



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 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       Dict			,     lowercase	:       Tuple			,     )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				parent
									__lowercase					=				13
									__lowercase					=				7
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				False
									__lowercase					=				False
									__lowercase					=				False
									__lowercase					=				2
									__lowercase					=				99
									__lowercase					=				0
									__lowercase					=				32
									__lowercase					=				2
									__lowercase					=				4
									__lowercase					=				0.1
									__lowercase					=				0.1
									__lowercase					=				512
									__lowercase					=				16
									__lowercase					=				2
									__lowercase					=				0.02
									__lowercase					=				3
									__lowercase					=				4
									__lowercase					=				"""last"""
									__lowercase					=				True
									__lowercase					=				None
									__lowercase					=				0





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )
									__lowercase					=				random_attention_mask([self.batch_size, self.seq_length]			,     dtype=tf.floataa   )

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

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

									__lowercase					=				None
									__lowercase					=				None
									__lowercase					=				None
									if self.use_labels:
															__lowercase					=				ids_tensor([self.batch_size]			,     self.type_sequence_label_size   )
															__lowercase					=				ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels   )
															__lowercase					=				ids_tensor([self.batch_size]			,     2			,     dtype=tf.floataa   )
															__lowercase					=				ids_tensor([self.batch_size]			,     self.num_choices   )

									__lowercase					=				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	:       Dict			,     lowercase	:       Optional[Any]			,     lowercase	:       Optional[int]			,     lowercase	:       Union[str, Any]			,     lowercase	:       List[str]			,     lowercase	:       int			,     lowercase	:       List[Any]			,     lowercase	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Tuple			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				TFFlaubertModel(config=lowercase   )
									__lowercase					=				{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase					=				model(lowercase   )

									__lowercase					=				[input_ids, input_mask]
									__lowercase					=				model(lowercase   )
									self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Union[str, Any]			,     lowercase	:       List[str]			,     lowercase	:       List[str]			,     lowercase	:       Dict			,     lowercase	:       Optional[Any]			,     lowercase	:       Any			,     lowercase	:       str			,     lowercase	:       List[str]			,     lowercase	:       str			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				TFFlaubertWithLMHeadModel(lowercase   )

									__lowercase					=				{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase					=				model(lowercase   )

									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)   )





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       List[Any]			,     lowercase	:       str			,     lowercase	:       List[Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Dict			,     lowercase	:       int			,     lowercase	:       List[Any]			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				TFFlaubertForQuestionAnsweringSimple(lowercase   )

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

									__lowercase					=				model(lowercase   )

									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	:       int			,     lowercase	:       Any			,     lowercase	:       int			,     lowercase	:       Union[str, Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Optional[int]			,     lowercase	:       List[str]			,     lowercase	:       Tuple			,     lowercase	:       str			,     lowercase	:       List[str]			,     )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				TFFlaubertForSequenceClassification(lowercase   )

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

									__lowercase					=				model(lowercase   )

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





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Dict			,     lowercase	:       Union[str, Any]			,     lowercase	:       str			,     lowercase	:       List[str]			,     lowercase	:       Any			,     lowercase	:       Any			,     lowercase	:       int			,     lowercase	:       str			,     lowercase	:       Optional[Any]			,     )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				self.num_labels
									__lowercase					=				TFFlaubertForTokenClassification(config=lowercase   )
									__lowercase					=				{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									__lowercase					=				model(lowercase   )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)   )





			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       Optional[Any]			,     lowercase	:       str			,     lowercase	:       Dict			,     lowercase	:       str			,     lowercase	:       int			,     lowercase	:       int			,     lowercase	:       Any			,     lowercase	:       Tuple			,     lowercase	:       List[str]			,     )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				self.num_choices
									__lowercase					=				TFFlaubertForMultipleChoice(config=lowercase   )
									__lowercase					=				tf.tile(tf.expand_dims(lowercase			,     1   )			,     (1, self.num_choices, 1)   )
									__lowercase					=				tf.tile(tf.expand_dims(lowercase			,     1   )			,     (1, self.num_choices, 1)   )
									__lowercase					=				tf.tile(tf.expand_dims(lowercase			,     1   )			,     (1, self.num_choices, 1)   )
									__lowercase					=				{
									    """input_ids""": multiple_choice_inputs_ids,
									    """attention_mask""": multiple_choice_input_mask,
									    """token_type_ids""": multiple_choice_token_type_ids,
									}
									__lowercase					=				model(lowercase   )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_choices)   )







			def 		snake_case__			(				self	:       List[Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.prepare_config_and_inputs()
									(
									    (
									    __lowercase
									)	,			(
									    __lowercase
									)	,			(
									    __lowercase
									)	,			(
									    __lowercase
									)	,			(
									    __lowercase
									)	,			(
									    __lowercase
									)	,			(
									    __lowercase
									)	,			(
									    __lowercase
									)	,			(
									    __lowercase
									)	,			
									)					=				config_and_inputs
									__lowercase					=				{
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """langs""": token_type_ids,
									    """lengths""": input_lengths,
									}
									return config, inputs_dict





@require_tf
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Dict      =			(
			    (
			        TFFlaubertModel,
			        TFFlaubertWithLMHeadModel,
			        TFFlaubertForSequenceClassification,
			        TFFlaubertForQuestionAnsweringSimple,
			        TFFlaubertForTokenClassification,
			        TFFlaubertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			lowercase__      :  List[str]      =			(
			    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
			)  # TODO (PVP): Check other models whether language generation is also applicable
			lowercase__      :  Any      =			(
			    {
			        """feature-extraction""": TFFlaubertModel,
			        """fill-mask""": TFFlaubertWithLMHeadModel,
			        """question-answering""": TFFlaubertForQuestionAnsweringSimple,
			        """text-classification""": TFFlaubertForSequenceClassification,
			        """token-classification""": TFFlaubertForTokenClassification,
			        """zero-shot""": TFFlaubertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowercase__      :  str      =			False
			lowercase__      :  Dict      =			False





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       Any			,     lowercase	:       Dict			,     lowercase	:       Dict			,     lowercase	:       Tuple			,     lowercase	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







									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	:       Optional[Any]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				TFFlaubertModelTester(self   )
									__lowercase					=				ConfigTester(self			,     config_class=lowercase			,     emb_dim=37   )





			def 		snake_case__			(				self	:       Optional[int]   )						->					str:
									"""simple docstring"""







									self.config_tester.run_common_tests()





			def 		snake_case__			(				self	:       str   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*lowercase   )





			def 		snake_case__			(				self	:       Tuple   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*lowercase   )





			def 		snake_case__			(				self	:       Any   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*lowercase   )





			def 		snake_case__			(				self	:       int   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_token_classification(*lowercase   )





			def 		snake_case__			(				self	:       Any   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowercase   )







			@slow
			def 		snake_case__			(				self	:       str   )						->					Tuple:
									"""simple docstring"""







									for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__lowercase					=				TFFlaubertModel.from_pretrained(lowercase   )
															self.assertIsNotNone(lowercase   )





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






			"""simple docstring"""





			@slow
			def 		snake_case__			(				self	:       int   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""   )

									__lowercase					=				tf.convert_to_tensor(
									    [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]			,     dtype=tf.intaa			,     )  # "J'aime flaubert !"

									__lowercase					=				model(lowercase   )[0]
									__lowercase					=				tf.TensorShape((1, 8, 512)   )
									self.assertEqual(output.shape			,     lowercase   )
									# compare the actual values for a slice.
									__lowercase					=				tf.convert_to_tensor(
									    [
									        [
									            [-1.876_8773, -1.56_6555, 0.2707_2418],
									            [-1.692_0038, -0.587_3505, 1.932_9599],
									            [-2.956_3985, -1.699_3835, 1.797_2052],
									        ]
									    ]			,     dtype=tf.floataa			,     )

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










def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				sum(lowercase__   )

						__lowercase					=				[[False for x in range(s + 1   )] for y in range(n + 1   )]

						for i in range(1						,       n + 1   ):
												__lowercase					=				True

						for i in range(1						,       s + 1   ):
												__lowercase					=				False

						for i in range(1						,       n + 1   ):
												for j in range(1						,       s + 1   ):
																		__lowercase					=				dp[i][j - 1]

																		if arr[i - 1] <= j:
																								__lowercase					=				dp[i][j] or dp[i - 1][j - arr[i - 1]]

						for j in range(int(s / 2   )						,       -1						,       -1   ):
												if dp[n][j] is True:
																		__lowercase					=				s - 2 * j
																		break

						return diff





