
"""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  ( lowerCAmelCase			):
				a__:      Tuple          =    'microsoft/speecht5_tts'
				a__:      Optional[Any]          =    (
				    '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.'
				)
				a__:      Dict          =    'text_reader'
				a__:      Tuple          =    SpeechTaProcessor
				a__:      int          =    SpeechTaForTextToSpeech
				a__:      str          =    SpeechTaHifiGan

				a__:      Any          =    ['text']
				a__:      Union[str, Any]          =    ['audio']




				def        UpperCAmelCase__						(				self   ):
							if self.post_processor is None:
										lowerCamelCase_					=  '''microsoft/speecht5_hifigan'''
							super().setup()




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							lowerCamelCase_					=  self.pre_processor(text=UpperCAmelCase							,						return_tensors='''pt'''							,						truncation=UpperCAmelCase   )

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

										lowerCamelCase_					=  load_dataset('''Matthijs/cmu-arctic-xvectors'''							,						split='''validation'''   )
										lowerCamelCase_					=  torch.tensor(embeddings_dataset[7305]['''xvector''']   ).unsqueeze(0   )

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							with torch.no_grad():
										return self.model.generate_speech(**UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							with torch.no_grad():
										return self.post_processor(UpperCAmelCase   ).cpu().detach()








"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    Optional[int]				=      {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

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





