import unittest

from transformers import MPNetConfig, 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 (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )

class    snake_case       :




  def __init__(   self  :						int  ,			a_  :						int  ,			a_  :						Union[str, Any]=13  ,			a_  :						List[str]=7  ,			a_  :						int=True  ,			a_  :						List[str]=True  ,			a_  :						List[Any]=False  ,			a_  :						Optional[Any]=True  ,			a_  :						Optional[Any]=99  ,			a_  :						List[Any]=64  ,			a_  :						Optional[int]=5  ,			a_  :						str=4  ,			a_  :						List[Any]=64  ,			a_  :						int="gelu"  ,			a_  :						List[str]=0.1  ,			a_  :						List[Any]=0.1  ,			a_  :						Optional[Any]=512  ,			a_  :						Union[str, Any]=16  ,			a_  :						int=2  ,			a_  :						Dict=0.02  ,			a_  :						Optional[Any]=3  ,			a_  :						List[Any]=4  ,			a_  :						Optional[int]=None  ,			)->							Any:




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




  def 						__lowercase(   self  :						Dict					)->							List[str]:




       """simple docstring"""
       return MPNetConfig.from_pretrained('microsoft/mpnet-base'					)




  def 						__lowercase(   self  :						Any					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		None
       if self.use_input_mask:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		random_attention_mask([self.batch_size, self.seq_length]					)

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

       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_config()
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




  def 						__lowercase(   self  :						Optional[int]					)->							Dict:




       """simple docstring"""
       return MPNetConfig(
           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  ,			initializer_range=self.initializer_range  ,			)




  def 						__lowercase(   self  :						int  ,			a_  :						Any  ,			a_  :						Dict  ,			a_  :						Dict  ,			a_  :						Optional[Any]  ,			a_  :						List[Any]  ,			a_  :						Optional[int]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		MPNetModel(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		model(a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		model(a_					)
       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 						__lowercase(   self  :						Tuple  ,			a_  :						int  ,			a_  :						Union[str, Any]  ,			a_  :						List[str]  ,			a_  :						Optional[Any]  ,			a_  :						Tuple  ,			a_  :						int					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		MPNetForQuestionAnswering(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		model(
           a_  ,			attention_mask=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 						__lowercase(   self  :						str  ,			a_  :						Union[str, Any]  ,			a_  :						Optional[int]  ,			a_  :						Dict  ,			a_  :						int  ,			a_  :						List[Any]  ,			a_  :						List[str]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.num_labels
       SCREAMING_SNAKE_CASE__					:					int      =		MPNetForSequenceClassification(a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Any      =		model(a_  ,			attention_mask=a_  ,			labels=a_					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_labels)					)




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						Union[str, Any]  ,			a_  :						Optional[Any]  ,			a_  :						Dict  ,			a_  :						Optional[int]  ,			a_  :						Union[str, Any]  ,			a_  :						int					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.num_choices
       SCREAMING_SNAKE_CASE__					:					Any      =		MPNetForMultipleChoice(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		input_ids.unsqueeze(1					).expand(-1  ,			self.num_choices  ,			-1					).contiguous()
       SCREAMING_SNAKE_CASE__					:					int      =		input_mask.unsqueeze(1					).expand(-1  ,			self.num_choices  ,			-1					).contiguous()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		model(
           a_  ,			attention_mask=a_  ,			labels=a_  ,			)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_choices)					)




  def 						__lowercase(   self  :						int  ,			a_  :						Optional[int]  ,			a_  :						Union[str, Any]  ,			a_  :						Any  ,			a_  :						int  ,			a_  :						List[str]  ,			a_  :						Any					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.num_labels
       SCREAMING_SNAKE_CASE__					:					Any      =		MPNetForTokenClassification(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					int      =		model(a_  ,			attention_mask=a_  ,			labels=a_					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.num_labels)					)




  def 						__lowercase(   self  :						str					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.prepare_config_and_inputs()
       ((SCREAMING_SNAKE_CASE__)      ,				(SCREAMING_SNAKE_CASE__)      ,				(SCREAMING_SNAKE_CASE__)      ,				(SCREAMING_SNAKE_CASE__)      ,				(SCREAMING_SNAKE_CASE__)      ,				(SCREAMING_SNAKE_CASE__))					:					Dict      =		config_and_inputs
       SCREAMING_SNAKE_CASE__					:					str      =		{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict




@require_torch
class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
      if is_torch_available()
      else ()
  )
  lowercase_								=  (
      {
          'feature-extraction': MPNetModel,
          'fill-mask': MPNetForMaskedLM,
          'question-answering': MPNetForQuestionAnswering,
          'text-classification': MPNetForSequenceClassification,
          'token-classification': MPNetForTokenClassification,
          'zero-shot': MPNetForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowercase_								=  False
  lowercase_								=  True




  def 						__lowercase(   self  :						Optional[Any]					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		MPNetModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		ConfigTester(self  ,			config_class=a_  ,			hidden_size=37					)




  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       self.config_tester.run_common_tests()




  def 						__lowercase(   self  :						int					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*a_					)




  def 						__lowercase(   self  :						Dict					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*a_					)




  def 						__lowercase(   self  :						str					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*a_					)




  def 						__lowercase(   self  :						List[str]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*a_					)




  def 						__lowercase(   self  :						Any					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*a_					)




@require_torch
class    snake_case       (   unittest.TestCase    ):




  @slow
  def 						__lowercase(   self  :						str					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		MPNetModel.from_pretrained('microsoft/mpnet-base'					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		model(a_					)[0]
       SCREAMING_SNAKE_CASE__					:					int      =		torch.Size((1, 11, 768)					)
       self.assertEqual(output.shape  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		torch.tensor(
           [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]					)
       # compare the actual values for a slice.
       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,
)


lowerCAmelCase__    :Tuple       =				{
    '''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:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''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

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
