



import copy
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
from ..auto import CONFIG_MAPPING


__snake_case						:Dict      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    List[str]											=						'''conditional_detr'''
       UpperCamelCase__  :    List[Any]											=						['''past_key_values''']
       UpperCamelCase__  :    Dict											=						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     List[str]=None     ,		__SCREAMING_SNAKE_CASE							:     int=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=300     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=6     ,		__SCREAMING_SNAKE_CASE							:     Dict=2_048     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=6     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     int=8     ,		__SCREAMING_SNAKE_CASE							:     str=0.0     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		__SCREAMING_SNAKE_CASE							:     Any=True     ,		__SCREAMING_SNAKE_CASE							:     List[str]="relu"     ,		__SCREAMING_SNAKE_CASE							:     Tuple=256     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Any=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.02     ,		__SCREAMING_SNAKE_CASE							:     Any=1.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="sine"     ,		__SCREAMING_SNAKE_CASE							:     int="resnet50"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     int=2     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=5     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Any=1     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=1     ,		__SCREAMING_SNAKE_CASE							:     Dict=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=5     ,		__SCREAMING_SNAKE_CASE							:     Dict=2     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=0.25     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''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.''')
                   __a						       =				CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
               elif isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
                   __a						       =				backbone_config.get('''model_type''')
                   __a						       =				CONFIG_MAPPING[backbone_model_type]
                   __a						       =				config_class.from_dict(__SCREAMING_SNAKE_CASE)

           __a						       =				use_timm_backbone
           __a						       =				backbone_config
           __a						       =				num_channels
           __a						       =				num_queries
           __a						       =				d_model
           __a						       =				encoder_ffn_dim
           __a						       =				encoder_layers
           __a						       =				encoder_attention_heads
           __a						       =				decoder_ffn_dim
           __a						       =				decoder_layers
           __a						       =				decoder_attention_heads
           __a						       =				dropout
           __a						       =				attention_dropout
           __a						       =				activation_dropout
           __a						       =				activation_function
           __a						       =				init_std
           __a						       =				init_xavier_std
           __a						       =				encoder_layerdrop
           __a						       =				decoder_layerdrop
           __a						       =				encoder_layers
           __a						       =				auxiliary_loss
           __a						       =				position_embedding_type
           __a						       =				backbone
           __a						       =				use_pretrained_backbone
           __a						       =				dilation
           # Hungarian matcher
           __a						       =				class_cost
           __a						       =				bbox_cost
           __a						       =				giou_cost
           # Loss coefficients
           __a						       =				mask_loss_coefficient
           __a						       =				dice_loss_coefficient
           __a						       =				cls_loss_coefficient
           __a						       =				bbox_loss_coefficient
           __a						       =				giou_loss_coefficient
           __a						       =				focal_alpha
           super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       @property
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           return self.encoder_attention_heads



       @property
       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           return self.d_model





       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

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


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    int											=						version.parse('''1.11'''    )



       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

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



       @property
       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           return 1E-5





       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           return 12










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests





def      __snake_case	(			_UpperCAmelCase = "isbn/0140328726"				):
    __a						       =				olid.strip().strip('''/'''				)  # Remove leading/trailing whitespace & slashes
    if new_olid.count('''/'''				) != 1:
        __a						       =				f'{olid} is not a valid Open Library olid'
        raise ValueError(_UpperCAmelCase				)
    return requests.get(f'https://openlibrary.org/{new_olid}.json'				).json()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{
        '''title''': '''Title''',
        '''publish_date''': '''Publish date''',
        '''authors''': '''Authors''',
        '''number_of_pages''': '''Number of pages:''',
        '''first_sentence''': '''First sentence''',
        '''isbn_10''': '''ISBN (10)''',
        '''isbn_13''': '''ISBN (13)''',
    }
    __a						       =				{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
    __a						       =				[
        get_openlibrary_data(author['''key''']				)['''name'''] for author in data['''Authors''']
    ]
    __a						       =				data['''First sentence''']['''value''']
    for key, value in data.items():
        if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
            __a						       =				''', '''.join(_UpperCAmelCase				)
    return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 while True:
  __snake_case						:List[Any]      		=     input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
  if isbn.lower() in ("", "q", "quit", "exit", "stop"):
   break

  if len(isbn) not in (10, 13) or not isbn.isdigit():
   print(f'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
   continue

  print(f'\nSearching Open Library for ISBN: {isbn}...\n')

  try:
   __snake_case						:Optional[Any]      		=     summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
   print('''\n'''.join(f'{key}: {value}' for key, value in book_summary.items()))
  except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
   print(f'Sorry, there are no results for ISBN: {isbn}.')





