




import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
				    FlaubertForMultipleChoice,
				    FlaubertForQuestionAnswering,
				    FlaubertForQuestionAnsweringSimple,
				    FlaubertForSequenceClassification,
				    FlaubertForTokenClassification,
				    FlaubertModel,
				    FlaubertWithLMHeadModel,
				)
				from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		_snake_case  (  A__					):






			def __init__(       self		,					a		,					a=13		,					a=7		,					a=True		,					a=True		,					a=True		,					a=True		,					a=True		,					a=False		,					a=False		,					a=False		,					a=2		,					a=99		,					a=0		,					a=32		,					a=5		,					a=4		,					a=0.1		,					a=0.1		,					a=512		,					a=12		,					a=2		,					a=0.02		,					a=3		,					a=4		,					a="last"		,					a=None		,					a=None		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= seq_length
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_input_lengths
						SCREAMING_SNAKE_CASE						= use_token_type_ids
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= gelu_activation
						SCREAMING_SNAKE_CASE						= sinusoidal_embeddings
						SCREAMING_SNAKE_CASE						= causal
						SCREAMING_SNAKE_CASE						= asm
						SCREAMING_SNAKE_CASE						= n_langs
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= n_special
						SCREAMING_SNAKE_CASE						= hidden_size
						SCREAMING_SNAKE_CASE						= num_hidden_layers
						SCREAMING_SNAKE_CASE						= num_attention_heads
						SCREAMING_SNAKE_CASE						= hidden_dropout_prob
						SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE						= max_position_embeddings
						SCREAMING_SNAKE_CASE						= type_vocab_size
						SCREAMING_SNAKE_CASE						= type_sequence_label_size
						SCREAMING_SNAKE_CASE						= initializer_range
						SCREAMING_SNAKE_CASE						= num_labels
						SCREAMING_SNAKE_CASE						= num_choices
						SCREAMING_SNAKE_CASE						= summary_type
						SCREAMING_SNAKE_CASE						= use_proj
						SCREAMING_SNAKE_CASE						= scope






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size)
						SCREAMING_SNAKE_CASE						= random_attention_mask([self.batch_size, self.seq_length])

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

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

						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None
						if self.use_labels:
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					self.type_sequence_label_size)
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.num_labels)
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					2).float()
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					self.num_choices)

						SCREAMING_SNAKE_CASE						= self.get_config()

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						return 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		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= FlaubertModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(a		,					lengths=a		,					langs=a)
						SCREAMING_SNAKE_CASE						= model(a		,					langs=a)
						SCREAMING_SNAKE_CASE						= model(a)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       int:
						SCREAMING_SNAKE_CASE						= FlaubertWithLMHeadModel(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a		,					token_type_ids=a		,					labels=a)
						self.parent.assertEqual(result.loss.shape		,					())
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= FlaubertForQuestionAnsweringSimple(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= model(a		,					start_positions=a		,					end_positions=a)
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       int:
						SCREAMING_SNAKE_CASE						= FlaubertForQuestionAnswering(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= model(
						    a		,					start_positions=a		,					end_positions=a		,					cls_index=a		,					is_impossible=a		,					p_mask=a		,					)

						SCREAMING_SNAKE_CASE						= model(
						    a		,					start_positions=a		,					end_positions=a		,					cls_index=a		,					is_impossible=a		,					)

						((SCREAMING_SNAKE_CASE						)				,												)						= result_with_labels.to_tuple()

						SCREAMING_SNAKE_CASE						= model(a		,					start_positions=a		,					end_positions=a)

						((SCREAMING_SNAKE_CASE						)				,												)						= result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape		,					())
						self.parent.assertEqual(result.start_top_log_probs.shape		,					(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(result.start_top_index.shape		,					(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(
						    result.end_top_log_probs.shape		,					(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(
						    result.end_top_index.shape		,					(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(result.cls_logits.shape		,					(self.batch_size,))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= FlaubertForSequenceClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a)
						SCREAMING_SNAKE_CASE						= model(a		,					labels=a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.num_labels
						SCREAMING_SNAKE_CASE						= FlaubertForTokenClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a		,					attention_mask=a		,					labels=a)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.num_choices
						SCREAMING_SNAKE_CASE						= FlaubertForMultipleChoice(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= input_ids.unsqueeze(1).expand(-1		,					self.num_choices		,					-1).contiguous()
						SCREAMING_SNAKE_CASE						= token_type_ids.unsqueeze(1).expand(-1		,					self.num_choices		,					-1).contiguous()
						SCREAMING_SNAKE_CASE						= input_mask.unsqueeze(1).expand(-1		,					self.num_choices		,					-1).contiguous()
						SCREAMING_SNAKE_CASE						= model(
						    a		,					attention_mask=a		,					token_type_ids=a		,					labels=a		,					)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices))







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,						(
						    SCREAMING_SNAKE_CASE						
						)				,												
						)						= config_and_inputs
						SCREAMING_SNAKE_CASE						= {
						    'input_ids': input_ids,
						    'token_type_ids': token_type_ids,
						    'lengths': input_lengths,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict



@require_torch
class 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    List[str]			     =							(
			    (
			        FlaubertModel,
			        FlaubertWithLMHeadModel,
			        FlaubertForQuestionAnswering,
			        FlaubertForQuestionAnsweringSimple,
			        FlaubertForSequenceClassification,
			        FlaubertForTokenClassification,
			        FlaubertForMultipleChoice,
			    )
			    if is_torch_available()
			    else ()
			)
			_lowercase		:    str			     =							(
			    {
			        '''feature-extraction''': FlaubertModel,
			        '''fill-mask''': FlaubertWithLMHeadModel,
			        '''question-answering''': FlaubertForQuestionAnsweringSimple,
			        '''text-classification''': FlaubertForSequenceClassification,
			        '''token-classification''': FlaubertForTokenClassification,
			        '''zero-shot''': FlaubertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a)      ->       str:
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a=False)      ->       int:
						SCREAMING_SNAKE_CASE						= super()._prepare_for_class(a		,					a		,					return_labels=a)

						if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
												SCREAMING_SNAKE_CASE						= torch.zeros(
												    self.model_tester.batch_size		,					dtype=torch.long		,					device=a)
												SCREAMING_SNAKE_CASE						= torch.zeros(
												    self.model_tester.batch_size		,					dtype=torch.long		,					device=a)

						return inputs_dict






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= FlaubertModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a		,					emb_dim=37)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						self.config_tester.run_common_tests()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_model(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_lm_head(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_simple_qa(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_qa(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_sequence_classif(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_token_classif(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_multiple_choice(*a)






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						= FlaubertModel.from_pretrained(a)
									self.assertIsNotNone(a)







			@slow
			@require_torch_gpu
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
												return

									SCREAMING_SNAKE_CASE						= True
									SCREAMING_SNAKE_CASE						= model_class(config=a)

									SCREAMING_SNAKE_CASE						= self._prepare_for_class(a		,					a)
									SCREAMING_SNAKE_CASE						= torch.jit.trace(
									    a		,					(inputs_dict['input_ids'].to('cpu'), inputs_dict['attention_mask'].to('cpu')))

									with tempfile.TemporaryDirectory() as tmp:
												torch.jit.save(a		,					os.path.join(a		,					'traced_model.pt'))
												SCREAMING_SNAKE_CASE						= torch.jit.load(os.path.join(a		,					'traced_model.pt')		,					map_location=a)
												loaded(inputs_dict['input_ids'].to(a)		,					inputs_dict['attention_mask'].to(a))



@require_torch
class 		_snake_case  (  unittest.TestCase					):






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= FlaubertModel.from_pretrained('flaubert/flaubert_base_cased')
						SCREAMING_SNAKE_CASE						= torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= model(a)[0]
						SCREAMING_SNAKE_CASE						= torch.Size((1, 11, 768))
						self.assertEqual(output.shape		,					a)
						SCREAMING_SNAKE_CASE						= torch.tensor(
						    [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]])

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


a_		:						int             =    {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




