






"""simple docstring"""
import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					Any      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					List[Any]=13      ,      lowerCAmelCase_   :					int=7      ,      lowerCAmelCase_   :					Tuple=True      ,      lowerCAmelCase_   :					Tuple=True      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					Dict=True      ,      lowerCAmelCase_   :					int=99      ,      lowerCAmelCase_   :					int=32      ,      lowerCAmelCase_   :					Any=5      ,      lowerCAmelCase_   :					Optional[int]=4      ,      lowerCAmelCase_   :					Union[str, Any]=37      ,      lowerCAmelCase_   :					Optional[int]="gelu"      ,      lowerCAmelCase_   :					List[Any]=0.1      ,      lowerCAmelCase_   :					str=0.1      ,      lowerCAmelCase_   :					Union[str, Any]=512      ,      lowerCAmelCase_   :					Dict=16      ,      lowerCAmelCase_   :					Optional[int]=2      ,      lowerCAmelCase_   :					Optional[int]=0.0_2      ,      lowerCAmelCase_   :					List[Any]=4      ,      )		->     List[str]:
				UpperCAmelCase_	:					Tuple     	=   parent
				UpperCAmelCase_	:					Optional[int]     	=   batch_size
				UpperCAmelCase_	:					Tuple     	=   seq_length
				UpperCAmelCase_	:					Optional[int]     	=   is_training
				UpperCAmelCase_	:					Any     	=   use_attention_mask
				UpperCAmelCase_	:					Tuple     	=   use_token_type_ids
				UpperCAmelCase_	:					Dict     	=   use_labels
				UpperCAmelCase_	:					int     	=   vocab_size
				UpperCAmelCase_	:					Optional[Any]     	=   hidden_size
				UpperCAmelCase_	:					Any     	=   num_hidden_layers
				UpperCAmelCase_	:					Dict     	=   num_attention_heads
				UpperCAmelCase_	:					int     	=   intermediate_size
				UpperCAmelCase_	:					List[str]     	=   hidden_act
				UpperCAmelCase_	:					Optional[Any]     	=   hidden_dropout_prob
				UpperCAmelCase_	:					Tuple     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					int     	=   max_position_embeddings
				UpperCAmelCase_	:					Union[str, Any]     	=   type_vocab_size
				UpperCAmelCase_	:					List[Any]     	=   type_sequence_label_size
				UpperCAmelCase_	:					Optional[int]     	=   initializer_range
				UpperCAmelCase_	:					str     	=   num_choices





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Dict:
				UpperCAmelCase_	:					List[str]     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

				UpperCAmelCase_	:					Dict     	=   None
				if self.use_attention_mask:
							UpperCAmelCase_	:					List[str]     	=   random_attention_mask([self.batch_size, self.seq_length]		)

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

				UpperCAmelCase_	:					Optional[Any]     	=   RobertaConfig(
				    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=lowercase_      ,      initializer_range=self.initializer_range      ,      )

				return config, input_ids, token_type_ids, attention_mask





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Any     	=   self.prepare_config_and_inputs()
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   config_and_inputs
				UpperCAmelCase_	:					Union[str, Any]     	=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Union[str, Any]:
				UpperCAmelCase_	:					str     	=   self.prepare_config_and_inputs()
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   config_and_inputs

				UpperCAmelCase_	:					str     	=   True
				UpperCAmelCase_	:					Any     	=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				UpperCAmelCase_	:					int     	=   ids_tensor([self.batch_size, self.seq_length]      ,      vocab_size=2		)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)







@require_flax
class      UpperCamelCase_			(SCREAMING_SNAKE_CASE_							,		unittest.TestCase	):
	__magic_name__       =							True

	__magic_name__       =							(
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   FlaxRobertaModelTester(self		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				for model_class_name in self.all_model_classes:
							UpperCAmelCase_	:					Optional[Any]     	=   model_class_name.from_pretrained("roberta-base"      ,      from_pt=lowercase_		)
							UpperCAmelCase_	:					List[str]     	=   model(np.ones((1, 1)		)		)
							self.assertIsNotNone(lowercase_		)





"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_            =    {
    '''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:
   SCREAMING_SNAKE_CASE_            =    ['''SpeechT5Tokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''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

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



