





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

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






def        _lowerCAmelCase			(  __snake_case  : Optional[Any]			)     ->     List[List[ImageInput]]:
     if isinstance(__lowerCamelCase ,							(list, tuple)			) and isinstance(videos[0] ,							(list, tuple)			) and is_valid_image(videos[0][0]			):
          return videos

     elif isinstance(__lowerCamelCase ,							(list, tuple)			) and is_valid_image(videos[0]			):
          return [videos]

     elif is_valid_image(__lowerCamelCase			):
          return [[videos]]

     raise ValueError(f'Could not make batched video from {videos}'			)







class  SCREAMING_SNAKE_CASE    (A_							):
     lowerCAmelCase					=       ["pixel_values"]


     def __init__(						self						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	_UpperCAmelCase = PILImageResampling.BILINEAR						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = 1 / 255						,	_UpperCAmelCase = True						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(**_snake_case)
          __A						: int      =       size if size is not None else {'''shortest_edge''': 256}
          __A						: Union[str, Any]      =       get_size_dict(_snake_case						,	default_to_square=_snake_case)
          __A						: List[str]      =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          __A						: Optional[Any]      =       get_size_dict(_snake_case						,	param_name='crop_size')

          __A						: List[Any]      =       do_resize
          __A						: Optional[int]      =       size
          __A						: Union[str, Any]      =       do_center_crop
          __A						: int      =       crop_size
          __A						: List[str]      =       resample
          __A						: int      =       do_rescale
          __A						: Tuple      =       rescale_factor
          __A						: List[Any]      =       offset
          __A						: Optional[int]      =       do_normalize
          __A						: List[Any]      =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __A						: Union[str, Any]      =       image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = PILImageResampling.BILINEAR						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Optional[int]      =       get_size_dict(_snake_case						,	default_to_square=_snake_case)
          if "shortest_edge" in size:
               __A						: Optional[int]      =       get_resize_output_image_size(_snake_case						,	size['shortest_edge']						,	default_to_square=_snake_case)
          elif "height" in size and "width" in size:
               __A						: Optional[Any]      =       (size['''height'''], size['''width'''])
          else:
               raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
          return resize(_snake_case						,	size=_snake_case						,	resample=_snake_case						,	data_format=_snake_case						,	**_snake_case)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Dict      =       get_size_dict(_snake_case)
          if "height" not in size or "width" not in size:
               raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
          return center_crop(_snake_case						,	size=(size['height'], size['width'])						,	data_format=_snake_case						,	**_snake_case)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: List[Any]      =       image.astype(np.floataa)
          if offset:
               __A						: List[str]      =       image - (scale / 2)
          return rescale(_snake_case						,	scale=_snake_case						,	data_format=_snake_case						,	**_snake_case)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = ChannelDimension.FIRST						,	):

          '''simple docstring'''






          if do_resize and size is None or resample is None:
               raise ValueError('Size and resample 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.')

          if offset and not do_rescale:
               raise ValueError('For offset, do_rescale must also be set to True.')

          # All transformations expect numpy arrays.
          __A						: Dict      =       to_numpy_array(_snake_case)

          if do_resize:
               __A						: Union[str, Any]      =       self.resize(image=_snake_case						,	size=_snake_case						,	resample=_snake_case)

          if do_center_crop:
               __A						: Optional[Any]      =       self.center_crop(_snake_case						,	size=_snake_case)

          if do_rescale:
               __A						: List[Any]      =       self.rescale(image=_snake_case						,	scale=_snake_case						,	offset=_snake_case)

          if do_normalize:
               __A						: List[Any]      =       self.normalize(image=_snake_case						,	mean=_snake_case						,	std=_snake_case)

          __A						: List[str]      =       to_channel_dimension_format(_snake_case						,	_snake_case)
          return image


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = ChannelDimension.FIRST						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Union[str, Any]      =       do_resize if do_resize is not None else self.do_resize
          __A						: int      =       resample if resample is not None else self.resample
          __A						: List[Any]      =       do_center_crop if do_center_crop is not None else self.do_center_crop
          __A						: str      =       do_rescale if do_rescale is not None else self.do_rescale
          __A						: int      =       rescale_factor if rescale_factor is not None else self.rescale_factor
          __A						: Optional[Any]      =       offset if offset is not None else self.offset
          __A						: Optional[Any]      =       do_normalize if do_normalize is not None else self.do_normalize
          __A						: Any      =       image_mean if image_mean is not None else self.image_mean
          __A						: List[str]      =       image_std if image_std is not None else self.image_std

          __A						: Union[str, Any]      =       size if size is not None else self.size
          __A						: Union[str, Any]      =       get_size_dict(_snake_case						,	default_to_square=_snake_case)
          __A						: Tuple      =       crop_size if crop_size is not None else self.crop_size
          __A						: Union[str, Any]      =       get_size_dict(_snake_case						,	param_name='crop_size')

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

          __A						: Any      =       make_batched(_snake_case)

          __A						: Optional[int]      =       [
              [
                  self._preprocess_image(
                      image=_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						,	offset=_snake_case						,	do_normalize=_snake_case						,	image_mean=_snake_case						,	image_std=_snake_case						,	data_format=_snake_case						,	)
                  for img in video
              ]
              for video in videos
          ]

          __A						: Dict      =       {'''pixel_values''': videos}
          return BatchFeature(data=_snake_case						,	tensor_type=_snake_case)




"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_   						=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
