
# 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       _snake_case					(     snake_case				):
			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 			SCREAMING_SNAKE_CASE    (				self		):
				if self.post_processor is None:
					__magic_name__		:		Union[str, Any]	   =     "microsoft/speecht5_hifigan"
				super().setup()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None		):
				__magic_name__		:		Tuple	   =     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."		)

					__magic_name__		:		int	   =     load_dataset("Matthijs/cmu-arctic-xvectors"						,   split="validation"		)
					__magic_name__		:		Dict	   =     torch.tensor(embeddings_dataset[7_305]["xvector"]		).unsqueeze(0		)

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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				with torch.no_grad():
					return self.model.generate_speech(**_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				with torch.no_grad():
					return self.post_processor(_a		).cpu().detach()


from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
