gr5PcEbQcNT6dzhwl6vv1GF3KkFZ73ze70vjb8rb0avrbTR1FuOyL4WeGm17PwCVl+t3i6uHM0PjdivsZG9/jDUoazfh2/uZcXfQZlfKvaFGKAw+vrDi+7UF4IbhcqGLcPcaWtFb2ToXEVD1c6Dd3/HL30mS/kqeEvq/h/hyJeW6F5fXjgbrZG1+OK8yfXK/vcsfYmPxk6DaHQfWE7+kGaUtKTEiU3fLL1ohUTI37hQRDy4RBS1jhepaSSa+ve4gN6EctWbfuC4m1z1kNaA+xIe9jffCPgo3ufPqvBKUifnW6lve+GT0qLfrbQtlYVFGLgjbVFZMWwvzih2sQ0l//BAJLqcvfeasuuUWRiDtGv9L4iqZf8v6+NFMedIGZG6yA/jI1Gk8CD2PMUG9UFi9s/2mfmF5J94LSVD4/hZPMHRShnaV9TqIyi/7FrtIJHhxvJrvpo0Qw+Isr7uCX6jPUJ1rG6f8Ind03d+Xy8I+iSy/ibrTurKESf+0MS7qjspofc9oLpPF1g6wQlDF8f+6wRXj4kHLvrNcENPd8vrTT0T0m3zrene1pwSb36Enlygmu7u7ucP2qhJUWMe7hpJ/+X0tcF13D+Svw7S21L9fX0omzfeXPcmS39OCfUkd3fL3BTu73Tve6EfRO3+CHHV9uxpeqBIW94NrfVp9v0/VAjvk2C9NE/v+EtsFV3u7cETtOj6pHtt2XiRerMacdjf34gju76pxM3hF9VaaoTF7vntl8R1t6nTsX6LU9pVJBEIvNadyeuupGQS94S0kI+OmMKKz8/3feFW4/7t3bjf7K86elP+xOt9dEXeXd366+8vrrid7n+fIR8mNL0vx5N5fW43T902Vm8I+Gy6Segkd39Bc5xe1uzX3rfNl30X5O7Pvvyf1XTRs/5P15JSMJXNr3y8KeCLNudtfjzO+9PhtCb110WUqglfXJk/btcbonr4y1gkLd3FQ3WWpbyF/cZnJ4VJ/X/k/Un8eR9+XY6u+SLtzeka70/4REuQkndTw2e/wnY3MR619DI3q0X1Yfq0rvGfPz17QRmBuIcdLSvDhfUnyeXPw98bAAAAVYQZqAFfBX5RmWxdbmrd6/MTd15T1eGC/+4QNnVKaPHt3i19grruMTfhN4/ZVNPOnuEb2e4JGY/G3V8v2eCXc08uelVvlljg37QyVBJ6tlWXIC297PAmeh2xKq8sIHu04TfWJYSLh5RG68t5tcKeCQ2Nm7SjL/7YXso/OC/4t8cBKyV6mj9btDSPcTYTvPXMS3zIpOjCQQKbunlUqOrsjEf1udL8q123/39/ZgLmHnkSta/HaMTNUIP0LGG/q3AvhmQ5bdAi125+uX9/FeWIblI4+4NesafCDgJjfnYcKr75IjOE3N8OvZv55jLl3+4c8Wpz6BO/f9PyeqX+OnhkZhwhEh3f9bnftfCHqxOl4zBSZ5PVOts8FfKCQo8ggdfE1JvnSpzTnWQmf7fLCN7vn/htFpL1JKdxtbgQTL+ntihngLNMlCvsVv2xhD51CVCw7sZA31d78+YporGxgyE2Q25afP/3NwvKWfgZBPHfc1oLazc7nbn+KcU1FVP9YvmCgyy0fS/gtKNBodkpcno5bJ6re6l4Haa+y3TjEx1kv0+1rZbv24QEXveVmQCXk/qvwUSKQ82piv3CAcLHKE3+PLO+M7sr7t7+4yumgL1V+P9iNFBP8fwxlLb2srrDcqdcKl6DuUB3uZCDtbW95D0W81kpS+q3Hmq8TFpfg+cHkK+j2ZYV+EeQWTyfAVbyOzsJvCurWsFZbMs/4Sds2tNfBw/pRus8JeHopS4y7/qHqPmmf98cG9on9j/f6UuhIlO3uEr43eKfl/TJcUIy8BVvE5x5K5Prt9ISTsbaLQ9PnyiyeEWRhHwWiC++RlNXV5v7WvoSWNjT5+ve6gm20s3vShbnuHRUCyqxUkjq+iUi8392yLqPUh//jT3fBNURlrMELdY/8Evh7/dJVM5HHNWsv3+NMhEV9wnqvjtm0Cb6zfvcUMrD+jhXQ74dxJv0A5nZLNfbjuno8FYnD8PsyJ927UtH5J69wWEzU+Mi1TffHvxaD+1Fxd/QpGZOqf7g7mJKG+38YRN1fLV3489yKveaaqgSd/FCJl4cp+BfS0VueO70sJn7TBeqm8+hhrhhLymfWT+/xpejuvBTOgg3hZu4vD0G5vAvdYLZCuNm/u/U9cEm7u3R+T0kkt7d99ZC3vJ66/YIo/3E+xv2lZCWVhSK73n8fiNOPW7/9duCGldt/oEfd3qtcPxEqDdKSXoXIJciF+T+38TIaSfr7K3Zv6xWS33vk9J9ckF3d3e+X0CS7y/UJP0h/Aie6f43BJzno8WmV0u3VHbveq1yqifXS0/WtdncJ+Ey2y/J/fPYJzPFd3d33rhITd3LCRXasrVjWSRC+q9d0CraLPNT5cdABpI/Cfk8jeqS+sVd743TtVlBJe89k+k2yueiGo3MX6hA71T5933eEfNVMrKycutQUEe7Ll75Ba5SGFz+X8n/FHdrONP3WdBm+RdZdX+6vd36WzMRveeXr80EWnc4OsJz9/G+8np6RUILk1X7c279RArJ+9F7Du7sJ5/G5tThKNktjh1YZpH4S8OSRrb6nl/HmnhG8tmhe6WXLTsTF934JD6nnbvQk4fn1/2QHVtWak79WL3pQ3Bku2rt7cbSElve9u1xgn5BBL3llM3qxJozfu/+FNsYOLm987Q+79p0qfaHHyg80iBtec+Or7vpdJ0J1RPiJt31dFQ4t73e28vv+F8kQYp+/G16VIsKFenz848N83Cxljz92iiL9b743OXSriCt3vvrHSC9osYlLLk5hb6gkJu528QxxeXC0MavHl/qoYyRBCn75M+SIh7R801Of6/8kQWfFy4j38PfFwAAAEmkGaoBXwV+YYUWqvy4JHrBL/4vKwGmhyeq/KTcwLXiz3Oy0KclQwX/7BOYj5AEUGn3cbHikPcPw+PbBQwMdooxzAJptP/Dsjr1dJlnh/pGH7hLyxsi+xv+e2+r/V+WOzm1bbYmke/8ex/GHM+73rILmjd51fcUblx7HbFhX1TDL/7iuSrhGui1bou9vBATYSn0xtdSgSzYhvrdMMgfNti2+b+xDjg/X/9e3vUyE9Q/lPp8n6b7Z2M5QM4i1JhlcA6JDzdy3u/DwYPYyIwNtYW2wD7Suhmpg7IQifpvUurkC3fQE273f/jStZlL+/VZY5XGs1wofRpYnwksjXQIwatgWB3DXhdsGlei/J9Jau43lHQcZnvvznhP/amn/VOoItymcXje+N5IhuC/eBNuHLvsHn9nuYndYrZk/3vQghEG9vf5uGh37lUv/WU+PSFsJl/8sUM4I68Cjo/axD7WO1vjiCt6ywYh7GcPpO3foL1LRqB0luN001+tuBJuwLjs0mF2orEoZFjnLqFw+H99yf/KjpT+M4+kC6ppf8NSrvtJ4tgqKP+tuX4SO0asBz3Lbrc279YnhB4OH4e3tOs8EmE/tlzg0qmlLor7EkCYi97RF+1LcFl90wS/m6mT1DTQuKfFbe0JebeK/gilhflk+klV3HX3fGXT5qyFZPSX9RPaCn+EXDu/T6TzxZAn1i/WMzUn73Vb5n7UvReyfpPiagk5l/WT6reRwS334y9v7Qg12qecJFo0/X8I+CItz/uyfWq/5LLDFxL/rvBKKgletNQw9WtPRa8Ugod855sWcP6r8m3Znu2/CBoKVcwrh9Jof4d7n+j9S/BNblxSvgtn4I5n3SZv33WWCe4zDVAowRK1frPkQV698ewv91wmLSxfnCPgrufvRx2svezb31XkPe+i+i9V2CYUGpYdvbyheYHRbrPBDIGk3y66TY1WOOcNve7yp3/BDZvwVRYJe6SdJlxPFtegRb3b4iEX+FO2WL7LApEQP38+gmT477t+tvdW7El3ZIuCW87HvFk93zXUlkdf7lFlx9UXqukK6qwRR2V86Zfbt6X8I+tb1sE97u9/+jvtbTeqvVfWvdeuSylffVZPX/2S7vpWPxbhF/QJx13cVuK3b8Sd5g1UJuQPj0e1CZXsnmb6LJOBbvvp+oIfLE6VWEqT48W/eT6U38xHv9kLe9LePETSvh1ijX5r6UskeV3M3u45fELBf4Tf4JobtnruRqV0ztSRBN2XlOmOpi0P3jC5dzX9RUobeOrtrsbZNle31Zkuvrd4kg493ve79u7lJe99Y7eKyP8dTDYcJe4j/f4KjRlfbY/Tt3G5EgS/5OLvq3gT791qtRrKcgq/nD+j+tUga/HdVR7u/fv167UJXx+cueH1DIkJNrP6+Md8J+QVOYdZeEzXd46+fJ90+mWCETDDsUHeSC1ZNdl99JegRxW+RMn1k9Zjz3hjJYiaVq8twUFxWf5clT16OwmV7880r8kVvJhYbF0Vgk7iHBfbWTHWsuZOyPvF+yk/hjJECFT6f2dH8kRMwGH6nynitmuskcUlu57mvPvgsgAAAE/UGawBXwV+LGc2Y6XL3rcXlntX/MTbDWa17mn7/ynujI4MF/9wRG2h6ZTmvwQa4r4eRNhjwOA5OD1u8PCXx8f3GXvuz4Td5qgjgR4IqSt7E7ZAd299nhCcmf5zUO+q5o21uHcdX7CNfnSgl8e+nfzTe/LCh7mc3Hfcepb1y3ezRaV6Qk2bWVuQfwr6pRv3BPEg8UYkfhKvadhhT5FmX+9xpG7DGRyB6fb62H5CiTBbfM5oWj+y9TZ2Tn0xipfL9rZrrD3lkHJqS/743WllRyO6mvj4rU8Fpqv9kKJZ9LcASPz/62V/yhwoI9NfG/+4U86TgRf1rP8Ev+nfgl8vcWbSRSVeKYpKposIFQFzBu/IvvOSMFPjvIpdULreH73rVnDt/decMoiOO1LsFN093yDxxXFtl+9Jw/hO97kKHUc/Th71eX9ELoVlzxWPk4KF/9wTiuGcEisxXlOrph7YUn5SWG5RqJq/yBaCEdX+chXBclv2h6v9+4H0d3Z0iddwoQO7xjXXXOF+ncaPUi4M56ijF8X93b5c7kXX7tnFpXwnaA/SrhtxFf9Fd19i4gs/nbPS9Nq5vJcntv7QpGnJQk/t3uCsQ7/GilL3wSCseEy/3khQt5YO+l77+aMdpItxeCFsZk/2YFv/Cke8JT+saf/vZcPOxr2fh+C4xhs5X7hFwoMfi42Y9mMOJKYO7yk22Csr74+UqFmLDGnEUKDAW7o0E981st/aTzwRSX/ddeTP4eb4v3Hk0S2nt7beZd4/y/9SsWT2eEfBYYZp+X3x9flp0XkLnqVumh90SVQ/xY12D975PTasa9oMjIdjmeHya+5H9U9glOG3K1avc7n7znTjTU3Uz0JLh7gjHsEP6x0vhfGQ5RKHLTq/6wRi2cCer+L2OsEhJ3tfvcFGORd3e/urnRUCkuCHZzje/e502lqCczhjfNFLsnJ1HUfe2MKlkj8In25/Kx3dlabhHwTU6dPeVZWCHn9+l7rzRBiCUhWaXk+v3xFM6e0qqgvr61f8FxyL+8v4ZPtvFq9e212W9G+qBZOxd333dz8Ed3fvCL9WITdN7zyWd75PrXplFly+f/WE+Z+T/VWZP7+x8Iwg0yt3vfd5f11HWpe9ne8m0u/X15PTapS8t7+IjCu73vdK0++sEQi9voTfagnve9z9tfkLe61Ld/r6qnHY31k7vr7TfH5OrvFFu937rtmuX99bveEi/62CeK7d4h93/EnQcklI7w0+/9bkl9aoS+s279iYLt3y5p1rLO6+8Vofe93vSd5Peut+nIR4UQzularsp/TwwthNcuCa90Y6mPc92NL0djjybMHpZ3iu1J9Vr/SQqyfLrteayS/6kvvqiFTP76wny5Rv+/U1yy71xJOTYrEPX4CfYomHXRZz93k+r6sIzbx5dJiTlr93vWQHT9fXVleT9ZRlI0xb3uhOhV773v2Q5Gfck/qzjFPhPogibfuMIWjl62cLpk8+Xd8N/SL7XU+tPPCBb73VhDoKby2+WHiITLza9l7Zr78sX3d77VXyeniNKmbSdrkkpv3lkgijfeXD0LaRNfV608PEfChXiVy0jeWVSq390W0MK2RrcuacwXhZbfBnlevsSwgV77uej0bnmT+lySw7fd1b5DRcGiCiN/5fehGhXdyjPL+STlbDXiCBG3m+WpAslFJ62SWaIiN7+6LqILPeev+5tPeCuAAAAEq0Ga4BXwWeEBXCD3HGFEGVME8G2u7v5yJl+TW14vG2RaepZfylu6hjwWGPpbN5jC2mznMPtRJ6L9qQ3u2EITrVWtPnG9cMbcSwgj/fVbO5YUz8BF+v0t929TjVbVFm6+rI2tvE1hrNtpoM5+vUXA7Se9QQvHef+ETserxM8rfzxevLCJ7ky3cv9nf7NmPv8py5WFS/+2YYP0/yxs+6lFjseCvCVUq6gSdrr0trSky1h6ntv8/eAg9NHl/xnW1j7xNnuvw32rc7C8zpVOJ+Nc1ikJnoe1rhHtSwMD4Zh/XMtIG9HiylCgwd095Lapp7CFk7zAu4/SdLrcq7+lUUj3y+1+bL5AaFC/+2KJwmd/PFGYrDewSMLCKI5YUIKMOuk9xm5NhFblmor8lL1dhK6Tck/e96IN47BTFvWxsJPMOl/lgevYaMH//qhx+/qNzXJerqn1Vfrti29LDj0rw7di1bJwKhpoXk/HuwObus5Rfjm2//Y3M6k7vTI7OznVGmvZQWpIx886CeV52sxRLFUoddLv+sYXIMkKqIh9gatchWZdvvmfpwV9I6eNl1QnpT211Ce7Y0Lt9+tE9KsqdzF5rk/SX12pVQ0UF3FthZe+7U6gg+VVQJOskx151BEXhLzWW8v9+CKftHnv/uOpUMNGvJ8Xf5hTw1yf4KrDltvP4sjr7z37altAqIhSeuCX9fjzPyVmC77VyQ7ejvfdaF/i/qWFudkkj85+3F8FR9zBrhDzDosG/rV42C4lDQ7JvpR+xspcZA+1TQJyHJH8NrvNtkX7VNJjCZt/Ld+UdcE785O7VrhHwWFSvvc/+qeXaKVKW040VGW7QI2v/Fm3t9Fe5F7U0A33X/48t+H2fhpJ/+G5byT2+vxhG+PiQWYaurzLhgq2hf0YI/gX9H0/oT2n8EVLLjDeECNA5nNxkni93tulLd2D8jR6yftLiyFRjHgNo/fW+deEi/+J/lEvf2vZQRQEG/XvP+mH4JhR3ZbkWvh+CUoA93+P07QLefsmb53Ne7IOgz37t3RXOiQQzL0Lh3gi3vB+C4r73ekd/f4IiO92hJ7eMu93d7htaBWcpLry4ftosdr/7Le9P83P3rrqu7EkRO6cEMbX3aEi+ralKCosQwxG/57+9z5FL1BH3fXYuW7+7EFFfllXZVb9Qnve76/FkeX93vd+y/EFLTf+M3u5fL3fvf7Fm3MIeONbtp03vPCX9/BOR3EOXM/xZf3ysSV3Llm6xOLEX2dZO9Ek3frCe73f+JvfhJil3Xr2N9+nxyy+tab3vuyFu7/IIwN3wqrPf7LmBV32EfNXVdlu4aevtelTcfrv/d77U+++ifX+uuRQle8boop4TX4zYo5tHZxoo//M9N3tfCX34VzdwzsNi2G7u4u0eXDGy/1WT7lKXL9r99Xk+1NtMlluZjp/9QiR7u9x0yTPl/2cn8KLhTHDHcVu+7w3Lbenyx5z4pJWb7/r//mvX3k+3VfKV5J5PSsknel9BEnL8j+XcnpPJlin5Lvd+sLLXBLd3HVhX+kCuvu91v66VyRBSpN6cV9OGfDWb2yIq8mm/zREN38NxXQSmJX6hOTMO7/WCyAAAAUcQZsAFfBX4sZx1rqpTqLxfPzUjui8v/l/i+RUlcqNavy+3C/i/L+TJf/cYTllwi4CFije25h2vwhwpDjRfYJWz/tD/sPbTKxLGWsbj5+4j2nDpHjTAv37it3u5r5P36PcJTpHfHyv5PpWnLTBHZhl6XAYrvxh8cW3d3t0WK4+4PiTPa6b2FvCPCHRzIL+4l9/ZrIKJGHrHcKEcpTPEINm+8aDiyoiDs6KYC//TlQyUpj44710cM3TfxJOqBKtOM8KthTkdufoDKJMSJlV4en78I0OV8p2ACvU1h9ue7c75yetU+43XTOP5SXLgy8t8GUBErkX7fkqj3RL672fX4072BQJnzJIsGu5El0F3mQd6K3SM4InXumpuv/8JQ/3TfTvjOZcSqI6gORvj2jc/+nXGUsJ96TDySHEtoPLPw25twjTXQU3cag6mcVBayumAn/ZlkL+7xSNpHqH96Sn7mF5ipiRhpDy2RlplRLvfVdDY7B3W5Z0u7Y7d8LsJ70ilt4P+k967cu3OrFsJl/8swoUZ+K3/tjJ8WhpARXeB41rXsJ/BNY1uQSA04vSFP2G5Wqefz/CfZ3tBQzVLyjF7O9zGsTQM5PJRfvGvkhsDE4XOv8FBY6G8jt4cTlH8PwSefuy/v4RK8TvlmH0VL2Vyq+v13XgnMX+QWKZT4+X0rboFEqbYb7p4cuVlSn+QmtPBFdG5Dx29wrr8aXka9xbV/yd+T0qK/ctqPxvpwpBEr/IdyTXb5AU7z5s0W9jYwh7CjvvsECHoDf/+UHorye7/eve5D7mv6Jm3/2Xt5PJ7WdFtKYh19Cuq2JunCaUsT6cm95PTvsnHioQjXXennj3Hk7p7FIGBdh68wFec88f37P/hAhRYNYP02Eg7HE6yeBL+LE4bIvY9d3/w5dzBvyzUv93iZiBB54G3vsSgR8Qi3usEJcuBmfa+T0rXTLFEsdzxIhm1qjRcJbieENNifvcvccpYR6BJ0z+qdVYTE5/3d0dgkFFC8Mod14fgvLeZd718n0/sitL/8FBXvd9j8J0n3y5dbu2Y19yFu+2nJRPi15gSW92QkX8srssZnLPfejvp+z+q19q/R/YvIT61fUm9+4Jt3u73hHynTl++8PZfu4l9u7hlsOqzX8n1VZeCsrzItrabzXKLdm4ML75/Uvt/qhiMjbt+4jd7ef7G9LhCCSUpuVPxPl736PHd3d96ZztJdBCJhru5YXu+kh3d3vufOlUzCd7EG0FQOIu/0Lu93c+fxIki95I8Irex4yIcfuxWIfJX9Cb3h6KuJQ71aQv1iCwxoHTG8HXsMcK9S3jInPdgi87S2T7csfv6y+M9TiUaTl/WP3d25xXkRjvtnTtpeixpL3ROBX89A1/z723PsPbT/tfd3MPVWE13Qm7ijcdp2+2XqxRxlBtTbzsPtLoQuv3Jq5dtoar6UEd7x21Zm1k9elkybu95OJ7iWArtz4Ey+pa480v3P3svc2P49gsPl1+y6/LzIU6bRaC0cTmne8RK55/zfgJa1uz8e7k9tu+pRWK3eSV+Iu6Lr3F3HxP+907c/b4g/TKo7b08IyPPdLl7vcKr8IGEvxW88dIkzmkr0n6vtqJL+Ak9yHf6Xuvo/f/v+EROq7vy5J7bf0kQ2TE+Mii3Ty58kpL39QsT+ifxF93JEV38XGfZYxR5YOqzFVfWbqUz82fJcpJokR5cJV7f7OW4W4YyQSiJt07k9COl5JCh96/1cZj3x/4e+MgAAAE2kGbIBXwV+YZcPy4SWWJXuWCD8jl7O/P9cXcXaOb2rv8xHovcXd+e/5T3KD3QDBf/sJGw/WmCeQue69oIVJf4eg9dyP4bivl/3xne0f7pWhsX13qf4m97HnX+N5ft3KHxkbQ+JFi7rV95Upyxb+t8LV3gUZqFXBUVw3+/q88JHvKLDqReditLEmjunVQ+te9wWL/7mI93vbcbapyC4BneRtbhbCqVohvV7IKjlvfVgr99N62iNmGGGV+xA3xw+YBX43cNqStQpdXCMVbl3Eux9oiIIZ5Rc4X8rlxOOs6/y/veFPjQd8+YKPH7Vp9siD1F0wfvJWpMu4QKvHtwb0tAvHPmDx1nolxbWp+F7xzWEjRHu0WGX3/uJoO9vpwe+c1J7b9NEYKaxQ21ejcM55bOFhor06+9rCPNu7+OqoCp8tcwmX/3DhhWKxWN3Fjfb+9toaQVnt2oOH7mVIkzBMi7OesVJxdcTBfuPpNzp6KwVEPqyPE7aN3zG1LbsZj42TNCzdJ3lpx7S6CB3COew6lh+m3Osy94Blf/8RLVFzaVuEY8d+a+ene+VxFKQ4q9e/5s5Wef2dKXv3BIIhG8yphzzqZff6G7nA3hhxPlL01piaJLWCRuRfTCIMIVM4XpVvq57K5Ywie1hHwvb06d1ywN/6KFObXhJ19/6t/FrzvY3RyYz1I0z8j7n9/hrPv3GPcnpJfuCggTaebec5Aqcua8iBVKF905BK4/HY3sdjWJpFvG43UzrQY0PXbfQvhC+fjBFcP+T028T8WfnwiMpb9QRGunfoqKXBI+pm+WQr73RLQo05JE03ITgRv/NWlqNJ3GGPa0bAVYBzD3u9px/Kt1UU4v/J9pOvGiRKb3O3svCL9Mpqb+3XXeINHS67onm/T0KTrmj9P4U+OXzROPqZfegZbGnFhkafEKUjwRiS+OhyYvWlLzSoN5PStt863Slgrvdykr5yRncvdWyfepa0Y0OrXHH+9aHlDKVot4fy+nzPhHwQ7pr2/WrvoiRa/BIIZucC7BUe5i4XPVPvwgW93e73eT1SL+jFLDf0C0mRru4Jvp00Zr8t7wlljJ/fZvR3e71ZfaX3VfXk/WvaJ3fTS92ezljwo/oE8Ppcnc9Hd0/ucrnz8306xaLPl69EFE49yHpIDeT6X/BD5bPeaKJe93e7q/cJlnXu0WHL930yXnEb/JywhIv3+S7u93i2CbuRAK9wxI1M6ushXf+HraXRQ/F0n70pVJ/pb+7xsFvmeu5mUNeq8TO5z55o67UpH0/X4a4JfHvcD4Vqa/DLaf0hnR41u97d9s7D+EfND8StnzPXYm6k1P397cOjht/wl3e7urH+vSeXTpz/yle+tzTd3vJy4QWenPeEfJh33+fh4733+PNdu8Swnw/vf4KxLRbdu33LLWPBHK3qUmAXp6Tfl6HmW/4SK+4MZ8vR/fqyclX5PVvJ6ye7i9fl8trJJfCg9uRyf4T8Jb1nz9jBDu7vb4cS+C927vL7reJLtO3Ar+el9+xrIV99F9+T0vRgh0UWpt6GWa97+svv5mS935HCy6xEdlWW3KT2l2yxW7n+7N3fQjuy/fWbzXySFkxcM5kCWfpJ1vwzwayJ+FYM/rBrvT4QdiezFWSpcdXu2qy//hm7+5/bPT5JJya4LIAAABZ1Bm0AV8FvmHarL/nuaNb/WSouVvdf5S3OicPBgv/uMNquoanSjkr28XenX2CC8774dzLKiViSXyq5Vzp/3NzAV7aPUs53mYpPLBJ0zvKjhVvibvlNlxE6SoPBk96E1cTBSe93FdyvuZovtVmRjO7wr4JOHUBmLmy/+WPl5/7hBZDga66uzet8aR+YOHxS/srfodTTPqGDFASLnRfAyP+z7o3alLxFng2B6wm420f3G4TWUzGnkmIDZwbmSI/qPP+k4K4WDrY/s6e7i0MErEG8bVn/GdJr8bTRfx44Zuz/o8F7xuyijd5gwyYqxLSr5KxvS6csPnMUjcFoso+jbJUZ8LsT4+WfbZN/2uSLyFoCcHQrAxMH0vu+gnCjQMS3b8ZWj5YKbzYZ4nbmEhjVgW6cZPpJc7LCN50Li6kOeXw7yS/7uDCfP4Yuh5NWM9/8tynEN3CqbCZf/cwgVitxX2xhBWdp+4dwm+9WKiPs/nFtCzeUyqPXGtoJ9dwvfxpA7NHmeCwT0Bj/t9XAuagk4eUbAZiWfXcNY46BFD50zX6ivSP+NOhQ3T856oq8JfpnE2NaD1iXArMkjmFrxcDV/8c38Ic4HMI8hssbyNpK2hN7357vfBMWa4drpEHc4vyV70WjQvk9JMpKssFHIFHj7LZ0fV9lvH39UpaNW96DvXxo2g+70IE+z4WOyr0XRX+EfIneEnp4+uURfbvfL+7VB267mT1xce8NW76z1W/+EKBt5nxmLBYF9z0YptLwm43MuX/3Csz8xCNZlG0yy2rkfb/O5Gaku9evNsn6XndAnJmGyl+dhxJ69q+Ci4Yd42hHBL+XBEHy7Gwkfc1o1ptLZ8k9pPy85FL2v6brDJb3XGdP+M7TcqFUM4/AJd/L9z/H2PxohfOflk8e370db0Kqvs2qb9KEfBYVM/Kx8dpb13/JffRS9Uy2W+0yzxAqPkXo87YXcl94uG5e70+N0+H4ZlvHqcoXgIv1fdy8bG17B2a8uh58tqWv9P4QONHX8xZJ794ry4f7p030yZK+WCkvLl7joldmT11gjhxWtS69snptYklpCiW5zKaHftFe+C4oE7+RfbU18fp0Iv4gE0OdnbeW0/EqWN0r8Iau5/XVXXq+rCZ333fdEjP2r/RILhUYEjN+KmMgjd8Q1YKShIv9D+jdiy1x4RIBB/w/794/Mg4h8EJL0X6cFewtPxRThfle99NZK97RKYcVh7v4kug83bu7+mjQb6bJ0Z//KLOovCOVhAU7u7u94+XvJ9U/sgsp4b7vyEVu3CeEXnm776sRu7gkGlb1z/7PBT89HfvvTre4+JC/tLSW/Y2pHm62usKQ0pP5jbt3fuf24k/afKRQjd3e73d/2c/8IehHb9RW73d/ohXv0JeT067N3u/X4pFI761CJ3tXPnZ4aJ7dr+E5Q0+93k9pdvFMTt3d30ldZPSX8khLvl+/4R1JLAv9KJK8POuyVXLGEvJ5VW2yuH357tciDWeEmxKiZf9r10WCKx3creoSvfP+1PxWXNK+/oI7u+UGEe77/FEu90b9P1j938jT33tVxPDi+K7d+lhIvlfO4yX9vp3cz43cfb6of3eivx196oSXlzewWEnqv8Tl/+cJW9b0nuW792RDYTleX3vfVlu76WxHJ60ukMifHaApOlp0a32EvcPaP37EgnMN6d7u47ayGIIBALR31EMH+6Ydi+iX6rWl2xZpUxA7UvN55guXKS76xOtaa9ZShK89Wu9XEE/Zr67EwR+MqW9fwju+5WM8v91cH8KP0ggIHcXhEvdlavt3eusQW1ElQcXrTLN+DQrr8JlvfSel7UwuiayQXcv3eUHRYThSkfStbUpOGPFxn+cPFdxmrVekir0qQhiJ85cu9a4Iu7p1zOGC+SSTqdK94wv+IKSZDhL3fBZAAAAUjQZtgFfBX5hkkz6u0vyy7U5f1tyyEQ4PPEvFyeyBP4z1V9SVQY8OZY1X+Lcv/uCMg8vkgmGX3b8IQnXVPmfh0RBNHDXP2T9NOjz9OeWGbvr5ffJ6qXu4KLjp4ftzqMouxMN6YcXC0qoxOn9VkQROY/Y8/LP3L7k/XJ6CZiztUt+pRJBWb6oaWCpf/lMMfDiTKXy76G28ImlOKGInAtvbmFWMhHr19gG+RD9vdLaClb7fjpH3ZEoJEThjB0dML3+M1pQNDB4LfsFFJOTJS9g0l+Xm8yCKzxkiUmnGXWxF9N3ufxtfBbBNIOxQ42XmH+7jiFD+EpeHykfTR2WECmWhJw6naNz3zavo/mCI+Fo4s0IGkvE8dqVB8eEr9ZLjNkT7vJh3lKe5yfJ9+Xq9Tv3iCO/Lv31+UvPgTL/7YcMKNxRhocpfk6PrdoaQVuKytCXwI/ggyrEzAcVc+8p+uEx/inuV5Uvy+/djZ9nXG42Aje7duvyg5FcjjNagAm10v2Jt/DEvcGyl+OiVrK649w2HpiHnfnqG9Khopff7CHHvR2XSimmz9LnQz78Fm/k+q99SC8kIlul3HZuSf++4TcG6Ev/75n696VbQKt33uQB8i50y/rWCsxEttDdhPh+ZTLxfwCCP+HvoTfpgnvU7u++qzwS9Xe5Fb2MKd9KV4U5ZjKd9xnAEfr1/H8+Hpb184sn3eL1gqpIuf6rzIXnBN48wdXtl5ilbsn7fb4K4cQ+G/hreiTW+dwhi9/NuNbgoJxma5xqlWvxYkZgHxhsQot5/wTkOgCP7mt8QhE6fRefOv2yU0D61aq2h5IPkjjhXXCewcMP6fTlE1SK4R9EzV7yj7/chX0xoTfvBUK5aCwbbAvvrLvCFDDMvl2/93+MjF4us+GsREzw2zOptvXgqSlRfpwFl+31Cx7JNkP2G9FF0nqFTmo290+5L8qGrxaZHm7K03XbYs5gMkc5byBkoj2gX33whew1HO6v1RHhw0FduXvDwl7lf+FCh6Tzh9iQ5/f3vL71CPgh8foVKL/91uQ5dv6hMVjsN3cp7+cqjSf//hUjxdgfOgt81VDDtf/bj+IPd8wWd13fTu706LpiNgy/XC579PpwTzpqUx3d3KYhJa4JBFGuDeV2EDy4fNXhobf7eJf7av069+r1ur6/EnPHcdX/f8grd6+hZ3u93p9OJxi6/u9dbp3hHwQ73rfpAnvcbKx7d3GvdEZaseoot3n/7gjpbnvVGc6L16oqfo+l/0VBHe73vH5L0oQu+7vvcI+auqeVuHSkbcduPuIYW5yDA5Rl/91VhK9vd+sJXfP/sWy6ZW+nFHbu9p+sogfp/4q+77qjo10b9iWTL3ulxknwg91O4fn2a612+yu939GV/4T5QhdrXtz97x3KV19m7vrKW9JaiCzNyiVd6rE6dKUgISbunWlE3fLnl9d8JEOv7vCb9QvLcVuK3cVqryw/qED4fe7cMve5x2rxscSHkH597lOOE/rOd+w2rGvf5TsRTz9bk9SEz/pZxDBRve7ylqiyJRfeRAvjpjVmNyVhD+CuzzJ/+rh9J/8KE/fscKUFQq0nP8Ium2Tnxytp8sIleE9v+DDCyu9v++iyFe+qf1otaSsjRCF7iSu97v5nrKbhZZOPIXvaJatpcfiPeWkW/3ekqXpSHnnye1QlakhG9Pn+5/y/q4iJt7on5fupKBNu701pF9nCNs17hjNBEIPm0RPxGaNXmsxmT631V2aHfiJCntZIgsgAAABMpBm4AV8FfhgZnLm+HooiUq/JxhnXuWMFpQm5ov/uUnNfy93DPgku49sC2v0JR7jI/pExN5fd9X28/BB88L6tzwT8YL/UFx+Rsn7v+P5zzvot37iZYXlmNAVp4s9Hvf1eeCi5Qcf+W6VfHnnwO3EPhn93P7+JNkiHblW/EB4W8UTljIXg2dJfftwpPTnRmD3T/5a8RFgFlNO8EH/Xc1rq+6JVqPkwsn0r29jZH7J0KfSjJ+7v+MhBXBQxYfvkMrXzti9LiypkS390bjNhiL99dXqPfHY2w6ecHXl6p8XCP/23/6sJvMVdqSTuPK5F/d+cmcDawVWirGQdmERoubbG3p1J9XlRbXShS+7n+f5aOXC9XlaXfxpeHc7wn5jXgTOtTthQgrG5H/bCPTPBHwrgj6vdxBZ/fNap7ntDZYmHAZG7za8fNnZIfT/OcyMo8tAW+nOJvQdr6+LTvCTdLVtAl2E3uVG7T5PSV/Ih1EvX7/2+W+1J6Vfbgn0mMw+WbSr5QUT637cJlkBs1N311R35ZrY0lBuLi2Fy5P6f6BWbhFzbtS0SBtuwaYDn8vWrG6Ey/lp4Ior7d26bBPAx8LS0lzT03yeqvu4uCP5uv8H2j56BLGZijyRPzgzuJPXvdR3drnqV6G/CfTDzSq8bGYYz16jBohHL4aLlOCMJbikjdQSe2mL5uFy2hsNA18063Nf9JPQKt52BytMXFdS9ywOmT6p9cTLfxwonm9e6poI2VU42994BE1e+e5tdwSCA80uitvqLYl3uEX+CchO6du6xLWdfhIp/kS3e9e4XFCGg7sA7yYe48Q44Z9YcJ53+nvElQxUn8cF9fk+3Fe8Ybyz1hzz53K6rMsRZwvyRYkEmj9TeywGfJdfdeCQhA278Q6hA77lnhL508WGIRtKZP6vzwQ9yKYtpOocI50DsKlTxr32q4UgL7k/3OhenvvdOtoR6F6He91tMTjOX8Te/JOqOwSCgSPjzuicn3VAhni895Uvch5nX+XKvfuTYij8HqyX35GCm0943T8c1ThqgldPya4SeXYUvd3cKqv13EvLZpy7x6NasEmfVcXtgj7vryJYutTp+C2Gt/9yS7XY1V4Tu73vulZRG+O+3aVYJN77PiIRf2KpR9nORW4rFfdYuizXf2eyu/earvfo+qPVPvFaKR99H9V+/DyST0S739wh7Le/xUuL6nmd/J+lTTKWJ27ZbLHuOuDksrcl7TJbBPd3MHd3TCierRbgjk5Pr/+vrZMt9ftCe7u/osm8Am1WmLVVCN4ycPd7hG/jCPitTR2xpIoSyLZRN7FZ4ivcvu5BcEJd0yWT0kvWUJbEVa7nHv5CXd+/WGfHdVjy/+SxO5nbE9J0T9oyRXuTtVxPhx6W3PwmXxBPwVEXq7it4VbmNNvJPKJuO3Lq1EspJYNPKXTlmPz1RfyVwV3z4GRGl7P4rVOUT97uiQn3dz50vXkJ9BPd3LaL5G47Z+FC+9k4Khhbd3e+fvbafxBzvqzShCMDXm5PtPE8hcn7ZOXJ6+I/gj3udKWUpPyQtkvG1vJcsXKYxWIc3enky4/K9UXop79Tp6j93iGj+731jCtrSu8+Pbe/8M5IIiHj508kRUJ86MWaZvEtWWjv1eH8vwVwAAABOlBm6AV8Fvix251ek/zXPS9e4u58qfLP7iyu/ntwwX/3HG5byAoMWFU1lLVL+94YuekNS9Fx1oVg92uoakv+X97wWcMpwrY2qm+5wdSqX2y+3K2fJ+27n4YojCrrz5yDdw4H49T+T+9csZvfgqwjvgj2P+YFngbO5Tdyf3q0yM4ehnf/gmNx3a4J/3dzC/Kd5shXzCnO9s1/8rbcEEJmVwK9gTA7YB0TdWcJ17MXgTMlYdv6Lt46aF3BTXdw+3d2hlGbYlAacen8vpFwvjHI0NUHvYMZ9c47ztX1WpRGxpOi/aaKx/3GXshCwfUwrbP0yg0830fgh/BCHktubnEJosFPhp/3zFTONfr/vz1Sp1vesUL9NF+02ytU65Zt316/RSl4dcHsJ+KNy+Xwl/85Ywgoz4Kr5NL/vK/QgTr0uiixebck1t9Xn5yMC7/3za94/wpHC7dNpFzu7VJske1irsOQxPzSHNav049j7wZ0dewTrnk5akRqVes/0ldVlYQ+ihHLwZ7hQ6ny3MPQVbS+CHQaCtYWl04QLu72z0SdwB+SCPu0W9+nNlz+JpvjYNm9P8I93vd2CD8P6uX/fChrnru78oUDzgQXzyxBdjmPzDRrmQm9MhQRe56PryodwHaVCy9M0X5V/WMlvd2C/3Gws9hilNbRgMrdR1iTpMUFz1yLxUtFHHma+L9NC2WMkFt3YOVw480EtoLX1N7WzD2Lvl/39avqs/sbKd0vvl+2msFQqi4M4/HefHKTaV316wi/sEJeX5fkurfWSYrnYk9L+5iF46K3l+3F7FChnaRdQ8PYyJdeQ7dbhUqrMGPqop92dRhP/8nr/4eMZgrZZ4f5WoBQ8Je2SkXKIvT+tcScrrHOk8SHTYu4sr3zRvbVYKjOUpXP1e/EJPpxWhawVnz7KBil2bi05Z0dJ4dNm5fpotIKEdk8sMqRt8pVeChJYGOP3BVMETJoIXf5Xy+O0eEfBdvPyfuV6Emqosvl8nr/4gVx8sSJ7/BSUkx8j6TfXBbvPLH0kfYohz8gnd3ekuwkJK9X3me/rRmOsnd+kKPe931kpo3b28El54+18ZCK8rLd3eT6yfoEZ7lzKiql8nLDpvfeSV4Brvh793J60f9nI5pl3/VdelyQTyknbw7ux9+tf7/y/X0r/QIt4h/Qk9JxU/d3dxW+hL7O96l/ij3fe/cERnv+ZPen/660lfsXEle990+8VSj0/7uEls/k+s6qsIc/ygUV77yfVjEXZYor7yV7yX7Fy3MDZQzdZSkE3fJ605OuT6r/MK3RbFoId3s7vPN939/hrwJklrDbsPbl4dZz3rYIZ9jZlfHZWR0hPUTdxL7dxWK+0inTV4lhK7sPcoL9FW0m69fUIFvefN36vF8n3rlRJCO/tJ93ppIkEd9x2bJ9+JCSqWHFR6W8Je4bOon35oJzCt3d3dzC3fYkW8dn7TkdP94YNLNq93X8urb4hCDyZu/1EktT1ecl/SXuEbu4UV3d398Kv3CZp/LwT99u3rLcaeMZdlbDMNpw5e9rLZcnTgiPur5Pe9bx59p7s7VvWWS9yLcvq4QuTu+vTupjbvvD5b3u7d7ZUrRr/ZOldpPC/hQh7l2nJWNrlsuaRrTN1+uX/yetixmd9wxkglEOpj+StV8ieSIlMmjkkcyy6/fLnkkKSL4LIAAAATZQZvAFfBX4sZxmLyecv5O4vPqsuSfL/7i+HJRiJewLe68tFIMv8t3wwX/ywiTNjKH80HBzSlMem+OX3+wnCd5zuo0ZbCD/v+WCiY+iYBDMtaepY4zOJlyx3V1fDWr6h9XL6TLLBby9hDUjxBx7RPk/b68I7ng/w9BGbJ8YKPr3CJX2d3PBsr76/BGR3yi/KJeCbyrfCpf/KzDj931vQyfN3MPp28PzaoBXpyfs1K/p6syulpttfjPcpJBFw95sjZbY8zYO6BMgtvw1wy8N2SA75f18YUbELaqt8eyl1IQCZ6p4d5RHBwg/UiFk/pVc8IE18IzPr5Xv+xbnLnJd33MD6acsQVtynuf+4KbvjQrUdDzYR8dPvlvyEn7/cl95fEeuE/NlXhP0csKGFdxoFkZlTWH1UEr9AV065r4+6350HilxRwE3/D3CHIR5B3vnFdRV2L9r8JV//pWa/dV3tEuLyBf/d+7zZ9jsItd/xpXq6vcef+90wPXoXPT02P9g44CooWqJax4BxhbSyyjp5mBg8f8b57MmvCDydD9NNvfA3MUq32uSnqtS3T06zxZQg4hOKGGTyD1ruKI+9wi5Sxcvt/goMdnmVJDtL3ZPX+uEvBFtgWG34/rcPaRG/cZDluLvL/7jIR7tZvjq/nDf/el5W19iNklZiRsXAJof73wtaROxmLZgeuGrY/tobcIYhPzabc0iCzmIqi1fGk3PKUB98A2HdW+HPD8nK+fs+r8TDHv64uXr8nJ6110uG/bGmfOudBpBuSb5hovYrhO1GOpb/y/W+U97hHwWbvfd5/i3ziGXdaJ/X5YIr8O4Kg/IICK7rJnQyavBdVEp/D5SL5PyyizXe3cNdt7/VDSP/T+Co3L2lsJV/DaIbyRPIGXaayQTCQ3SumX05G+M3nPPtSKXebx4T96IwvPc5+6mhl6KT9/Epl39AnNuO+dqWHbnNvvBDIH9rjsYoS8FBXttqmqetdfk/kPHwet13hcUH5dvdScq86lZ8kW/4Ipw0ixL5vwS8qIQHY+k/UAp9Plr3ECx+nZYHv+3BGI3d/UJFnIzjuCP5856PDm71Uv/tJVBFtDRX5QfQJO71CL5SNiFdcv4nlgrPLjvwOC6dt6IJXoyN9+y+87XXusXRWCeQ9fu/VuCPu/j9lbIGn95Lu790ZvoEZXnQ7diWE73vvr2tantZLwi/SFbiufLv1WLWvtXeqPuj+qBFu/XcisbaVfrWmsJPK8SU/vG1ux0MnLpeoidI5ENvfJ/VPlKQXJL6iBV7vfX/rpdom9j25t73muQkIXvorOvXBdvT5/aEvVI/gmvu7u77eQqEpBl+XSxWrnZjaQ+6P0uTpdI3L/TUoO/fp/Ydvhp0D5n/Fa+n6nVcdxhHyYZ6/ovk/ijPFbwomLFpfkqjwXi2aHI1/Gak4qOVV91Z7cXUxnnX6KxBxWze79rUnWTy9F/WyzZl/cpuSJkuyQ5l/wBz7f6zFUVnFJfPn9wnFnn8KE+/snBUKLdisVlyW7uK3SqlVscWeL7tlBZBp75Ppp5qn7LZW3+vv3mJu/GWWtb7xe7uNyXwvmiDOOmXdMkgs4f73cTTe7vdJf1aR4Iz8uHTdJlRr78klLcM6qdPNEYZuF8woPtWbW+O5+CPdG66XPL5pPwWQAAATdQZvgFfBX5hko8N5oc6+EfHGpaVzVmGyf8XZSO442a82Y+rYBjzcwkePwUEKas3nJJJ1Xlh/JiK2MQmXSIOmT6ME2x3lr73KtL6b9gov343zDdy2iv8tyQmXk/afEyxe0Ybe+8gvVueJ5/0yiioAzYmKKX+6lyvcEhJe3mF6lEvNkKeYRlh+EfCS9S1TafJm41e3jSeHLNDLbJk+V6BnAj80/4oAw/TrmGd65vTLVRgo5/aXnhShyINphyg0INJW4zagWd7A7sFjj6K5G1DqgJavn/y7iZHfWZpZC/gVQC5WRFy89xpwBP3Sv9/9/S3Q/oBtKbqj68XTtLHabcHDWHWjq0+PEy5x/+gZ49OsiU1kXHvv/Gk8OX/u3srWeN8L+QYBmVneS032cBnMtT3cJ1x0/1SljL7mCOdIO9d328m4fe2Yfprw8UOXK4LnO2w9GUD8YgJd/yfdk0lhSW0mauF2827sPZYPJdcUyKm1xnP3TcdjKMM0+1Ni2g8w+FK9FgguyG7Fp15jTbtiwV3WVTfqpt+XuG5VmFNsKDj4/RvD0+ceD0UpVq97iLe4q2mQvG20JuHs01LrdMJ/2hfAME3epC1YdNPlHx8DL+thFs8hjNP40qGpT09Og79j72KRUqFkuFyhqlzBdJfvt/jPCD348iZBNO+DyH6LWMvpob4q7y/NsovRh3ZPX2fk9JLvp935kWVXKr/NdiBH9Pz5PVS+nBQYoFOGp1AbgpUslmq3/KEbk8JP8IhB7nXuf799HgutNF/nx2+wjDKVl/HmPkHua/gl30r6s17hHZnUWcoEwzZVCJz2/txLU1t/blg3F3xZPbbf/Vt/0KEvd+Xe2jf1rgqJnTfKwHYcpJdWRPff7O77hHw9nf0SbveuK19VkvdF6fnnsaxQqUjKNXnS21kh89ElSPTSYzy13Q1DP3/k/Sv8UZ5zxxkOX3Q8Enl96+JOOQesE/6JBfHsK/uv1/+CDzY+8NEi6uC/4arn+gpAo2Un30lXP43T3Zt7vnhHwTZcn96bq1Kd99/RKphpLowozX9UCUqkHsJXeE885qev0SCTu7ntfosvw0Tc+LxzvwnlYUu7u97u7vPQ7Nho8Wd74d0tbvvyJErsSUk7QGOq8/SXasVX177wQ3f/7S9k9Uj+lBEVylbpmE39CrwhDm5+K71gjlK2J0Xq83fd9/f1qgC6xZy5o3mNF+xPtSGd71a08HZfk+ltS8FV3e7uf3edMnru67u/k/p/yb3rtYR8lkJe1+ghrdsaBJdq72srtKfKCMrpacdV1SIRPJ6+tSp19Zc/14ruAhquD99q1ifcZLbd3CPmpLMHFooK9t7u7vEOUw0pMgREqOfwyxpZJezlCPnwo65R+zGVB1b91iCW77T2umJK7v032eUz78kMi8MJO4zpPun/UQSXvbv6tSgp1pKnV7WJy97HYxeeFZheoS8hL3v8UR3d3d3rLSEicMOVq1y2+0ikyFzTt3NZxmw+t+tEMLvyf1/TF5e1Sqv0hBbXaqju+sVfc/8K9gqFXvn9lu5p9oKHmVP5R8be6BGvb/T90zu/pP+rEadt3+IX2Eiamvkjo8Vffd5fat/5GXP4WX4TEH7it1f7Zb3Lml28hXa8kcJNVm9veeHzP+GPZFNmsllJWpSZPh74qAAAFmEGaD0pAK+CzwXisNMtcgVVmJV+kMy/lIQPSwZ15f4vnWLHPT8WdSYeLbP7RVwwX/ywTiOHYITFLiRdx8Pqz+93coqGWPOnLy7tBxypPXEyXt236aLL015ckryf37R4Iy3eLYv1eowiU7uXNO+7ULF/9sORL7v2nPv9xpMhc1gi+Z+tAZ+Pxh+regCVfKab3DCa4dq7zC++DsX0/DLwKL9wsrhQOTT/uCjci2isSGwUWixwGag/zEvqdzx6fX409OwACH+b4LUGm3ynBTtRXYa8CX9gbPpFuO6yVumNFDkkFK8h9ZKfCxNuQKq+5f+MOdLpPOr4W2OlfBbe5eRSYzKfKntjCl1P3zjutE5+UWOUwRvHvnNOD8v+C/JUXAnfVG/+FAKbpvX4J7kp+X/1BLbfsN5xINK66WQvcZx8ZNtgXMrplQ7P9IhL5cjAWRwPkE/MK3Kavdw+QVu8NwfJyU3cmKfa4Jty/zJp6MBT/PyyNP/hTQzOEnmZGU3fEt7rtK1RAODdevxL+uaaRN+5rzbdlY0ub0L2+2dfooffHDilWXDULWNPGnXvd5Pd/8KYS6evYayplR7o9T329VnvzXUwFpU5bgi4Ru1unW+vJPn2VC97ucTKHnu7hQnD0kt22nf0ySTiemZ867b8o/jV3CT+wTjntE8/317QKtlPpjD7blbLzFRmH794RkK0MpPaP4QafXgl+WpTTngl5nf1CX1nYtH6kg9mhLZk/S3fDvZoyXaauIx9l5R5tX2DcI92000Yz1+mN2ycifNrUo6xvCCxNLeHU4XHLk/vaUTLX7MAU0WL0o39n10PLe6i+QWla6+u8qn0CEzYOBzyfNCT9CQkW79S/eSnqrcFpR3vzC4/i7pVpxQyD8jHQ8+V9LikHj5wpfnQCIUn0Yl6o4/jvf+Cwx12NBF8fReKCfOuwNLxObwj34QKgQaX7rJ9rCuii77jsq9DZC3fpRRtw5bsr/1h8/lsEPxg1HsdC+VkatbR8u5sPPcMffBHbKqM3ub3BIaHeBC3FvXBMULC/l9EK9fKJWz/jQiT+tfFR+vj3l/Ry8/y/J/rksEx0ky+33SgnrhcUjlfvuULOwys+V/4KSlGWn3hB6DiXhb5zZy+/Z4ISUSeGr/d4gWceKLHQbnC/vBEIe9u6Flzq3xt0e1ePv8O3vYiru06+N8v4zp2p1MPnz3fLsfp18cUTn8IrcrD4hy5u4VebQ9atMNmvq9uX/2w2Xl6/L30/b1u0Ku0H7v3fgmI5/zjz7nernf3iMsN3+IYJfMgZbvsd9XKC6TvvuwCRfJ1sEW7fL8FJRtr+9yLToJC9sE8+dy5dP++5adRV773+E7u80nf3ZSbfXr0J9YgjjKve7v7y+/4Sone98npJFJ6gn3Kgt3uO0I+P3vi9tPr8RVNU0hmn+He05/cwlbpkugK9vurrJ7dl/l3fvE3e8/vTultQqvJ9vlb/v0+kujXv+Jvsrt/kJPCtq+C6HOnuL8j7IR8UWk3tSZ8lzfvlcE930kTygpdUY86iPi8rTcUR6d75P6/Gumo3hkSNlBsr6P//+qGEjOH438095/SWkCSbeKU1upPG/V2oIr26RS+lrhKHx75SjnNYTL/1ijCuKN4Slpb+ERLvnNHPHtx3iekhrUUYg/e8mIdV7Ohl05/uk6BIR9yppcdFlcgPPDCJ48e2/RIk+XNT+r7EWIvenfCXjcvG1b5fUqpIm6Jwp5Lr3e4KhVnsJZXLbu4rEDQ4QpciHHhjC3+Uffd5t38ioIKVCxXJQ+tUR59jfEziL1JMeIaEOyawYp0Lhsm32Htybk/fz6Nd76fS46Yj6HbtEu935L7vC/kIOytfKvli73c5vfVk3fxnVll9qYstP4k5ArVKsxHDOSQw6v+SSXM10/+IspNvBZAAAAUXQZogFfBYX/3Fisrgmz/OfHoPvS+hZF3dz5hkv/2N7QdaieWOcG+F780Pq+vY1GnXkTDnVIOsyvcbPaS4pKQxBj99aSFfJ3ngY41oQD/Ad49VtDlK7lRWa7aBeTKVMWRartWGO/uFC7juiV3HcciJp9gw46Eo1Tli8BjKv5YTsQ/pJEvee/J9pliX4fkRRPwSfO/1p5T5STw4/2fDQq/eCOXt6L8v+1gm5CYReZfDamsyZf/SKd5sMDQtlgnCRH3gn2dNSE2IlTZZbVFl/rcVzCx3zSIDlNRpvC/cx+HUncnrR39CSXLeG/7g30C9P5O3q4XW2WNu4rMFLYbEDekw6BJ3k3an5bw/NYSMQvYWOGkHp8exWNPOpz0lmv7jSTORDDpDtTu0errOsa8u5jO7/luhewUPK7pPApviaPvc+HtpsW/4fOrJKa6auOBeOOzBNHjgSdf3/3fZX7jJXQyQyj/eolmtI5/4qcLtXH3XLupdHuncbW+zf5MpaSXqfufPS/T/4S4ZbTvfJ9uW/9WXhF0zQ9J6p/yFjwv9o95YJ8bp4ev2n70SVS+/WCsym9sGX3cORbwLweCkcnBNYffJwk/TBV4rFd3d3t9gquSpxfP9rm5b9vxm680csVm+hyed5JV8OJGPjYaNf+zx7kyfWD/ka+sk/9e4ykU1kL80pQrCNg8IMW8/bfcI3Ym8BLfa7j/QMe99nYI5+gQeuF9trfsTExMVbzv/niddeCIt7j7L764LBG4dl+PHol7+4vqPEnd+eVBuEfDl34P5O67JIVIP6f4XEOHT/G5gtnkbLBDqWP/xxTBay3h78WHffnx3eGt5PfFv8LkDiSRg+wTfVIivicsWWHLWpz/wgV5oZP0kVd30VCk8PLPvJ7f/QTO8wbfmlugRCAj9w/7uJfpW6CB30rb3O3unKgU9y695CeMV8H2CQkdU7HW/wXTL13M92+ZCHmLeO0+H/LKV/J66slr8v6fFidXu71l9iyo4+YfgiGMUZSnj8vwWyo3p+UeeWvcEOiQwofY/Eicbvbdf3ozepS7mpsWllu3EkJu8JeYQ1VO/oFgmkeW5RWCdvdd+IbqbXey7R/J695OCbIGn8m0ztMkshJF/uQo/lXvX4VNFe5x/eP9/9jddAkvf2t9e7a6KQxXe+zoE5Jfd3v/qoRf4J773d378pcfn/y6cvusvr/JvGZzk9Hk1fr9Pe9Pe/TFcuXTvL+lq97hLyZYP/DF78I3bz7bV/l/3lBMc/8/6cfhGnSvkQO/k9vO+3MS76voQLP12qSPrtcy62IPClvLoEtE+5kWUG/KgRXbem/IR9KvJBTB32b/dC/F923Vi8Kgm/s0699b4JD3d0zr8Tu97gk2J1L5V2n0XT8lS3qS+9+m973tfYkgq+73/E+WDnrI6JzCXuGOSOll//IQQ0cVlz4kSjwnJLFGf3pJCzcIrhZsf2JYbPj2+Kba/pt7KR7/hAuem9y8xb92ecVbqvVrL/8gsz5nZ79PyQQcvxunRuG5Fc/F373Ii85Z8KP7HiDabX3W/hV7LGE1z6Fd0+4q5gdHg17vWMVBAb1OW9zuzq/uGycOeqO49fpX+xPJ9JW/J6qlcn7flIo/Vd56tPHnwvmiDR3G2IWGsuS/5qhG93uO942vVEn5fvN+8xd1ryS3SvJ6W1+CIt6SL4Y8hCXnz4jm+Irv3G6XzbL/+IwQ9fxvi3MMXl+r1B/kXxBSlqktpvNIFkAAABPNBmkAV8FfmGbQfPpf/cXtTve9/XuEe7bVd3/LeMTWGS/+4LyZugqhjJuPkrevwScbjMre4y7uHXR2/UoMmEn5f/cXMKkDb2O2bz1k9Jt8vLlL26vxeYGnO+EjD5L+Ey3vel9EKkLF/9lCOob5hl0ZSxEROr2/Okys8aR+fSgtb//srh5mgynNZDbPGAU27r+3jFo4Eel/hJqf0luNvjNbaeWzehET32vxVKFlilAmpcDQ2b2UvR9iT/D/qo+TG/TE///ytehlp9pO4dOApf9bu6o8PZd4CK1VPX6mHn7v/gTZw2r/syT6/+q6Gk7lm+OxuADTu56puG9jma4sdoUxoVZUji6V0fyfSWruMok5Qq3dHpmYTR1BkZen20+OK4QYlY7Gfnq8o7ZRY5C0xk9pMTP3CNNlD9/oZO2IOYj3k/aarcfd4d3cQ4GUlHkfalyDLjdmPfju7mcpcumXJx75acBCFfcv52GgU2woOFduUCrd+VNLeGUEZMzTWLOAc+w042GpMJ6gowpTdL6djR0s6BO2v8S3Zu+dwKZXZ+yvduI5+9tMyx6/pBQvTSIXu6iG7ieRr9fzgUAxvz+5sybow6iLWtPZhfhHOH2mGZpTxv+XIXPD04IuVelVueSEy2T3yp/BHzHCID34Ld7yi5Udnsn1/qYk4LHNXh+96ChO7n58fd9x8qxTVx2hNaLgt7gk/Nu97iqaEsRkvmrzgesuH9YIJB+AjA624euNE243j/HTguImO3/gp/wCb9x5igJ2qMl39vl9l/bfCBHkh0KLHyvyi99Xi5eR5zOst5494g7lXjlUPSVNp/E27+TQ0S0XJ1pSfQLDO8MOwZ7h8Sf5Ve+UJeCct5++/v3P9fViCkrSyFn9OCr+M2/SGBL28tk+7xruiCj4OlzuT3f7xN2/dcn6+fhGZdx1LvKLmC9CIF6Pp/BWVWz3VYkEM/YIg7rLuh5jz4zL4r3gpEnFr7uUT3cA73BeKJEuTnmt+2br+haGHu7u97ng4WH4yr9tT8aKhRryy3e1Vx1DbgE+7rD2lhOnKLuf6z9v4oUdryG5hqXPhKGUETBctuxP+CmtP1gk/TIuq/d85+xJcsgW9YJjZfd9+rHFvcxPd7/kvfosI7vu93d6S1FZVF5fcJPLbCl3d3vL7cgaehZT0ypW15lk99//wUxKGn3et3dvwQ4flb9zpshav04JyQ4g3Xc596kq00Wnn7fWCK99oSL5P4IpbxXqu1l7u+/eSvqtpdUX99flhDyFbt14Qu7s3Ezj2CPx+a/5faT2iFfenFvHXfOUu2eP6ohEUJf1dCfeq8n6m3/p9iUCXJfInDp4dsnq7kiLOyababCPqwT9IE/Ll5/dvJsEouf9y/Squ0hVPd97yJfzdXryUITrX1+5ju/1J3S5O98TNb6QIM8q/AT8hHeIfeV4k77hOmTGWy5orCJAkclH+BGr9/NvuwPZ6aKfXY9elzXP2/RbPufOosm7u+i/QvJQTu8uPTy/lJ4Q53qWLIyhRLfrCj9R4wp+973u9L4dLbm7X///dM18ys1cfdi4Ukc59ntf9EixZlU2MOr9FgiJLuZPd03vxHVOLQSIll27unlkKpQQvkj5f2eMyqGy7Td+WCb47lWt08Wp0SXzXr61Km6M8xUrhwe4iTKdfDPkkQhtqP8k5MhPrfe5SV8lzfgsgAAAEt0Gab0pAK+CvzDJckA/xfNa1rty2hpk0wCLxcpR3IDm14Y83RBvTfCJK1LFlze48R37YQnFw9nWm+vfgmoz0Clu5sFGFRyf0/Z4IOQmQuyvgHl+w4VcFWAxhcXNpsyV+nfBJrJVLpvcEFDu8Ez4/0utO/BY918lywyfu7rgo0ocEFIIvHsIZwa6dlLTWvwmTU4cP2rQXW5YQEPcdDDdDCLxda0CHdFcv19wpY8m0Wvu+wf3yBKm0QLpRrrGH2RFbxWjzG3y9gZgt5wb3/F5TxQi0OoQcS7F/UdD8SrRBv4z7coO5f13BFyHQzBik6S9xc38bnOFPBXmuYcyaO+8BB1cvyiy/KWXjDO0KwwMMj2+xeyerBB2qrn7P15NfHSewvZiWWzOU065iX1/GxkyIW5BXm0K3UyP+/t3b+6dq3gPV2fr+5+e8dIG3nCwNUPEQKa4DZWRkXtjMEju3K8KbWl7oHtyJwhQbRNwH54ZwbGECWUHvQJXSQluFCuUu/ppa4BBVW+Pl/VWZzgWbut1cTxfSqmitxu4J/m8eJm0QlyNDd93fCT/Q413/bxNT63LyX7gk3ulVfid3cZ5itUNfeiFhPe8q8MTny/W7h826opd8K4l3uPR0E5x94eVFDrttU/6KNpTHHMJPXBOEBX27u79fYj59vTVln/QKa1ov37eGXr0EGT93yvBTeGkm/+LZB6Sl3KSutJbhCcXYX8oPtELkuO5PSS/oJUGu/q4ze1Qm7FRF7B8r9Wf2qOlWhKCe93vtVbKKudB9qEX8SCg6zMJF+9U876r0l0JNZXKG86vsiBBgl2kwb+pPbPgg+Nx24dz9f471LmierfPhK4LKcJyf1lqNWn180ExzRkOzrmCUvfJ9v76xdYJyXcxzMm77VcJzBnU+2mEi/5P91dbXFSTBFhOcf+C2HIN+kiVeVR4W6xsRuby4HcgXfk9pon3QlKPwTmIi3vdsn9dONWT3frcJlvfd6dpMEt3vx2EbjuKve73CSzosZe7tz8bsuCTZu6fL7vwz32z9Xt9k9r+C6Ihq+7ulT6Ld36Gy7K9t2rvvvd9/iZd3vf0UuTwjmMPJ00z5d3e9t390ssnr5nqJK6b33l+/yd32f79D+1X6XzRO9y9r/dBPd3vfaQT3n13uEfCdPetZfnndoEWapts6ecwyNr9N59LS8grlLvbvRi3d/h21u5cvMvqe9P9DZiXfT8nX0vZEE59Z777Vz/6Eyb3rIzol2uT3780JkD3kclN3d4R80r86VPbc0/v+YXP+T9JPFtot8yK63yeryxtDBQrh19wn9fXtLUTd2t76vsRNd/kiZov7vyMpHbeEy+kjahe6Qo3m3TfdXy+4ie5Sj9LLG/oxME31gO6yTnU21/d54QJe7t+S9WK5yv9cZw7cI33ffdO03URfd95flkyMXvd6/JCj9QmKhLe003PMVlu9vGlu/zxI9hbd7c3rD+0/7crcJi173fb4iiGF0Jk1ftLyTE3en6BJe8W3kf2zu7KFi+aReQRivL6pmr2T3lKX2Hppt1Km/IxGcWfr3+JKGb7vSk/9lA3eWnn0vQWGPIIC1tvf4jDfjT2vzXWXvV/JEFHNOK71BZAAAASDQZqAFfBX4sZx6nhWy+ask974S6STxlo/myNmHvhubFhkt/49osbQyX/ywkTjM3UoF69QVW44W+HcOoMQtsmbL9l7gonLbL5Ac+VMnpU/tqlUnq35fqy8FGGrS3cwyQfSqq6DheXltuW/vfC1y4P+fjfdJxZKvLS2975SijJK0FS/+5hQrCy4bmeWNj8nlIlDYJT7zYwt6NpzJrvglu5Zc6yV/u8Ei9a/cWrMCTmtUS+CNPaRdN9DjggfnCX79xt8TD7bQahS15GltZguz7n66ZZcdd9/A3d++UK/X83XkL2jmF50G5OIR1nPy8Tnionq0uki3GnssW8imkeEH/7U8174QWs76VB2eKsP0See0K9DN5L1of+qTxhMCbumXpb93pSfeu5FW/9P8PpRfX7952DfrGzPyETIONB9KH95F3B/7hL7FPyenStLj8Nd929A/PzRtXwVc9gl2LF7HbAanl74rRx00FLkFmbfPS3d3000U5P0q+ycP9HL/vl7j8gUywoMFG5/ONG835oDLa3CR5xgLRmd2a4msz3Pxv6XY6XbxYb00fN93b6zxWYToFI2JbIBq3HTb9f6sft0P45VLkDuYryRRd513j1W95p9bf0K3fd8v/WCghQ2VQ+ZNyhptPwn2Cy87zqTIJeeA3J8t+Muki1f9Yy0YiZ3sgxKfdbgtJaIvwzeX8tK5Kxa+gSSryhLhq8bF+HYsbZPXuCPcor6yek0ep+CIukUP9qvExn37x1/XyYkrv7un00FRBpBm5H6UPZ97NQm/1W1CPgnLe9ysZ1Nd177S0uxbIaW/dhvkHjavh3lfk9//CNzJz0feGboLtX+EMJ08tt4Dl72BbyhoWKX9csg12XJ7dn5uKGTv7v0eLKNoP3c+In6tqWoIvNFhXqFIM5+pTBC8r3fn8r1luR31sfy/6EV3EAl7hVo/fz8u5WPt9WjpVtyCWq5fFfoUEHKCMayrLfJ72N/klc9Pr3V+j0dh3RnyfS1TaaO5SrKglKyX/Oxf4u773hIv57bYUnxqXvu9uH0kH2RbDs77f8X3d39i10Vj4EN0Bn/d95ZXd/Z2CKHkkn3Mn0//1Qu7vfe197xtl8I3vd73d5f0v1rIPq+73fcJLIKzbult+3WVVjiy07u7d/Iu2u3RNdO937PLe9v9ahEv/2Ei8mxX/Jcole7O1F3itwl4/zbxkgWtTchXcZkuurH3pZy7Le7yfSv9j/l7vabxuEXa1iDnz32W/vOKU/v/jGS++0+0hN3e7/oVoppH/1pEYVIAn9f+t/Rp3uotfwl5ZO4e93qcvS6XITr8n9XOi/rX2PE3u93d+npxF9939k3fL9/hKf+N9wk/pkH0PW+I8z4fcm+pb3S2LZrzDz6wXle/NX021/J9pWL3N19l1Yvuk3BHloix23pYS3vLuFdRgh73ivd3cI3ch77iCw2l4uU3vCPg9NW+EzhB0v+73q6lR13JvT2l0T6QJKJ5opVJ9a5JJrt/WFskhi5vX4u76UuOl6opBdLS8mGtSU0puskQfk6ruCyAAAEu0GaoBXwV+LGZ+1hCyvuX/3LR3C4tdwxXCX6C3+x/H/6XuLl4XffPkoz8pV0EEdRAYL/7hERPbUz2v2hz8vu94yJ3+Z/1D573vwJd+dbR2WEdxl6Wy2kPEXmAoIPrbW/quxl73fcv73eT+n3LGd05Z8Er0HRxfL8n6r7gs7mk6+EY4N0cjKN+Wy5O7l/34WL/5Yo3NI/+9toPWuHdfRLaXu1g/2Ca/PxmtuwCJ3dz9XrdX0pLsW7EH8E/XWN+7isZxyBfXj7t7WZT23dpVoR9v7YHXyPXsXpWLvgBzdWc/gz/6D8JwvCpwuULI5JNO7Un37q405G2fBu8lSrcuZsE6bTZRYATOGroLK2Mqhkl2mUS4gwvimVEyPNcP2+n/Gkicol1DyEzYj+knG7/cqzlotWx/i3mtFJ9elWIrf+CjZhMwRaDWIbDW8NVlA5l//CkiwKsCoew6fo3/vCbhgx0q7ib5h+zLk8t13j7Zhd+5syc6S/c0Z57cv9FuNq4/hTbCgx3dTR446pQINxGCRsS/GkMKs7GT00izt8b3BEbRXKHUNY+Qhr5HmIezYn9+xNW4XababNZa3qvBX9w5nKP1x4ToKSeXRYSLebtTIi5J6r+4Q+P3I/LBPlAuiwjwi55l7x7d6ova05a3/N42Jcvv2oKyO7kXhpLsOnW6J0nt+UXh6JzCT/37RBRkYQq7cv2uRAmJ9zAuUlOPJ/yc4aIvbQltguh3bHy1JIOotV4diX04I75KOdniyj9HhLu/dE4vy0wxLPtUfqugVEdzHbfCXh7QUfrWb66fB2UgkqddR/ane2EfBFpywd9iLe97enJhiGe/Z+T+t86JBdZYRk87301yjjOyNuz7QVv94Jvj9MdNVjd2UkcEi2vr810UvX1nnLX+IgmvHZxX0C0zJk+/EjTuPR2h84Ut32Nihm2+leT0rE3JKgUFIL73G3bY6oE/MUfL502lagnmn85crHlk/dFU5FBCRyLh73tCPgkKtvd699OgtyD2j4vrEBDQNXyrwm5/qgXyng2pfDkGT0aWPfGUgqf+sE8eDy4Nfdk+6z0V+tEY/HHfe99zSk/uvFpWNWSRgo87GP9adW9tE3d68Q2fGcsIvyoEYqVTDTR+Re7E80V4ITO+/tCy7vd/WvBHSnuW9MmzFe1cSkZ4S4hf+mbe71veqPqnRcusE93u99QmtzxhW3Lnc5W3iuEWBkNUx35d315rFnNk1/R/5hU6SU3anqqcfWurNe+03aJvenxRQSkMXCbq8l9l72dITf4JIl8V0q76pysom8EXlvHo8hAl5X/+vrEeXjMR7Ektf2zvfXmhKf8rF9ZOqdfBHCT+Yhu009FqJGaEvZBpfWX8wjshHu9LYk5Su0l2WbLdaTf6+vSmhLRfRN5PKile+tyMfNH8vI/9iQhCj8iCYqfN3d6fyFDWa8fezjU5xas3JvfXpJf6y+9p0IjePfIceu6d8qALeTH/eXyfwSkqH1+mf6V3bZJpriHHpumjlU3plv7UkR6JyeqW9CJe70pYiHSuf/DAoFaudJNTxv/+SzmHO4YzQ0KCZ/ch24nbQUmJ7a/+I6uo9nZllpfETTxVDf3+IK4qje4Uaa+sFcAAAE60GawBXwWeLFWhzk09TXf5SY7JX8Xz082/NuUz/KdY/lhjxYgaymIjOQuefcPx/TQueq+81gTNP3VK+cKKZmZGc5/cf65+/KRShGDn3He4fSkb790VW54LZaXvuil1ZeF+btmB2YY0AX8kV0eML3d3d7vPDfVamI27OFvFeXx/zvOVuWESOnXiD67Wg9hzQv4TeegL0AZc5Svq7jK11+tjvE7aqjgX4g+9xsUXNU/+/TaK9WW9gscgW+3GHI5ncnK8quoO6d4hx8HpA7K9h6a91liSeayrBb+9sVesFvDt+HPQwuuUHSjCjAPT03/YgdOPyuxXohkO8n1SurjtyhOtJuH4NKpCTwP0lqE6p9MbE4N4uvcKXiGF6Ce3IXt7umH5eCI2P7uMJ+YRwm+7a3LGkEuOKzBe7DCDiqo4Zq2bAoFnQY3+IIZwRRe9SP/4Ulf8Cb0Zbb1JH5mt2YEvq84vUocQZQ9Tmi5/p/xpbNm6Ew2Xdt1f8d4Q+vWuHoT1t//eIOYX674O9l8i3hFvVRe9Wizv02yeT97xLaCU7ii46ejLlR9NexcIXhe/lIluY9l7Vq5IS5Qype96vFxZXn88N6pzwheyV8hUblIlGvpawX7u+EfBVvKv6zXk9UtU3BQTd5x4u6XJ+r6WFCbwh4TD4QVVmy35BhTVNDF/7jkR7f2cDr5RcHD1iwkvlBaOfPwe4OfH4NeVBKqC0U5o5u/4zZpUI2yvo6fhyf6sQcdefJ7SYtpeInzeen0W56xtik/tVxsFE00sumBQ7GbDbmnhAvp1aBnNuCOwZYZKOdawe4iXULS+71ZV7RCeN7l9/VljfcJ/qsT2+5MtOnvBSY2eLcORdf+8V6cTlKkJ0Rwu42QGtDXUw296U9yCt3k93G6pv7y+/VgkPmFTheLL9fguNmLnDV5e8JeUry/peveFboalS8FI695x6QfdONkbUnG+if2u41di0KO993yfq+Wo/L7+SNyD/6Cc6B93e4Q9Uu/UEIh06dZPqvLTBWJe3e5YO5vc+m0Zk9NKz+mVyhf1mulRyetVqii/LS75fV7n6PEEMysdXyb68vviJ7Pu+xcUR3d70tuMmCmykrCdJ7vdPr9sYW98s+73nhvrBMYqvd32HbhEn9Gn+CLu7KL8s/O9/Viu75/1TK/T1Yi93n/r6vz+iTbKX6oIyrmPXe/u7uX7PPZ/U/t1wWXIe3u7u9Ltz0kSUJeCLefGPsIFfuf7u7lWYPyek0ll7LMfKi9bmuz3kpDL3CPzTffcJ+k+6sf62S95PrN/e992Kv3u72mbk5/totRhUt59vn+Zr9Kpo8w61RIugbj7Tu3cVl+7hHzZXwUXPqf0Cetlyy5Yrct5GUTljpc8I2N/L973+Yj2WT6V/+/b9KnW2um0U9pc0ly32kmSp4vV/jsviHmbsv5YW8I+TU4/2Tm6L5CQh/p+WCMr3crZPWzl8SxXd3chvtNxtlMmlT2peYnP9khA5o7uUSf9NPvbJ7XdmiSAkI75U69qrQu9939YU0x4iX42uWywcSffrH7rUsF5w8uex91a5LbrDrqMX15k+YKvJ7pie/k+m6v+7pakqLm1660uFvJIRZXk/UssnLvOd5ITK9y9yLadF5J2Je8v15oIt3pX1ZQ6lCWO4Z8EJJ2NKeSpLX75r81lWHzoQbeCyAAAAE3UGa4BXwV+LGaT8NmiX/3L035mfNlvrVwl1a3OS+E+0a62gx5vDLc5f/LCJNTh8oUps12T+Okc16helke9wjwIv14/75fy7cFHPgz4bILAlnaYW3EvE3mI7f/F4aUzCT7nxsq/BR4yyOKuN+I5jODVueUt6dfRiPd/lE4X1YV8wqM4sEHljd7J0SEMjVrCvrXqwtW9IZYpYAr0/vs3OBg9pEmvWGO6UJF3J7ZKTRV2dggmHcMRujrmCjeW7Mr1M/749IrBskZoXlCut6kXa2LtJbOxKSWuVbfP9HhQs90o+GzxPb704eX2DZ/k533H/q2klMxUVuTdfRSqroF5OehuAnGmzP6p6FnP46n9V4R3P89mBnz95PdIT/EFiMXR9NX/vLSn3BBSNzatDq8ZldCyI/sPcrfyek756gwqXuNQstxffq9FL/u4JOTU0YqX/cku7uFC/K2+FBz3LmwQIPFQ+eg20AL+/vqI6CSFem/RYUqwM6F3DzyekSlFuK54hjTt39O/c4EuvhA/kQ0uANPAIYvW9+v7uqVN9TGNz65g9/9lfa4UIndZEuTjy9/tLbCUP3L+GYsmNHfw/J/CXggfPzzdXpX73NhI0mgUv6Xh8jnSdAs/HppgcTJJonl97nDQTenYRivZStTy+3ye1Z2tFheMkTYe/C8Nz4exL/J6S/kQLJs+YvMP5jb7xuctVuMuh3IWiAV71XeIp5QaQfeM3+4yJa56X2TqRs+tswP6TDLrNpEUsCaTPexrCkxE5Qr9op95utmfPpSju6D9y8W2UfI/zCKFy5N+uGrjv961k/b/oJ9m+0+v8hzVu9pFSjBCKXIW+PH1793k/WvwgV6bJvFd3pwj4LCPutc/e2fkn176922LeT2np7xBXs4zW2R96fFIFIpcyqfXi3nXnFOXREKk7Qd0n3YrhDc7nYHcgRHxAeIJH/zxWvCBRr2nlwZYV37vpvenQJSuf+78tr4kxl0BEQ2dDkThOinrVBFMtf4Ix25Cvxo8EUid5F+SINutW61c31goOm8z8m5fueECf2tc9P3HTqPd3d8JPysI3iu4bk/dhy7x8bSL/DBX1d3XGvx/8Re7Q7B9/VL3BCSCUfXn2yoh2Ju79PkiRO73fJ6++T7giFXv2X3+gTHR3cuXMaLfIbkfrXBKQNopT+4rLzpCXm3v3BDa2zhfiy3SufOt8dNvtPyzSXqjvRC9SebrT6XeTLR/ZVCXgiw4ukVOiO7y6FT0mDTgj+Ne/2J61T9e6zwlJPuf9/iy3enrsagld3d970v1CBgCOq9Mpe13x0sTyffd8TDMIeEwtL+tJcoSyv3n/tAjEy6Sei/fqYhzZEfo8JZ1+ens8x3ob0qjnujcojd5PS/yRBTR9p8vonSj9t8kecLv/Ne+T7rLldSXpkjcnwl7IEecl6dZjJAiwomOmPJE+O/7it79wT3OR8BH+zbZq5Kru703dFu/e7guLx7pF9KtielmFdqQupRa6y3vpWlBbfEPvcW0KvVsFQyWXu73fvoInh+kb1on/LmUtfab4yX/0u/vrT9denC5fLu6CBbo7krXLndit3vd95e7yftBDkyGLdp7yaCEdX7npHtxXeX8kn/Yk/HJWwxkQVFTT8S5Vx8Z7/BLmRNZvkouerUQU5y7qVUywWQAAABLxBmw9KQCvgr8wzaVbhufKPXcn/4vmy5Cz/xfcIvMkzfQMMebwzAa+LJD/kchneTY/7uMj/RNLtIkj3u4nwTdqQEfv2rrPCm6PtAb4+d+yCZc0i9/lTrU9povDkIuDFP8UvhE/SVJdCYrRG+cVy3aqdhMt7Tp4XL/5ZjCXkDrKe2Mv989cQ5bvOCmTFRLSRXQQO8UsS9VdivaGXL3lY/PdDSW+5AGJrq8/sNlE/oL7Ml68uFFF1ahULeB9fX4wphY6F0jaGHlokuywM+0R3/7V9RZ7Ll6jgsnrtk6h8m43HG5C3+DE/DRgaJHDcXa7h+0f1eWCucXbuH5n/lQXdOavxBRyKJh+vv4LR/69QpnxvOUPzdBEDQ4l59WMn2eQvt+4KowcWnvDqJsd7yIXyi9yXLj8v/QiW5kCE1wUyxg4UbvyhIbyGbqbUO7pryGZu3jBdwgY/G6Ksb+8Hnpf+CP3QXsRaVC47x/5F6P17avhjRs0v2VjfeZULNWUv1f7Mr+Sr9x3F/8NlMPLFXH+/3ql1RJ+1E3KUsL9lhKXvlpU+8E0pfacacui3L7rrl+trBYIywsnGTXYPuRRtCfKNu98/S3P3IS6+6s/wVYfv2MtgMTM0eQVhiWibT+CzKLQQ/PniaSh0vzipv2n8JQ1nvClYbbNS0scErv3C8Ivpe5XGbPyQxLr0fl/m0vk+//CPdvI99q0IbB5YC6e8ITppv288HSw8YdH7+9r+kjpsfs73d8ktq3MUW7v0VEJTvVXhQRYzT93d9Sobxx6fK+EfBQU+zv6bacfhOK95f/1uCHR760kLdDhA61qzI85cbMa9aWhUaXh6/LQ4Qnb/SNC3qf33fEpV/W3DSOdeanxsIF1vtrS19tudfsmil4ImwZX4q+q+6afU9mkgFqG/QK6BL+WLv8qW+bN3ThaxJ8891wn+qCRonvwi/t1hObXajNvd3js56yFcJFdzyYmzkcPkhoK04Lrn+SsNooWqL2w+QIfmwo7KkEJ8X+dab5aBJJbacq6hx2/+EX9Apy+DOpbv9W/Z+Cjdz99as/q32k24orgi2/NvqgI1tPZ4dImZh61lmOijFw+0PI0iC3MJ0OWdW80+vcK0kWi/m/r1oV72Cirv+vwQ4CkTeP1q5+Q7lIuplS/0uFrg+5Nu55OMr9GM5/7bF0r0cm/Q+9e97l7t9wkvLChHW6vF3P3d75Ar/r7IV7+5bvdX+rkGUyDE/Q2iO8m9QTl3e70/cmX+p8xHdqEskJFnx7V/4QpHH1y2Ufu9LL99b7vV2f3lvevROqz17V1kKiDaIl5P33pTb33ZN3+gpuTMve9ye7u5aE152MvPnNt4reYNTsNb9bqlXXL/9d1k96/G+/bapGJl8I+r0/wSXvbeab/2eUt775rahXLKWk+3fNedQCJTvyk9+zVXb6lu+lq/eQ7veT1sXfpEMm7W/xHP8vwqvwVCnd73u7zJ+C04Ahk7+1+/3n/PCnWhrfhyDX9by/giO9ztFdu0321KJRCO/L5NLe9PBLvd7udO+F8iF3it8uZPe5Cf0mL/pUvJIdLfkwxkiN5XKoQRF+tfL5pfgqpxuH67HEo+xG1p2+S8kR0ETet5fUn4LIAAAAUDQZsgFfBX5RnDH3xfUclbL3uv5q117i7nzapfiyhqZo3DbB/jrSGC/+WKESH4fL4m+vcZCddH7gxyX+iM93aE2C8v724KqLk01eYGkNp4vCdW+zJ+M57eyflDr/K/8IxmMhZDr7iFPXiXdXR4T8vD5e92xDGf1eiCiuy+4fK/vLwgSMxF9vpveoW8EEP/frNl2xk8rxjX63LMYjzwhJJO7jNu3huVpsKhwX2LOOr/mxUIVjvruxnJJt4Zdxh3jp49WB38B62SJgc0Tsp7Hl937Cl0GiOHn+0HYBfvp+4+IRY4f/2CTNb06NUPdFhCcXf9a/Ud03rtXHDjXk+3JcvNcI+V5/gqvKD+kYNGuPXV0odIKXd6/kCr0n2QEt6cLUvCdqU20SdkEfUKeY3LlbbY0j2FXHw9LFRzjsaA3fA3cofYCoSb3pKYJeNQBeCn9/je4PhmEVI3QWIennpspnLyeIvfx0QdcXF9jV8vIkLx7BHj6TzP+FOIv+Evx+ZlDUxnAErzkp5/efzA7Ntio5c22T6Tsuzw6eHHRLzh9zCliU9z99u/WRRp0nH+n6W0gpVJXotpWkP2jswYY3VBBWlmG7DP81pTdE42+8lnXg3AVZJx21klGINTZ/e/+K815YVZSD97vd3LpC1UnrTVN4UNuOo3dShXaVTT/iVIMJHGGpISf4LCPfL932a9wRb075bU/BP6beCN8vnsK9xFwK96ibye9624LuDNUYFsF8hcMZ9Rw4Fr8VujMhoQA9S7HKv7i8Opys0Tv+OLPj8qOdBDyLq9fgunfYe4JTQ+k2gTEET9L/y/W2oIjZ4bm+7KUqh3hOsUVzJvu/rH7u/kOXnzu1FsRee771dUHRWPnnTxfXvzndlHCUxZgj/8YfBN+TXEEvHifDcuf5SQH+CLQXwTG5FzA6gTwtHA49Rfv6DRa0d3vgyWv/xd2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ozhCmHcsojn0ve9J6vo3F1atfrU1nSludw7TxPV5ubVWuTuiVWntvoFVlejD9jy3FG1zaMw9HVwYsVkDaPxVdVVEbYKQ2piJWJVsfXVB8qFpPYCLWIJlAQami+oxdakNUdo0gp/a1pauFM/SGnlWnNlVcAfKUoPVNeVZbQkUfOkFW2pYS319JY+vVucrphjWxIdccaecCpTf6JNclJdcBWTTniWiRkFzkJARLTZGmZSFcCahgqoH0rNs4dCDVc1g1VkDnZkXsRSWtvsHCbkwj5iyHUGtOavCREQOPzZ6ZK6uMSY6/yaBCvi5ESoob2Lj4RCkbw8ESKmUuwp5jhS1gf16X1xBw8EFjk1FHAfTYBv66qH5cP9fr/Y4LUdYZqFSqRpYlWsrtGs9FYK54eqI7xWtGzrakmeIlqaulpOmNcGEMnt8n+SkiPm4Il/DmdKj/KGYRGW8h+a62PtFZMDFROnV2nlzbVx31ZUnlaLtQ890RbnIj1Kq5R0Hud4uiJbEZQzTiiLSpU9oubin1EG9ZwOkxlRKSVMGxVx1o6JYbXV7++mUYd+VFjJ4qRZfJqRYaHT68NPq582PXuzggnjVVlROb252XpaG0ItNuBZ8QIRT5VVfq00QlPwZObiX4e+baig6vRIGC4rFQqIv5goXj1NMT3OV+MS0dmn9ygkuubmUQFtVHNtc12H3jQroDkDzZ18O9/evKi8titwOvQta9Mjo66rhq8a2DA8FJxKWgJszcSWMFszeXpVJz6ztTWVVa2c8dLakuqWHLRVdWpEYSnlQiqEoqKJClUwLLKVm6V+emeYqEm2qlIg67M7GEmZuUvGaHYHj8mcXTIOmRqThaVMXCLHlFZWnRo98pGsxmcdLO7CAXs6vlUclMlSw27Nx0rNGZlZmL3LmeUgs6dDj7bb7SVTwHzZbrNJ5ptwtj0BXFCzMF84IYFPsWhOJ9DqcAC9UtKhfxXuabcbp1jSfJnORGHqtCbAzGkX/Tk1pmEV0o7QZbswlYywBnMMw0rm23bRC5jvwrAHV5M1fIY35PwmJK+aEceBI+LVmsMAKhpxfISg/v1KV4QHK+kms9FsMKtm1ZjqTfNyo81qtyZYFWNySlJKjxTFmK54/MydCPCaM/wsxeryUyjEQqE8XFexmgEuf4EnxZPiTk7iiTyQ6y8YPGTw4EEDgz2DAf9d7PtHp19ZvbRx3KU371kVbWGFNz/Qv3zsbfPHbYruNmxJzjxnVnTvzoei0YfrCjYN7l/+yYMffpuXhbXfi/OL+E60UXs42WjIMptNJlJU4XyrJctGZhOCNJzvdA80VSpna1YtgVvTElQLF/6zSI9arGIjLN325bF2i+WERDr1aJdT7cPP9YbGOb8Kdbl1rPTrOOc3NWO/ev+kT92F+SOcwrVwSrI/TveqOT/epYR+/IdytWHLpmjRn6IJmzCj+xFd2WKFzN5JCVhMSo/kgaqSZbHebd1n5VYD5zYzdqYryMxdQWYWQWYRazNrJpOxQ/9crgnMl2GbWJTRKVaE+sFwns1mnGJkYj3GmqYElsBt0kM26SGb9JAt5iHhTyum8J9cFbZJX5lFr6dHX0rcUVoC0xImJNQmLEpQh1d7QzWLu2LxZDTWxCTwlEA4r2hEYU2+DEkWGuCC70AB4P3b+bHt248bDVuOP8inHxvF246PxUzXITby4DkD/SZsZxw+M5BZU5nawR8K+01ckUtU5BIVuUSl20HwzU8eKOPPPVAf1sT2XOy02Ot12/lLhi3H/rVJ5I3Z+keGtw378X2dzlLCFWkOluRMSkr3pKerqlNNsnls6erDns2JzyQqHo83nWuZYdf4HuM94bQqQ5VlmnOKa2aP6Z6Z3qlp09LXeu7gztQsRXFn2SzJXbGQ3BULySIW5BKTg5qJ4aH4SsrBfNwuVmsS4SEWCeaoXCSYT9vFBkplsUaT2NkisW5TWlMmy3RI/zmk/xyyc0dQuM8sBGQXAjLKEDBKZ6VmzJ5x4vGoGSuyzLiuTe4SUNHhosPY35rFVFNTs7iHk/wFqkgZaiA7hw9x0oACcg3kwUA2zWZr2OAX2KhH26Obt+6Nbtn4HMt89U2WvuKTm1+Mvsp3sQXsj9ujD7x1IHr3E8+x6U9Hv43uZQNZehuz/S76Afx/D56sTYgPL2XzYWG/25bI3IMzpvvONy/wqRanWLJZokliDiJfeiZBOEQw9i7G1sW4O/RDbe60gSiPtmX3HOgS9cyeA53x0hEv0f5aW2Yw1g59Z7wU7eGzwOQmnp1xtjbZNiNjQcYSy/LEFY5V1jWO2xIednQ4Pk78yOFMtNs1lyPJ5XK4HHaLO53701KsRnzLJNgNXoslxZOWmuURM46/b7aFk8VWeDzkzxbZkbxehyPRnNUVKlldoZJ1Im1kBRPvNIoAiaeN2FMg88VAmTmMwi3GGi1nUU5TjpKT7ZUB4ZUB4ZUB4f1fPlDxVGH8aaqIP1eB4Rt/LqfGAyf1fW/8beXEHc+todBxVArz3Z5C5vIUrk7sGzJc4dwpwip06kWiP5zQwlZz2FHocA5zuYdBVM0WQ9hJifo74bTUQld2aqEblBjOKHRmJ4F8oOTCeCfV4sWWgg9JowlvN0+PgNKX45UWcEEs328B/z28eefuS3e9PPaMKefoX22fctG0Pv6Kd9k9q9aNu+2+aD/DlvHPrbjzlczcnHHLootZ/6uvG2ozHV+mDBiyYnTDNeKvsalEpotFpPLLO8mhR4XTSqZw6e7EWFbCw9ehH483KCca5LMpjhG9BKcaY7lOIJfbpMqDhCKR2+Nm4nGXZp92MV/JERDv+9ttkBjA4J0BrhcFXb3cQW8hDXYVugd7z6LRrrPco71VNM1V5Z7mdd5uvt3BW4zi/BQe4GRpqaHkgYaB9jJDmb0iudJQaT83eY5hjv3C5KWGpfbLkh2GZLtCzG0mswMnNcRpkbhc2Moa5nIXFqaHsxTVYOBGE156VizXkpDocNjxGe9OTvF4vch0I9oM5NVEaXe7RBmenmy2aIQ3pcYoiTHyGszmrGRvUnKy1223WLKS3WDdLrvDoTldSU6ny22xm73JBofLaSeOKRkUr9PhsFjMZo45ed1ul4vMaR5PmrPYwiaSRnZgMihMBjZxs6YxxlJTO9jalljw1qSljj2e5j1+PC31uHdceX3Zhyci1hm/RbBifa4uKixcPbZvaPUVO1f39f60QOCtTnTu3AkYsbOLOxVYRcQxuSLiwoG11W314pEbOrQawlwI8yDsJBKnd6qI2CBJhKTNHjaEoVSN52RJTezsdvrlZwN6pHgGD0HhRtFjAAuwYE+jibG7opc9eyAnbaiVeT59aXwgo8+HO6IXPRl9oafJkxR93rDlx6Lbfv/PHOWd42nRz/61tl157NgoteY6rX70D/fhCN1JlcoZbUGvb99TSi86COJKr9ZQpq9T6alktg73hTuUQJs7ucBR3EcR+SRfogZcCHoctFURv8WYqYgzoRO4EtQEehy0FbQPZCQCilYNtBC0AXRQtCiZSkar5nMW91RSYZuKt4ND8dARkA5SyAfMB40HzQTdCNoAMko9IVkIWgnaCjoqW8KKp/WWAZi7p3WtLNoumF8gq3Wx6owaWW2bVh0rx06MlWVnxdSGxdT6D4yJ+5bEyp69Y6U7t6BJlNaEgm3FKUoKFpmCiS8CMr6THAh0H92tJFMExBVjXBJW3G05wYINWxWVmMIVRnPIp29TWGuCq6DYynV+hNzk45/zw7EWfrgt0VWwofhsfogeB20FKfwQ7nf5u7SSHxQ+BxaBNoC2gvaCjoCM/CDuA7jf4e+Qg79N+aAi0EzQBtBW0BGQib8NdPK3xDe+RMEXgbj47Qtqb2JZbwId/A1wb/A3MLWXW4cUFnRKJpQfZ3y5ccaTHmfcKQUd/KXW73shooLYaUTUk0o2jaQBSnZrbn9fh+JtHTHP18Hfa9NCvruL+/H9FAFxzGQ/Rt5PGmgCqBa0CGQE9wq4V6gJdBPoblAEhCgDOkEa3wXaDXqF+oHCoAkgM9/XimE6+N7WYImvOIW/yJ8lDzy+hz8ny938GVm+wP8my+dRZqHcxZ9pzfJRsQ3tBBsnSifKfLQb+F/bctw+vdjFt8J3PmA+qAg0HjQTdCPIyLfy7NY5Pjc6eZJ2mQmarfSJLB+ke80UvsAXDpYiADUBwWFnggNs0DYEeTi47g5UBQRvuAWcgODV14ETELz0KnACgvMvBicgOOcCcAKC02eCExAcXwkO0MHv+nNOT9+Q8RcyrdjBL4GXLoGXLoGXLiGVXyJu+l4Vc/tDa14ePLY+HOqV52vawpqeYk2TWNO9rKmeNV3Jmq5iTSNY03msKcSaMlhTFmsKs6Yn2VC4oomF20+rFoa9rGkXa9rEmhpZU5A15bKmHNaksSHhDu5vPWuALMpl0VYsHjqUZ45E9nFwPzzqR8z7kRO2AveCdFkLQ0nLjimnZokyuy2vKFbvO6xgYfEYvgOGO7ANO+gASMUG7UAY7UAnO9CBA1gEmgnaBjoC0kFGaGdj4jdKdADzQUWgmaCVoCMgo5zOERCnhfEpPi4nlh+f9HhR4ztwiz9i+bk/nOnMcIacY5QbM5gji43P0rP4EEoRv4lwu8yuDpaw+duE775NIEuxhd/Ab6RMbMRN8fLG1u8zfR3s9tbgk77iZHYbZamIOlZIQZaLcig1yvogyjCLciBl8EdRFrRmTIWZozXY27eFJQqrzb7vM973fZLRwcF+nPGk71WtQ2Wtvn9A8uhm3/6Ma33P53eYIXkq2MFQbNGkamfGUN+mXVL1KjSsb/VdKYrNvisyRvsuzJAN9bGG8xpRCzt8k4LTfWPQX1nGLF+4EX1u9hVlnOcbEdMaJGw2+/phCqEYm4fJ9sqQgwayZIdThnSwhnBv0zpTlWm8abCpwNTb5Df5TJmmdFOS2W12mhPNdrPVbDYbzaqZ4xiTJM7LIXGKSzLKH2gaVfkDO8k7Ocmf1Mmf3XFm5nQ2RXooFbxic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-)
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",
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- "label": None,
- "show_label": True,
- "style": {},
- "elem_id": None,
- "interactive": None,
- "visible": True,
-}
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",
-}
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/anyio.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/anyio.py
deleted file mode 100644
index 1ed5228dbde1732de50677e9a3bd6f04a3017433..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/anyio.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import ssl
-import typing
-
-import anyio
-
-from .._exceptions import (
- ConnectError,
- ConnectTimeout,
- ReadError,
- ReadTimeout,
- WriteError,
- WriteTimeout,
- map_exceptions,
-)
-from .._utils import is_socket_readable
-from .base import SOCKET_OPTION, AsyncNetworkBackend, AsyncNetworkStream
-
-
-class AnyIOStream(AsyncNetworkStream):
- def __init__(self, stream: anyio.abc.ByteStream) -> None:
- self._stream = stream
-
- async def read(
- self, max_bytes: int, timeout: typing.Optional[float] = None
- ) -> bytes:
- exc_map = {
- TimeoutError: ReadTimeout,
- anyio.BrokenResourceError: ReadError,
- anyio.ClosedResourceError: ReadError,
- }
- with map_exceptions(exc_map):
- with anyio.fail_after(timeout):
- try:
- return await self._stream.receive(max_bytes=max_bytes)
- except anyio.EndOfStream: # pragma: nocover
- return b""
-
- async def write(
- self, buffer: bytes, timeout: typing.Optional[float] = None
- ) -> None:
- if not buffer:
- return
-
- exc_map = {
- TimeoutError: WriteTimeout,
- anyio.BrokenResourceError: WriteError,
- anyio.ClosedResourceError: WriteError,
- }
- with map_exceptions(exc_map):
- with anyio.fail_after(timeout):
- await self._stream.send(item=buffer)
-
- async def aclose(self) -> None:
- await self._stream.aclose()
-
- async def start_tls(
- self,
- ssl_context: ssl.SSLContext,
- server_hostname: typing.Optional[str] = None,
- timeout: typing.Optional[float] = None,
- ) -> AsyncNetworkStream:
- exc_map = {
- TimeoutError: ConnectTimeout,
- anyio.BrokenResourceError: ConnectError,
- }
- with map_exceptions(exc_map):
- try:
- with anyio.fail_after(timeout):
- ssl_stream = await anyio.streams.tls.TLSStream.wrap(
- self._stream,
- ssl_context=ssl_context,
- hostname=server_hostname,
- standard_compatible=False,
- server_side=False,
- )
- except Exception as exc: # pragma: nocover
- await self.aclose()
- raise exc
- return AnyIOStream(ssl_stream)
-
- def get_extra_info(self, info: str) -> typing.Any:
- if info == "ssl_object":
- return self._stream.extra(anyio.streams.tls.TLSAttribute.ssl_object, None)
- if info == "client_addr":
- return self._stream.extra(anyio.abc.SocketAttribute.local_address, None)
- if info == "server_addr":
- return self._stream.extra(anyio.abc.SocketAttribute.remote_address, None)
- if info == "socket":
- return self._stream.extra(anyio.abc.SocketAttribute.raw_socket, None)
- if info == "is_readable":
- sock = self._stream.extra(anyio.abc.SocketAttribute.raw_socket, None)
- return is_socket_readable(sock)
- return None
-
-
-class AnyIOBackend(AsyncNetworkBackend):
- async def connect_tcp(
- self,
- host: str,
- port: int,
- timeout: typing.Optional[float] = None,
- local_address: typing.Optional[str] = None,
- socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None,
- ) -> AsyncNetworkStream:
- if socket_options is None:
- socket_options = [] # pragma: no cover
- exc_map = {
- TimeoutError: ConnectTimeout,
- OSError: ConnectError,
- anyio.BrokenResourceError: ConnectError,
- }
- with map_exceptions(exc_map):
- with anyio.fail_after(timeout):
- stream: anyio.abc.ByteStream = await anyio.connect_tcp(
- remote_host=host,
- remote_port=port,
- local_host=local_address,
- )
- # By default TCP sockets opened in `asyncio` include TCP_NODELAY.
- for option in socket_options:
- stream._raw_socket.setsockopt(*option) # type: ignore[attr-defined] # pragma: no cover
- return AnyIOStream(stream)
-
- async def connect_unix_socket(
- self,
- path: str,
- timeout: typing.Optional[float] = None,
- socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None,
- ) -> AsyncNetworkStream: # pragma: nocover
- if socket_options is None:
- socket_options = []
- exc_map = {
- TimeoutError: ConnectTimeout,
- OSError: ConnectError,
- anyio.BrokenResourceError: ConnectError,
- }
- with map_exceptions(exc_map):
- with anyio.fail_after(timeout):
- stream: anyio.abc.ByteStream = await anyio.connect_unix(path)
- for option in socket_options:
- stream._raw_socket.setsockopt(*option) # type: ignore[attr-defined] # pragma: no cover
- return AnyIOStream(stream)
-
- async def sleep(self, seconds: float) -> None:
- await anyio.sleep(seconds) # pragma: nocover
diff --git a/spaces/Dagfinn1962/Dreamlikeart-Anime-1.0/style.css b/spaces/Dagfinn1962/Dreamlikeart-Anime-1.0/style.css
deleted file mode 100644
index 85afb3cc45fb3b89191feaa5d8ecc758976a583f..0000000000000000000000000000000000000000
--- a/spaces/Dagfinn1962/Dreamlikeart-Anime-1.0/style.css
+++ /dev/null
@@ -1,94 +0,0 @@
-#col-container {
- background:rgb(9, 2, 27);
- color:#FFFFFF;
- width: 100%;
- margin-left: auto;
- margin-right: auto;
-}
-a {
- color: inherit;
- text-decoration: underline;
-}
-.gradio-container {
- background:rgb(9, 2, 27);
- color:#FFFFFF;
- font-family: 'IBM Plex Sans', sans-serif;
-}
-.gr-button {
- color: white;
- border-color: #9d66e5;
- background: #9e5bf6;
-}
-input[type='range'] {
- accent-color: #9d66e5;
-}
-.dark input[type='range'] {
- accent-color: #dfdfdf;
-}
-.container {
- background:rgb(9, 2, 27);
- width: 100%;
- margin: auto;
- padding-top: 1.5rem;
- border-radius: 20px;
-}
-#gallery {
- min-height: 22rem;
- margin-bottom: 15px;
- margin-left: auto;
- margin-right: auto;
- border-bottom-right-radius: .5rem !important;
- border-bottom-left-radius: .5rem !important;
-}
-#gallery>div>.h-full {
- min-height: 20rem;
-}
-.details:hover {
- text-decoration: underline;
-}
-.gr-button {
- white-space: nowrap;
-}
-.gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
-}
-#advanced-options {
- margin-bottom: 20px;
-}
-.footer {
- background: #090956;
- color:#FFFFFF;
- margin-bottom: 45px;
- margin-top: 35px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
-}
-.footer>p {
- color:#FFFFFF;
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
-}
-.dark .logo{ filter: invert(1); }
-.dark .footer {
- border-color: #303030;
-}
-.dark .footer>p {
- background: #0b0f19;
-}
-.acknowledgments h4{
- color:#FFFFFF;
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
-}
-
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/pano_s2d3d_mix_dataset.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/pano_s2d3d_mix_dataset.py
deleted file mode 100644
index d8f8444b20f89b1c1b1ad274c7c7d0274ef5aa2f..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/pano_s2d3d_mix_dataset.py
+++ /dev/null
@@ -1,91 +0,0 @@
-"""
-@date: 2021/6/16
-@description:
-"""
-
-import os
-
-from dataset.pano_s2d3d_dataset import PanoS2D3DDataset
-from utils.logger import get_logger
-
-
-class PanoS2D3DMixDataset(PanoS2D3DDataset):
- def __init__(self, root_dir, mode, shape=None, max_wall_num=0, aug=None, camera_height=1.6, logger=None,
- split_list=None, patch_num=256, keys=None, for_test_index=None, subset=None):
- assert subset == 's2d3d' or subset == 'pano', 'error subset'
- super().__init__(root_dir, None, shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, subset)
- if logger is None:
- logger = get_logger()
- self.mode = mode
- if mode == 'train':
- if subset == 'pano':
- s2d3d_train_data = PanoS2D3DDataset(root_dir, 'train', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 's2d3d').data
- s2d3d_val_data = PanoS2D3DDataset(root_dir, 'val', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 's2d3d').data
- s2d3d_test_data = PanoS2D3DDataset(root_dir, 'test', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 's2d3d').data
- s2d3d_all_data = s2d3d_train_data + s2d3d_val_data + s2d3d_test_data
-
- pano_train_data = PanoS2D3DDataset(root_dir, 'train', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 'pano').data
- self.data = s2d3d_all_data + pano_train_data
- elif subset == 's2d3d':
- pano_train_data = PanoS2D3DDataset(root_dir, 'train', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 'pano').data
- pano_val_data = PanoS2D3DDataset(root_dir, 'val', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 'pano').data
- pano_test_data = PanoS2D3DDataset(root_dir, 'test', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 'pano').data
- pano_all_data = pano_train_data + pano_val_data + pano_test_data
-
- s2d3d_train_data = PanoS2D3DDataset(root_dir, 'train', shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, 's2d3d').data
- self.data = pano_all_data + s2d3d_train_data
- else:
- self.data = PanoS2D3DDataset(root_dir, mode, shape, max_wall_num, aug, camera_height, logger,
- split_list, patch_num, keys, None, subset).data
-
- if for_test_index is not None:
- self.data = self.data[:for_test_index]
- logger.info(f"Build dataset mode: {self.mode} valid: {len(self.data)}")
-
-
-if __name__ == '__main__':
- import numpy as np
- from PIL import Image
-
- from tqdm import tqdm
- from visualization.boundary import draw_boundaries
- from visualization.floorplan import draw_floorplan
- from utils.boundary import depth2boundaries
- from utils.conversion import uv2xyz
-
- modes = ['test', 'val', 'train']
- for i in range(1):
- for mode in modes:
- print(mode)
- mp3d_dataset = PanoS2D3DMixDataset(root_dir='../src/dataset/pano_s2d3d', mode=mode, aug={
- # 'STRETCH': True,
- # 'ROTATE': True,
- # 'FLIP': True,
- # 'GAMMA': True
- }, subset='pano')
- continue
- save_dir = f'../src/dataset/pano_s2d3d/visualization1/{mode}'
- if not os.path.isdir(save_dir):
- os.makedirs(save_dir)
-
- bar = tqdm(mp3d_dataset, ncols=100)
- for data in bar:
- bar.set_description(f"Processing {data['id']}")
- boundary_list = depth2boundaries(data['ratio'], data['depth'], step=None)
- pano_img = draw_boundaries(data['image'].transpose(1, 2, 0), boundary_list=boundary_list, show=False)
- Image.fromarray((pano_img * 255).astype(np.uint8)).save(
- os.path.join(save_dir, f"{data['id']}_boundary.png"))
-
- floorplan = draw_floorplan(uv2xyz(boundary_list[0])[..., ::2], show=False,
- marker_color=None, center_color=0.8, show_radius=None)
- Image.fromarray((floorplan.squeeze() * 255).astype(np.uint8)).save(
- os.path.join(save_dir, f"{data['id']}_floorplan.png"))
diff --git a/spaces/DemoLou/moe-tts/transforms.py b/spaces/DemoLou/moe-tts/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/ECCV2022/bytetrack/yolox/data/data_augment.py b/spaces/ECCV2022/bytetrack/yolox/data/data_augment.py
deleted file mode 100644
index 99fb30a284eeb5851e4c776aafd61b44d485196b..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/yolox/data/data_augment.py
+++ /dev/null
@@ -1,299 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-"""
-Data augmentation functionality. Passed as callable transformations to
-Dataset classes.
-
-The data augmentation procedures were interpreted from @weiliu89's SSD paper
-http://arxiv.org/abs/1512.02325
-"""
-
-import cv2
-import numpy as np
-
-import torch
-
-from yolox.utils import xyxy2cxcywh
-
-import math
-import random
-
-
-def augment_hsv(img, hgain=0.015, sgain=0.7, vgain=0.4):
- r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
- hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
- dtype = img.dtype # uint8
-
- x = np.arange(0, 256, dtype=np.int16)
- lut_hue = ((x * r[0]) % 180).astype(dtype)
- lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
- lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
- img_hsv = cv2.merge(
- (cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))
- ).astype(dtype)
- cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.2):
- # box1(4,n), box2(4,n)
- # Compute candidate boxes which include follwing 5 things:
- # box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
- w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
- w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
- ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio
- return (
- (w2 > wh_thr)
- & (h2 > wh_thr)
- & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr)
- & (ar < ar_thr)
- ) # candidates
-
-
-def random_perspective(
- img,
- targets=(),
- degrees=10,
- translate=0.1,
- scale=0.1,
- shear=10,
- perspective=0.0,
- border=(0, 0),
-):
- # targets = [cls, xyxy]
- height = img.shape[0] + border[0] * 2 # shape(h,w,c)
- width = img.shape[1] + border[1] * 2
-
- # Center
- C = np.eye(3)
- C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
- C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
-
- # Rotation and Scale
- R = np.eye(3)
- a = random.uniform(-degrees, degrees)
- # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
- s = random.uniform(scale[0], scale[1])
- # s = 2 ** random.uniform(-scale, scale)
- R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
- # Shear
- S = np.eye(3)
- S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
- S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
-
- # Translation
- T = np.eye(3)
- T[0, 2] = (
- random.uniform(0.5 - translate, 0.5 + translate) * width
- ) # x translation (pixels)
- T[1, 2] = (
- random.uniform(0.5 - translate, 0.5 + translate) * height
- ) # y translation (pixels)
-
- # Combined rotation matrix
- M = T @ S @ R @ C # order of operations (right to left) is IMPORTANT
-
- ###########################
- # For Aug out of Mosaic
- # s = 1.
- # M = np.eye(3)
- ###########################
-
- if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
- if perspective:
- img = cv2.warpPerspective(
- img, M, dsize=(width, height), borderValue=(114, 114, 114)
- )
- else: # affine
- img = cv2.warpAffine(
- img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)
- )
-
- # Transform label coordinates
- n = len(targets)
- if n:
- # warp points
- xy = np.ones((n * 4, 3))
- xy[:, :2] = targets[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(
- n * 4, 2
- ) # x1y1, x2y2, x1y2, x2y1
- xy = xy @ M.T # transform
- if perspective:
- xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale
- else: # affine
- xy = xy[:, :2].reshape(n, 8)
-
- # create new boxes
- x = xy[:, [0, 2, 4, 6]]
- y = xy[:, [1, 3, 5, 7]]
- xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
- # clip boxes
- #xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
- #xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
-
- # filter candidates
- i = box_candidates(box1=targets[:, :4].T * s, box2=xy.T)
- targets = targets[i]
- targets[:, :4] = xy[i]
-
- targets = targets[targets[:, 0] < width]
- targets = targets[targets[:, 2] > 0]
- targets = targets[targets[:, 1] < height]
- targets = targets[targets[:, 3] > 0]
-
- return img, targets
-
-
-def _distort(image):
- def _convert(image, alpha=1, beta=0):
- tmp = image.astype(float) * alpha + beta
- tmp[tmp < 0] = 0
- tmp[tmp > 255] = 255
- image[:] = tmp
-
- image = image.copy()
-
- if random.randrange(2):
- _convert(image, beta=random.uniform(-32, 32))
-
- if random.randrange(2):
- _convert(image, alpha=random.uniform(0.5, 1.5))
-
- image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
-
- if random.randrange(2):
- tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
- tmp %= 180
- image[:, :, 0] = tmp
-
- if random.randrange(2):
- _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
-
- image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
-
- return image
-
-
-def _mirror(image, boxes):
- _, width, _ = image.shape
- if random.randrange(2):
- image = image[:, ::-1]
- boxes = boxes.copy()
- boxes[:, 0::2] = width - boxes[:, 2::-2]
- return image, boxes
-
-
-def preproc(image, input_size, mean, std, swap=(2, 0, 1)):
- if len(image.shape) == 3:
- padded_img = np.ones((input_size[0], input_size[1], 3)) * 114.0
- else:
- padded_img = np.ones(input_size) * 114.0
- img = np.array(image)
- r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
- resized_img = cv2.resize(
- img,
- (int(img.shape[1] * r), int(img.shape[0] * r)),
- interpolation=cv2.INTER_LINEAR,
- ).astype(np.float32)
- padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
-
- padded_img = padded_img[:, :, ::-1]
- padded_img /= 255.0
- if mean is not None:
- padded_img -= mean
- if std is not None:
- padded_img /= std
- padded_img = padded_img.transpose(swap)
- padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
- return padded_img, r
-
-
-class TrainTransform:
- def __init__(self, p=0.5, rgb_means=None, std=None, max_labels=100):
- self.means = rgb_means
- self.std = std
- self.p = p
- self.max_labels = max_labels
-
- def __call__(self, image, targets, input_dim):
- boxes = targets[:, :4].copy()
- labels = targets[:, 4].copy()
- ids = targets[:, 5].copy()
- if len(boxes) == 0:
- targets = np.zeros((self.max_labels, 6), dtype=np.float32)
- image, r_o = preproc(image, input_dim, self.means, self.std)
- image = np.ascontiguousarray(image, dtype=np.float32)
- return image, targets
-
- image_o = image.copy()
- targets_o = targets.copy()
- height_o, width_o, _ = image_o.shape
- boxes_o = targets_o[:, :4]
- labels_o = targets_o[:, 4]
- ids_o = targets_o[:, 5]
- # bbox_o: [xyxy] to [c_x,c_y,w,h]
- boxes_o = xyxy2cxcywh(boxes_o)
-
- image_t = _distort(image)
- image_t, boxes = _mirror(image_t, boxes)
- height, width, _ = image_t.shape
- image_t, r_ = preproc(image_t, input_dim, self.means, self.std)
- # boxes [xyxy] 2 [cx,cy,w,h]
- boxes = xyxy2cxcywh(boxes)
- boxes *= r_
-
- mask_b = np.minimum(boxes[:, 2], boxes[:, 3]) > 1
- boxes_t = boxes[mask_b]
- labels_t = labels[mask_b]
- ids_t = ids[mask_b]
-
- if len(boxes_t) == 0:
- image_t, r_o = preproc(image_o, input_dim, self.means, self.std)
- boxes_o *= r_o
- boxes_t = boxes_o
- labels_t = labels_o
- ids_t = ids_o
-
- labels_t = np.expand_dims(labels_t, 1)
- ids_t = np.expand_dims(ids_t, 1)
-
- targets_t = np.hstack((labels_t, boxes_t, ids_t))
- padded_labels = np.zeros((self.max_labels, 6))
- padded_labels[range(len(targets_t))[: self.max_labels]] = targets_t[
- : self.max_labels
- ]
- padded_labels = np.ascontiguousarray(padded_labels, dtype=np.float32)
- image_t = np.ascontiguousarray(image_t, dtype=np.float32)
- return image_t, padded_labels
-
-
-class ValTransform:
- """
- Defines the transformations that should be applied to test PIL image
- for input into the network
-
- dimension -> tensorize -> color adj
-
- Arguments:
- resize (int): input dimension to SSD
- rgb_means ((int,int,int)): average RGB of the dataset
- (104,117,123)
- swap ((int,int,int)): final order of channels
-
- Returns:
- transform (transform) : callable transform to be applied to test/val
- data
- """
-
- def __init__(self, rgb_means=None, std=None, swap=(2, 0, 1)):
- self.means = rgb_means
- self.swap = swap
- self.std = std
-
- # assume input is cv2 img for now
- def __call__(self, img, res, input_size):
- img, _ = preproc(img, input_size, self.means, self.std, self.swap)
- return img, np.zeros((1, 5))
diff --git a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py b/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py
deleted file mode 100644
index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(SeperableConv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nin,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- groups=nin,
- bias=False,
- ),
- nn.Conv2d(nin, nout, kernel_size=1, bias=False),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
- def __call__(self, x):
- skip = self.conv1(x)
- h = self.conv2(skip)
-
- return h, skip
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
- h = self.conv(x)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
- self.conv3 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = nn.Sequential(
- Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
- )
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
- bottle = self.bottleneck(out)
- return bottle
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/_base_/det_datasets/toy_data.py b/spaces/EuroPython2022/mmocr-demo/configs/_base_/det_datasets/toy_data.py
deleted file mode 100644
index 512d1d20372a3fa3f662cc908c8cf4b66b35b797..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/_base_/det_datasets/toy_data.py
+++ /dev/null
@@ -1,41 +0,0 @@
-root = 'tests/data/toy_dataset'
-
-# dataset with type='TextDetDataset'
-train1 = dict(
- type='TextDetDataset',
- img_prefix=f'{root}/imgs',
- ann_file=f'{root}/instances_test.txt',
- loader=dict(
- type='AnnFileLoader',
- repeat=4,
- file_format='txt',
- parser=dict(
- type='LineJsonParser',
- keys=['file_name', 'height', 'width', 'annotations'])),
- pipeline=None,
- test_mode=False)
-
-# dataset with type='IcdarDataset'
-train2 = dict(
- type='IcdarDataset',
- ann_file=f'{root}/instances_test.json',
- img_prefix=f'{root}/imgs',
- pipeline=None)
-
-test = dict(
- type='TextDetDataset',
- img_prefix=f'{root}/imgs',
- ann_file=f'{root}/instances_test.txt',
- loader=dict(
- type='AnnFileLoader',
- repeat=1,
- file_format='txt',
- parser=dict(
- type='LineJsonParser',
- keys=['file_name', 'height', 'width', 'annotations'])),
- pipeline=None,
- test_mode=True)
-
-train_list = [train1, train2]
-
-test_list = [test]
diff --git a/spaces/EyeSeeThru/openjourney/app.py b/spaces/EyeSeeThru/openjourney/app.py
deleted file mode 100644
index bea4accb45793c8e748731c184dee0ffaf509dd5..0000000000000000000000000000000000000000
--- a/spaces/EyeSeeThru/openjourney/app.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import gradio as gr
-
-description = """
-
-
")
-
- def load_dataset(self, split, epoch=1, combine=False, **kwargs):
- """Load a given dataset split.
-
- Args:
- split (str): name of the split (e.g., train, valid, test)
- """
- paths = utils.split_paths(self.args.data)
- assert len(paths) > 0
- data_path = paths[(epoch - 1) % len(paths)]
-
- # infer langcode
- src, tgt = self.args.source_lang, self.args.target_lang
-
- self.datasets[split] = load_langpair_dataset(
- data_path,
- split,
- src,
- self.src_dict,
- tgt,
- self.tgt_dict,
- combine=combine,
- dataset_impl=self.args.dataset_impl,
- upsample_primary=self.args.upsample_primary,
- left_pad_source=self.args.left_pad_source,
- left_pad_target=self.args.left_pad_target,
- max_source_positions=getattr(self.args, "max_source_positions", 1024),
- max_target_positions=getattr(self.args, "max_target_positions", 1024),
- load_alignments=self.args.load_alignments,
- prepend_bos=getattr(self.args, "prepend_bos", False),
- append_source_id=True,
- )
-
- def build_generator(self, models, args, **unused):
- if getattr(args, "score_reference", False):
- from fairseq.sequence_scorer import SequenceScorer
-
- return SequenceScorer(
- self.target_dictionary,
- eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)),
- )
- else:
- from fairseq.sequence_generator import SequenceGenerator
-
- return SequenceGenerator(
- models,
- self.target_dictionary,
- beam_size=getattr(args, "beam", 5),
- max_len_a=getattr(args, "max_len_a", 0),
- max_len_b=getattr(args, "max_len_b", 200),
- min_len=getattr(args, "min_len", 1),
- normalize_scores=(not getattr(args, "unnormalized", False)),
- len_penalty=getattr(args, "lenpen", 1),
- unk_penalty=getattr(args, "unkpen", 0),
- temperature=getattr(args, "temperature", 1.0),
- match_source_len=getattr(args, "match_source_len", False),
- no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0),
- eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)),
- )
-
- def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None):
- src_lang_id = self.source_dictionary.index("[{}]".format(self.args.source_lang))
- source_tokens = []
- for s_t in src_tokens:
- s_t = torch.cat([s_t, s_t.new(1).fill_(src_lang_id)])
- source_tokens.append(s_t)
- dataset = LanguagePairDataset(
- source_tokens,
- src_lengths,
- self.source_dictionary,
- tgt_dict=self.target_dictionary,
- constraints=constraints,
- )
- return dataset
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/scripts/average_checkpoints.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/scripts/average_checkpoints.py
deleted file mode 100644
index c512f802bce6b3395cc42a0e4eb39181e9f8c873..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/scripts/average_checkpoints.py
+++ /dev/null
@@ -1,158 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import collections
-import os
-import re
-
-import torch
-from fairseq.file_io import PathManager
-
-
-def average_checkpoints(inputs):
- """Loads checkpoints from inputs and returns a model with averaged weights.
-
- Args:
- inputs: An iterable of string paths of checkpoints to load from.
-
- Returns:
- A dict of string keys mapping to various values. The 'model' key
- from the returned dict should correspond to an OrderedDict mapping
- string parameter names to torch Tensors.
- """
- params_dict = collections.OrderedDict()
- params_keys = None
- new_state = None
- num_models = len(inputs)
-
- for fpath in inputs:
- with PathManager.open(fpath, "rb") as f:
- state = torch.load(
- f,
- map_location=(
- lambda s, _: torch.serialization.default_restore_location(s, "cpu")
- ),
- )
- # Copies over the settings from the first checkpoint
- if new_state is None:
- new_state = state
-
- model_params = state["model"]
-
- model_params_keys = list(model_params.keys())
- if params_keys is None:
- params_keys = model_params_keys
- elif params_keys != model_params_keys:
- raise KeyError(
- "For checkpoint {}, expected list of params: {}, "
- "but found: {}".format(f, params_keys, model_params_keys)
- )
-
- for k in params_keys:
- p = model_params[k]
- if isinstance(p, torch.HalfTensor):
- p = p.float()
- if k not in params_dict:
- params_dict[k] = p.clone()
- # NOTE: clone() is needed in case of p is a shared parameter
- else:
- params_dict[k] += p
-
- averaged_params = collections.OrderedDict()
- for k, v in params_dict.items():
- averaged_params[k] = v
- if averaged_params[k].is_floating_point():
- averaged_params[k].div_(num_models)
- else:
- averaged_params[k] //= num_models
- new_state["model"] = averaged_params
- return new_state
-
-
-def last_n_checkpoints(paths, n, update_based, upper_bound=None):
- assert len(paths) == 1
- path = paths[0]
- if update_based:
- pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt")
- else:
- pt_regexp = re.compile(r"checkpoint(\d+)\.pt")
- files = PathManager.ls(path)
-
- entries = []
- for f in files:
- m = pt_regexp.fullmatch(f)
- if m is not None:
- sort_key = int(m.group(1))
- if upper_bound is None or sort_key <= upper_bound:
- entries.append((sort_key, m.group(0)))
- if len(entries) < n:
- raise Exception(
- "Found {} checkpoint files but need at least {}", len(entries), n
- )
- return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]]
-
-
-def main():
- parser = argparse.ArgumentParser(
- description="Tool to average the params of input checkpoints to "
- "produce a new checkpoint",
- )
- # fmt: off
- parser.add_argument('--inputs', required=True, nargs='+',
- help='Input checkpoint file paths.')
- parser.add_argument('--output', required=True, metavar='FILE',
- help='Write the new checkpoint containing the averaged weights to this path.')
- num_group = parser.add_mutually_exclusive_group()
- num_group.add_argument('--num-epoch-checkpoints', type=int,
- help='if set, will try to find checkpoints with names checkpoint_xx.pt in the path specified by input, '
- 'and average last this many of them.')
- num_group.add_argument('--num-update-checkpoints', type=int,
- help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by input, '
- 'and average last this many of them.')
- parser.add_argument('--checkpoint-upper-bound', type=int,
- help='when using --num-epoch-checkpoints, this will set an upper bound on which epoch to use, '
- 'when using --num-update-checkpoints, this will set an upper bound on which update to use'
- 'e.g., with --num-epoch-checkpoints=10 --checkpoint-upper-bound=50, checkpoints 41-50 would be averaged.'
- 'e.g., with --num-update-checkpoints=10 --checkpoint-upper-bound=50000, checkpoints 40500-50000 would be averaged assuming --save-interval-updates 500'
- )
- # fmt: on
- args = parser.parse_args()
- print(args)
-
- num = None
- is_update_based = False
- if args.num_update_checkpoints is not None:
- num = args.num_update_checkpoints
- is_update_based = True
- elif args.num_epoch_checkpoints is not None:
- num = args.num_epoch_checkpoints
-
- assert args.checkpoint_upper_bound is None or (
- args.num_epoch_checkpoints is not None
- or args.num_update_checkpoints is not None
- ), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints"
- assert (
- args.num_epoch_checkpoints is None or args.num_update_checkpoints is None
- ), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints"
-
- if num is not None:
- args.inputs = last_n_checkpoints(
- args.inputs,
- num,
- is_update_based,
- upper_bound=args.checkpoint_upper_bound,
- )
- print("averaging checkpoints: ", args.inputs)
-
- new_state = average_checkpoints(args.inputs)
- with PathManager.open(args.output, "wb") as f:
- torch.save(new_state, f)
- print("Finished writing averaged checkpoint to {}".format(args.output))
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Hexamind/swarms/settings.py b/spaces/Hexamind/swarms/settings.py
deleted file mode 100644
index 1f0e00461142930c25d554bedd432dee5c0a5304..0000000000000000000000000000000000000000
--- a/spaces/Hexamind/swarms/settings.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from dataclasses import dataclass
-
-import param_
-import streamlit as st
-import numpy as np
-
-
-@dataclass
-class Settings:
-
- perimeter: int = param_.PERIMETER
- perimeter_z: int = param_.PERIMETER_Z
- groundzone: int = param_.GROUNDZONE
-
- latlon = param_.LATLON['Paris']['lat'], param_.LATLON['Paris']['lon']
-
- blues: int = param_.BLUES
-
- blues_per_circle = np.array(param_.BLUES_PER_CIRCLE)
- blue_circles_rho = param_.BLUE_CIRCLES_RHO
- blue_circles_theta = param_.BLUE_CIRCLES_THETA
- blue_circles_zed = param_.BLUE_CIRCLES_ZED
- blue_distance_factor: float = param_.BLUE_DISTANCE_FACTOR
-
- is_unkillable: bool = param_.BLUE_IS_UNKILLABLE
-
- blue_speed_init: int = param_.BLUE_SPEED_INIT
-
- reds: int = param_.REDS
-
- red_squads = param_.RED_SQUADS
- red_squads_rho = np.array(param_.RED_SQUADS_RHO)
- red_squads_theta = param_.RED_SQUADS_THETA
- red_squads_zed = param_.RED_SQUADS_ZED
- red_distance_factor: float = param_.RED_DISTANCE_FACTOR
-
- red_rho_noise = np.array(param_.RED_RHO_NOISE)
- red_theta_noise = np.array(param_.RED_THETA_NOISE)
- red_zed_noise = np.array(param_.RED_ZED_NOISE)
-
- red_speed_init: int = param_.RED_SPEED_INIT
-
- policy_folder: str = param_.POLICY_FOLDER
-
-
-def define_(with_streamlit: bool = True, blues: int = Settings.blues, reds: int = Settings.reds):
- """"
- shows the blue and red swarms in Streamlit
- :return:
- """
- blues = blues
- reds = reds
-
- if with_streamlit:
- st.title('Blues against Reds by hexamind.ai')
- st.write('controlled by Reinforcement Learning.')
- st.text('<- Set parameters')
-
- st.sidebar.subheader("Define the battlefield")
- blues = st.sidebar.slider("how many blues on defense?", 1, 20, 6)
- Settings.blues = blues
- blue_dispersion = st.sidebar.slider("set the average blue dispersion", 0.3, 1.0, 0.8)
- Settings.reds = reds
- reds = st.sidebar.slider("how many reds are on the attack?", 1, 20, 6)
- Settings.reds = reds
- red_dispersion = st.sidebar.slider("set the average red dispersion", 0.3, 1.0, 0.7)
-
- Settings.blue_distance_factor = 3 * blue_dispersion
- Settings.red_distance_factor = 3 * red_dispersion
-
- location = st.sidebar.radio("Location", ['Paris', 'Puilaurens', 'San Francisco'])
-
- lat_tg = param_.LATLON[location]['lat']
- lon_tg = param_.LATLON[location]['lon']
-
- Settings.latlon = lat_tg, lon_tg
-
- st.sidebar.write(
- 'you probably need more drones '
- 'No worries, we have plenty at www.hexamind.ai ')
-
- return blues, reds
diff --git a/spaces/HoangHa/IELTS_Speaking_GPT/audio_recorder.py b/spaces/HoangHa/IELTS_Speaking_GPT/audio_recorder.py
deleted file mode 100644
index 256f017c7c5086f0cc82128b0d5be25c4885192e..0000000000000000000000000000000000000000
--- a/spaces/HoangHa/IELTS_Speaking_GPT/audio_recorder.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import pyaudio
-import wave
-
-# Set audio parameters
-FORMAT = pyaudio.paInt16 # Audio format
-CHANNELS = 1 # Number of audio channels
-RATE = 16000 # Sampling rate
-CHUNK = 1024 # Number of audio frames per buffer
-RECORD_SECONDS = 10 # Duration of recording in seconds
-RECORDING_FILENAME = 'recording.wav' # Name of output file
-
-
-def record(seconds=RECORD_SECONDS, filename=RECORDING_FILENAME):
- # Initialize PyAudio object
- audio = pyaudio.PyAudio()
-
- # Open audio stream
- stream = audio.open(
- format=FORMAT, channels=CHANNELS,
- rate=RATE, input=True,
- frames_per_buffer=CHUNK)
-
- # Record audio
- frames = []
- for i in range(0, int(RATE / CHUNK * seconds)):
- data = stream.read(CHUNK)
- frames.append(data)
-
- # Stop audio stream and PyAudio object
- stream.stop_stream()
- stream.close()
- audio.terminate()
-
- # Write frames to a WAV file
- wave_file = wave.open(filename, 'wb')
- wave_file.setnchannels(CHANNELS)
- wave_file.setsampwidth(audio.get_sample_size(FORMAT))
- wave_file.setframerate(RATE)
- wave_file.writeframes(b''.join(frames))
- wave_file.close()
-
-if __name__ == '__main__':
- # Run the record function with default parameters
- record()
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/wav2vec_manifest.py b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/wav2vec_manifest.py
deleted file mode 100644
index 9b8aa180e88d9ee98bdca7089aed5046ec0d9cb9..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/wav2vec_manifest.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Data pre-processing: build vocabularies and binarize training data.
-"""
-
-import argparse
-import glob
-import os
-import random
-
-import soundfile
-
-
-def get_parser():
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "root", metavar="DIR", help="root directory containing flac files to index"
- )
- parser.add_argument(
- "--valid-percent",
- default=0.01,
- type=float,
- metavar="D",
- help="percentage of data to use as validation set (between 0 and 1)",
- )
- parser.add_argument(
- "--dest", default=".", type=str, metavar="DIR", help="output directory"
- )
- parser.add_argument(
- "--ext", default="flac", type=str, metavar="EXT", help="extension to look for"
- )
- parser.add_argument("--seed", default=42, type=int, metavar="N", help="random seed")
- parser.add_argument(
- "--path-must-contain",
- default=None,
- type=str,
- metavar="FRAG",
- help="if set, path must contain this substring for a file to be included in the manifest",
- )
- return parser
-
-
-def main(args):
- assert args.valid_percent >= 0 and args.valid_percent <= 1.0
-
- if not os.path.exists(args.dest):
- os.makedirs(args.dest)
-
- dir_path = os.path.realpath(args.root)
- search_path = os.path.join(dir_path, "**/*." + args.ext)
- rand = random.Random(args.seed)
-
- valid_f = (
- open(os.path.join(args.dest, "valid.tsv"), "w")
- if args.valid_percent > 0
- else None
- )
-
- with open(os.path.join(args.dest, "train.tsv"), "w") as train_f:
- print(dir_path, file=train_f)
-
- if valid_f is not None:
- print(dir_path, file=valid_f)
-
- for fname in glob.iglob(search_path, recursive=True):
- file_path = os.path.realpath(fname)
-
- if args.path_must_contain and args.path_must_contain not in file_path:
- continue
-
- frames = soundfile.info(fname).frames
- dest = train_f if rand.random() > args.valid_percent else valid_f
- print(
- "{}\t{}".format(os.path.relpath(file_path, dir_path), frames), file=dest
- )
- if valid_f is not None:
- valid_f.close()
-
-
-if __name__ == "__main__":
- parser = get_parser()
- args = parser.parse_args()
- main(args)
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/metrics/niqe.py b/spaces/Iceclear/StableSR/StableSR/basicsr/metrics/niqe.py
deleted file mode 100644
index e3c1467f61d809ec3b2630073118460d9d61a861..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/metrics/niqe.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-from scipy.ndimage import convolve
-from scipy.special import gamma
-
-from basicsr.metrics.metric_util import reorder_image, to_y_channel
-from basicsr.utils.matlab_functions import imresize
-from basicsr.utils.registry import METRIC_REGISTRY
-
-
-def estimate_aggd_param(block):
- """Estimate AGGD (Asymmetric Generalized Gaussian Distribution) parameters.
-
- Args:
- block (ndarray): 2D Image block.
-
- Returns:
- tuple: alpha (float), beta_l (float) and beta_r (float) for the AGGD
- distribution (Estimating the parames in Equation 7 in the paper).
- """
- block = block.flatten()
- gam = np.arange(0.2, 10.001, 0.001) # len = 9801
- gam_reciprocal = np.reciprocal(gam)
- r_gam = np.square(gamma(gam_reciprocal * 2)) / (gamma(gam_reciprocal) * gamma(gam_reciprocal * 3))
-
- left_std = np.sqrt(np.mean(block[block < 0]**2))
- right_std = np.sqrt(np.mean(block[block > 0]**2))
- gammahat = left_std / right_std
- rhat = (np.mean(np.abs(block)))**2 / np.mean(block**2)
- rhatnorm = (rhat * (gammahat**3 + 1) * (gammahat + 1)) / ((gammahat**2 + 1)**2)
- array_position = np.argmin((r_gam - rhatnorm)**2)
-
- alpha = gam[array_position]
- beta_l = left_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))
- beta_r = right_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))
- return (alpha, beta_l, beta_r)
-
-
-def compute_feature(block):
- """Compute features.
-
- Args:
- block (ndarray): 2D Image block.
-
- Returns:
- list: Features with length of 18.
- """
- feat = []
- alpha, beta_l, beta_r = estimate_aggd_param(block)
- feat.extend([alpha, (beta_l + beta_r) / 2])
-
- # distortions disturb the fairly regular structure of natural images.
- # This deviation can be captured by analyzing the sample distribution of
- # the products of pairs of adjacent coefficients computed along
- # horizontal, vertical and diagonal orientations.
- shifts = [[0, 1], [1, 0], [1, 1], [1, -1]]
- for i in range(len(shifts)):
- shifted_block = np.roll(block, shifts[i], axis=(0, 1))
- alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block)
- # Eq. 8
- mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha))
- feat.extend([alpha, mean, beta_l, beta_r])
- return feat
-
-
-def niqe(img, mu_pris_param, cov_pris_param, gaussian_window, block_size_h=96, block_size_w=96):
- """Calculate NIQE (Natural Image Quality Evaluator) metric.
-
- ``Paper: Making a "Completely Blind" Image Quality Analyzer``
-
- This implementation could produce almost the same results as the official
- MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip
-
- Note that we do not include block overlap height and width, since they are
- always 0 in the official implementation.
-
- For good performance, it is advisable by the official implementation to
- divide the distorted image in to the same size patched as used for the
- construction of multivariate Gaussian model.
-
- Args:
- img (ndarray): Input image whose quality needs to be computed. The
- image must be a gray or Y (of YCbCr) image with shape (h, w).
- Range [0, 255] with float type.
- mu_pris_param (ndarray): Mean of a pre-defined multivariate Gaussian
- model calculated on the pristine dataset.
- cov_pris_param (ndarray): Covariance of a pre-defined multivariate
- Gaussian model calculated on the pristine dataset.
- gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the
- image.
- block_size_h (int): Height of the blocks in to which image is divided.
- Default: 96 (the official recommended value).
- block_size_w (int): Width of the blocks in to which image is divided.
- Default: 96 (the official recommended value).
- """
- assert img.ndim == 2, ('Input image must be a gray or Y (of YCbCr) image with shape (h, w).')
- # crop image
- h, w = img.shape
- num_block_h = math.floor(h / block_size_h)
- num_block_w = math.floor(w / block_size_w)
- img = img[0:num_block_h * block_size_h, 0:num_block_w * block_size_w]
-
- distparam = [] # dist param is actually the multiscale features
- for scale in (1, 2): # perform on two scales (1, 2)
- mu = convolve(img, gaussian_window, mode='nearest')
- sigma = np.sqrt(np.abs(convolve(np.square(img), gaussian_window, mode='nearest') - np.square(mu)))
- # normalize, as in Eq. 1 in the paper
- img_nomalized = (img - mu) / (sigma + 1)
-
- feat = []
- for idx_w in range(num_block_w):
- for idx_h in range(num_block_h):
- # process ecah block
- block = img_nomalized[idx_h * block_size_h // scale:(idx_h + 1) * block_size_h // scale,
- idx_w * block_size_w // scale:(idx_w + 1) * block_size_w // scale]
- feat.append(compute_feature(block))
-
- distparam.append(np.array(feat))
-
- if scale == 1:
- img = imresize(img / 255., scale=0.5, antialiasing=True)
- img = img * 255.
-
- distparam = np.concatenate(distparam, axis=1)
-
- # fit a MVG (multivariate Gaussian) model to distorted patch features
- mu_distparam = np.nanmean(distparam, axis=0)
- # use nancov. ref: https://ww2.mathworks.cn/help/stats/nancov.html
- distparam_no_nan = distparam[~np.isnan(distparam).any(axis=1)]
- cov_distparam = np.cov(distparam_no_nan, rowvar=False)
-
- # compute niqe quality, Eq. 10 in the paper
- invcov_param = np.linalg.pinv((cov_pris_param + cov_distparam) / 2)
- quality = np.matmul(
- np.matmul((mu_pris_param - mu_distparam), invcov_param), np.transpose((mu_pris_param - mu_distparam)))
-
- quality = np.sqrt(quality)
- quality = float(np.squeeze(quality))
- return quality
-
-
-@METRIC_REGISTRY.register()
-def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y', **kwargs):
- """Calculate NIQE (Natural Image Quality Evaluator) metric.
-
- ``Paper: Making a "Completely Blind" Image Quality Analyzer``
-
- This implementation could produce almost the same results as the official
- MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip
-
- > MATLAB R2021a result for tests/data/baboon.png: 5.72957338 (5.7296)
- > Our re-implementation result for tests/data/baboon.png: 5.7295763 (5.7296)
-
- We use the official params estimated from the pristine dataset.
- We use the recommended block size (96, 96) without overlaps.
-
- Args:
- img (ndarray): Input image whose quality needs to be computed.
- The input image must be in range [0, 255] with float/int type.
- The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order)
- If the input order is 'HWC' or 'CHW', it will be converted to gray
- or Y (of YCbCr) image according to the ``convert_to`` argument.
- crop_border (int): Cropped pixels in each edge of an image. These
- pixels are not involved in the metric calculation.
- input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'.
- Default: 'HWC'.
- convert_to (str): Whether converted to 'y' (of MATLAB YCbCr) or 'gray'.
- Default: 'y'.
-
- Returns:
- float: NIQE result.
- """
- ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
- # we use the official params estimated from the pristine dataset.
- niqe_pris_params = np.load(os.path.join(ROOT_DIR, 'niqe_pris_params.npz'))
- mu_pris_param = niqe_pris_params['mu_pris_param']
- cov_pris_param = niqe_pris_params['cov_pris_param']
- gaussian_window = niqe_pris_params['gaussian_window']
-
- img = img.astype(np.float32)
- if input_order != 'HW':
- img = reorder_image(img, input_order=input_order)
- if convert_to == 'y':
- img = to_y_channel(img)
- elif convert_to == 'gray':
- img = cv2.cvtColor(img / 255., cv2.COLOR_BGR2GRAY) * 255.
- img = np.squeeze(img)
-
- if crop_border != 0:
- img = img[crop_border:-crop_border, crop_border:-crop_border]
-
- # round is necessary for being consistent with MATLAB's result
- img = img.round()
-
- niqe_result = niqe(img, mu_pris_param, cov_pris_param, gaussian_window)
-
- return niqe_result
diff --git a/spaces/Ikaros521/moe-tts/text/mandarin.py b/spaces/Ikaros521/moe-tts/text/mandarin.py
deleted file mode 100644
index ff71de9788e4f20c897b971a775d1ecfbfe1c7b7..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/moe-tts/text/mandarin.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import os
-import sys
-import re
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba
-import cn2an
-import logging
-
-logging.getLogger('jieba').setLevel(logging.WARNING)
-jieba.initialize()
-
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'ㄟˉ'),
- ('b', 'ㄅㄧˋ'),
- ('c', 'ㄙㄧˉ'),
- ('d', 'ㄉㄧˋ'),
- ('e', 'ㄧˋ'),
- ('f', 'ㄝˊㄈㄨˋ'),
- ('g', 'ㄐㄧˋ'),
- ('h', 'ㄝˇㄑㄩˋ'),
- ('i', 'ㄞˋ'),
- ('j', 'ㄐㄟˋ'),
- ('k', 'ㄎㄟˋ'),
- ('l', 'ㄝˊㄛˋ'),
- ('m', 'ㄝˊㄇㄨˋ'),
- ('n', 'ㄣˉ'),
- ('o', 'ㄡˉ'),
- ('p', 'ㄆㄧˉ'),
- ('q', 'ㄎㄧㄡˉ'),
- ('r', 'ㄚˋ'),
- ('s', 'ㄝˊㄙˋ'),
- ('t', 'ㄊㄧˋ'),
- ('u', 'ㄧㄡˉ'),
- ('v', 'ㄨㄧˉ'),
- ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
- ('x', 'ㄝˉㄎㄨˋㄙˋ'),
- ('y', 'ㄨㄞˋ'),
- ('z', 'ㄗㄟˋ')
-]]
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'ʧ⁼'),
- ('ㄑ', 'ʧʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ʦ`⁼'),
- ('ㄔ', 'ʦ`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ʦ⁼'),
- ('ㄘ', 'ʦʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'e'),
- ('ㄞ', 'ai'),
- ('ㄟ', 'ei'),
- ('ㄠ', 'au'),
- ('ㄡ', 'ou'),
- ('ㄧㄢ', 'yeNN'),
- ('ㄢ', 'aNN'),
- ('ㄧㄣ', 'iNN'),
- ('ㄣ', 'əNN'),
- ('ㄤ', 'aNg'),
- ('ㄧㄥ', 'iNg'),
- ('ㄨㄥ', 'uNg'),
- ('ㄩㄥ', 'yuNg'),
- ('ㄥ', 'əNg'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-# List of (romaji, ipa) pairs:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('ʃy', 'ʃ'),
- ('ʧʰy', 'ʧʰ'),
- ('ʧ⁼y', 'ʧ⁼'),
- ('NN', 'n'),
- ('Ng', 'ŋ'),
- ('y', 'j'),
- ('h', 'x')
-]]
-
-# List of (bopomofo, ipa) pairs:
-_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'x'),
- ('ㄐ', 'tʃ⁼'),
- ('ㄑ', 'tʃʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ts`⁼'),
- ('ㄔ', 'ts`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ts⁼'),
- ('ㄘ', 'tsʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'ɛ'),
- ('ㄞ', 'aɪ'),
- ('ㄟ', 'eɪ'),
- ('ㄠ', 'ɑʊ'),
- ('ㄡ', 'oʊ'),
- ('ㄧㄢ', 'jɛn'),
- ('ㄩㄢ', 'ɥæn'),
- ('ㄢ', 'an'),
- ('ㄧㄣ', 'in'),
- ('ㄩㄣ', 'ɥn'),
- ('ㄣ', 'ən'),
- ('ㄤ', 'ɑŋ'),
- ('ㄧㄥ', 'iŋ'),
- ('ㄨㄥ', 'ʊŋ'),
- ('ㄩㄥ', 'jʊŋ'),
- ('ㄥ', 'əŋ'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-# List of (bopomofo, ipa2) pairs:
-_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄅㄛ', 'pwo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'tɕ'),
- ('ㄑ', 'tɕʰ'),
- ('ㄒ', 'ɕ'),
- ('ㄓ', 'tʂ'),
- ('ㄔ', 'tʂʰ'),
- ('ㄕ', 'ʂ'),
- ('ㄖ', 'ɻ'),
- ('ㄗ', 'ts'),
- ('ㄘ', 'tsʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ɤ'),
- ('ㄝ', 'ɛ'),
- ('ㄞ', 'aɪ'),
- ('ㄟ', 'eɪ'),
- ('ㄠ', 'ɑʊ'),
- ('ㄡ', 'oʊ'),
- ('ㄧㄢ', 'jɛn'),
- ('ㄩㄢ', 'yæn'),
- ('ㄢ', 'an'),
- ('ㄧㄣ', 'in'),
- ('ㄩㄣ', 'yn'),
- ('ㄣ', 'ən'),
- ('ㄤ', 'ɑŋ'),
- ('ㄧㄥ', 'iŋ'),
- ('ㄨㄥ', 'ʊŋ'),
- ('ㄩㄥ', 'jʊŋ'),
- ('ㄥ', 'ɤŋ'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'y'),
- ('ˉ', '˥'),
- ('ˊ', '˧˥'),
- ('ˇ', '˨˩˦'),
- ('ˋ', '˥˩'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-
-def number_to_chinese(text):
- numbers = re.findall(r'\d+(?:\.?\d+)?', text)
- for number in numbers:
- text = text.replace(number, cn2an.an2cn(number), 1)
- return text
-
-
-def chinese_to_bopomofo(text):
- text = text.replace('、', '，').replace('；', '，').replace('：', '，')
- words = jieba.lcut(text, cut_all=False)
- text = ''
- for word in words:
- bopomofos = lazy_pinyin(word, BOPOMOFO)
- if not re.search('[\u4e00-\u9fff]', word):
- text += word
- continue
- for i in range(len(bopomofos)):
- bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
- if text != '':
- text += ' '
- text += ''.join(bopomofos)
- return text
-
-
-def latin_to_bopomofo(text):
- for regex, replacement in _latin_to_bopomofo:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def bopomofo_to_romaji(text):
- for regex, replacement in _bopomofo_to_romaji:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def bopomofo_to_ipa(text):
- for regex, replacement in _bopomofo_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def bopomofo_to_ipa2(text):
- for regex, replacement in _bopomofo_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def chinese_to_romaji(text):
- text = number_to_chinese(text)
- text = chinese_to_bopomofo(text)
- text = latin_to_bopomofo(text)
- text = bopomofo_to_romaji(text)
- text = re.sub('i([aoe])', r'y\1', text)
- text = re.sub('u([aoəe])', r'w\1', text)
- text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
- r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
- text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
- return text
-
-
-def chinese_to_lazy_ipa(text):
- text = chinese_to_romaji(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def chinese_to_ipa(text):
- text = number_to_chinese(text)
- text = chinese_to_bopomofo(text)
- text = latin_to_bopomofo(text)
- text = bopomofo_to_ipa(text)
- text = re.sub('i([aoe])', r'j\1', text)
- text = re.sub('u([aoəe])', r'w\1', text)
- text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
- r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
- text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
- return text
-
-
-def chinese_to_ipa2(text):
- text = number_to_chinese(text)
- text = chinese_to_bopomofo(text)
- text = latin_to_bopomofo(text)
- text = bopomofo_to_ipa2(text)
- text = re.sub(r'i([aoe])', r'j\1', text)
- text = re.sub(r'u([aoəe])', r'w\1', text)
- text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
- text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
- return text
diff --git a/spaces/Illumotion/Koboldcpp/k_quants.h b/spaces/Illumotion/Koboldcpp/k_quants.h
deleted file mode 100644
index 9de089e7a471956849a9f9df3edaeefc5e93a7ad..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/k_quants.h
+++ /dev/null
@@ -1,165 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-
-#include
-#include
-#include
-
-// Super-block size
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
-#endif
-
-#ifndef static_assert
-#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
-#define static_assert(cond, msg) _Static_assert(cond, msg)
-#else
-#define static_assert(cond, msg) struct global_scope_noop_trick
-#endif
-#endif
-
-//
-// Super-block quantization structures
-//
-
-// 2-bit quantization
-// weight is represented as x = a * q + b
-// 16 blocks of 16 elements each
-// Effectively 2.5625 bits per weight
-typedef struct {
- uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
- uint8_t qs[QK_K/4]; // quants
- ggml_fp16_t d; // super-block scale for quantized scales
- ggml_fp16_t dmin; // super-block scale for quantized mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-// 3-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 3.4375 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
- uint8_t hmask[QK_K/8]; // quants - high bit
- uint8_t qs[QK_K/4]; // quants - low 2 bits
- uint8_t scales[2];
- ggml_fp16_t d; // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
-#else
-typedef struct {
- uint8_t hmask[QK_K/8]; // quants - high bit
- uint8_t qs[QK_K/4]; // quants - low 2 bits
- uint8_t scales[12]; // scales, quantized with 6 bits
- ggml_fp16_t d; // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
-#endif
-
-// 4-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 4.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
- ggml_fp16_t d[2]; // super-block scales/mins
- uint8_t scales[2]; // 4-bit block scales/mins
- uint8_t qs[QK_K/2]; // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct {
- ggml_fp16_t d; // super-block scale for quantized scales
- ggml_fp16_t dmin; // super-block scale for quantized mins
- uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
- uint8_t qs[QK_K/2]; // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-// 5-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 5.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
- ggml_fp16_t d; // super-block scale
- int8_t scales[QK_K/16]; // 8-bit block scales
- uint8_t qh[QK_K/8]; // quants, high bit
- uint8_t qs[QK_K/2]; // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct {
- ggml_fp16_t d; // super-block scale for quantized scales
- ggml_fp16_t dmin; // super-block scale for quantized mins
- uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
- uint8_t qh[QK_K/8]; // quants, high bit
- uint8_t qs[QK_K/2]; // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-// 6-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 6.5625 bits per weight
-typedef struct {
- uint8_t ql[QK_K/2]; // quants, lower 4 bits
- uint8_t qh[QK_K/4]; // quants, upper 2 bits
- int8_t scales[QK_K/16]; // scales, quantized with 8 bits
- ggml_fp16_t d; // super-block scale
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
-
-// This is only used for intermediate quantization and dot products
-typedef struct {
- float d; // delta
- int8_t qs[QK_K]; // quants
- int16_t bsums[QK_K/16]; // sum of quants in groups of 16
-} block_q8_K;
-static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
-
-
-// Quantization
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
-
-void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
-
-// Dequantization
-void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
-void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
-void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
-void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
-void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
-
-// Dot product
-void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-
-// Quantization with histogram collection
-size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
-
diff --git a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/model/apply_lora.py b/spaces/Intel/NeuralChat-ICX-INT4/fastchat/model/apply_lora.py
deleted file mode 100644
index 870e64a3b4bfb6e44df70bf922bb8e3f6d3c9d42..0000000000000000000000000000000000000000
--- a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/model/apply_lora.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""
-Apply the LoRA weights on top of a base model.
-
-Usage:
-python3 -m fastchat.model.apply_lora --base ~/model_weights/llama-7b --target ~/model_weights/baize-7b --lora project-baize/baize-lora-7B
-
-Dependency:
-pip3 install git+https://github.com/huggingface/peft.git@2822398fbe896f25d4dac5e468624dc5fd65a51b
-"""
-import argparse
-
-import torch
-from peft import PeftModel
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-
-def apply_lora(base_model_path, target_model_path, lora_path):
- print(f"Loading the base model from {base_model_path}")
- base = AutoModelForCausalLM.from_pretrained(
- base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
- )
- base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False)
-
- print(f"Loading the LoRA adapter from {lora_path}")
-
- lora_model = PeftModel.from_pretrained(
- base,
- lora_path,
- torch_dtype=torch.float16,
- )
-
- print("Applying the LoRA")
- model = lora_model.merge_and_unload()
-
- print(f"Saving the target model to {target_model_path}")
- model.save_pretrained(target_model_path)
- base_tokenizer.save_pretrained(target_model_path)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--base-model-path", type=str, required=True)
- parser.add_argument("--target-model-path", type=str, required=True)
- parser.add_argument("--lora-path", type=str, required=True)
-
- args = parser.parse_args()
-
- apply_lora(args.base_model_path, args.target_model_path, args.lora_path)
diff --git a/spaces/Jasonyoyo/CodeFormer/CodeFormer/basicsr/utils/download_util.py b/spaces/Jasonyoyo/CodeFormer/CodeFormer/basicsr/utils/download_util.py
deleted file mode 100644
index 2a267915743ee3f3232bc8fe992466b52468979a..0000000000000000000000000000000000000000
--- a/spaces/Jasonyoyo/CodeFormer/CodeFormer/basicsr/utils/download_util.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import math
-import os
-import requests
-from torch.hub import download_url_to_file, get_dir
-from tqdm import tqdm
-from urllib.parse import urlparse
-
-from .misc import sizeof_fmt
-
-
-def download_file_from_google_drive(file_id, save_path):
- """Download files from google drive.
- Ref:
- https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501
- Args:
- file_id (str): File id.
- save_path (str): Save path.
- """
-
- session = requests.Session()
- URL = 'https://docs.google.com/uc?export=download'
- params = {'id': file_id}
-
- response = session.get(URL, params=params, stream=True)
- token = get_confirm_token(response)
- if token:
- params['confirm'] = token
- response = session.get(URL, params=params, stream=True)
-
- # get file size
- response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
- print(response_file_size)
- if 'Content-Range' in response_file_size.headers:
- file_size = int(response_file_size.headers['Content-Range'].split('/')[1])
- else:
- file_size = None
-
- save_response_content(response, save_path, file_size)
-
-
-def get_confirm_token(response):
- for key, value in response.cookies.items():
- if key.startswith('download_warning'):
- return value
- return None
-
-
-def save_response_content(response, destination, file_size=None, chunk_size=32768):
- if file_size is not None:
- pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')
-
- readable_file_size = sizeof_fmt(file_size)
- else:
- pbar = None
-
- with open(destination, 'wb') as f:
- downloaded_size = 0
- for chunk in response.iter_content(chunk_size):
- downloaded_size += chunk_size
- if pbar is not None:
- pbar.update(1)
- pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}')
- if chunk: # filter out keep-alive new chunks
- f.write(chunk)
- if pbar is not None:
- pbar.close()
-
-
-def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
- """Load file form http url, will download models if necessary.
- Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
- Args:
- url (str): URL to be downloaded.
- model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
- Default: None.
- progress (bool): Whether to show the download progress. Default: True.
- file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.
- Returns:
- str: The path to the downloaded file.
- """
- if model_dir is None: # use the pytorch hub_dir
- hub_dir = get_dir()
- model_dir = os.path.join(hub_dir, 'checkpoints')
-
- os.makedirs(model_dir, exist_ok=True)
-
- parts = urlparse(url)
- filename = os.path.basename(parts.path)
- if file_name is not None:
- filename = file_name
- cached_file = os.path.abspath(os.path.join(model_dir, filename))
- if not os.path.exists(cached_file):
- print(f'Downloading: "{url}" to {cached_file}\n')
- download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
- return cached_file
\ No newline at end of file
diff --git a/spaces/Jeff2323/ai-comic-factory/src/app/store/index.ts b/spaces/Jeff2323/ai-comic-factory/src/app/store/index.ts
deleted file mode 100644
index e85dd4d052996e9b4120bef57abb6c72c509d41a..0000000000000000000000000000000000000000
--- a/spaces/Jeff2323/ai-comic-factory/src/app/store/index.ts
+++ /dev/null
@@ -1,203 +0,0 @@
-"use client"
-
-import { create } from "zustand"
-
-import { FontName } from "@/lib/fonts"
-import { Preset, PresetName, defaultPreset, getPreset, getRandomPreset } from "@/app/engine/presets"
-import { LayoutName, defaultLayout, getRandomLayoutName, getRandomLayoutNames } from "../layouts"
-import html2canvas from "html2canvas"
-import { RenderedScene } from "@/types"
-
-export const useStore = create<{
- prompt: string
- font: FontName
- preset: Preset
- nbFrames: number
- panels: string[]
- captions: string[]
- upscaleQueue: Record
- showCaptions: boolean
- renderedScenes: Record
- layout: LayoutName
- layouts: LayoutName[]
- zoomLevel: number
- page: HTMLDivElement
- isGeneratingStory: boolean
- panelGenerationStatus: Record
- isGeneratingText: boolean
- atLeastOnePanelIsBusy: boolean
- setRendered: (panelId: string, renderedScene: RenderedScene) => void
- addToUpscaleQueue: (panelId: string, renderedScene: RenderedScene) => void
- removeFromUpscaleQueue: (panelId: string) => void
- setPrompt: (prompt: string) => void
- setFont: (font: FontName) => void
- setPreset: (preset: Preset) => void
- setPanels: (panels: string[]) => void
- setShowCaptions: (showCaptions: boolean) => void
- setLayout: (layout: LayoutName) => void
- setLayouts: (layouts: LayoutName[]) => void
- setCaptions: (captions: string[]) => void
- setZoomLevel: (zoomLevel: number) => void
- setPage: (page: HTMLDivElement) => void
- setGeneratingStory: (isGeneratingStory: boolean) => void
- setGeneratingImages: (panelId: string, value: boolean) => void
- setGeneratingText: (isGeneratingText: boolean) => void
- pageToImage: () => Promise
- download: () => Promise
- generate: (prompt: string, presetName: PresetName, layoutName: LayoutName) => void
-}>((set, get) => ({
- prompt: "",
- font: "actionman",
- preset: getPreset(defaultPreset),
- nbFrames: 1,
- panels: [],
- captions: [],
- upscaleQueue: {} as Record,
- renderedScenes: {} as Record,
- showCaptions: false,
- layout: defaultLayout,
- layouts: [defaultLayout, defaultLayout],
- zoomLevel: 60,
- page: undefined as unknown as HTMLDivElement,
- isGeneratingStory: false,
- panelGenerationStatus: {},
- isGeneratingText: false,
- atLeastOnePanelIsBusy: false,
- setRendered: (panelId: string, renderedScene: RenderedScene) => {
- const { renderedScenes } = get()
- set({
- renderedScenes: {
- ...renderedScenes,
- [panelId]: renderedScene
- }
- })
- },
- addToUpscaleQueue: (panelId: string, renderedScene: RenderedScene) => {
- const { upscaleQueue } = get()
- set({
- upscaleQueue: {
- ...upscaleQueue,
- [panelId]: renderedScene
- },
- })
- },
- removeFromUpscaleQueue: (panelId: string) => {
- const upscaleQueue = { ...get().upscaleQueue }
- delete upscaleQueue[panelId]
- set({
- upscaleQueue,
- })
- },
- setPrompt: (prompt: string) => {
- const existingPrompt = get().prompt
- if (prompt === existingPrompt) { return }
- set({
- prompt,
- })
- },
- setFont: (font: FontName) => {
- const existingFont = get().font
- if (font === existingFont) { return }
- set({
- font,
- })
- },
- setPreset: (preset: Preset) => {
- const existingPreset = get().preset
- if (preset.label === existingPreset.label) { return }
- set({
- preset,
- })
- },
- setNbFrames: (nbFrames: number) => {
- const existingNbFrames = get().nbFrames
- if (nbFrames === existingNbFrames) { return }
- set({
- nbFrames,
- })
- },
- setPanels: (panels: string[]) => set({ panels }),
- setCaptions: (captions: string[]) => {
- set({
- captions,
- })
- },
- setShowCaptions: (showCaptions: boolean) => {
- set({
- showCaptions,
- })
- },
- setLayout: (layoutName: LayoutName) => {
- const layout = layoutName === "random"
- ? getRandomLayoutName()
- : layoutName
-
- set({
- layout,
- layouts: [layout, layout]
- })
- },
- setLayouts: (layouts: LayoutName[]) => set({ layouts }),
- setZoomLevel: (zoomLevel: number) => set({ zoomLevel }),
- setPage: (page: HTMLDivElement) => {
- if (!page) { return }
- set({ page })
- },
- setGeneratingStory: (isGeneratingStory: boolean) => set({ isGeneratingStory }),
- setGeneratingImages: (panelId: string, value: boolean) => {
- const panelGenerationStatus: Record = {
- ...get().panelGenerationStatus,
- [panelId]: value
- }
-
- const atLeastOnePanelIsBusy = Object.values(panelGenerationStatus).includes(true)
-
- set({
- panelGenerationStatus,
- atLeastOnePanelIsBusy
- })
- },
- setGeneratingText: (isGeneratingText: boolean) => set({ isGeneratingText }),
- pageToImage: async () => {
- const { page } = get()
- if (!page) { return "" }
-
-
- const canvas = await html2canvas(page)
- console.log("canvas:", canvas)
-
- const data = canvas.toDataURL('image/jpeg', 0.5)
- return data
- },
- download: async () => {
- const { pageToImage } = get()
- const data = await pageToImage()
-
- const link = document.createElement('a')
-
- if (typeof link.download === 'string') {
- link.href = data
- link.download = 'comic.jpg'
- document.body.appendChild(link)
- link.click()
- document.body.removeChild(link)
- } else {
- window.open(data)
- }
- },
- generate: (prompt: string, presetName: PresetName, layoutName: LayoutName) => {
- const layout = layoutName === "random"
- ? getRandomLayoutName()
- : layoutName
- set({
- prompt,
- panels: [],
- captions: [],
- preset: presetName === "random"
- ? getRandomPreset()
- : getPreset(presetName),
- layout,
- layouts: [layout, layout],
- })
- }
-}))
diff --git a/spaces/JingyeChen22/TextDiffuser/test.py b/spaces/JingyeChen22/TextDiffuser/test.py
deleted file mode 100644
index 0c5264b59075634aaff87ae484c71b61d2e88ed8..0000000000000000000000000000000000000000
--- a/spaces/JingyeChen22/TextDiffuser/test.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import os
-
-os.system('wget https://layoutlm.blob.core.windows.net/textdiffuser/textdiffuser-ckpt.zip')
-os.system('unzip textdiffuser-ckpt.zip')
-os.system('finish')
\ No newline at end of file
diff --git a/spaces/Jojelf/dreamlike-photoreal-2.0/README.md b/spaces/Jojelf/dreamlike-photoreal-2.0/README.md
deleted file mode 100644
index dd4eaa77c97d886042d6d692b2f708c0484dbf9f..0000000000000000000000000000000000000000
--- a/spaces/Jojelf/dreamlike-photoreal-2.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dreamlike Photoreal 2.0
-emoji: 👀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kayson/InstructDiffusion/stable_diffusion/ldm/modules/distributions/distributions.py b/spaces/Kayson/InstructDiffusion/stable_diffusion/ldm/modules/distributions/distributions.py
deleted file mode 100644
index f2b8ef901130efc171aa69742ca0244d94d3f2e9..0000000000000000000000000000000000000000
--- a/spaces/Kayson/InstructDiffusion/stable_diffusion/ldm/modules/distributions/distributions.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
- def sample(self):
- raise NotImplementedError()
-
- def mode(self):
- raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
- def __init__(self, value):
- self.value = value
-
- def sample(self):
- return self.value
-
- def mode(self):
- return self.value
-
-
-class DiagonalGaussianDistribution(object):
- def __init__(self, parameters, deterministic=False):
- self.parameters = parameters
- self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
- self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
- self.deterministic = deterministic
- self.std = torch.exp(0.5 * self.logvar)
- self.var = torch.exp(self.logvar)
- if self.deterministic:
- self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
- def sample(self):
- x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
- return x
-
- def kl(self, other=None):
- if self.deterministic:
- return torch.Tensor([0.])
- else:
- if other is None:
- return 0.5 * torch.sum(torch.pow(self.mean, 2)
- + self.var - 1.0 - self.logvar,
- dim=[1, 2, 3])
- else:
- return 0.5 * torch.sum(
- torch.pow(self.mean - other.mean, 2) / other.var
- + self.var / other.var - 1.0 - self.logvar + other.logvar,
- dim=[1, 2, 3])
-
- def nll(self, sample, dims=[1,2,3]):
- if self.deterministic:
- return torch.Tensor([0.])
- logtwopi = np.log(2.0 * np.pi)
- return 0.5 * torch.sum(
- logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
- dim=dims)
-
- def mode(self):
- return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
- """
- source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
- Compute the KL divergence between two gaussians.
- Shapes are automatically broadcasted, so batches can be compared to
- scalars, among other use cases.
- """
- tensor = None
- for obj in (mean1, logvar1, mean2, logvar2):
- if isinstance(obj, torch.Tensor):
- tensor = obj
- break
- assert tensor is not None, "at least one argument must be a Tensor"
-
- # Force variances to be Tensors. Broadcasting helps convert scalars to
- # Tensors, but it does not work for torch.exp().
- logvar1, logvar2 = [
- x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
- for x in (logvar1, logvar2)
- ]
-
- return 0.5 * (
- -1.0
- + logvar2
- - logvar1
- + torch.exp(logvar1 - logvar2)
- + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
- )
diff --git a/spaces/Kevin676/Real-Time-Voice-Cloning/synthesizer/__init__.py b/spaces/Kevin676/Real-Time-Voice-Cloning/synthesizer/__init__.py
deleted file mode 100644
index 4287ca8617970fa8fc025b75cb319c7032706910..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Real-Time-Voice-Cloning/synthesizer/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#
\ No newline at end of file
diff --git a/spaces/Kevin676/Shanghainese-TTS-demo/transforms.py b/spaces/Kevin676/Shanghainese-TTS-demo/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Shanghainese-TTS-demo/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/KevinQHLin/UniVTG/model/univtg_qfvs.py b/spaces/KevinQHLin/UniVTG/model/univtg_qfvs.py
deleted file mode 100644
index 0c4b133857affa8f34e4c77e753539291cb96a75..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/model/univtg_qfvs.py
+++ /dev/null
@@ -1,476 +0,0 @@
-import pdb
-import torch
-import torch.nn.functional as F
-from torch import nn
-import numpy as np
-
-from model.transformer_encoder_droppath import build_transformer
-from model.matcher import build_matcher
-from model.position_encoding import build_position_encoding
-from utils.span_utils import generalized_temporal_iou, span_cxw_to_xx
-
-def init_weights(module):
- if isinstance(module, (nn.Linear, nn.Embedding)):
- module.weight.data.normal_(mean=0.0, std=0.02)
- elif isinstance(module, nn.LayerNorm):
- module.bias.data.zero_()
- module.weight.data.fill_(1.0)
-
- if isinstance(module, nn.Linear) and module.bias is not None:
- module.bias.data.zero_()
-
-def mask_logits(inputs, mask, mask_value=-1e30):
- mask = mask.type(torch.float32)
- return inputs + (1.0 - mask) * mask_value
-
-def sim_matrix(a, b, eps=1e-8):
- """
- added eps for numerical stability
- """
- a_n, b_n = a.norm(dim=1)[:, None], b.norm(dim=1)[:, None]
- a_norm = a / torch.max(a_n, eps * torch.ones_like(a_n))
- b_norm = b / torch.max(b_n, eps * torch.ones_like(b_n))
- sim_mt = torch.mm(a_norm, b_norm.transpose(0, 1))
- return sim_mt
-
-class WeightedPool(nn.Module):
- def __init__(self, dim):
- super(WeightedPool, self).__init__()
- weight = torch.empty(dim, 1)
- nn.init.xavier_uniform_(weight)
- self.weight = nn.Parameter(weight, requires_grad=True)
-
- def forward(self, x, mask):
- alpha = torch.tensordot(x, self.weight, dims=1) # shape = (batch_size, seq_length, 1)
- alpha = mask_logits(alpha, mask=mask.unsqueeze(2))
- alphas = nn.Softmax(dim=1)(alpha)
- pooled_x = torch.matmul(x.transpose(1, 2), alphas) # (batch_size, dim, 1)
- pooled_x = pooled_x.squeeze(2)
- return pooled_x
-
-class Model(nn.Module):
- """ This is the UniVTG module that performs moment localization. """
-
- def __init__(self, transformer, position_embed, txt_position_embed, txt_dim, vid_dim,
- input_dropout, aux_loss=False,
- max_v_l=75, span_loss_type="l1", use_txt_pos=False, n_input_proj=2):
- """ Initializes the model.
- Parameters:
- transformer: torch module of the transformer architecture. See transformer.py
- position_embed: torch module of the position_embedding, See position_encoding.py
- txt_position_embed: position_embedding for text
- txt_dim: int, text query input dimension
- vid_dim: int, video feature input dimension
- max_v_l: int, maximum #clips in videos
- span_loss_type: str, one of [l1, ce]
- l1: (center-x, width) regression.
- ce: (st_idx, ed_idx) classification.
- # foreground_thd: float, intersection over prediction >= foreground_thd: labeled as foreground
- # background_thd: float, intersection over prediction <= background_thd: labeled background
- """
- super().__init__()
- self.transformer = transformer
- self.position_embed = position_embed
- self.txt_position_embed = txt_position_embed
- hidden_dim = transformer.d_model
- self.span_loss_type = span_loss_type
- self.max_v_l = max_v_l
- span_pred_dim = 2 if span_loss_type == "l1" else max_v_l * 2
-
- self.token_type_embeddings = nn.Embedding(2, hidden_dim)
- self.token_type_embeddings.apply(init_weights)
-
- # Conv projector
- self.span_embed = Conv(hidden_dim, hidden_dim, span_pred_dim, 3, kernel_size=3)
- self.class_embed = Conv(hidden_dim, hidden_dim, 1, 3, kernel_size=3) # 0: background, 1: foreground
-
- self.use_txt_pos = use_txt_pos
- self.n_input_proj = n_input_proj
- relu_args = [True] * 3
- relu_args[n_input_proj-1] = False
- self.input_txt_proj = nn.Sequential(*[
- LinearLayer(txt_dim, hidden_dim, layer_norm=True, dropout=input_dropout, relu=relu_args[0]),
- LinearLayer(hidden_dim, hidden_dim, layer_norm=True, dropout=input_dropout, relu=relu_args[1]),
- LinearLayer(hidden_dim, hidden_dim, layer_norm=True, dropout=input_dropout, relu=relu_args[2])
- ][:n_input_proj])
- self.input_vid_proj = nn.Sequential(*[
- LinearLayer(vid_dim, hidden_dim, layer_norm=True, dropout=input_dropout, relu=relu_args[0]),
- LinearLayer(hidden_dim, hidden_dim, layer_norm=True, dropout=input_dropout, relu=relu_args[1]),
- LinearLayer(hidden_dim, hidden_dim, layer_norm=True, dropout=input_dropout, relu=relu_args[2])
- ][:n_input_proj])
-
- # MLP Projector
- self.weightedpool = WeightedPool(hidden_dim)
-
- def forward(self, src_txt, src_txt_mask, src_vid, src_vid_mask, src_cls=None, src_cls_mask=None):
- bs = src_vid.shape[0]
- src_vid = self.input_vid_proj(src_vid)
- src_txt = self.input_txt_proj(src_txt)
- if src_cls is not None:
- src_cls = self.input_txt_proj(src_cls)
-
- # type token.
- src_vid = src_vid + self.token_type_embeddings(torch.full_like(src_vid_mask.long(), 1))
- src_txt = src_txt + self.token_type_embeddings(torch.zeros_like(src_txt_mask.long()))
- if src_cls is not None:
- src_cls = src_cls + self.token_type_embeddings(torch.zeros_like(src_cls_mask.long()))
-
- src = torch.cat([src_vid, src_txt], dim=1) # (bsz, L_vid+L_txt, d)
- mask = torch.cat([src_vid_mask, src_txt_mask], dim=1).bool() # (bsz, L_vid+L_txt)
-
- pos_vid = self.position_embed(src_vid, src_vid_mask) # (bsz, L_vid, d)
- pos_txt = self.txt_position_embed(src_txt) if self.use_txt_pos else torch.zeros_like(src_txt) # (bsz, L_txt, d)
- pos = torch.cat([pos_vid, pos_txt], dim=1)
-
- memory = self.transformer(src, ~mask, pos)
- vid_mem = memory[:, :src_vid.shape[1], :] # (bsz, L_vid, d)
-
- outputs_class = self.class_embed(vid_mem).sigmoid() # (#layers, batch_size, #queries, #classes)
- outputs_coord = self.span_embed(vid_mem) # (#layers, bsz, #queries, 2 or max_v_l * 2)
-
- if self.span_loss_type == "l1":
- outputs_coord = outputs_coord.sigmoid()
- idx_mask = torch.tensor((-1, 1)).unsqueeze(0).unsqueeze(0).cuda()
- idx_mask = idx_mask.repeat(outputs_coord.shape[0], outputs_coord.shape[1], 1)
- outputs_coord = outputs_coord * idx_mask
- else:
- raise NotImplementedError
-
- out = {'pred_logits': outputs_class, 'pred_spans': outputs_coord,
- 'src_vid_mask': src_vid_mask}
-
- vid_mem_proj = src_vid
-
- # word-level -> sentence-level
- txt_mem_proj = self.weightedpool(src_txt, src_txt_mask).unsqueeze(1)
- sim = F.cosine_similarity(vid_mem_proj, txt_mem_proj, dim=-1) + (src_vid_mask + 1e-45).log()
-
- out["vid_mem_proj"] = vid_mem_proj
- out["txt_mem_proj"] = txt_mem_proj
- if src_cls is not None:
- cls_mem_proj = self.weightedpool(src_cls, src_cls_mask)
- out["cls_mem_proj"] = cls_mem_proj
- out["saliency_scores"] = sim
- return out
-
-class SetCriterion(nn.Module):
- """ This class computes the loss for DETR.
- The process happens in two steps:
- 1) we compute hungarian assignment between ground truth boxes and the outputs of the model
- 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
- """
-
- def __init__(self, matcher, weight_dict, eos_coef, losses, temperature, span_loss_type, max_v_l,
- saliency_margin=1):
- """ Create the criterion.
- Parameters:
- matcher: module able to compute a matching between targets and proposals
- weight_dict: dict containing as key the names of the losses and as values their relative weight.
- eos_coef: relative classification weight applied to the no-object category
- losses: list of all the losses to be applied. See get_loss for list of available losses.
- temperature: float, temperature for NCE loss
- span_loss_type: str, [l1, ce]
- max_v_l: int,
- saliency_margin: float
- """
- super().__init__()
- self.matcher = matcher
- self.weight_dict = weight_dict
- self.losses = losses
- self.temperature = temperature
- self.span_loss_type = span_loss_type
- self.max_v_l = max_v_l
- self.saliency_margin = saliency_margin
- self.temperature = 0.07
-
- # foreground and background classification
- self.foreground_label = 0
- self.background_label = 1
- self.eos_coef = eos_coef
- empty_weight = torch.ones(2)
- empty_weight[-1] = self.eos_coef # lower weight for background (index 1, foreground index 0)
- self.register_buffer('empty_weight', empty_weight)
-
- def loss_spans(self, outputs, targets, indices):
- assert 'pred_spans' in outputs
-
- start_spans = targets['timestamp']
- pred_spans = outputs['pred_spans']
- src_spans = start_spans + pred_spans
- gt_spans = targets['span_labels_nn']
-
- mask = targets['timestamp_mask'].bool()
- mask_full = targets['timestamp_mask'].unsqueeze(2).repeat(1, 1, 2)
- mask_valid = targets['timestamp_window'].bool()
- mask_valid_full = targets['timestamp_window'].unsqueeze(2).repeat(1, 1, 2)
-
- loss_span = F.smooth_l1_loss(src_spans, gt_spans, reduction='none') * mask_valid_full
- loss_giou = 1 - torch.diag(generalized_temporal_iou(src_spans[mask_valid], gt_spans[mask_valid]))
-
- losses = {}
- losses['loss_b'] = loss_span.sum() / mask_valid.sum()
- losses['loss_g'] = loss_giou.mean()
- return losses
-
- def loss_labels(self, outputs, targets, indices, log=True):
- saliency_scores = targets["saliency_scores"]
- if saliency_scores.sum() == 0:
- return {"loss_f": 0.}
-
- src_logits = outputs['pred_logits'].squeeze(-1) # (batch_size, #queries, #classes=2)
- target_classes = targets["saliency_scores"].squeeze()
-
- weights = torch.ones_like(target_classes).float() * self.empty_weight[1]
- weights[target_classes.bool()] = self.empty_weight[0]
-
- loss_ce = F.binary_cross_entropy(src_logits, target_classes.float(), reduction="none")
- return {"loss_f": loss_ce.sum() / target_classes.sum()}
- # return {"loss_f": loss_ce.sum() / len(target_classes)}
-
- # mask = targets['timestamp_mask'].bool()
- # mask_valid = targets['timestamp_window'].bool()
- # target_classes = torch.full(src_logits.shape[:2], 0, dtype=torch.int64, device=src_logits.device) # (batch_size, #queries)
- # target_classes[mask_valid] = 1
- # # target_classes = targets['timestamp_window'] # soft cls.
- # target_classes.float()
- # # pdb.set_trace()
-
- # weights = torch.zeros_like(target_classes).float()
- # weights[mask] = self.empty_weight[1]
- # weights[mask_valid] = self.empty_weight[0]
-
- # loss_ce = F.binary_cross_entropy(src_logits, target_classes.float(), weight=weights, reduction="none") * mask
- # # return {"loss_f": loss_ce.sum() / mask.sum()}
- # return {"loss_f": loss_ce.sum() / mask_valid.sum()}
-
- def loss_saliency(self, outputs, targets, indices, log=True):
- """higher scores for positive clips"""
- if "saliency_pos_labels" not in targets:
- return {"loss_s_inter": 0., "loss_s_intra": 0.}
- saliency_scores = targets["saliency_scores"]
- if saliency_scores.sum() == 0:
- return {"loss_s_inter": 0., "loss_s_intra": 0.}
-
- # * qfvs mil-nce mode
- pos_indices = saliency_scores.squeeze() > 0
-
- sim = outputs['saliency_scores']
- sim_soft = F.softmax(sim / self.temperature, dim=0)
- sim_log = torch.log(sim_soft[pos_indices])
- loss_saliency_intra = -sim_log.sum() / len(sim_log)
- return {"loss_s_inter": 0., "loss_s_intra": loss_saliency_intra}
-
- # * inter-vid mode
- # vid_mem_proj = outputs["vid_mem_proj"]
- # pos_indices = targets["saliency_pos_labels"][:,0].long() # (N, #pairs)
- # batch_indices = torch.arange(len(vid_mem_proj)).to(vid_mem_proj.device)
-
- # vid_feats = vid_mem_proj[batch_indices, pos_indices]
- # txt_feats = outputs["txt_mem_proj"].squeeze(1)
- # sim = sim_matrix(vid_feats, txt_feats)
-
- # i_logsm = F.log_softmax(sim / self.temperature, dim=1)
- # j_logsm = F.log_softmax(sim.t() /self.temperature, dim=1)
-
- # # sum over positives
- # idiag = torch.diag(i_logsm)
- # jdiag = torch.diag(j_logsm)
- # loss_i = idiag.sum() / len(idiag)
- # loss_j = jdiag.sum() / len(jdiag)
-
- # loss_saliency_inter = - loss_i - loss_j
-
- # # * intra-vid mode
- # mask = targets['timestamp_mask']
- # selected_scores = saliency_scores[batch_indices, pos_indices].unsqueeze(-1)
- # neg_indices_in = (saliency_scores < selected_scores)
- # neg_indices_in[batch_indices, pos_indices] = True
- # mask_invalid = neg_indices_in * mask.bool()
-
- # sim_in = F.cosine_similarity(vid_mem_proj, txt_feats.unsqueeze(1), dim=-1)
- # sim_in = sim_in + (mask_invalid + 1e-45).log()
- # logsm_in_i = F.log_softmax(sim_in / self.temperature, dim=1)
- # logsm_in_j = F.log_softmax(sim_in.t() / self.temperature, dim=1)
-
- # pos_logsm_in_i = logsm_in_i[batch_indices, pos_indices]
- # pos_logsm_in_j = logsm_in_j[pos_indices, batch_indices]
- # loss_in_i = pos_logsm_in_i.sum() / len(pos_logsm_in_i)
- # loss_in_j = pos_logsm_in_j.sum() / len(pos_logsm_in_j)
-
- # loss_saliency_intra = - loss_in_i - loss_in_j
-
- # return {"loss_s_inter": loss_saliency_inter, "loss_s_intra": loss_saliency_intra}
-
- def loss_saliency_cls(self, outputs, targets, indices, log=True):
- """higher scores for positive clips"""
- if "saliency_pos_labels" not in targets:
- return {"loss_s_inter": 0., "loss_s_intra": 0.}
- saliency_scores = targets["saliency_scores"]
- if saliency_scores.sum() == 0:
- return {"loss_s_inter": 0., "loss_s_intra": 0.}
-
- # * inter-vid mode
- vid_mem_proj = outputs["vid_mem_proj"]
- pos_indices = targets["saliency_pos_labels"][:,0].long() # (N, #pairs)
- batch_indices = torch.arange(len(vid_mem_proj)).to(vid_mem_proj.device)
-
- vid_feats = vid_mem_proj[batch_indices, pos_indices]
- txt_feats = outputs["txt_mem_proj"].squeeze(1)
- sim = sim_matrix(vid_feats, txt_feats)
-
- i_logsm = F.log_softmax(sim / self.temperature, dim=1)
- j_logsm = F.log_softmax(sim.t() /self.temperature, dim=1)
-
- # sum over positives
- idiag = torch.diag(i_logsm)
- jdiag = torch.diag(j_logsm)
- loss_i = idiag.sum() / len(idiag)
- loss_j = jdiag.sum() / len(jdiag)
-
- loss_saliency_inter = - loss_i - loss_j
-
- # * intra-vid mode
- if 'cls_idx' not in targets.keys(): # eval
- return {"loss_s_inter": loss_saliency_inter}
-
- cls_indices = targets['cls_idx'].bool()
- cls_feats = outputs["cls_mem_proj"].squeeze(1)
- sim_cls = sim_matrix(vid_feats, cls_feats)
-
- i_logsm_cls = F.log_softmax(sim_cls / self.temperature, dim=1)
- idiag_cls = i_logsm_cls[cls_indices]
- loss_cls_i = idiag_cls.sum() / len(idiag_cls)
-
- loss_saliency_intra = - loss_cls_i
-
- return {"loss_s_inter": loss_saliency_inter, "loss_s_intra": loss_saliency_intra}
-
- def get_loss(self, loss, outputs, targets, indices, **kwargs):
- loss_map = {
- "spans": self.loss_spans,
- "labels": self.loss_labels,
- "saliency": self.loss_saliency,
- "saliency_cls": self.loss_saliency_cls,
- }
- assert loss in loss_map, f'do you really want to compute {loss} loss?'
- return loss_map[loss](outputs, targets, indices, **kwargs)
-
- def forward(self, outputs, targets, mask_GT=None):
- """ This performs the loss computation.
- Parameters:
- outputs: dict of tensors, see the output specification of the model for the format
- targets: list of dicts, such that len(targets) == batch_size.
- The expected keys in each dict depends on the losses applied, see each loss' doc
- """
- indices = None
- # Compute all the requested losses
- losses = {}
- outputs['pred_logits'] = outputs['pred_logits'].reshape(1, -1).masked_select(mask_GT[0])
- count = mask_GT.sum()
- outputs['saliency_scores'] = outputs['saliency_scores'].reshape(1, -1).masked_select(mask_GT[0])
- # targets['saliency_scores'] = targets['saliency_scores'].masked_select(mask_GT[0])
- targets['saliency_scores'] = targets['saliency_scores'][0,:count]
-
- for loss in self.losses:
- losses.update(self.get_loss(loss, outputs, targets, indices))
-
- return losses
-
-class MLP(nn.Module):
- """ Very simple multi-layer perceptron (also called FFN)"""
-
- def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
- super().__init__()
- self.num_layers = num_layers
- h = [hidden_dim] * (num_layers - 1)
- self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
-
- def forward(self, x):
- for i, layer in enumerate(self.layers):
- x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
- return x
-
-class Conv(nn.Module):
- """ Very simple multi-layer perceptron (also called FFN)"""
-
- def __init__(self, input_dim, hidden_dim, output_dim, num_layers, kernel_size):
- super().__init__()
- self.num_layers = num_layers
- h = [hidden_dim] * (num_layers - 1)
- # self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
- self.layers = nn.ModuleList(
- nn.Conv1d(n, k, kernel_size=kernel_size, stride=1, padding=kernel_size//2, dilation=1, groups=1, bias=True, padding_mode='zeros')
- for n, k in zip([input_dim] + h, h + [output_dim]))
- def forward(self, x):
- x = x.permute(0,2,1)
- for i, layer in enumerate(self.layers):
- x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
- return x.permute(0, 2, 1)
-
-class LinearLayer(nn.Module):
- """linear layer configurable with layer normalization, dropout, ReLU."""
-
- def __init__(self, in_hsz, out_hsz, layer_norm=True, dropout=0.1, relu=True):
- super(LinearLayer, self).__init__()
- self.relu = relu
- self.layer_norm = layer_norm
- if layer_norm:
- self.LayerNorm = nn.LayerNorm(in_hsz)
- layers = [
- nn.Dropout(dropout),
- nn.Linear(in_hsz, out_hsz)
- ]
- self.net = nn.Sequential(*layers)
-
- def forward(self, x):
- """(N, L, D)"""
- if self.layer_norm:
- x = self.LayerNorm(x)
- x = self.net(x)
- if self.relu:
- x = F.relu(x, inplace=True)
- return x # (N, L, D)
-
-
-def build_model(args):
- device = torch.device(args.device)
-
- transformer = build_transformer(args)
- position_embedding, txt_position_embedding = build_position_encoding(args)
-
- model = Model(
- transformer,
- position_embedding,
- txt_position_embedding,
- txt_dim=args.t_feat_dim,
- vid_dim=args.v_feat_dim,
- input_dropout=args.input_dropout,
- span_loss_type=args.span_loss_type,
- use_txt_pos=args.use_txt_pos,
- n_input_proj=args.n_input_proj,
- )
-
- matcher = build_matcher(args)
- weight_dict = {"loss_b": args.b_loss_coef,
- "loss_g": args.g_loss_coef,
- "loss_f": args.f_loss_coef,
- "loss_s_intra": args.s_loss_intra_coef,
- "loss_s_inter": args.s_loss_inter_coef}
-
- if args.dset_type in ['mr', 'vlp']:
- if 'tal' not in args.train_path:
- losses = ['spans', 'labels', 'saliency']
- else:
- losses = ['spans', 'labels', 'saliency_cls']
- elif args.dset_type in ['hl', 'vs']:
- losses = ['labels', 'saliency']
-
- criterion = SetCriterion(
- matcher=matcher,
- weight_dict=weight_dict, losses=losses,
- eos_coef=args.eos_coef, temperature=args.temperature,
- span_loss_type=args.span_loss_type, max_v_l=args.max_v_l,
- saliency_margin=args.saliency_margin,
- )
- criterion.to(device)
- return model, criterion
diff --git a/spaces/KevinQHLin/UniVTG/run_on_video/data_utils.py b/spaces/KevinQHLin/UniVTG/run_on_video/data_utils.py
deleted file mode 100644
index 44c073f4689b20bd8f7adb2aac77c0b7523ed8dd..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/run_on_video/data_utils.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import torch
-import os
-import numpy as np
-import ffmpeg
-import math
-from run_on_video import clip
-
-
-class ClipFeatureExtractor:
- def __init__(self, framerate=1/2, size=224, centercrop=True, model_name_or_path="ViT-B/32", device="cuda"):
- self.video_loader = VideoLoader(framerate=framerate, size=size, centercrop=centercrop)
- print("Loading CLIP models")
- self.clip_extractor, _ = clip.load(model_name_or_path, device=device, jit=False)
- self.tokenizer = clip.tokenize
- self.video_preprocessor = Preprocessing()
- self.device = device
-
- @torch.no_grad()
- def encode_video(self, video_path: str, bsz=60):
- video_frames = self.video_loader.read_video_from_file(video_path) # (T, H, W, 3)
- video_frames = self.video_preprocessor(video_frames)
- n_frames = len(video_frames)
- n_batch = int(math.ceil(n_frames / bsz))
- video_features = []
- for i in range(n_batch):
- st_idx = i * bsz
- ed_idx = (i+1) * bsz
- _video_frames = video_frames[st_idx:ed_idx].to(self.device)
- _video_features = self.clip_extractor.encode_image(_video_frames)
- video_features.append(_video_features)
- video_features = torch.cat(video_features, dim=0)
- return video_features # (T=#frames, d) torch tensor
-
- @torch.no_grad()
- def encode_text(self, text_list, bsz=60):
- n_text = len(text_list)
- n_batch = int(math.ceil(n_text / bsz))
- text_features = []
- for i in range(n_batch):
- st_idx = i * bsz
- ed_idx = (i+1) * bsz
- encoded_texts = self.tokenizer(text_list[st_idx:ed_idx], context_length=77).to(self.device)
- output = self.clip_extractor.encode_text(encoded_texts)
- valid_lengths = (encoded_texts != 0).sum(1).tolist()
- batch_last_hidden_states = output["last_hidden_state"]
- for j, valid_len in enumerate(valid_lengths):
- text_features.append(batch_last_hidden_states[j, :valid_len])
- return text_features # List([L_j, d]) torch tensor
-
-
-def convert_to_float(frac_str):
- try:
- return float(frac_str)
- except ValueError:
- try:
- num, denom = frac_str.split('/')
- except ValueError:
- return None
- try:
- leading, num = num.split(' ')
- except ValueError:
- return float(num) / float(denom)
- if float(leading) < 0:
- sign_mult = -1
- else:
- sign_mult = 1
- return float(leading) + sign_mult * (float(num) / float(denom))
-
-
-class Normalize(object):
-
- def __init__(self, mean, std):
- self.mean = torch.FloatTensor(mean).view(1, 3, 1, 1)
- self.std = torch.FloatTensor(std).view(1, 3, 1, 1)
-
- def __call__(self, tensor):
- tensor = (tensor - self.mean) / (self.std + 1e-8)
- return tensor
-
-
-class Preprocessing(object):
-
- def __init__(self):
- self.norm = Normalize(
- mean=[0.48145466, 0.4578275, 0.40821073],
- std=[0.26862954, 0.26130258, 0.27577711])
-
- def __call__(self, tensor):
- tensor = tensor / 255.0
- tensor = self.norm(tensor)
- return tensor
-
-
-class VideoLoader:
- """Pytorch video loader.
- Copied and modified from:
- https://github.com/linjieli222/HERO_Video_Feature_Extractor/blob/main/clip/video_loader.py
- """
- def __init__(
- self,
- framerate=1/2,
- size=224,
- centercrop=True,
- ):
- self.centercrop = centercrop
- self.size = size
- self.framerate = framerate
-
- def _get_video_info(self, video_path):
- probe = ffmpeg.probe(video_path)
- video_stream = next((stream for stream in probe['streams']
- if stream['codec_type'] == 'video'), None)
- width = int(video_stream['width'])
- height = int(video_stream['height'])
- fps = math.floor(convert_to_float(video_stream['avg_frame_rate']))
- try:
- frames_length = int(video_stream['nb_frames'])
- duration = float(video_stream['duration'])
- except Exception:
- frames_length, duration = -1, -1
- info = {"duration": duration, "frames_length": frames_length,
- "fps": fps, "height": height, "width": width}
- return info
-
- def _get_output_dim(self, h, w):
- if isinstance(self.size, tuple) and len(self.size) == 2:
- return self.size
- elif h >= w:
- return int(h * self.size / w), self.size
- else:
- return self.size, int(w * self.size / h)
-
- def read_video_from_file(self, video_path):
- try:
- info = self._get_video_info(video_path)
- h, w = info["height"], info["width"]
- except Exception:
- print('ffprobe failed at: {}'.format(video_path))
- return {'video': torch.zeros(1), 'input': video_path,
- 'info': {}}
- height, width = self._get_output_dim(h, w)
- try:
- duration = info["duration"]
- fps = self.framerate
- if duration > 0 and duration < 1/fps+0.1:
- fps = 2/max(int(duration), 1)
- print(duration, fps)
- except Exception:
- fps = self.framerate
- cmd = (
- ffmpeg
- .input(video_path)
- .filter('fps', fps=fps)
- .filter('scale', width, height)
- )
- if self.centercrop:
- x = int((width - self.size) / 2.0)
- y = int((height - self.size) / 2.0)
- cmd = cmd.crop(x, y, self.size, self.size)
- out, _ = (
- cmd.output('pipe:', format='rawvideo', pix_fmt='rgb24')
- .run(capture_stdout=True, quiet=True)
- )
- if self.centercrop and isinstance(self.size, int):
- height, width = self.size, self.size
- video = np.frombuffer(out, np.uint8).reshape(
- [-1, height, width, 3])
- video = torch.from_numpy(video.astype('float32'))
- video = video.permute(0, 3, 1, 2)
- return video
diff --git a/spaces/LeoLM/leo-hessianai-7b-chat/README.md b/spaces/LeoLM/leo-hessianai-7b-chat/README.md
deleted file mode 100644
index bee564c2342318e83c1bb25080eebb73938908a5..0000000000000000000000000000000000000000
--- a/spaces/LeoLM/leo-hessianai-7b-chat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LeoLM 7b Chat
-emoji: 🦁
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: llama2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/textsnake_r50_fpn_unet.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/textsnake_r50_fpn_unet.py
deleted file mode 100644
index 7d74f376b8c635451a3036e780ffc88e7640bf2c..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/textsnake_r50_fpn_unet.py
+++ /dev/null
@@ -1,22 +0,0 @@
-model = dict(
- type='TextSnake',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- norm_eval=True,
- style='caffe'),
- neck=dict(
- type='FPN_UNet', in_channels=[256, 512, 1024, 2048], out_channels=32),
- bbox_head=dict(
- type='TextSnakeHead',
- in_channels=32,
- loss=dict(type='TextSnakeLoss'),
- postprocessor=dict(
- type='TextSnakePostprocessor', text_repr_type='poly')),
- train_cfg=None,
- test_cfg=None)
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/locales/es.ts b/spaces/LuxOAI/ChatGpt-Web/app/locales/es.ts
deleted file mode 100644
index 4204506e4404c1d17439467cc8811f7f42d3f1b6..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/locales/es.ts
+++ /dev/null
@@ -1,244 +0,0 @@
-import { SubmitKey } from "../store/config";
-import type { LocaleType } from "./index";
-
-const es: LocaleType = {
- WIP: "En construcción...",
- Error: {
- Unauthorized:
- "Acceso no autorizado, por favor ingrese el código de acceso en la página de configuración.",
- },
- ChatItem: {
- ChatItemCount: (count: number) => `${count} mensajes`,
- },
- Chat: {
- SubTitle: (count: number) => `${count} mensajes con ChatGPT`,
- Actions: {
- ChatList: "Ir a la lista de chats",
- CompressedHistory: "Historial de memoria comprimido",
- Export: "Exportar todos los mensajes como Markdown",
- Copy: "Copiar",
- Stop: "Detener",
- Retry: "Reintentar",
- Delete: "Delete",
- },
- Rename: "Renombrar chat",
- Typing: "Escribiendo...",
- Input: (submitKey: string) => {
- var inputHints = `Escribe algo y presiona ${submitKey} para enviar`;
- if (submitKey === String(SubmitKey.Enter)) {
- inputHints += ", presiona Shift + Enter para nueva línea";
- }
- return inputHints;
- },
- Send: "Enviar",
- Config: {
- Reset: "Reset to Default",
- SaveAs: "Save as Mask",
- },
- },
- Export: {
- Title: "Todos los mensajes",
- Copy: "Copiar todo",
- Download: "Descargar",
- MessageFromYou: "Mensaje de ti",
- MessageFromChatGPT: "Mensaje de ChatGPT",
- },
- Memory: {
- Title: "Historial de memoria",
- EmptyContent: "Aún no hay nada.",
- Copy: "Copiar todo",
- Send: "Send Memory",
- Reset: "Reset Session",
- ResetConfirm:
- "Resetting will clear the current conversation history and historical memory. Are you sure you want to reset?",
- },
- Home: {
- NewChat: "Nuevo chat",
- DeleteChat: "¿Confirmar eliminación de la conversación seleccionada?",
- DeleteToast: "Chat Deleted",
- Revert: "Revert",
- },
- Settings: {
- Title: "Configuración",
- SubTitle: "Todas las configuraciones",
- Actions: {
- ClearAll: "Borrar todos los datos",
- ResetAll: "Restablecer todas las configuraciones",
- Close: "Cerrar",
- ConfirmResetAll: "Are you sure you want to reset all configurations?",
- ConfirmClearAll: "Are you sure you want to reset all chat?",
- },
- Lang: {
- Name: "Language",
- All: "All Languages",
- Options: {
- cn: "简体中文",
- en: "Inglés",
- tw: "繁體中文",
- es: "Español",
- it: "Italiano",
- tr: "Türkçe",
- jp: "日本語",
- de: "Deutsch",
- },
- },
- Avatar: "Avatar",
- FontSize: {
- Title: "Tamaño de fuente",
- SubTitle: "Ajustar el tamaño de fuente del contenido del chat",
- },
- Update: {
- Version: (x: string) => `Versión: ${x}`,
- IsLatest: "Última versión",
- CheckUpdate: "Buscar actualizaciones",
- IsChecking: "Buscando actualizaciones...",
- FoundUpdate: (x: string) => `Se encontró una nueva versión: ${x}`,
- GoToUpdate: "Actualizar",
- },
- SendKey: "Tecla de envío",
- Theme: "Tema",
- TightBorder: "Borde ajustado",
- SendPreviewBubble: {
- Title: "Enviar burbuja de vista previa",
- SubTitle: "Preview markdown in bubble",
- },
- Mask: {
- Title: "Mask Splash Screen",
- SubTitle: "Show a mask splash screen before starting new chat",
- },
- Prompt: {
- Disable: {
- Title: "Desactivar autocompletado",
- SubTitle: "Escribe / para activar el autocompletado",
- },
- List: "Lista de autocompletado",
- ListCount: (builtin: number, custom: number) =>
- `${builtin} incorporado, ${custom} definido por el usuario`,
- Edit: "Editar",
- Modal: {
- Title: "Prompt List",
- Add: "Add One",
- Search: "Search Prompts",
- },
- EditModal: {
- Title: "Edit Prompt",
- },
- },
- HistoryCount: {
- Title: "Cantidad de mensajes adjuntos",
- SubTitle: "Número de mensajes enviados adjuntos por solicitud",
- },
- CompressThreshold: {
- Title: "Umbral de compresión de historial",
- SubTitle:
- "Se comprimirán los mensajes si la longitud de los mensajes no comprimidos supera el valor",
- },
- Token: {
- Title: "Clave de API",
- SubTitle: "Utiliza tu clave para ignorar el límite de código de acceso",
- Placeholder: "Clave de la API de OpenAI",
- },
- Usage: {
- Title: "Saldo de la cuenta",
- SubTitle(used: any, total: any) {
- return `Usado $${used}, subscription $${total}`;
- },
- IsChecking: "Comprobando...",
- Check: "Comprobar de nuevo",
- NoAccess: "Introduzca la clave API para comprobar el saldo",
- },
- AccessCode: {
- Title: "Código de acceso",
- SubTitle: "Control de acceso habilitado",
- Placeholder: "Necesita código de acceso",
- },
- Bot: "Proveedores de IA (bot)",
- Model: "Modelo",
- Temperature: {
- Title: "Temperatura",
- SubTitle: "Un valor mayor genera una salida más aleatoria",
- },
- MaxTokens: {
- Title: "Máximo de tokens",
- SubTitle: "Longitud máxima de tokens de entrada y tokens generados",
- },
- PresencePenlty: {
- Title: "Penalización de presencia",
- SubTitle:
- "Un valor mayor aumenta la probabilidad de hablar sobre nuevos temas",
- },
- },
- Store: {
- DefaultTopic: "Nueva conversación",
- BotHello: "¡Hola! ¿Cómo puedo ayudarte hoy?",
- Error: "Algo salió mal, por favor intenta nuevamente más tarde.",
- Prompt: {
- History: (content: string) =>
- "Este es un resumen del historial del chat entre la IA y el usuario como recapitulación: " +
- content,
- Topic:
- "Por favor, genera un título de cuatro a cinco palabras que resuma nuestra conversación sin ningún inicio, puntuación, comillas, puntos, símbolos o texto adicional. Elimina las comillas que lo envuelven.",
- Summarize:
- "Resuma nuestra discusión brevemente en 200 caracteres o menos para usarlo como un recordatorio para futuros contextos.",
- },
- },
- Copy: {
- Success: "Copiado al portapapeles",
- Failed:
- "La copia falló, por favor concede permiso para acceder al portapapeles",
- },
- Context: {
- Toast: (x: any) => `With ${x} contextual prompts`,
- Edit: "Contextual and Memory Prompts",
- Add: "Add One",
- },
- Plugin: {
- Name: "Plugin",
- },
- Mask: {
- Name: "Mask",
- Page: {
- Title: "Prompt Template",
- SubTitle: (count: number) => `${count} prompt templates`,
- Search: "Search Templates",
- Create: "Create",
- },
- Item: {
- Info: (count: number) => `${count} prompts`,
- Chat: "Chat",
- View: "View",
- Edit: "Edit",
- Delete: "Delete",
- DeleteConfirm: "Confirm to delete?",
- },
- EditModal: {
- Title: (readonly: boolean) =>
- `Edit Prompt Template ${readonly ? "(readonly)" : ""}`,
- Download: "Download",
- Clone: "Clone",
- },
- Config: {
- Avatar: "Bot Avatar",
- Name: "Bot Name",
- },
- },
- NewChat: {
- Return: "Return",
- Skip: "Skip",
- Title: "Pick a Mask",
- SubTitle: "Chat with the Soul behind the Mask",
- More: "Find More",
- NotShow: "Not Show Again",
- ConfirmNoShow: "Confirm to disable？You can enable it in settings later.",
- },
-
- UI: {
- Confirm: "Confirm",
- Cancel: "Cancel",
- Close: "Close",
- Create: "Create",
- Edit: "Edit",
- },
-};
-
-export default es;
diff --git a/spaces/LuxOAI/guanaco-playground-tgi/share_btn.py b/spaces/LuxOAI/guanaco-playground-tgi/share_btn.py
deleted file mode 100644
index 8ff61abe298d71349f565b5d47228986b42d1f96..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/guanaco-playground-tgi/share_btn.py
+++ /dev/null
@@ -1,98 +0,0 @@
-community_icon_html = """
- """
-
-loading_icon_html = """WARNING: Extremely Slow. 65s/Iteration. Expect 25-50mins an image for 25-50 iterations respectively. This model is capable of producing NSFW (Softcore) images.",
- article = "If You Enjoyed this Demo and would like to Donate, you can send to any of these Wallets. Manjushri ").launch(debug=True, max_threads=80)
\ No newline at end of file
diff --git a/spaces/Marne/MockingBird/mockingbirdforuse/encoder/audio.py b/spaces/Marne/MockingBird/mockingbirdforuse/encoder/audio.py
deleted file mode 100644
index 90fd8b9230a22f4d1c9fc46e8c0e6d071b390a1f..0000000000000000000000000000000000000000
--- a/spaces/Marne/MockingBird/mockingbirdforuse/encoder/audio.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import struct
-import librosa
-import webrtcvad
-import numpy as np
-from pathlib import Path
-from typing import Optional, Union
-from scipy.ndimage.morphology import binary_dilation
-
-from .hparams import hparams as hp
-
-
-def preprocess_wav(
- fpath_or_wav: Union[str, Path, np.ndarray],
- source_sr: Optional[int] = None,
- normalize: Optional[bool] = True,
- trim_silence: Optional[bool] = True,
-):
- """
- Applies the preprocessing operations used in training the Speaker Encoder to a waveform
- either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
-
- :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not
- just .wav), either the waveform as a numpy array of floats.
- :param source_sr: if passing an audio waveform, the sampling rate of the waveform before
- preprocessing. After preprocessing, the waveform's sampling rate will match the data
- hyperparameters. If passing a filepath, the sampling rate will be automatically detected and
- this argument will be ignored.
- """
- # Load the wav from disk if needed
- if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
- wav, source_sr = librosa.load(str(fpath_or_wav))
- else:
- wav = fpath_or_wav
-
- # Resample the wav if needed
- if source_sr is not None and source_sr != hp.sampling_rate:
- wav = librosa.resample(wav, orig_sr=source_sr, target_sr=hp.sampling_rate)
-
- # Apply the preprocessing: normalize volume and shorten long silences
- if normalize:
- wav = normalize_volume(wav, hp.audio_norm_target_dBFS, increase_only=True)
- if webrtcvad and trim_silence:
- wav = trim_long_silences(wav)
-
- return wav
-
-
-def wav_to_mel_spectrogram(wav):
- """
- Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
- Note: this not a log-mel spectrogram.
- """
- frames = librosa.feature.melspectrogram(
- y=wav,
- sr=hp.sampling_rate,
- n_fft=int(hp.sampling_rate * hp.mel_window_length / 1000),
- hop_length=int(hp.sampling_rate * hp.mel_window_step / 1000),
- n_mels=hp.mel_n_channels,
- )
- return frames.astype(np.float32).T
-
-
-def trim_long_silences(wav):
- """
- Ensures that segments without voice in the waveform remain no longer than a
- threshold determined by the VAD parameters in params.py.
-
- :param wav: the raw waveform as a numpy array of floats
- :return: the same waveform with silences trimmed away (length <= original wav length)
- """
- # Compute the voice detection window size
- samples_per_window = (hp.vad_window_length * hp.sampling_rate) // 1000
-
- # Trim the end of the audio to have a multiple of the window size
- wav = wav[: len(wav) - (len(wav) % samples_per_window)]
-
- # Convert the float waveform to 16-bit mono PCM
- int16_max = (2**15) - 1
- pcm_wave = struct.pack(
- "%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)
- )
-
- # Perform voice activation detection
- voice_flags = []
- vad = webrtcvad.Vad(mode=3)
- for window_start in range(0, len(wav), samples_per_window):
- window_end = window_start + samples_per_window
- voice_flags.append(
- vad.is_speech(
- pcm_wave[window_start * 2 : window_end * 2],
- sample_rate=hp.sampling_rate,
- )
- )
- voice_flags = np.array(voice_flags)
-
- # Smooth the voice detection with a moving average
- def moving_average(array, width):
- array_padded = np.concatenate(
- (np.zeros((width - 1) // 2), array, np.zeros(width // 2))
- )
- ret = np.cumsum(array_padded, dtype=float)
- ret[width:] = ret[width:] - ret[:-width]
- return ret[width - 1 :] / width
-
- audio_mask = moving_average(voice_flags, hp.vad_moving_average_width)
- audio_mask = np.round(audio_mask).astype(np.bool8)
-
- # Dilate the voiced regions
- audio_mask = binary_dilation(audio_mask, np.ones(hp.vad_max_silence_length + 1))
- audio_mask = np.repeat(audio_mask, samples_per_window)
-
- return wav[audio_mask == True]
-
-
-def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
- if increase_only and decrease_only:
- raise ValueError("Both increase only and decrease only are set")
- dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav**2))
- if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
- return wav
- return wav * (10 ** (dBFS_change / 20))
diff --git a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/jquery.min.js b/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/jquery.min.js
deleted file mode 100644
index b8c4187de18dd413ad3029839ce0773549e92a14..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/jquery.min.js
+++ /dev/null
@@ -1,4 +0,0 @@
-/*! jQuery v2.2.3 | (c) jQuery Foundation | jquery.org/license */
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