


from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__=1			,						lowerCAmelCase__=0			,						lowerCAmelCase__=2			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__="cls"			,						lowerCAmelCase__=False			,						lowerCAmelCase__=True			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(pad_token_id=lowerCAmelCase__			,						bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					project_dim
										SCREAMING_SNAKE_CASE_		:     List[str]					=					pooler_fn
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					learn_encoder
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_attention_mask







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						[r"""pooler""", r"""logit_scale"""]
							_UpperCAmelCase      =						[r"""position_ids""", r"""predictions.decoder.bias"""]
							_UpperCAmelCase      =						"""roberta"""
							_UpperCAmelCase      =						RobertaSeriesConfig






							def __init__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					XLMRobertaModel(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					nn.Linear(config.hidden_size			,						config.project_dim       )
										SCREAMING_SNAKE_CASE_		:     str					=					getattr(lowerCAmelCase__			,						'has_pre_transformation'			,						lowerCAmelCase__       )
										if self.has_pre_transformation:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(config.hidden_size			,						config.project_dim       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					nn.LayerNorm(config.hidden_size			,						eps=config.layer_norm_eps       )
										self.post_init()






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					return_dict if return_dict is not None else self.config.use_return_dict

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.base_model(
										    input_ids=lowerCAmelCase__			,						attention_mask=lowerCAmelCase__			,						token_type_ids=lowerCAmelCase__			,						position_ids=lowerCAmelCase__			,						head_mask=lowerCAmelCase__			,						inputs_embeds=lowerCAmelCase__			,						encoder_hidden_states=lowerCAmelCase__			,						encoder_attention_mask=lowerCAmelCase__			,						output_attentions=lowerCAmelCase__			,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states			,						return_dict=lowerCAmelCase__			,						)

										if self.has_pre_transformation:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					outputs['hidden_states'][-2]
													SCREAMING_SNAKE_CASE_		:     List[str]					=					self.pre_LN(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.transformation_pre(lowerCAmelCase__       )

													return TransformationModelOutput(
													    projection_state=lowerCAmelCase__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)
										else:
													SCREAMING_SNAKE_CASE_		:     int					=					self.transformation(outputs.last_hidden_state       )
													return TransformationModelOutput(
													    projection_state=lowerCAmelCase__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)









# 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 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""microsoft/speecht5_tts"""
							_UpperCAmelCase      =						(
							    """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."""
							)
							_UpperCAmelCase      =						"""text_reader"""
							_UpperCAmelCase      =						SpeechTaProcessor
							_UpperCAmelCase      =						SpeechTaForTextToSpeech
							_UpperCAmelCase      =						SpeechTaHifiGan

							_UpperCAmelCase      =						["""text"""]
							_UpperCAmelCase      =						["""audio"""]






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										if self.post_processor is None:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					'microsoft/speecht5_hifigan'
										super().setup()






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.pre_processor(text=lowerCAmelCase__			,						return_tensors='pt'			,						truncation=lowerCAmelCase__       )

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

													SCREAMING_SNAKE_CASE_		:     List[Any]					=					load_dataset('Matthijs/cmu-arctic-xvectors'			,						split='validation'       )
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.tensor(embeddings_dataset[7_3_0_5]['xvector']       ).unsqueeze(0       )

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										with torch.no_grad():
													return self.model.generate_speech(**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										with torch.no_grad():
													return self.post_processor(lowerCAmelCase__       ).cpu().detach()





