





'''simple docstring'''




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


lowercase__    :      int						=    logging.get_logger(__name__)






def        _lowerCAmelCase			(  __snake_case  : Union[str, Any] ,							__snake_case  : int ,							__snake_case  : List[str] ,							__snake_case  : Optional[Any]			)     ->     Tuple[int, int]:
     def constraint_to_multiple_of(__snake_case  : List[str] ,							__snake_case  : int ,							__snake_case  : Union[str, Any]=0 ,							__snake_case  : Dict=None			):
          __A						: Dict      =       round(val / multiple			) * multiple

          if max_val is not None and x > max_val:
               __A						: Optional[int]      =       math.floor(val / multiple			) * multiple

          if x < min_val:
               __A						: int      =       math.ceil(val / multiple			) * multiple

          return x

     __A						: Dict      =       (output_size, output_size) if isinstance(__lowerCamelCase ,							__lowerCamelCase			) else output_size

     __A						: str      =       get_image_size(__lowerCamelCase			)
     __A						: str      =       output_size

     # determine new height and width
     __A						: str      =       output_height / input_height
     __A						: Any      =       output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width			) < abs(1 - scale_height			):
               # fit width
               __A						: List[Any]      =       scale_width
          else:
               # fit height
               __A						: int      =       scale_height

     __A						: List[Any]      =       constraint_to_multiple_of(scale_height * input_height ,							multiple=__lowerCamelCase			)
     __A						: Dict      =       constraint_to_multiple_of(scale_width * input_width ,							multiple=__lowerCamelCase			)

     return (new_height, new_width)







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


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

          '''simple docstring'''






          super().__init__(**_snake_case)
          __A						: List[str]      =       size if size is not None else {'''height''': 384, '''width''': 384}
          __A						: Dict      =       get_size_dict(_snake_case)
          __A						: str      =       do_resize
          __A						: List[Any]      =       size
          __A						: Optional[Any]      =       keep_aspect_ratio
          __A						: Optional[int]      =       ensure_multiple_of
          __A						: Dict      =       resample
          __A						: Union[str, Any]      =       do_rescale
          __A						: Optional[int]      =       rescale_factor
          __A						: Any      =       do_normalize
          __A						: List[Any]      =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __A						: str      =       image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = False						,	_UpperCAmelCase = 1						,	_UpperCAmelCase = PILImageResampling.BICUBIC						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: str      =       get_size_dict(_snake_case)
          if "height" not in size or "width" not in size:
               raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}')
          __A						: List[str]      =       get_resize_output_image_size(
              _snake_case						,	output_size=(size['height'], size['width'])						,	keep_aspect_ratio=_snake_case						,	multiple=_snake_case						,	)
          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'''






          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						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Tuple      =       do_resize if do_resize is not None else self.do_resize
          __A						: Dict      =       size if size is not None else self.size
          __A						: Dict      =       get_size_dict(_snake_case)
          __A						: Union[str, Any]      =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
          __A						: Optional[Any]      =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
          __A						: List[Any]      =       resample if resample is not None else self.resample
          __A						: List[str]      =       do_rescale if do_rescale is not None else self.do_rescale
          __A						: Optional[Any]      =       rescale_factor if rescale_factor is not None else self.rescale_factor
          __A						: List[str]      =       do_normalize if do_normalize is not None else self.do_normalize
          __A						: Union[str, Any]      =       image_mean if image_mean is not None else self.image_mean
          __A						: Tuple      =       image_std if image_std is not None else self.image_std

          __A						: int      =       make_list_of_images(_snake_case)

          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.')

          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_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.')

          # All transformations expect numpy arrays.
          __A						: Tuple      =       [to_numpy_array(_snake_case) for image in images]

          if do_resize:
               __A						: Dict      =       [self.resize(image=_snake_case						,	size=_snake_case						,	resample=_snake_case) for image in images]

          if do_rescale:
               __A						: str      =       [self.rescale(image=_snake_case						,	scale=_snake_case) for image in images]

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

          __A						: Union[str, Any]      =       [to_channel_dimension_format(_snake_case						,	_snake_case) for image in images]

          __A						: Any      =       {'''pixel_values''': images}
          return BatchFeature(data=_snake_case						,	tensor_type=_snake_case)


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

          '''simple docstring'''






          __A						: List[str]      =       outputs.logits

          # Resize logits and compute semantic segmentation maps
          if target_sizes is not None:
               if len(_snake_case) != len(_snake_case):
                    raise ValueError(
                        'Make sure that you pass in as many target sizes as the batch dimension of the logits')

               if is_torch_tensor(_snake_case):
                    __A						: Optional[int]      =       target_sizes.numpy()

               __A						: Tuple      =       []

               for idx in range(len(_snake_case)):
                    __A						: int      =       torch.nn.functional.interpolate(
                        logits[idx].unsqueeze(dim=0)						,	size=target_sizes[idx]						,	mode='bilinear'						,	align_corners=_snake_case)
                    __A						: Any      =       resized_logits[0].argmax(dim=0)
                    semantic_segmentation.append(_snake_case)
          else:
               __A						: Optional[Any]      =       logits.argmax(dim=1)
               __A						: List[Any]      =       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

          return semantic_segmentation




"""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__)
