# 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__						(   UpperCamelCase_):
						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        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												if self.post_processor is None:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''microsoft/speecht5_hifigan'''
												super().setup()


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Optional[int]    ,		UpperCamelCase__	:				int=None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							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.'''						)

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

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


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





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



						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





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






from typing import TYPE_CHECKING

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


__UpperCamelCase		:       Any									=     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     ['MBartTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Dict									=     ['MBartTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MBartForCausalLM',
					    'MBartForConditionalGeneration',
					    'MBartForQuestionAnswering',
					    'MBartForSequenceClassification',
					    'MBartModel',
					    'MBartPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'TFMBartForConditionalGeneration',
					    'TFMBartModel',
					    'TFMBartPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'FlaxMBartForConditionalGeneration',
					    'FlaxMBartForQuestionAnswering',
					    'FlaxMBartForSequenceClassification',
					    'FlaxMBartModel',
					    'FlaxMBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart import MBartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart_fast import MBartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mbart import (
										    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MBartForCausalLM,
										    MBartForConditionalGeneration,
										    MBartForQuestionAnswering,
										    MBartForSequenceClassification,
										    MBartModel,
										    MBartPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mbart import (
										    FlaxMBartForConditionalGeneration,
										    FlaxMBartForQuestionAnswering,
										    FlaxMBartForSequenceClassification,
										    FlaxMBartModel,
										    FlaxMBartPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





