

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase_       	=	logging.get_logger(__name__)


if is_vision_available():
					import PIL





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   str									=				["pixel_values"]







			def __init__(     self :				List[str]							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				Dict[str, int] = None							,   _UpperCamelCase :				PILImageResampling = PILImageResampling.BICUBIC							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				Dict[str, int] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				Union[int, float] = 1 / 2_5_5							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				Optional[Union[float, List[float]]] = None							,   _UpperCamelCase :				Optional[Union[float, List[float]]] = None							,   _UpperCamelCase :				bool = True							,   **_UpperCamelCase :				Union[str, Any]							,   )      ->None:
									super().__init__(**_UpperCamelCase							)
									snake_case_   =	size if size is not None else {'''shortest_edge''': 2_2_4}
									snake_case_   =	get_size_dict(_UpperCamelCase							,   default_to_square=_UpperCamelCase							)
									snake_case_   =	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
									snake_case_   =	get_size_dict(_UpperCamelCase							,   default_to_square=_UpperCamelCase							,   param_name='''crop_size'''							)

									snake_case_   =	do_resize
									snake_case_   =	size
									snake_case_   =	resample
									snake_case_   =	do_center_crop
									snake_case_   =	crop_size
									snake_case_   =	do_rescale
									snake_case_   =	rescale_factor
									snake_case_   =	do_normalize
									snake_case_   =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									snake_case_   =	image_std if image_std is not None else OPENAI_CLIP_STD
									snake_case_   =	do_convert_rgb







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Dict[str, int]							,   _UpperCamelCase :				PILImageResampling = PILImageResampling.BICUBIC							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							,   **_UpperCamelCase :				Optional[Any]							,   )      ->np.ndarray:
									snake_case_   =	get_size_dict(_UpperCamelCase							,   default_to_square=_UpperCamelCase							)
									if "shortest_edge" not in size:
															raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
									snake_case_   =	get_resize_output_image_size(_UpperCamelCase							,   size=size['''shortest_edge''']							,   default_to_square=_UpperCamelCase							)
									return resize(_UpperCamelCase							,   size=_UpperCamelCase							,   resample=_UpperCamelCase							,   data_format=_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Dict[str, int]							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							,   **_UpperCamelCase :				int							,   )      ->np.ndarray:
									snake_case_   =	get_size_dict(_UpperCamelCase							)
									if "height" not in size or "width" not in size:
															raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
									return center_crop(_UpperCamelCase							,   size=(size['''height'''], size['''width'''])							,   data_format=_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Union[int, float]							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							,   **_UpperCamelCase :				str							,   )      ->Optional[Any]:
									return rescale(_UpperCamelCase							,   scale=_UpperCamelCase							,   data_format=_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Union[float, List[float]]							,   _UpperCamelCase :				Union[float, List[float]]							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							,   **_UpperCamelCase :				Optional[Any]							,   )      ->np.ndarray:
									return normalize(_UpperCamelCase							,   mean=_UpperCamelCase							,   std=_UpperCamelCase							,   data_format=_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				ImageInput							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				Dict[str, int] = None							,   _UpperCamelCase :				PILImageResampling = None							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				int = None							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				float = None							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				Optional[Union[float, List[float]]] = None							,   _UpperCamelCase :				Optional[Union[float, List[float]]] = None							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				Optional[Union[str, TensorType]] = None							,   _UpperCamelCase :				Optional[ChannelDimension] = ChannelDimension.FIRST							,   **_UpperCamelCase :				Tuple							,   )      ->PIL.Image.Image:
									snake_case_   =	do_resize if do_resize is not None else self.do_resize
									snake_case_   =	size if size is not None else self.size
									snake_case_   =	get_size_dict(_UpperCamelCase							,   param_name='''size'''							,   default_to_square=_UpperCamelCase							)
									snake_case_   =	resample if resample is not None else self.resample
									snake_case_   =	do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case_   =	crop_size if crop_size is not None else self.crop_size
									snake_case_   =	get_size_dict(_UpperCamelCase							,   param_name='''crop_size'''							,   default_to_square=_UpperCamelCase							)
									snake_case_   =	do_rescale if do_rescale is not None else self.do_rescale
									snake_case_   =	rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case_   =	do_normalize if do_normalize is not None else self.do_normalize
									snake_case_   =	image_mean if image_mean is not None else self.image_mean
									snake_case_   =	image_std if image_std is not None else self.image_std
									snake_case_   =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									snake_case_   =	make_list_of_images(_UpperCamelCase							)

									if not valid_images(_UpperCamelCase							):
															raise ValueError(
															    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
															    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

									if do_resize and size is None:
															raise ValueError('''Size must be specified if do_resize is True.'''							)

									if do_center_crop and crop_size is None:
															raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)

									if do_rescale and rescale_factor is None:
															raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

									if do_normalize and (image_mean is None or image_std is None):
															raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															snake_case_   =	[convert_to_rgb(_UpperCamelCase							) for image in images]

									# All transformations expect numpy arrays.
									snake_case_   =	[to_numpy_array(_UpperCamelCase							) for image in images]

									if do_resize:
															snake_case_   =	[self.resize(image=_UpperCamelCase							,   size=_UpperCamelCase							,   resample=_UpperCamelCase							) for image in images]

									if do_center_crop:
															snake_case_   =	[self.center_crop(image=_UpperCamelCase							,   size=_UpperCamelCase							) for image in images]

									if do_rescale:
															snake_case_   =	[self.rescale(image=_UpperCamelCase							,   scale=_UpperCamelCase							) for image in images]

									if do_normalize:
															snake_case_   =	[self.normalize(image=_UpperCamelCase							,   mean=_UpperCamelCase							,   std=_UpperCamelCase							) for image in images]

									snake_case_   =	[to_channel_dimension_format(_UpperCamelCase							,   _UpperCamelCase							) for image in images]

									snake_case_   =	{'''pixel_values''': images}
									return BatchFeature(data=_UpperCamelCase							,   tensor_type=_UpperCamelCase							)

# 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		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "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		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

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

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

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







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