





import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import 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 (
							    NystromformerForMaskedLM,
							    NystromformerForMultipleChoice,
							    NystromformerForQuestionAnswering,
							    NystromformerForSequenceClassification,
							    NystromformerForTokenClassification,
							    NystromformerModel,
							)
							from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST

class  __lowercase       :


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_=13				,	UpperCAmelCase_=7				,	UpperCAmelCase_=True				,	UpperCAmelCase_=True				,	UpperCAmelCase_=True				,	UpperCAmelCase_=True				,	UpperCAmelCase_=99				,	UpperCAmelCase_=32				,	UpperCAmelCase_=5				,	UpperCAmelCase_=4				,	UpperCAmelCase_=37				,	UpperCAmelCase_="gelu"				,	UpperCAmelCase_=0.1				,	UpperCAmelCase_=0.1				,	UpperCAmelCase_=512				,	UpperCAmelCase_=16				,	UpperCAmelCase_=2				,	UpperCAmelCase_=0.0_2				,	UpperCAmelCase_=3				,	UpperCAmelCase_=4				,	UpperCAmelCase_=None				,	):
								lowerCamelCase			=parent
								lowerCamelCase			=batch_size
								lowerCamelCase			=seq_length
								lowerCamelCase			=is_training
								lowerCamelCase			=use_input_mask
								lowerCamelCase			=use_token_type_ids
								lowerCamelCase			=use_labels
								lowerCamelCase			=vocab_size
								lowerCamelCase			=hidden_size
								lowerCamelCase			=num_hidden_layers
								lowerCamelCase			=num_attention_heads
								lowerCamelCase			=intermediate_size
								lowerCamelCase			=hidden_act
								lowerCamelCase			=hidden_dropout_prob
								lowerCamelCase			=attention_probs_dropout_prob
								lowerCamelCase			=max_position_embeddings
								lowerCamelCase			=type_vocab_size
								lowerCamelCase			=type_sequence_label_size
								lowerCamelCase			=initializer_range
								lowerCamelCase			=num_labels
								lowerCamelCase			=num_choices
								lowerCamelCase			=scope


				def _snake_case    (						self							):
								lowerCamelCase			=ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size							)

								lowerCamelCase			=None
								if self.use_input_mask:
												lowerCamelCase			=random_attention_mask([self.batch_size, self.seq_length]							)

								lowerCamelCase			=None
								if self.use_token_type_ids:
												lowerCamelCase			=ids_tensor([self.batch_size, self.seq_length]				,	self.type_vocab_size							)

								lowerCamelCase			=None
								lowerCamelCase			=None
								lowerCamelCase			=None
								if self.use_labels:
												lowerCamelCase			=ids_tensor([self.batch_size]				,	self.type_sequence_label_size							)
												lowerCamelCase			=ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels							)
												lowerCamelCase			=ids_tensor([self.batch_size]				,	self.num_choices							)

								lowerCamelCase			=self.get_config()

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


				def _snake_case    (						self							):
								return NystromformerConfig(
								    vocab_size=self.vocab_size				,	hidden_size=self.hidden_size				,	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				,	is_decoder=UpperCAmelCase_				,	initializer_range=self.initializer_range				,	)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=NystromformerModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase			=model(UpperCAmelCase_				,	attention_mask=UpperCAmelCase_				,	token_type_ids=UpperCAmelCase_							)
								lowerCamelCase			=model(UpperCAmelCase_				,	token_type_ids=UpperCAmelCase_							)
								lowerCamelCase			=model(UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=NystromformerForMaskedLM(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase			=model(UpperCAmelCase_				,	attention_mask=UpperCAmelCase_				,	token_type_ids=UpperCAmelCase_				,	labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size)							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=NystromformerForQuestionAnswering(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase			=model(
								    UpperCAmelCase_				,	attention_mask=UpperCAmelCase_				,	token_type_ids=UpperCAmelCase_				,	start_positions=UpperCAmelCase_				,	end_positions=UpperCAmelCase_				,	)
								self.parent.assertEqual(result.start_logits.shape				,	(self.batch_size, self.seq_length)							)
								self.parent.assertEqual(result.end_logits.shape				,	(self.batch_size, self.seq_length)							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=self.num_labels
								lowerCamelCase			=NystromformerForSequenceClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase			=model(UpperCAmelCase_				,	attention_mask=UpperCAmelCase_				,	token_type_ids=UpperCAmelCase_				,	labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels)							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=self.num_labels
								lowerCamelCase			=NystromformerForTokenClassification(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase			=model(UpperCAmelCase_				,	attention_mask=UpperCAmelCase_				,	token_type_ids=UpperCAmelCase_				,	labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels)							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=self.num_choices
								lowerCamelCase			=NystromformerForMultipleChoice(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase			=input_ids.unsqueeze(1							).expand(-1				,	self.num_choices				,	-1							).contiguous()
								lowerCamelCase			=token_type_ids.unsqueeze(1							).expand(-1				,	self.num_choices				,	-1							).contiguous()
								lowerCamelCase			=input_mask.unsqueeze(1							).expand(-1				,	self.num_choices				,	-1							).contiguous()
								lowerCamelCase			=model(
								    UpperCAmelCase_				,	attention_mask=UpperCAmelCase_				,	token_type_ids=UpperCAmelCase_				,	labels=UpperCAmelCase_				,	)
								self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_choices)							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase					
								)	,					(
								    lowerCamelCase					
								)	,					(
								    lowerCamelCase					
								)	,					(
								    lowerCamelCase					
								)	,					(
								    lowerCamelCase					
								)	,					(
								    lowerCamelCase					
								)	,					(
								    lowerCamelCase					
								)	,										
								)			=config_and_inputs
								lowerCamelCase			={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class  __lowercase       (  a      ,   a      ,   unittest.TestCase      ):
				__A								=			(
				    (
				        NystromformerModel,
				        NystromformerForMaskedLM,
				        NystromformerForMultipleChoice,
				        NystromformerForQuestionAnswering,
				        NystromformerForSequenceClassification,
				        NystromformerForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__A								=			(
				    {
				        """feature-extraction""": NystromformerModel,
				        """fill-mask""": NystromformerForMaskedLM,
				        """question-answering""": NystromformerForQuestionAnswering,
				        """text-classification""": NystromformerForSequenceClassification,
				        """token-classification""": NystromformerForTokenClassification,
				        """zero-shot""": NystromformerForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__A								=			False
				__A								=			False


				def _snake_case    (						self							):
								lowerCamelCase			=NystromformerModelTester(self							)
								lowerCamelCase			=ConfigTester(self				,	config_class=UpperCAmelCase_				,	hidden_size=37							)


				def _snake_case    (						self							):
								self.config_tester.run_common_tests()


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												lowerCamelCase			=type
												self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_							)


				@slow
				def _snake_case    (						self							):
								for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase			=NystromformerModel.from_pretrained(UpperCAmelCase_							)
												self.assertIsNotNone(UpperCAmelCase_							)




@require_torch
class  __lowercase       (  unittest.TestCase      ):


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""							)
								lowerCamelCase			=torch.tensor([[0, 1, 2, 3, 4, 5]]							)

								with torch.no_grad():
												lowerCamelCase			=model(UpperCAmelCase_							)[0]

								lowerCamelCase			=torch.Size((1, 6, 768)							)
								self.assertEqual(output.shape				,	UpperCAmelCase_							)

								lowerCamelCase			=torch.tensor(
								    [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]							)

								self.assertTrue(torch.allclose(output[:, :3, :3]				,	UpperCAmelCase_				,	atol=1E-4							)							)


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			="""the [MASK] of Belgium is Brussels"""

								lowerCamelCase			=AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""							)
								lowerCamelCase			=NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""							)

								lowerCamelCase			=tokenizer(UpperCAmelCase_				,	return_tensors="""pt"""							)

								with torch.no_grad():
												lowerCamelCase			=model(encoding.input_ids							).logits

								lowerCamelCase			=token_logits[:, 2, :].argmax(-1							)[0]

								self.assertEqual(tokenizer.decode(UpperCAmelCase_							)				,	"""capital"""							)












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

import requests





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





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


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase__  :     List[str]   			=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__  :     Dict   			=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}.")





