





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import (
							    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    FlaubertConfig,
							    TFFlaubertForMultipleChoice,
							    TFFlaubertForQuestionAnsweringSimple,
							    TFFlaubertForSequenceClassification,
							    TFFlaubertForTokenClassification,
							    TFFlaubertModel,
							    TFFlaubertWithLMHeadModel,
							)

class  __lowercase       :


				def __init__(						self				,	UpperCAmelCase_				,	):
								lowerCamelCase			=parent
								lowerCamelCase			=13
								lowerCamelCase			=7
								lowerCamelCase			=True
								lowerCamelCase			=True
								lowerCamelCase			=True
								lowerCamelCase			=True
								lowerCamelCase			=True
								lowerCamelCase			=False
								lowerCamelCase			=False
								lowerCamelCase			=False
								lowerCamelCase			=2
								lowerCamelCase			=99
								lowerCamelCase			=0
								lowerCamelCase			=32
								lowerCamelCase			=2
								lowerCamelCase			=4
								lowerCamelCase			=0.1
								lowerCamelCase			=0.1
								lowerCamelCase			=512
								lowerCamelCase			=16
								lowerCamelCase			=2
								lowerCamelCase			=0.0_2
								lowerCamelCase			=3
								lowerCamelCase			=4
								lowerCamelCase			="""last"""
								lowerCamelCase			=True
								lowerCamelCase			=None
								lowerCamelCase			=0


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

								lowerCamelCase			=None
								if self.use_input_lengths:
												lowerCamelCase			=(
												    ids_tensor([self.batch_size]				,	vocab_size=2							) + self.seq_length - 2
												)  # small variation of seq_length

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

								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]				,	2				,	dtype=tf.floataa							)
												lowerCamelCase			=ids_tensor([self.batch_size]				,	self.num_choices							)

								lowerCamelCase			=FlaubertConfig(
								    vocab_size=self.vocab_size				,	n_special=self.n_special				,	emb_dim=self.hidden_size				,	n_layers=self.num_hidden_layers				,	n_heads=self.num_attention_heads				,	dropout=self.hidden_dropout_prob				,	attention_dropout=self.attention_probs_dropout_prob				,	gelu_activation=self.gelu_activation				,	sinusoidal_embeddings=self.sinusoidal_embeddings				,	asm=self.asm				,	causal=self.causal				,	n_langs=self.n_langs				,	max_position_embeddings=self.max_position_embeddings				,	initializer_range=self.initializer_range				,	summary_type=self.summary_type				,	use_proj=self.use_proj				,	bos_token_id=self.bos_token_id				,	)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_lengths,
								    sequence_labels,
								    token_labels,
								    is_impossible_labels,
								    choice_labels,
								    input_mask,
								)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	):
								lowerCamelCase			=TFFlaubertModel(config=__snake_case							)
								lowerCamelCase			={"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
								lowerCamelCase			=model(__snake_case							)

								lowerCamelCase			=[input_ids, input_mask]
								lowerCamelCase			=model(__snake_case							)
								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_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	):
								lowerCamelCase			=TFFlaubertWithLMHeadModel(__snake_case							)

								lowerCamelCase			={"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
								lowerCamelCase			=model(__snake_case							)

								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_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	):
								lowerCamelCase			=TFFlaubertForQuestionAnsweringSimple(__snake_case							)

								lowerCamelCase			={"""input_ids""": input_ids, """lengths""": input_lengths}

								lowerCamelCase			=model(__snake_case							)

								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_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	):
								lowerCamelCase			=TFFlaubertForSequenceClassification(__snake_case							)

								lowerCamelCase			={"""input_ids""": input_ids, """lengths""": input_lengths}

								lowerCamelCase			=model(__snake_case							)

								self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.type_sequence_label_size)							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	):
								lowerCamelCase			=self.num_labels
								lowerCamelCase			=TFFlaubertForTokenClassification(config=__snake_case							)
								lowerCamelCase			={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCamelCase			=model(__snake_case							)
								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_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	):
								lowerCamelCase			=self.num_choices
								lowerCamelCase			=TFFlaubertForMultipleChoice(config=__snake_case							)
								lowerCamelCase			=tf.tile(tf.expand_dims(__snake_case				,	1							)				,	(1, self.num_choices, 1)							)
								lowerCamelCase			=tf.tile(tf.expand_dims(__snake_case				,	1							)				,	(1, self.num_choices, 1)							)
								lowerCamelCase			=tf.tile(tf.expand_dims(__snake_case				,	1							)				,	(1, self.num_choices, 1)							)
								lowerCamelCase			={
								    """input_ids""": multiple_choice_inputs_ids,
								    """attention_mask""": multiple_choice_input_mask,
								    """token_type_ids""": multiple_choice_token_type_ids,
								}
								lowerCamelCase			=model(__snake_case							)
								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 
								)	,					(
								    lowerCamelCase 
								)	,					(
								    lowerCamelCase 
								)	,					 
								)			=config_and_inputs
								lowerCamelCase			={
								    """input_ids""": input_ids,
								    """token_type_ids""": token_type_ids,
								    """langs""": token_type_ids,
								    """lengths""": input_lengths,
								}
								return config, inputs_dict




@require_tf
class  __lowercase       (  lowerCamelCase_      ,   lowerCamelCase_      ,   unittest.TestCase      ):
				__A								=			(
				    (
				        TFFlaubertModel,
				        TFFlaubertWithLMHeadModel,
				        TFFlaubertForSequenceClassification,
				        TFFlaubertForQuestionAnsweringSimple,
				        TFFlaubertForTokenClassification,
				        TFFlaubertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__A								=			(
				    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				__A								=			(
				    {
				        '''feature-extraction''': TFFlaubertModel,
				        '''fill-mask''': TFFlaubertWithLMHeadModel,
				        '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
				        '''text-classification''': TFFlaubertForSequenceClassification,
				        '''token-classification''': TFFlaubertForTokenClassification,
				        '''zero-shot''': TFFlaubertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__A								=			False
				__A								=			False


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("""Fast"""							)
								):
												# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
												# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
												# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
												return True

								return False


				def _snake_case    (						self							):
								lowerCamelCase			=TFFlaubertModelTester(self							)
								lowerCamelCase			=ConfigTester(self				,	config_class=__snake_case				,	emb_dim=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_flaubert_model(*__snake_case							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_lm_head(*__snake_case							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_qa(*__snake_case							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_sequence_classif(*__snake_case							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_token_classification(*__snake_case							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_multiple_choice(*__snake_case							)


				@slow
				def _snake_case    (						self							):
								for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase			=TFFlaubertModel.from_pretrained(__snake_case							)
												self.assertIsNotNone(__snake_case							)




@require_tf
@require_sentencepiece
@require_tokenizers
class  __lowercase       (  unittest.TestCase      ):


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""							)

								lowerCamelCase			=tf.convert_to_tensor(
								    [[0, 158, 735, 2592, 1424, 6727, 82, 1]]				,	dtype=tf.intaa				,	)  # "J'aime flaubert !"

								lowerCamelCase			=model(__snake_case							)[0]
								lowerCamelCase			=tf.TensorShape((1, 8, 512)							)
								self.assertEqual(output.shape				,	__snake_case							)
								# compare the actual values for a slice.
								lowerCamelCase			=tf.convert_to_tensor(
								    [
								        [
								            [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
								            [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
								            [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
								        ]
								    ]				,	dtype=tf.floataa				,	)

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()				,	expected_slice.numpy()				,	atol=1E-4							)							)












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}.")





