


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__			:      List[Any]								=logging.get_logger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""pixel_values"""]






							def __init__(      self			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = PILImageResampling.BICUBIC			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = 1 / 2_5_5			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
										SCREAMING_SNAKE_CASE_		:     Any					=					get_size_dict(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__			,						param_name='crop_size'       )

										SCREAMING_SNAKE_CASE_		:     int					=					do_resize
										SCREAMING_SNAKE_CASE_		:     Any					=					do_rescale
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					do_normalize
										SCREAMING_SNAKE_CASE_		:     str					=					do_center_crop
										SCREAMING_SNAKE_CASE_		:     List[str]					=					crop_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					size
										SCREAMING_SNAKE_CASE_		:     str					=					resample
										SCREAMING_SNAKE_CASE_		:     Tuple					=					rescale_factor
										SCREAMING_SNAKE_CASE_		:     Any					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										SCREAMING_SNAKE_CASE_		:     Any					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = PILImageResampling.BILINEAR			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					get_size_dict(lowerCAmelCase__       )

										if "shortest_edge" in size:
													SCREAMING_SNAKE_CASE_		:     int					=					get_resize_output_image_size(lowerCAmelCase__			,						size=size['shortest_edge']			,						default_to_square=lowerCAmelCase__       )
													# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
										elif "height" in size and "width" in size:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					(size['height'], size['width'])
										else:
													raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
										return resize(lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






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





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					get_size_dict(lowerCAmelCase__       )
										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(lowerCAmelCase__			,						size=(size['height'], size['width'])			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






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





										"""simple docstring"""




										return rescale(lowerCAmelCase__			,						scale=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






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





										"""simple docstring"""




										return normalize(lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = ChannelDimension.FIRST			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					do_resize if do_resize is not None else self.do_resize
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					do_rescale if do_rescale is not None else self.do_rescale
										SCREAMING_SNAKE_CASE_		:     List[str]					=					do_normalize if do_normalize is not None else self.do_normalize
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_center_crop if do_center_crop is not None else self.do_center_crop
										SCREAMING_SNAKE_CASE_		:     int					=					crop_size if crop_size is not None else self.crop_size
										SCREAMING_SNAKE_CASE_		:     Any					=					get_size_dict(lowerCAmelCase__			,						param_name='crop_size'			,						default_to_square=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					resample if resample is not None else self.resample
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					rescale_factor if rescale_factor is not None else self.rescale_factor
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_mean if image_mean is not None else self.image_mean
										SCREAMING_SNAKE_CASE_		:     str					=					image_std if image_std is not None else self.image_std

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					size if size is not None else self.size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					get_size_dict(lowerCAmelCase__       )

										if not is_batched(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     str					=					[images]

										if not valid_images(lowerCAmelCase__       ):
													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.'       )

										# All transformations expect numpy arrays.
										SCREAMING_SNAKE_CASE_		:     List[str]					=					[to_numpy_array(lowerCAmelCase__       ) for image in images]

										if do_resize:
													SCREAMING_SNAKE_CASE_		:     str					=					[self.resize(image=lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__       ) for image in images]

										if do_center_crop:
													SCREAMING_SNAKE_CASE_		:     int					=					[self.center_crop(image=lowerCAmelCase__			,						size=lowerCAmelCase__       ) for image in images]

										if do_rescale:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					[self.rescale(image=lowerCAmelCase__			,						scale=lowerCAmelCase__       ) for image in images]

										if do_normalize:
													SCREAMING_SNAKE_CASE_		:     str					=					[self.normalize(image=lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     int					=					[to_channel_dimension_format(lowerCAmelCase__			,						lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     Dict					=					{'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase__			,						tensor_type=lowerCAmelCase__       )









# 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()





