

'''simple docstring'''






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      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    str							     =     ['pixel_values']






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

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






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






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	np.ndarray   ,lowercase__  :	Dict[str, int]   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	Optional[int]   ,):
   __lowercase				    =					get_size_dict(lowercase__     )
   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(lowercase__   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	np.ndarray   ,lowercase__  :	Union[int, float]   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	str   ,):
   return rescale(lowercase__   ,scale=lowercase__   ,data_format=lowercase__   ,**lowercase__     )






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






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	ImageInput   ,lowercase__  :	bool = None   ,lowercase__  :	Dict[str, int] = None   ,lowercase__  :	PILImageResampling = None   ,lowercase__  :	bool = None   ,lowercase__  :	int = None   ,lowercase__  :	bool = None   ,lowercase__  :	float = None   ,lowercase__  :	bool = None   ,lowercase__  :	Optional[Union[float, List[float]]] = None   ,lowercase__  :	Optional[Union[float, List[float]]] = None   ,lowercase__  :	bool = None   ,lowercase__  :	Optional[Union[str, TensorType]] = None   ,lowercase__  :	Optional[ChannelDimension] = ChannelDimension.FIRST   ,**lowercase__  :	Dict   ,):
   __lowercase				    =					do_resize if do_resize is not None else self.do_resize
   __lowercase				    =					size if size is not None else self.size
   __lowercase				    =					get_size_dict(lowercase__   ,param_name='''size'''   ,default_to_square=lowercase__     )
   __lowercase				    =					resample if resample is not None else self.resample
   __lowercase				    =					do_center_crop if do_center_crop is not None else self.do_center_crop
   __lowercase				    =					crop_size if crop_size is not None else self.crop_size
   __lowercase				    =					get_size_dict(lowercase__   ,param_name='''crop_size'''   ,default_to_square=lowercase__     )
   __lowercase				    =					do_rescale if do_rescale is not None else self.do_rescale
   __lowercase				    =					rescale_factor if rescale_factor is not None else self.rescale_factor
   __lowercase				    =					do_normalize if do_normalize is not None else self.do_normalize
   __lowercase				    =					image_mean if image_mean is not None else self.image_mean
   __lowercase				    =					image_std if image_std is not None else self.image_std
   __lowercase				    =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __lowercase				    =					make_list_of_images(lowercase__     )

   if not valid_images(lowercase__     ):
    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:
    __lowercase				    =					[convert_to_rgb(lowercase__     ) for image in images]

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

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

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

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

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

   __lowercase				    =					[to_channel_dimension_format(lowercase__   ,lowercase__     ) for image in images]

   __lowercase				    =					{'''pixel_values''': images}
   return BatchFeature(data=lowercase__   ,tensor_type=lowercase__     )



import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'MCTCTFeatureExtractor'
	lowerCAmelCase__   =					'AutoTokenizer'



	def __init__(       self		,    lowercase		,    lowercase					)							->			str:
					super().__init__(lowercase		,    lowercase					)
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False



	def __call__(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase		,    **lowercase					)

					if "raw_speech" in kwargs:
									warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."					)
									lowerCamelCase_         =     kwargs.pop("raw_speech"					)
					else:
									lowerCamelCase_         =     kwargs.pop("audio"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("sampling_rate"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("text"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process."					)

					if audio is not None:
									lowerCamelCase_         =     self.feature_extractor(lowercase		,    *lowercase		,    sampling_rate=lowercase		,    **lowercase					)
					if text is not None:
									lowerCamelCase_         =     self.tokenizer(lowercase		,    **lowercase					)

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_         =     encodings["input_ids"]
									return inputs



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					return self.tokenizer.batch_decode(*lowercase		,    **lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor.pad(*lowercase		,    **lowercase					)

					lowerCamelCase_         =     kwargs.pop("input_features"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("labels"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if input_features is not None:
									lowerCamelCase_         =     self.feature_extractor.pad(lowercase		,    *lowercase		,    **lowercase					)
					if labels is not None:
									lowerCamelCase_         =     self.tokenizer.pad(lowercase		,    **lowercase					)

					if labels is None:
									return input_features
					elif input_features is None:
									return labels
					else:
									lowerCamelCase_         =     labels["input_ids"]
									return input_features



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					return self.tokenizer.decode(*lowercase		,    **lowercase					)



	@contextmanager
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your audio inputs, or in a separate call."					)
					lowerCamelCase_         =     True
					lowerCamelCase_         =     self.tokenizer
					yield
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False


