

'''simple docstring'''
import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
         BioGptForCausalLM,
         BioGptForSequenceClassification,
         BioGptForTokenClassification,
         BioGptModel,
         BioGptTokenizer,
     )
     from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class 							A__    :





  def __init__(		self      :	str							,					_a      :	List[Any]							,					_a      :	Optional[Any]=13							,					_a      :	Tuple=7							,					_a      :	int=True							,					_a      :	Tuple=True							,					_a      :	Any=False							,					_a      :	Tuple=True							,					_a      :	Optional[int]=99							,					_a      :	List[Any]=32							,					_a      :	Dict=5							,					_a      :	List[str]=4							,					_a      :	str=37							,					_a      :	Dict="gelu"							,					_a      :	Optional[int]=0.1							,					_a      :	int=0.1							,					_a      :	List[Any]=512							,					_a      :	int=16							,					_a      :	List[str]=2							,					_a      :	Union[str, Any]=0.02							,					_a      :	Any=3							,					_a      :	str=4							,					_a      :	str=None							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =parent
       _SCREAMING_SNAKE_CASE							    =batch_size
       _SCREAMING_SNAKE_CASE							    =seq_length
       _SCREAMING_SNAKE_CASE							    =is_training
       _SCREAMING_SNAKE_CASE							    =use_input_mask
       _SCREAMING_SNAKE_CASE							    =use_token_type_ids
       _SCREAMING_SNAKE_CASE							    =use_labels
       _SCREAMING_SNAKE_CASE							    =vocab_size
       _SCREAMING_SNAKE_CASE							    =hidden_size
       _SCREAMING_SNAKE_CASE							    =num_hidden_layers
       _SCREAMING_SNAKE_CASE							    =num_attention_heads
       _SCREAMING_SNAKE_CASE							    =intermediate_size
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _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							    =scope





  def 						A  (		self      :	Optional[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size )

       _SCREAMING_SNAKE_CASE							    =None
       if self.use_input_mask:
            _SCREAMING_SNAKE_CASE							    =random_attention_mask([self.batch_size, self.seq_length] )

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

       _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]							,					self.num_choices )

       _SCREAMING_SNAKE_CASE							    =self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





  def 						A  (		self      :	Optional[int] )	->    Any:





       '''simple docstring'''






       return BioGptConfig(
           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=_a							,					initializer_range=self.initializer_range							,					)





  def 						A  (		self      :	Tuple							,					_a      :	str							,					_a      :	str							,					_a      :	Optional[int]							,					_a      :	List[str]							,					_a      :	Dict							,					_a      :	Union[str, Any]							,					_a      :	int )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptModel(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a )
       _SCREAMING_SNAKE_CASE							    =model(_a )
       self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size) )





  def 						A  (		self      :	Union[str, Any]							,					_a      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	str							,					_a      :	Optional[int]							,					_a      :	Optional[Any]							,					_a      :	Dict							,					_a      :	Optional[int]							,					_a      :	List[Any]							,					_a      :	str							,					)	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptForCausalLM(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a )
       self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size) )





  def 						A  (		self      :	int							,					_a      :	Dict							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	str							,					_a      :	Optional[int]							,					*_a      :	str )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptModel(config=_a )
       model.to(_a )
       model.eval()

       # create attention mask
       _SCREAMING_SNAKE_CASE							    =torch.ones(input_ids.shape							,					dtype=torch.long							,					device=_a )
       _SCREAMING_SNAKE_CASE							    =self.seq_length // 2
       _SCREAMING_SNAKE_CASE							    =0

       # first forward pass
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a ).to_tuple()

       # create hypothetical next token and extent to next_input_ids
       _SCREAMING_SNAKE_CASE							    =ids_tensor((self.batch_size, 1)							,					config.vocab_size )

       # change a random masked slice from input_ids
       _SCREAMING_SNAKE_CASE							    =ids_tensor((1,)							,					_a ).item() + 1
       _SCREAMING_SNAKE_CASE							    =ids_tensor((self.batch_size, 1)							,					config.vocab_size ).squeeze(-1 )
       _SCREAMING_SNAKE_CASE							    =random_other_next_tokens

       # append to next input_ids and attn_mask
       _SCREAMING_SNAKE_CASE							    =torch.cat([input_ids, next_tokens]							,					dim=-1 )
       _SCREAMING_SNAKE_CASE							    =torch.cat(
           [attn_mask, torch.ones((attn_mask.shape[0], 1)							,					dtype=torch.long							,					device=_a )]							,					dim=1							,					)

       # get two different outputs
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a )['last_hidden_state']
       _SCREAMING_SNAKE_CASE							    =model(_a							,					past_key_values=_a							,					attention_mask=_a )['last_hidden_state']

       # select random slice
       _SCREAMING_SNAKE_CASE							    =ids_tensor((1,)							,					output_from_past.shape[-1] ).item()
       _SCREAMING_SNAKE_CASE							    =output_from_no_past[:, -1, random_slice_idx].detach()
       _SCREAMING_SNAKE_CASE							    =output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(_a							,					_a							,					atol=1e-3 ) )





  def 						A  (		self      :	Any							,					_a      :	Dict							,					_a      :	List[Any]							,					_a      :	str							,					_a      :	List[str]							,					_a      :	Any							,					*_a      :	List[str] )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptModel(config=_a ).to(_a ).eval()

       _SCREAMING_SNAKE_CASE							    =torch.ones(input_ids.shape							,					dtype=torch.long							,					device=_a )

       # first forward pass
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					use_cache=_a )

       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       _SCREAMING_SNAKE_CASE							    =ids_tensor((self.batch_size, 3)							,					config.vocab_size )
       _SCREAMING_SNAKE_CASE							    =ids_tensor((self.batch_size, 3)							,					2 )

       # append to next input_ids and
       _SCREAMING_SNAKE_CASE							    =torch.cat([input_ids, next_tokens]							,					dim=-1 )
       _SCREAMING_SNAKE_CASE							    =torch.cat([attention_mask, next_attn_mask]							,					dim=-1 )

       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a )['last_hidden_state']
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					past_key_values=_a )[
           'last_hidden_state'
       ]

       # select random slice
       _SCREAMING_SNAKE_CASE							    =ids_tensor((1,)							,					output_from_past.shape[-1] ).item()
       _SCREAMING_SNAKE_CASE							    =output_from_no_past[:, -3:, random_slice_idx].detach()
       _SCREAMING_SNAKE_CASE							    =output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(_a							,					_a							,					atol=1e-3 ) )





  def 						A  (		self      :	Optional[int]							,					_a      :	Dict							,					_a      :	Any							,					_a      :	List[Any]							,					_a      :	Tuple							,					_a      :	Dict							,					*_a      :	Optional[Any]							,					_a      :	int=False )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptForCausalLM(_a )
       model.to(_a )
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       _SCREAMING_SNAKE_CASE							    =model(_a							,					labels=_a )
       self.parent.assertEqual(result.loss.shape							,					() )
       self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size) )
       result.loss.backward()





  def 						A  (		self      :	int							,					_a      :	Dict							,					*_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptModel(_a )
       _SCREAMING_SNAKE_CASE							    =model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
       for key in model.state_dict().keys():
            if "c_proj" in key and "weight" in key:
                 self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std )							,					0.0_01 )
                 self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )							,					0.01 )





  def 						A  (		self      :	Optional[int]							,					_a      :	Dict							,					_a      :	int							,					_a      :	Tuple							,					_a      :	Union[str, Any]							,					_a      :	Optional[int]							,					*_a      :	int )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.num_labels
       _SCREAMING_SNAKE_CASE							    =BioGptForTokenClassification(_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					token_type_ids=_a )
       self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels) )





  def 						A  (		self      :	int )	->    Optional[Any]:





       '''simple docstring'''






       _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
       )    ,						
       )							    =config_and_inputs
       _SCREAMING_SNAKE_CASE							    ={'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class 							A__    (       A__     ,   A__     ,   A__     ,   unittest.TestCase  ):
  A__		 =				(
      (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
      if is_torch_available()
      else ()
  )
  A__		 =				(BioGptForCausalLM,) if is_torch_available() else ()
  A__		 =				(
      {
          'feature-extraction': BioGptModel,
          'text-classification': BioGptForSequenceClassification,
          'text-generation': BioGptForCausalLM,
          'token-classification': BioGptForTokenClassification,
          'zero-shot': BioGptForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  A__		 =				False





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptModelTester(self )
       _SCREAMING_SNAKE_CASE							    =ConfigTester(self							,					config_class=_a							,					hidden_size=37 )





  def 						A  (		self      :	str )	->    Tuple:





       '''simple docstring'''






       self.config_tester.run_common_tests()





  def 						A  (		self      :	Optional[int] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a )





  def 						A  (		self      :	Tuple )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            _SCREAMING_SNAKE_CASE							    =type
            self.model_tester.create_and_check_model(*_a )





  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_a )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*_a							,					gradient_checkpointing=_a )





  def 						A  (		self      :	Any )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_a )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*_a )





  def 						A  (		self      :	Any )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*_a )





  @slow
  def 						A  (		self      :	List[str] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
       model.to(_a )
       _SCREAMING_SNAKE_CASE							    =BioGptTokenizer.from_pretrained('microsoft/biogpt' )

       _SCREAMING_SNAKE_CASE							    ='left'

       # Define PAD Token = EOS Token = 50256
       _SCREAMING_SNAKE_CASE							    =tokenizer.eos_token
       _SCREAMING_SNAKE_CASE							    =model.config.eos_token_id

       # use different length sentences to test batching
       _SCREAMING_SNAKE_CASE							    =[
           'Hello, my dog is a little',
           'Today, I',
       ]

       _SCREAMING_SNAKE_CASE							    =tokenizer(_a							,					return_tensors='pt'							,					padding=_a )
       _SCREAMING_SNAKE_CASE							    =inputs['input_ids'].to(_a )

       _SCREAMING_SNAKE_CASE							    =model.generate(
           input_ids=_a							,					attention_mask=inputs['attention_mask'].to(_a )							,					)

       _SCREAMING_SNAKE_CASE							    =tokenizer(sentences[0]							,					return_tensors='pt' ).input_ids.to(_a )
       _SCREAMING_SNAKE_CASE							    =model.generate(input_ids=_a )

       _SCREAMING_SNAKE_CASE							    =inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
       _SCREAMING_SNAKE_CASE							    =tokenizer(sentences[1]							,					return_tensors='pt' ).input_ids.to(_a )
       _SCREAMING_SNAKE_CASE							    =model.generate(input_ids=_a							,					max_length=model.config.max_length - num_paddings )

       _SCREAMING_SNAKE_CASE							    =tokenizer.batch_decode(_a							,					skip_special_tokens=_a )
       _SCREAMING_SNAKE_CASE							    =tokenizer.decode(output_non_padded[0]							,					skip_special_tokens=_a )
       _SCREAMING_SNAKE_CASE							    =tokenizer.decode(output_padded[0]							,					skip_special_tokens=_a )

       _SCREAMING_SNAKE_CASE							    =[
           'Hello, my dog is a little bit bigger than a little bit.',
           'Today, I have a good idea of how to use the information',
       ]
       self.assertListEqual(_a							,					_a )
       self.assertListEqual(_a							,					[non_padded_sentence, padded_sentence] )





  @slow
  def 						A  (		self      :	str )	->    Any:





       '''simple docstring'''






       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE							    =BioGptModel.from_pretrained(_a )
            self.assertIsNotNone(_a )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs_for_common()
       _SCREAMING_SNAKE_CASE							    =3
       _SCREAMING_SNAKE_CASE							    =input_dict['input_ids']
       _SCREAMING_SNAKE_CASE							    =input_ids.ne(1 ).to(_a )
       _SCREAMING_SNAKE_CASE							    =ids_tensor([self.model_tester.batch_size]							,					self.model_tester.type_sequence_label_size )
       _SCREAMING_SNAKE_CASE							    =BioGptForSequenceClassification(_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					labels=_a )
       self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels) )





  def 						A  (		self      :	Tuple )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs_for_common()
       _SCREAMING_SNAKE_CASE							    =3
       _SCREAMING_SNAKE_CASE							    ='multi_label_classification'
       _SCREAMING_SNAKE_CASE							    =input_dict['input_ids']
       _SCREAMING_SNAKE_CASE							    =input_ids.ne(1 ).to(_a )
       _SCREAMING_SNAKE_CASE							    =ids_tensor(
           [self.model_tester.batch_size, config.num_labels]							,					self.model_tester.type_sequence_label_size ).to(torch.float )
       _SCREAMING_SNAKE_CASE							    =BioGptForSequenceClassification(_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					labels=_a )
       self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels) )







@require_torch
class 							A__    (       unittest.TestCase  ):





  @slow
  def 						A  (		self      :	Any )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
       _SCREAMING_SNAKE_CASE							    =torch.tensor([[2, 4805, 9, 656, 21]] )
       _SCREAMING_SNAKE_CASE							    =model(_a )[0]

       _SCREAMING_SNAKE_CASE							    =4_2384

       _SCREAMING_SNAKE_CASE							    =torch.Size((1, 5, vocab_size) )
       self.assertEqual(output.shape							,					_a )

       _SCREAMING_SNAKE_CASE							    =torch.tensor(
           [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]] )

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





  @slow
  def 						A  (		self      :	Any )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =BioGptTokenizer.from_pretrained('microsoft/biogpt' )
       _SCREAMING_SNAKE_CASE							    =BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
       model.to(_a )

       torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =tokenizer('COVID-19 is'							,					return_tensors='pt' ).to(_a )
       _SCREAMING_SNAKE_CASE							    =model.generate(
           **_a							,					min_length=100							,					max_length=1024							,					num_beams=5							,					early_stopping=_a							,					)
       _SCREAMING_SNAKE_CASE							    =tokenizer.decode(output_ids[0]							,					skip_special_tokens=_a )

       _SCREAMING_SNAKE_CASE							    =(
           'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
           ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
           ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
           ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
           ' more than 800,000 deaths.'
       )
       self.assertEqual(_a							,					_a )





"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_   						=   {
    '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_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    '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_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
