




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase			:	Optional[Any]							 =     logging.get_logger(__name__)

_lowerCamelCase			:	List[str]							 =     {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''yolos'''


    def __init__(    self   :			str      ,				UpperCAmelCase__   :			Tuple=768      ,				UpperCAmelCase__   :			Any=12      ,				UpperCAmelCase__   :			str=12      ,				UpperCAmelCase__   :			Optional[Any]=3_072      ,				UpperCAmelCase__   :			str="gelu"      ,				UpperCAmelCase__   :			Any=0.0      ,				UpperCAmelCase__   :			str=0.0      ,				UpperCAmelCase__   :			str=0.02      ,				UpperCAmelCase__   :			Tuple=1e-12      ,				UpperCAmelCase__   :			Union[str, Any]=[512, 864]      ,				UpperCAmelCase__   :			Tuple=16      ,				UpperCAmelCase__   :			Union[str, Any]=3      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			int=100      ,				UpperCAmelCase__   :			List[str]=True      ,				UpperCAmelCase__   :			str=False      ,				UpperCAmelCase__   :			Dict=1      ,				UpperCAmelCase__   :			str=5      ,				UpperCAmelCase__   :			Optional[int]=2      ,				UpperCAmelCase__   :			List[Any]=5      ,				UpperCAmelCase__   :			Optional[Any]=2      ,				UpperCAmelCase__   :			Any=0.1      ,				**UpperCAmelCase__   :			Optional[Any]      ,				)		->Union[str, Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__   				=					hidden_size
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					intermediate_size
           A__   				=					hidden_act
           A__   				=					hidden_dropout_prob
           A__   				=					attention_probs_dropout_prob
           A__   				=					initializer_range
           A__   				=					layer_norm_eps
           A__   				=					image_size
           A__   				=					patch_size
           A__   				=					num_channels
           A__   				=					qkv_bias
           A__   				=					num_detection_tokens
           A__   				=					use_mid_position_embeddings
           A__   				=					auxiliary_loss
           # Hungarian matcher
           A__   				=					class_cost
           A__   				=					bbox_cost
           A__   				=					giou_cost
           # Loss coefficients
           A__   				=					bbox_loss_coefficient
           A__   				=					giou_loss_coefficient
           A__   				=					eos_coefficient


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   version.parse('''1.11'''  )


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ])


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->float:





           '''simple docstring'''

           return 1e-4





    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->int:





           '''simple docstring'''

           return 12








import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowerCamelCase			:	List[str]							 =     (720, 1280)  # Height, Width
_lowerCamelCase			:	Optional[Any]							 =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCamelCase			:	List[Any]							 =     1 / 100
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	Union[str, Any]							 =     250







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__				,							A__   				=					get_dataset(lowercase_				,       lowercase_		)
       for index in range(lowercase_		):
              A__   				=					random.sample(range(len(lowercase_		)		)				,       4		)
              A__				,							A__				,							A__   				=					update_image_and_anno(
                  lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       filter_scale=lowercase_				,       )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A__   				=					random_chars(32		)
              A__   				=					path.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              A__   				=					f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""				,       lowercase_				,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""		)
              A__   				=					[]
              for anno in new_annos:
                     A__   				=					anno[3] - anno[1]
                     A__   				=					anno[4] - anno[2]
                     A__   				=					anno[1] + width / 2
                     A__   				=					anno[2] + height / 2
                     A__   				=					f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(lowercase_		)
              with open(f"""{file_root}.txt"""				,       '''w'''		) as outfile:
                     outfile.write('''\n'''.join(line for line in annos_list		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

       A__   				=					[]
       A__   				=					[]
       for label_file in glob.glob(os.path.join(lowercase_				,       '''*.txt'''		)		):
              A__   				=					label_file.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              with open(lowercase_		) as in_file:
                     A__   				=					in_file.readlines()
              A__   				=					os.path.join(lowercase_				,       f"""{label_name}.jpg"""		)

              A__   				=					[]
              for obj_list in obj_lists:
                     A__   				=					obj_list.rstrip('''\n'''		).split(''' '''		)
                     A__   				=					float(obj[1]		) - float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) - float(obj[4]		) / 2
                     A__   				=					float(obj[1]		) + float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) + float(obj[4]		) / 2

                     boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
              if not boxes:
                     continue
              img_paths.append(lowercase_		)
              labels.append(lowercase_		)
       return img_paths, labels







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

       A__   				=					np.zeros([output_size[0], output_size[1], 3]				,       dtype=np.uinta		)
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					int(scale_x * output_size[1]		)
       A__   				=					int(scale_y * output_size[0]		)

       A__   				=					[]
       A__   				=					[]
       for i, index in enumerate(lowercase_		):
              A__   				=					all_img_list[index]
              path_list.append(lowercase_		)
              A__   				=					all_annos[index]
              A__   				=					cva.imread(lowercase_		)
              if i == 0:  # top-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					bbox[2] * scale_y
                            A__   				=					bbox[3] * scale_x
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 1:  # top-right
                     A__   				=					cva.resize(lowercase_				,       (output_size[1] - divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					bbox[2] * scale_y
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 2:  # bottom-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					bbox[3] * scale_x
                            A__   				=					scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              else:  # bottom-right
                     A__   				=					cva.resize(
                         lowercase_				,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              A__   				=					[
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       assert number_char > 1, "The number of character should greater than 1"
       A__   				=					ascii_lowercase + digits
       return "".join(random.choice(lowercase_		) for _ in range(lowercase_		)		)


if __name__ == "__main__":
  main()
  print("""DONE ✅""")


