




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		__A      (			nn.Module			):

						def __init__(		self   :int    , __snake_case   :List[Any] = 16    , __snake_case   :int = 88    , __snake_case   :int = None    , __snake_case   :Dict = 1    , __snake_case   :List[Any] = 0.0    , __snake_case   :Union[str, Any] = 32    , __snake_case   :Union[str, Any] = None    , __snake_case   :List[Any] = False    , __snake_case   :int = None    , __snake_case   :Optional[Any] = None    , __snake_case   :List[str] = "geglu"    , __snake_case   :Optional[Any] = None    , ):
										'''simple docstring'''

										super().__init__()
										__magic_name__	:	Any								=nn.ModuleList(
										    [
										        TransformeraDModel(
										            num_attention_heads=_A    , attention_head_dim=_A    , in_channels=_A    , num_layers=_A    , dropout=_A    , norm_num_groups=_A    , cross_attention_dim=_A    , attention_bias=_A    , sample_size=_A    , num_vector_embeds=_A    , activation_fn=_A    , num_embeds_ada_norm=_A    , )
										        for _ in range(2 )
										    ] )

										# Variables that can be set by a pipeline:

										# The ratio of transformer1 to transformer2's output states to be combined during inference
										__magic_name__	:	List[Any]								=0.5

										# The shape of `encoder_hidden_states` is expected to be
										# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
										__magic_name__	:	Any								=[77, 2_57]

										# Which transformer to use to encode which condition.
										# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
										__magic_name__	:	Dict								=[1, 0]


						def     A__						(		self   :Any    , __snake_case   :Dict    , __snake_case   :Union[str, Any]    , __snake_case   :List[Any]=None    , __snake_case   :Any=None    , __snake_case   :List[str]=None    , __snake_case   :List[Any] = True    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=hidden_states

										__magic_name__	:	Union[str, Any]								=[]
										__magic_name__	:	Union[str, Any]								=0
										# attention_mask is not used yet
										for i in range(2 ):
														# for each of the two transformers, pass the corresponding condition tokens
														__magic_name__	:	Union[str, Any]								=encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
														__magic_name__	:	str								=self.transformer_index_for_condition[i]
														__magic_name__	:	Optional[Any]								=self.transformers[transformer_index](
														    _A    , encoder_hidden_states=_A    , timestep=_A    , cross_attention_kwargs=_A    , return_dict=_A    , )[0]
														encoded_states.append(encoded_state - input_states )
														tokens_start += self.condition_lengths[i]

										__magic_name__	:	List[Any]								=encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
										__magic_name__	:	Optional[Any]								=output_states + input_states

										if not return_dict:
														return (output_states,)

										return TransformeraDModelOutput(sample=_A )










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

import requests




def 		lowerCAmelCase_   (	lowerCamelCase = "isbn/0140328726"   ):
				__magic_name__	:	Optional[Any]								=olid.strip().strip("""/"""   )  # Remove leading/trailing whitespace & slashes
				if new_olid.count("""/"""   ) != 1:
								__magic_name__	:	Union[str, Any]								=F"{olid} is not a valid Open Library olid"
								raise ValueError(lowerCamelCase   )
				return requests.get(F"https://openlibrary.org/{new_olid}.json"   ).json()




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								={
				    """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)""",
				}
				__magic_name__	:	List[Any]								={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
				__magic_name__	:	Dict								=[
				    get_openlibrary_data(author["""key"""]   )["""name"""] for author in data["""Authors"""]
				]
				__magic_name__	:	List[str]								=data["""First sentence"""]["""value"""]
				for key, value in data.items():
								if isinstance(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Tuple								=""", """.join(lowerCamelCase   )
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase_		:				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:
																					UpperCAmelCase_		:				str									=     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}.""")




