






'''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 					lowercase__				(   )->				Optional[int]:
						UpperCamelCase       						=      argparse.ArgumentParser()
						parser.add_argument(
						    """-m"""	,		"""--pretrained_model_name_or_path"""	,		type=_lowercase	,		default=_lowercase	,		required=_lowercase	,		help="""Path to pretrained model or model identifier from huggingface.co/models."""	,		)
						parser.add_argument(
						    """-c"""	,		"""--caption"""	,		type=_lowercase	,		default="""robotic cat with wings"""	,		help="""Text used to generate images."""	,		)
						parser.add_argument(
						    """-n"""	,		"""--images_num"""	,		type=_lowercase	,		default=4	,		help="""How much images to generate."""	,		)
						parser.add_argument(
						    """-s"""	,		"""--seed"""	,		type=_lowercase	,		default=42	,		help="""Seed for random process."""	,		)
						parser.add_argument(
						    """-ci"""	,		"""--cuda_id"""	,		type=_lowercase	,		default=0	,		help="""cuda_id."""	,		)
						UpperCamelCase       						=      parser.parse_args()
						return args







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Dict:
						if not len(_lowercase					) == rows * cols:
												raise ValueError("""The specified number of rows and columns are not correct."""					)

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

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







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase="robotic cat with wings"	,		__UpperCamelCase=7.5	,		__UpperCamelCase=50	,		__UpperCamelCase=1	,		__UpperCamelCase=42	,		)->				Optional[Any]:
						UpperCamelCase       						=      torch.Generator(pipeline.device					).manual_seed(_lowercase					)
						UpperCamelCase       						=      pipeline(
						    _lowercase	,		guidance_scale=_lowercase	,		num_inference_steps=_lowercase	,		generator=_lowercase	,		num_images_per_prompt=_lowercase	,		).images
						UpperCamelCase       						=      int(math.sqrt(_lowercase					)					)
						UpperCamelCase       						=      image_grid(_lowercase	,		rows=_rows	,		cols=num_images_per_prompt // _rows					)
						return grid, images


SCREAMING_SNAKE_CASE__											=     parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__											=     CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
SCREAMING_SNAKE_CASE__											=     CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
SCREAMING_SNAKE_CASE__											=     AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
SCREAMING_SNAKE_CASE__											=     UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

SCREAMING_SNAKE_CASE__											=     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__											=     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
					SCREAMING_SNAKE_CASE__											=     load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, 'unet', unet)
else:
					SCREAMING_SNAKE_CASE__											=     unet.to(torch.device('cuda', args.cuda_id))
SCREAMING_SNAKE_CASE__											=     pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__											=     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()))))
SCREAMING_SNAKE_CASE__											=     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


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class        a_		(    lowerCamelCase       ):
  lowercase  	=   """deformable_detr"""
  lowercase  	=   {
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=300    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="relu"    ,	_SCREAMING_SNAKE_CASE=256    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=1.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE="sine"    ,	_SCREAMING_SNAKE_CASE="resnet50"    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=300    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.2_5    ,	_SCREAMING_SNAKE_CASE=False    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[str]:



        """simple docstring"""






        if backbone_config is not None and use_timm_backbone:
              raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""   )

        if not use_timm_backbone:
              if backbone_config is None:
                    logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
                    UpperCamelCase       						=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]   )
              elif isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      backbone_config.get("""model_type"""   )
                    UpperCamelCase       						=      CONFIG_MAPPING[backbone_model_type]
                    UpperCamelCase       						=      config_class.from_dict(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      use_timm_backbone
        UpperCamelCase       						=      backbone_config
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      num_queries
        UpperCamelCase       						=      max_position_embeddings
        UpperCamelCase       						=      d_model
        UpperCamelCase       						=      encoder_ffn_dim
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      encoder_attention_heads
        UpperCamelCase       						=      decoder_ffn_dim
        UpperCamelCase       						=      decoder_layers
        UpperCamelCase       						=      decoder_attention_heads
        UpperCamelCase       						=      dropout
        UpperCamelCase       						=      attention_dropout
        UpperCamelCase       						=      activation_dropout
        UpperCamelCase       						=      activation_function
        UpperCamelCase       						=      init_std
        UpperCamelCase       						=      init_xavier_std
        UpperCamelCase       						=      encoder_layerdrop
        UpperCamelCase       						=      auxiliary_loss
        UpperCamelCase       						=      position_embedding_type
        UpperCamelCase       						=      backbone
        UpperCamelCase       						=      use_pretrained_backbone
        UpperCamelCase       						=      dilation
        # deformable attributes
        UpperCamelCase       						=      num_feature_levels
        UpperCamelCase       						=      encoder_n_points
        UpperCamelCase       						=      decoder_n_points
        UpperCamelCase       						=      two_stage
        UpperCamelCase       						=      two_stage_num_proposals
        UpperCamelCase       						=      with_box_refine
        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
        UpperCamelCase       						=      class_cost
        UpperCamelCase       						=      bbox_cost
        UpperCamelCase       						=      giou_cost
        # Loss coefficients
        UpperCamelCase       						=      mask_loss_coefficient
        UpperCamelCase       						=      dice_loss_coefficient
        UpperCamelCase       						=      bbox_loss_coefficient
        UpperCamelCase       						=      giou_loss_coefficient
        UpperCamelCase       						=      eos_coefficient
        UpperCamelCase       						=      focal_alpha
        UpperCamelCase       						=      disable_custom_kernels
        super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return self.encoder_attention_heads

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return self.d_model


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      copy.deepcopy(self.__dict__   )
        if self.backbone_config is not None:
              UpperCamelCase       						=      self.backbone_config.to_dict()
        UpperCamelCase       						=      self.__class__.model_type
        return output
