





'''simple docstring'''






import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_PRETRAINING_MAPPING,
					    NezhaForMaskedLM,
					    NezhaForMultipleChoice,
					    NezhaForNextSentencePrediction,
					    NezhaForPreTraining,
					    NezhaForQuestionAnswering,
					    NezhaForSequenceClassification,
					    NezhaForTokenClassification,
					    NezhaModel,
					)
					from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class 		lowerCamelCase  :



	def __init__(   self		,      a_		,      a_=13		,      a_=7		,      a_=True		,      a_=True		,      a_=True		,      a_=True		,      a_=99		,      a_=32		,      a_=5		,      a_=4		,      a_=37		,      a_="gelu"		,      a_=0.1		,      a_=0.1		,      a_=128		,      a_=32		,      a_=16		,      a_=2		,      a_=0.02		,      a_=3		,      a_=4		,      a_=None		,      ):
			lowerCAmelCase     :    Any						=   parent
			lowerCAmelCase     :    Optional[Any]						=   batch_size
			lowerCAmelCase     :    List[Any]						=   seq_length
			lowerCAmelCase     :    Optional[Any]						=   is_training
			lowerCAmelCase     :    Dict						=   use_input_mask
			lowerCAmelCase     :    Tuple						=   use_token_type_ids
			lowerCAmelCase     :    int						=   use_labels
			lowerCAmelCase     :    Optional[Any]						=   vocab_size
			lowerCAmelCase     :    Union[str, Any]						=   hidden_size
			lowerCAmelCase     :    Any						=   num_hidden_layers
			lowerCAmelCase     :    Union[str, Any]						=   num_attention_heads
			lowerCAmelCase     :    Union[str, Any]						=   intermediate_size
			lowerCAmelCase     :    str						=   hidden_act
			lowerCAmelCase     :    Optional[Any]						=   hidden_dropout_prob
			lowerCAmelCase     :    Dict						=   attention_probs_dropout_prob
			lowerCAmelCase     :    Dict						=   max_position_embeddings
			lowerCAmelCase     :    int						=   type_vocab_size
			lowerCAmelCase     :    List[Any]						=   type_sequence_label_size
			lowerCAmelCase     :    Union[str, Any]						=   initializer_range
			lowerCAmelCase     :    Union[str, Any]						=   num_labels
			lowerCAmelCase     :    Optional[int]						=   num_choices
			lowerCAmelCase     :    List[str]						=   scope



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size	)

			lowerCAmelCase     :    str						=   None
			if self.use_input_mask:
					lowerCAmelCase     :    int						=   random_attention_mask([self.batch_size, self.seq_length]	)

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

			lowerCAmelCase     :    Tuple						=   None
			lowerCAmelCase     :    List[Any]						=   None
			lowerCAmelCase     :    List[str]						=   None
			if self.use_labels:
					lowerCAmelCase     :    Optional[Any]						=   ids_tensor([self.batch_size]		,      self.type_sequence_label_size	)
					lowerCAmelCase     :    List[str]						=   ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels	)
					lowerCAmelCase     :    int						=   ids_tensor([self.batch_size]		,      self.num_choices	)

			lowerCAmelCase     :    List[str]						=   self.get_config()

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



	def   _lowerCamelCase    (   self	):
			return NezhaConfig(
			    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=_lowerCamelCase		,      initializer_range=self.initializer_range		,      )



	def   _lowerCamelCase    (   self	):
			(
			    (
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,			
			)     :    Dict						=   self.prepare_config_and_inputs()

			lowerCAmelCase     :    Dict						=   True
			lowerCAmelCase     :    Tuple						=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
			lowerCAmelCase     :    Any						=   ids_tensor([self.batch_size, self.seq_length]		,      vocab_size=2	)

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    input_mask,
			    sequence_labels,
			    token_labels,
			    choice_labels,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    List[str]						=   NezhaModel(config=_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    List[str]						=   model(_lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase	)
			lowerCAmelCase     :    int						=   model(_lowerCamelCase		,      token_type_ids=_lowerCamelCase	)
			lowerCAmelCase     :    Any						=   model(_lowerCamelCase	)
			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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      ):
			lowerCAmelCase     :    Any						=   True
			lowerCAmelCase     :    List[str]						=   NezhaModel(_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    List[str]						=   model(
			    _lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      encoder_hidden_states=_lowerCamelCase		,      encoder_attention_mask=_lowerCamelCase		,      )
			lowerCAmelCase     :    Optional[Any]						=   model(
			    _lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      encoder_hidden_states=_lowerCamelCase		,      )
			lowerCAmelCase     :    Optional[int]						=   model(_lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase	)
			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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   NezhaForMaskedLM(config=_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    str						=   model(_lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      labels=_lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   NezhaForNextSentencePrediction(config=_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    List[Any]						=   model(
			    _lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      labels=_lowerCamelCase		,      )
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, 2)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Tuple						=   NezhaForPreTraining(config=_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    List[str]						=   model(
			    _lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      labels=_lowerCamelCase		,      next_sentence_label=_lowerCamelCase		,      )
			self.parent.assertEqual(result.prediction_logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)	)
			self.parent.assertEqual(result.seq_relationship_logits.shape		,      (self.batch_size, 2)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    str						=   NezhaForQuestionAnswering(config=_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    Dict						=   model(
			    _lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      start_positions=_lowerCamelCase		,      end_positions=_lowerCamelCase		,      )
			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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Union[str, Any]						=   self.num_labels
			lowerCAmelCase     :    Union[str, Any]						=   NezhaForSequenceClassification(_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    Optional[Any]						=   model(_lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      labels=_lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    List[Any]						=   self.num_labels
			lowerCAmelCase     :    Union[str, Any]						=   NezhaForTokenClassification(config=_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    str						=   model(_lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      labels=_lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.num_labels)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Tuple						=   self.num_choices
			lowerCAmelCase     :    Optional[Any]						=   NezhaForMultipleChoice(config=_lowerCamelCase	)
			model.to(_lowerCamelCase	)
			model.eval()
			lowerCAmelCase     :    Tuple						=   input_ids.unsqueeze(1	).expand(-1		,      self.num_choices		,      -1	).contiguous()
			lowerCAmelCase     :    Optional[Any]						=   token_type_ids.unsqueeze(1	).expand(-1		,      self.num_choices		,      -1	).contiguous()
			lowerCAmelCase     :    str						=   input_mask.unsqueeze(1	).expand(-1		,      self.num_choices		,      -1	).contiguous()
			lowerCAmelCase     :    int						=   model(
			    _lowerCamelCase		,      attention_mask=_lowerCamelCase		,      token_type_ids=_lowerCamelCase		,      labels=_lowerCamelCase		,      )
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices)	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self.prepare_config_and_inputs()
			(
			    (
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,			
			)     :    Optional[Any]						=   config_and_inputs
			lowerCAmelCase     :    Tuple						=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
			return config, inputs_dict






@require_torch
class 		lowerCamelCase  (      __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   unittest.TestCase       ):
	snake_case_      =   (
	    (
	        NezhaModel,
	        NezhaForMaskedLM,
	        NezhaForMultipleChoice,
	        NezhaForNextSentencePrediction,
	        NezhaForPreTraining,
	        NezhaForQuestionAnswering,
	        NezhaForSequenceClassification,
	        NezhaForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case_      =   (
	    {
	        "feature-extraction": NezhaModel,
	        "fill-mask": NezhaForMaskedLM,
	        "question-answering": NezhaForQuestionAnswering,
	        "text-classification": NezhaForSequenceClassification,
	        "token-classification": NezhaForTokenClassification,
	        "zero-shot": NezhaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case_      =   True



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_=False	):
			lowerCAmelCase     :    Tuple						=   super()._prepare_for_class(_lowerCamelCase		,      _lowerCamelCase		,      return_labels=_lowerCamelCase	)

			if return_labels:
					if model_class in get_values(_lowerCamelCase	):
							lowerCAmelCase     :    int						=   torch.zeros(
							    (self.model_tester.batch_size, self.model_tester.seq_length)		,      dtype=torch.long		,      device=_lowerCamelCase	)
							lowerCAmelCase     :    List[str]						=   torch.zeros(
							    self.model_tester.batch_size		,      dtype=torch.long		,      device=_lowerCamelCase	)
			return inputs_dict



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   NezhaModelTester(self	)
			lowerCAmelCase     :    Optional[Any]						=   ConfigTester(self		,      config_class=_lowerCamelCase		,      hidden_size=37	)



	def   _lowerCamelCase    (   self	):
			self.config_tester.run_common_tests()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			# This regression test was failing with PyTorch < 1.3
			(
			    (
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,		(
			    lowerCAmelCase	
			)	,			
			)     :    List[Any]						=   self.model_tester.prepare_config_and_inputs_for_decoder()

			lowerCAmelCase     :    List[Any]						=   None

			self.model_tester.create_and_check_model_as_decoder(
			    _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      )



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_next_sequence_prediction(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase	)



	@slow
	def   _lowerCamelCase    (   self	):
			for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase     :    Union[str, Any]						=   NezhaModel.from_pretrained(_lowerCamelCase	)
					self.assertIsNotNone(_lowerCamelCase	)





	@slow
	@require_torch_gpu
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase	,		lowerCAmelCase     :    List[Any]						=   self.model_tester.prepare_config_and_inputs_for_common()
			for model_class in self.all_model_classes:
					# NezhaForMultipleChoice behaves incorrectly in JIT environments.
					if model_class == NezhaForMultipleChoice:
							return

					lowerCAmelCase     :    str						=   True
					lowerCAmelCase     :    Any						=   model_class(config=_lowerCamelCase	)

					lowerCAmelCase     :    str						=   self._prepare_for_class(_lowerCamelCase		,      _lowerCamelCase	)
					lowerCAmelCase     :    Any						=   torch.jit.trace(
					    _lowerCamelCase		,      (inputs_dict["input_ids"].to("cpu"	), inputs_dict["attention_mask"].to("cpu"	))	)

					with tempfile.TemporaryDirectory() as tmp:
							torch.jit.save(_lowerCamelCase		,      os.path.join(_lowerCamelCase		,      "bert.pt"	)	)
							lowerCAmelCase     :    Optional[int]						=   torch.jit.load(os.path.join(_lowerCamelCase		,      "bert.pt"	)		,      map_location=_lowerCamelCase	)
							loaded(inputs_dict["input_ids"].to(_lowerCamelCase	)		,      inputs_dict["attention_mask"].to(_lowerCamelCase	)	)






@require_torch
class 		lowerCamelCase  (      unittest.TestCase       ):



	@slow
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   NezhaModel.from_pretrained("sijunhe/nezha-cn-base"	)
			lowerCAmelCase     :    List[Any]						=   torch.tensor([[0, 1, 2, 3, 4, 5]]	)
			lowerCAmelCase     :    Optional[Any]						=   torch.tensor([[0, 1, 1, 1, 1, 1]]	)
			with torch.no_grad():
					lowerCAmelCase     :    Dict						=   model(_lowerCamelCase		,      attention_mask=_lowerCamelCase	)[0]
			lowerCAmelCase     :    Dict						=   torch.Size((1, 6, 768)	)
			self.assertEqual(output.shape		,      _lowerCamelCase	)
			lowerCAmelCase     :    int						=   torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]	)

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





	@slow
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"	)
			lowerCAmelCase     :    str						=   torch.tensor([[0, 1, 2, 3, 4, 5]]	)
			lowerCAmelCase     :    Optional[int]						=   torch.tensor([[1, 1, 1, 1, 1, 1]]	)
			with torch.no_grad():
					lowerCAmelCase     :    Union[str, Any]						=   model(_lowerCamelCase		,      attention_mask=_lowerCamelCase	)[0]
			lowerCAmelCase     :    str						=   torch.Size((1, 6, 21_128)	)
			self.assertEqual(output.shape		,      _lowerCamelCase	)
			lowerCAmelCase     :    Optional[int]						=   torch.tensor(
			    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]	)

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









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



