



'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""trocr"""
  a_				   =["""past_key_values"""]
  a_				   ={
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }




  def __init__(					self :      List[str]     ,      _a :      Optional[Any]=5_0265     ,      _a :      Tuple=1024     ,      _a :      Optional[int]=12     ,      _a :      int=16     ,      _a :      Any=4096     ,      _a :      Union[str, Any]="gelu"     ,      _a :      Union[str, Any]=512     ,      _a :      Optional[int]=0.1     ,      _a :      List[str]=0.0     ,      _a :      List[Any]=0.0     ,      _a :      str=2     ,      _a :      List[str]=0.02     ,      _a :      Tuple=0.0     ,      _a :      Union[str, Any]=True     ,      _a :      List[str]=False     ,      _a :      int=True     ,      _a :      Tuple=True     ,      _a :      List[str]=1     ,      _a :      Tuple=0     ,      _a :      int=2     ,      **_a :      Tuple     ,      )   ->   Union[str, Any]:
    __lowerCamelCase			:			Optional[Any]       			=					vocab_size
    __lowerCamelCase			:			List[Any]       			=					d_model
    __lowerCamelCase			:			List[str]       			=					decoder_layers
    __lowerCamelCase			:			Any       			=					decoder_attention_heads
    __lowerCamelCase			:			Union[str, Any]       			=					decoder_ffn_dim
    __lowerCamelCase			:			Any       			=					activation_function
    __lowerCamelCase			:			str       			=					max_position_embeddings
    __lowerCamelCase			:			List[Any]       			=					dropout
    __lowerCamelCase			:			Optional[Any]       			=					attention_dropout
    __lowerCamelCase			:			List[Any]       			=					activation_dropout
    __lowerCamelCase			:			Union[str, Any]       			=					init_std
    __lowerCamelCase			:			Union[str, Any]       			=					decoder_layerdrop
    __lowerCamelCase			:			Optional[int]       			=					use_cache
    __lowerCamelCase			:			List[Any]       			=					scale_embedding
    __lowerCamelCase			:			Dict       			=					use_learned_position_embeddings
    __lowerCamelCase			:			Optional[int]       			=					layernorm_embedding

    super().__init__(
        pad_token_id=_a     ,      bos_token_id=_a     ,      eos_token_id=_a     ,      decoder_start_token_id=_a     ,      **_a     ,      )











'''simple docstring'''





import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			Optional[Any]       			=					1.5
  __lowerCamelCase			:			Tuple       			=					int(factor * num_class_images			)
  __lowerCamelCase			:			Tuple       			=					ClipClient(
      url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			)

  os.makedirs(F'{class_data_dir}/images'			,exist_ok=_lowerCAmelCase			)
  if len(list(Path(F'{class_data_dir}/images'			).iterdir()			)			) >= num_class_images:
    return

  while True:
    __lowerCamelCase			:			Dict       			=					client.query(text=_lowerCAmelCase			)
    if len(_lowerCAmelCase			) >= factor * num_class_images or num_images > 1E4:
      break
    else:
      __lowerCamelCase			:			Union[str, Any]       			=					int(factor * num_images			)
      __lowerCamelCase			:			List[str]       			=					ClipClient(
          url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			,)

  __lowerCamelCase			:			List[Any]       			=					0
  __lowerCamelCase			:			Tuple       			=					0
  __lowerCamelCase			:			int       			=					tqdm(desc='downloading real regularization images'			,total=_lowerCAmelCase			)

  with open(F'{class_data_dir}/caption.txt'			,'w'			) as fa, open(F'{class_data_dir}/urls.txt'			,'w'			) as fa, open(
      F'{class_data_dir}/images.txt'			,'w'			) as fa:
    while total < num_class_images:
      __lowerCamelCase			:			List[str]       			=					class_images[count]
      count += 1
      try:
        __lowerCamelCase			:			int       			=					requests.get(images['url']			)
        if img.status_code == 200:
          __lowerCamelCase			:			str       			=					Image.open(BytesIO(img.content			)			)
          with open(F'{class_data_dir}/images/{total}.jpg'			,'wb'			) as f:
            f.write(img.content			)
          fa.write(images['caption'] + '\n'			)
          fa.write(images['url'] + '\n'			)
          fa.write(F'{class_data_dir}/images/{total}.jpg' + '\n'			)
          total += 1
          pbar.update(1			)
        else:
          continue
      except Exception:
        continue
  return

def 			a_	(				) -> Dict:
  __lowerCamelCase			:			Any       			=					argparse.ArgumentParser(''			,add_help=_lowerCAmelCase			)
  parser.add_argument('--class_prompt'			,help='text prompt to retrieve images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--class_data_dir'			,help='path to save images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--num_class_images'			,help='number of images to download'			,default=200			,type=_lowerCAmelCase			)
  return parser.parse_args()


if __name__ == "__main__":
   _UpperCamelCase											=		parse_args()
   retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






