"""simple docstring"""






import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def      UpperCAmelCase__			(							)					->       List[str]:
      '''simple docstring'''


      lowercase								=      argparse.ArgumentParser()
      parser.add_argument(
          """-m"""   ,						"""--pretrained_model_name_or_path"""   ,						type=lowerCAmelCase__   ,						default=lowerCAmelCase__   ,						required=lowerCAmelCase__   ,						help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,						)
      parser.add_argument(
          """-c"""   ,						"""--caption"""   ,						type=lowerCAmelCase__   ,						default="""robotic cat with wings"""   ,						help="""Text used to generate images."""   ,						)
      parser.add_argument(
          """-n"""   ,						"""--images_num"""   ,						type=lowerCAmelCase__   ,						default=4   ,						help="""How much images to generate."""   ,						)
      parser.add_argument(
          """-s"""   ,						"""--seed"""   ,						type=lowerCAmelCase__   ,						default=4_2   ,						help="""Seed for random process."""   ,						)
      parser.add_argument(
          """-ci"""   ,						"""--cuda_id"""   ,						type=lowerCAmelCase__   ,						default=0   ,						help="""cuda_id."""   ,						)
      lowercase								=      parser.parse_args()
      return args


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[str]   ,						lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int						)					->       List[str]:
      '''simple docstring'''


      if not len(lowerCAmelCase__						) == rows * cols:
            raise ValueError("""The specified number of rows and columns are not correct."""						)

      lowercase				,				lowercase								=      imgs[0].size
      lowercase								=      Image.new("""RGB"""   ,						size=(cols * w, rows * h)						)
      lowercase				,				lowercase								=      grid.size

      for i, img in enumerate(lowerCAmelCase__						):
            grid.paste(lowerCAmelCase__   ,						box=(i % cols * w, i // cols * h)						)
      return grid


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int="robotic cat with wings"   ,						lowerCAmelCase__	:Any=7.5   ,						lowerCAmelCase__	:int=5_0   ,						lowerCAmelCase__	:Any=1   ,						lowerCAmelCase__	:Optional[Any]=4_2   ,						)					->       str:
      '''simple docstring'''


      lowercase								=      torch.Generator(pipeline.device						).manual_seed(lowerCAmelCase__						)
      lowercase								=      pipeline(
          lowerCAmelCase__   ,						guidance_scale=lowerCAmelCase__   ,						num_inference_steps=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_images_per_prompt=lowerCAmelCase__   ,						).images
      lowercase								=      int(math.sqrt(lowerCAmelCase__						)						)
      lowercase								=      image_grid(lowerCAmelCase__   ,						rows=_rows   ,						cols=num_images_per_prompt // _rows						)
      return grid, images


__lowerCAmelCase       :			str   	=parse_args()
# Load models and create wrapper for stable diffusion
__lowerCAmelCase       :			int   	=CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__lowerCAmelCase       :			List[str]   	=CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__lowerCAmelCase       :			str   	=AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__lowerCAmelCase       :			str   	=UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__lowerCAmelCase       :			List[str]   	=StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__lowerCAmelCase       :			str   	=lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
       __lowerCAmelCase       :			Union[str, Any]   	=load(args.pretrained_model_name_or_path, model=unet)
       unet.eval()
       setattr(pipeline, """unet""", unet)
else:
       __lowerCAmelCase       :			Optional[int]   	=unet.to(torch.device("""cuda""", args.cuda_id))
__lowerCAmelCase       :			Optional[Any]   	=pipeline.to(unet.device)
__lowerCAmelCase	,	__lowerCAmelCase       :			Any   	=generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
__lowerCAmelCase       :			Any   	=os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
       image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))




"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Dict   	={
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'deta'
      snake_case__     :  Dict				    =      {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(		self						,			__lowerCAmelCase=None						,			__lowerCAmelCase=900						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=6						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=8						,			__lowerCAmelCase=6						,			__lowerCAmelCase=1024						,			__lowerCAmelCase=8						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase="relu"						,			__lowerCAmelCase=256						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase=False						,			__lowerCAmelCase="sine"						,			__lowerCAmelCase=5						,			__lowerCAmelCase=4						,			__lowerCAmelCase=4						,			__lowerCAmelCase=True						,			__lowerCAmelCase=300						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=1						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.2_5						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if backbone_config is None:
                  logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                  lowercase								=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
            else:
                  if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                        lowercase								=      backbone_config.pop("""model_type"""		)
                        lowercase								=      CONFIG_MAPPING[backbone_model_type]
                        lowercase								=      config_class.from_dict(__lowerCAmelCase		)

            lowercase								=      backbone_config
            lowercase								=      num_queries
            lowercase								=      max_position_embeddings
            lowercase								=      d_model
            lowercase								=      encoder_ffn_dim
            lowercase								=      encoder_layers
            lowercase								=      encoder_attention_heads
            lowercase								=      decoder_ffn_dim
            lowercase								=      decoder_layers
            lowercase								=      decoder_attention_heads
            lowercase								=      dropout
            lowercase								=      attention_dropout
            lowercase								=      activation_dropout
            lowercase								=      activation_function
            lowercase								=      init_std
            lowercase								=      init_xavier_std
            lowercase								=      encoder_layerdrop
            lowercase								=      auxiliary_loss
            lowercase								=      position_embedding_type
            # deformable attributes
            lowercase								=      num_feature_levels
            lowercase								=      encoder_n_points
            lowercase								=      decoder_n_points
            lowercase								=      two_stage
            lowercase								=      two_stage_num_proposals
            lowercase								=      with_box_refine
            lowercase								=      assign_first_stage
            if two_stage is True and with_box_refine is False:
                  raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
            # Hungarian matcher
            lowercase								=      class_cost
            lowercase								=      bbox_cost
            lowercase								=      giou_cost
            # Loss coefficients
            lowercase								=      mask_loss_coefficient
            lowercase								=      dice_loss_coefficient
            lowercase								=      bbox_loss_coefficient
            lowercase								=      giou_loss_coefficient
            lowercase								=      eos_coefficient
            lowercase								=      focal_alpha
            super().__init__(is_encoder_decoder=__lowerCAmelCase						,			**__lowerCAmelCase		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.encoder_attention_heads

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.d_model







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      copy.deepcopy(self.__dict__		)
            lowercase								=      self.backbone_config.to_dict()
            lowercase								=      self.__class__.model_type
            return output



