



"""simple docstring"""







from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Any   	=				["pixel_values"]







		def __init__(	self						:  int			,_snake_case						:  bool = True			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  PILImageResampling = PILImageResampling.BICUBIC			,_snake_case						:  bool = True			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  bool = True			,_snake_case						:  Union[int, float] = 1 / 255			,_snake_case						:  bool = True			,_snake_case						:  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN			,_snake_case						:  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD			,**_snake_case						:  Optional[int]			,)					->		None:






				"""simple docstring"""



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

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







		def 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  np.ndarray			,_snake_case						:  Dict[str, int]			,_snake_case						:  PILImageResampling = PILImageResampling.BICUBIC			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  List[Any]			,)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    List[str]				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
						lowercase__   :    Optional[Any]				=			int((256 / 224) * size['''shortest_edge''']						)
						lowercase__   :    List[str]				=			get_resize_output_image_size(_snake_case			,size=_snake_case			,default_to_square=_snake_case						)
						lowercase__   :    List[str]				=			{'''height''': output_size[0], '''width''': output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
						raise ValueError(
						    f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""						)
				return resize(
				    _snake_case			,size=(size_dict['''height'''], size_dict['''width'''])			,resample=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  np.ndarray			,_snake_case						:  Dict[str, int]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  List[str]			,)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    List[str]				=			get_size_dict(_snake_case						)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""						)
				return center_crop(_snake_case			,size=(size['''height'''], size['''width'''])			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  np.ndarray			,_snake_case						:  Union[int, float]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Any			,)					->		np.ndarray:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  np.ndarray			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Tuple			,)					->		np.ndarray:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  ImageInput			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[Dict[str, int]] = None			,_snake_case						:  PILImageResampling = None			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[Dict[str, int]] = None			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[float] = None			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[Union[float, Iterable[float]]] = None			,_snake_case						:  Optional[Union[float, Iterable[float]]] = None			,_snake_case						:  Optional[TensorType] = None			,_snake_case						:  ChannelDimension = ChannelDimension.FIRST			,**_snake_case						:  Dict			,)					->		BatchFeature:






				"""simple docstring"""



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

				lowercase__   :    Optional[Any]				=			size if size is not None else self.size
				lowercase__   :    Tuple				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)
				lowercase__   :    Optional[Any]				=			crop_size if crop_size is not None else self.crop_size
				lowercase__   :    int				=			get_size_dict(_snake_case			,param_name='''crop_size'''						)

				lowercase__   :    Union[str, Any]				=			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:
						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.'''						)

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

				if do_resize:
						lowercase__   :    Any				=			[self.resize(_snake_case			,_snake_case			,_snake_case						) for image in images]

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

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

				if do_normalize:
						lowercase__   :    List[str]				=			[self.normalize(_snake_case			,_snake_case			,_snake_case						) for image in images]

				lowercase__   :    int				=			[to_channel_dimension_format(_snake_case			,_snake_case						) for image in images]

				lowercase__   :    Union[str, Any]				=			{'''pixel_values''': images}
				return BatchFeature(data=_snake_case			,tensor_type=_snake_case						)








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowercase  							=					{"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}


if TYPE_CHECKING:
	from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





