




import functools
import operator

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


lowerCamelCase__			:       Tuple         =							logging.get_logger(__name__)

lowerCamelCase__			:       int         =							{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class 	UpperCAmelCase_							(				lowercase__ ):


 '''simple docstring'''







 lowercase_       =   """sew-d"""



 def __init__(							self  :		int    ,    _lowerCAmelCase  :		Optional[int]=32    ,    _lowerCAmelCase  :		Optional[Any]=768    ,    _lowerCAmelCase  :		str=12    ,    _lowerCAmelCase  :		List[str]=12    ,    _lowerCAmelCase  :		List[Any]=3_072    ,    _lowerCAmelCase  :		List[Any]=2    ,    _lowerCAmelCase  :		Union[str, Any]=512    ,    _lowerCAmelCase  :		Dict=256    ,    _lowerCAmelCase  :		str=True    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		List[Any]=("p2c", "c2p")    ,    _lowerCAmelCase  :		Dict="layer_norm"    ,    _lowerCAmelCase  :		Union[str, Any]="gelu_python"    ,    _lowerCAmelCase  :		Optional[Any]=0.1    ,    _lowerCAmelCase  :		str=0.1    ,    _lowerCAmelCase  :		List[Any]=0.1    ,    _lowerCAmelCase  :		Any=0.0    ,    _lowerCAmelCase  :		str=0.1    ,    _lowerCAmelCase  :		List[Any]=0.02    ,    _lowerCAmelCase  :		Dict=1E-7    ,    _lowerCAmelCase  :		Any=1E-5    ,    _lowerCAmelCase  :		List[Any]="group"    ,    _lowerCAmelCase  :		Optional[Any]="gelu"    ,    _lowerCAmelCase  :		Optional[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,    _lowerCAmelCase  :		Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,    _lowerCAmelCase  :		Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,    _lowerCAmelCase  :		Dict=False    ,    _lowerCAmelCase  :		List[str]=128    ,    _lowerCAmelCase  :		List[Any]=16    ,    _lowerCAmelCase  :		int=True    ,    _lowerCAmelCase  :		Tuple=0.05    ,    _lowerCAmelCase  :		Tuple=10    ,    _lowerCAmelCase  :		str=2    ,    _lowerCAmelCase  :		Union[str, Any]=0.0    ,    _lowerCAmelCase  :		Union[str, Any]=10    ,    _lowerCAmelCase  :		Dict=0    ,    _lowerCAmelCase  :		Optional[Any]="mean"    ,    _lowerCAmelCase  :		int=False    ,    _lowerCAmelCase  :		List[Any]=False    ,    _lowerCAmelCase  :		Tuple=256    ,    _lowerCAmelCase  :		Union[str, Any]=0    ,    _lowerCAmelCase  :		str=1    ,    _lowerCAmelCase  :		List[str]=2    ,    **_lowerCAmelCase  :		int    ,    ):
       super().__init__(**lowercase_    ,    pad_token_id=lowercase_    ,    bos_token_id=lowercase_    ,    eos_token_id=lowercase_						)
       SCREAMING_SNAKE_CASE_     						=      hidden_size
       SCREAMING_SNAKE_CASE_     						=      feat_extract_norm
       SCREAMING_SNAKE_CASE_     						=      feat_extract_activation
       SCREAMING_SNAKE_CASE_     						=      list(lowercase_						)
       SCREAMING_SNAKE_CASE_     						=      list(lowercase_						)
       SCREAMING_SNAKE_CASE_     						=      list(lowercase_						)
       SCREAMING_SNAKE_CASE_     						=      conv_bias
       SCREAMING_SNAKE_CASE_     						=      num_conv_pos_embeddings
       SCREAMING_SNAKE_CASE_     						=      num_conv_pos_embedding_groups
       SCREAMING_SNAKE_CASE_     						=      len(self.conv_dim						)
       SCREAMING_SNAKE_CASE_     						=      num_hidden_layers
       SCREAMING_SNAKE_CASE_     						=      intermediate_size
       SCREAMING_SNAKE_CASE_     						=      squeeze_factor
       SCREAMING_SNAKE_CASE_     						=      max_position_embeddings
       SCREAMING_SNAKE_CASE_     						=      position_buckets
       SCREAMING_SNAKE_CASE_     						=      share_att_key
       SCREAMING_SNAKE_CASE_     						=      relative_attention
       SCREAMING_SNAKE_CASE_     						=      norm_rel_ebd
       SCREAMING_SNAKE_CASE_     						=      list(lowercase_						)
       SCREAMING_SNAKE_CASE_     						=      hidden_act
       SCREAMING_SNAKE_CASE_     						=      num_attention_heads
       SCREAMING_SNAKE_CASE_     						=      hidden_dropout
       SCREAMING_SNAKE_CASE_     						=      attention_dropout
       SCREAMING_SNAKE_CASE_     						=      activation_dropout
       SCREAMING_SNAKE_CASE_     						=      feat_proj_dropout
       SCREAMING_SNAKE_CASE_     						=      final_dropout
       SCREAMING_SNAKE_CASE_     						=      layer_norm_eps
       SCREAMING_SNAKE_CASE_     						=      feature_layer_norm_eps
       SCREAMING_SNAKE_CASE_     						=      initializer_range
       SCREAMING_SNAKE_CASE_     						=      vocab_size

       if (
           (len(self.conv_stride						) != self.num_feat_extract_layers)
           or (len(self.conv_kernel						) != self.num_feat_extract_layers)
           or (len(self.conv_dim						) != self.num_feat_extract_layers)
       ):
             raise ValueError(
                 'Configuration for convolutional layers is incorrect.'
                 'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                 F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                 F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       SCREAMING_SNAKE_CASE_     						=      apply_spec_augment
       SCREAMING_SNAKE_CASE_     						=      mask_time_prob
       SCREAMING_SNAKE_CASE_     						=      mask_time_length
       SCREAMING_SNAKE_CASE_     						=      mask_time_min_masks
       SCREAMING_SNAKE_CASE_     						=      mask_feature_prob
       SCREAMING_SNAKE_CASE_     						=      mask_feature_length
       SCREAMING_SNAKE_CASE_     						=      mask_feature_min_masks

       # ctc loss
       SCREAMING_SNAKE_CASE_     						=      ctc_loss_reduction
       SCREAMING_SNAKE_CASE_     						=      ctc_zero_infinity

       # sequence classification
       SCREAMING_SNAKE_CASE_     						=      use_weighted_layer_sum
       SCREAMING_SNAKE_CASE_     						=      classifier_proj_size




 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       return functools.reduce(operator.mul    ,    self.conv_stride    ,    1						)





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

import requests

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     dict							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      {
          '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)',
      }
      SCREAMING_SNAKE_CASE_     						=      {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      SCREAMING_SNAKE_CASE_     						=      [
          get_openlibrary_data(author['key']							)['name'] for author in data['Authors']
      ]
      SCREAMING_SNAKE_CASE_     						=      data['First sentence']['value']
      for key, value in data.items():
            if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      ', '.join(__UpperCAmelCase							)
      return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   while True:
      lowerCamelCase__			:       Optional[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:
         lowerCamelCase__			:       Union[str, 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}.''')