




import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
			from transformers.models.esm.modeling_esm import (
			    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    EsmEmbeddings,
			    create_position_ids_from_input_ids,
			)




class 		__A      :

						def __init__(		self   :List[str]    , __snake_case   :Tuple    , __snake_case   :List[Any]=13    , __snake_case   :List[Any]=7    , __snake_case   :List[Any]=False    , __snake_case   :List[Any]=True    , __snake_case   :Union[str, Any]=False    , __snake_case   :List[str]=True    , __snake_case   :Any=33    , __snake_case   :Any=32    , __snake_case   :int=5    , __snake_case   :List[str]=4    , __snake_case   :int=37    , __snake_case   :Dict="gelu"    , __snake_case   :int=0.1    , __snake_case   :Optional[Any]=0.1    , __snake_case   :List[Any]=5_12    , __snake_case   :Any=16    , __snake_case   :str=2    , __snake_case   :Dict=0.02    , __snake_case   :Any=3    , __snake_case   :int=4    , __snake_case   :Optional[int]=None    , ):
										'''simple docstring'''

										__magic_name__	:	Dict								=parent
										__magic_name__	:	int								=batch_size
										__magic_name__	:	Tuple								=seq_length
										__magic_name__	:	List[Any]								=is_training
										__magic_name__	:	Tuple								=use_input_mask
										__magic_name__	:	Dict								=use_token_type_ids
										__magic_name__	:	Optional[int]								=use_labels
										__magic_name__	:	List[Any]								=vocab_size
										__magic_name__	:	Optional[int]								=hidden_size
										__magic_name__	:	List[Any]								=num_hidden_layers
										__magic_name__	:	Optional[Any]								=num_attention_heads
										__magic_name__	:	List[str]								=intermediate_size
										__magic_name__	:	str								=hidden_act
										__magic_name__	:	List[str]								=hidden_dropout_prob
										__magic_name__	:	str								=attention_probs_dropout_prob
										__magic_name__	:	Tuple								=max_position_embeddings
										__magic_name__	:	List[str]								=type_vocab_size
										__magic_name__	:	Optional[int]								=type_sequence_label_size
										__magic_name__	:	Optional[int]								=initializer_range
										__magic_name__	:	Optional[int]								=num_labels
										__magic_name__	:	List[Any]								=num_choices
										__magic_name__	:	Tuple								=scope

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

										__magic_name__	:	Optional[Any]								=None
										if self.use_input_mask:
														__magic_name__	:	Any								=random_attention_mask([self.batch_size, self.seq_length] )

										__magic_name__	:	str								=None
										__magic_name__	:	Dict								=None
										__magic_name__	:	Union[str, Any]								=None
										if self.use_labels:
														__magic_name__	:	int								=ids_tensor([self.batch_size]    , self.type_sequence_label_size )
														__magic_name__	:	str								=ids_tensor([self.batch_size, self.seq_length]    , self.num_labels )
														__magic_name__	:	List[Any]								=ids_tensor([self.batch_size]    , self.num_choices )

										__magic_name__	:	Any								=self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

						def     A__						(		self   :Any ):
										'''simple docstring'''

										return EsmConfig(
										    vocab_size=self.vocab_size    , hidden_size=self.hidden_size    , pad_token_id=1    , num_hidden_layers=self.num_hidden_layers    , num_attention_heads=self.num_attention_heads    , intermediate_size=self.intermediate_size    , hidden_act=self.hidden_act    , hidden_dropout_prob=self.hidden_dropout_prob    , attention_probs_dropout_prob=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , type_vocab_size=self.type_vocab_size    , initializer_range=self.initializer_range    , )

						def     A__						(		self   :Optional[Any]    , __snake_case   :Optional[Any]    , __snake_case   :Any    , __snake_case   :List[Any]    , __snake_case   :int    , __snake_case   :Union[str, Any]    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	str								=EsmModel(config=__snake_case )
										model.to(__snake_case )
										model.eval()
										__magic_name__	:	List[str]								=model(__snake_case    , attention_mask=__snake_case )
										__magic_name__	:	Tuple								=model(__snake_case )
										__magic_name__	:	Optional[int]								=model(__snake_case )

										self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size) )
										self.parent.assertEqual(result.pooler_output.shape    , (self.batch_size, self.hidden_size) )

						def     A__						(		self   :Optional[Any]    , __snake_case   :Union[str, Any]    , __snake_case   :int    , __snake_case   :int    , __snake_case   :str    , __snake_case   :Any    , __snake_case   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=EsmForMaskedLM(config=__snake_case )
										model.to(__snake_case )
										model.eval()
										__magic_name__	:	Union[str, Any]								=model(__snake_case    , attention_mask=__snake_case    , labels=__snake_case )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size) )

						def     A__						(		self   :Any    , __snake_case   :Any    , __snake_case   :Dict    , __snake_case   :Dict    , __snake_case   :Union[str, Any]    , __snake_case   :Union[str, Any]    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.num_labels
										__magic_name__	:	List[str]								=EsmForTokenClassification(config=__snake_case )
										model.to(__snake_case )
										model.eval()
										__magic_name__	:	List[str]								=model(__snake_case    , attention_mask=__snake_case    , labels=__snake_case )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.num_labels) )


						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.prepare_config_and_inputs()
										(
										    __magic_name__ 
										)	:	List[str]								=config_and_inputs
										__magic_name__	:	List[str]								={"""input_ids""": input_ids, """attention_mask""": input_mask}
										return config, inputs_dict




@require_torch
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					False

						UpperCamelCase     						=					(
						    (
						        EsmForMaskedLM,
						        EsmModel,
						        EsmForSequenceClassification,
						        EsmForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase     						=					()
						UpperCamelCase     						=					(
						    {
						        """feature-extraction""": EsmModel,
						        """fill-mask""": EsmForMaskedLM,
						        """text-classification""": EsmForSequenceClassification,
						        """token-classification""": EsmForTokenClassification,
						        """zero-shot""": EsmForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase     						=					True

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=EsmModelTester(self )
										__magic_name__	:	Any								=ConfigTester(self    , config_class=__snake_case    , hidden_size=37 )

						def     A__						(		self   :int ):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														__magic_name__	:	str								=type
														self.model_tester.create_and_check_model(*__snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__snake_case )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__snake_case )

						@slow
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__	:	str								=EsmModel.from_pretrained(__snake_case )
														self.assertIsNotNone(__snake_case )

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.model_tester.prepare_config_and_inputs()[0]
										__magic_name__	:	Dict								=EsmEmbeddings(config=__snake_case )

										__magic_name__	:	int								=torch.as_tensor([[12, 31, 13, model.padding_idx]] )
										__magic_name__	:	Optional[int]								=torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ] )
										__magic_name__	:	Union[str, Any]								=create_position_ids_from_input_ids(__snake_case    , model.padding_idx )
										self.assertEqual(position_ids.shape    , expected_positions.shape )
										self.assertTrue(torch.all(torch.eq(__snake_case    , __snake_case ) ) )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Any								=self.model_tester.prepare_config_and_inputs()[0]
										__magic_name__	:	Tuple								=EsmEmbeddings(config=__snake_case )

										__magic_name__	:	Dict								=torch.empty(2    , 4    , 30 )
										__magic_name__	:	List[str]								=[
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										__magic_name__	:	List[Any]								=torch.as_tensor([expected_single_positions, expected_single_positions] )
										__magic_name__	:	Any								=embeddings.create_position_ids_from_inputs_embeds(__snake_case )
										self.assertEqual(position_ids.shape    , expected_positions.shape )
										self.assertTrue(torch.all(torch.eq(__snake_case    , __snake_case ) ) )

						@unittest.skip("""Esm does not support embedding resizing""" )
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										pass

						@unittest.skip("""Esm does not support embedding resizing""" )
						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass


						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										pass




@require_torch
class 		__A      (			UpperCamelCase__			):

						@slow
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										with torch.no_grad():
														__magic_name__	:	Any								=EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
														model.eval()
														__magic_name__	:	List[str]								=torch.tensor([[0, 1, 2, 3, 4, 5]] )
														__magic_name__	:	List[Any]								=model(__snake_case )[0]

														__magic_name__	:	Dict								=33

														__magic_name__	:	Optional[Any]								=torch.Size((1, 6, vocab_size) )
														self.assertEqual(output.shape    , __snake_case )

														__magic_name__	:	Optional[Any]								=torch.tensor(
														    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
														self.assertTrue(torch.allclose(output[:, :3, :3]    , __snake_case    , atol=1E-4 ) )


						@slow
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										with torch.no_grad():
														__magic_name__	:	Union[str, Any]								=EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
														model.eval()

														__magic_name__	:	int								=torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
														__magic_name__	:	int								=model(__snake_case )[0]
														# compare the actual values for a slice.
														__magic_name__	:	Any								=torch.tensor(
														    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
														self.assertTrue(torch.allclose(output[:, :3, :3]    , __snake_case    , atol=1E-4 ) )










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}.""")




