"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__A  =				logging.get_logger(__name__)
class  _snake_case     (					a__		):
						snake_case__			  =			["pixel_values"]






						def __init__(       self      :			str							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Dict[str, int] = None							,					UpperCAmelCase      :			PILImageResampling = PIL.Image.BICUBIC							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Dict[str, int] = None							,					UpperCAmelCase      :			Union[int, float] = 1 / 255							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Union[float, List[float]]] = None							,					UpperCAmelCase      :			Optional[Union[float, List[float]]] = None							,					**UpperCAmelCase      :			str							,					):
													super().__init__(**UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     size if size is not None else {"height": 256, "width": 256}
													__lowerCamelCase					:		Any		   =     get_size_dict(UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     crop_size if crop_size is not None else {"height": 224, "width": 224}
													__lowerCamelCase					:		Tuple		   =     get_size_dict(UpperCAmelCase							,					param_name="crop_size"    )

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






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			np.ndarray							,					UpperCAmelCase      :			Dict[str, int]							,					UpperCAmelCase      :			PILImageResampling = PIL.Image.BICUBIC							,					UpperCAmelCase      :			Optional[Union[str, ChannelDimension]] = None							,					**UpperCAmelCase      :			List[str]							,					):
													__lowerCamelCase					:		int		   =     get_size_dict(UpperCAmelCase    )
													if "height" not in size or "width" not in size:
																				raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
													return resize(
													    UpperCAmelCase							,					size=(size["height"], size["width"])							,					resample=UpperCAmelCase							,					data_format=UpperCAmelCase							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			np.ndarray							,					UpperCAmelCase      :			Dict[str, int]							,					UpperCAmelCase      :			Optional[Union[str, ChannelDimension]] = None							,					**UpperCAmelCase      :			Dict							,					):
													__lowerCamelCase					:		Tuple		   =     get_size_dict(UpperCAmelCase    )
													if "height" not in size or "width" not in size:
																				raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
													return center_crop(UpperCAmelCase							,					size=(size["height"], size["width"])							,					data_format=UpperCAmelCase							,					**UpperCAmelCase    )






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






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







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			ImageInput							,					UpperCAmelCase      :			bool = None							,					UpperCAmelCase      :			Dict[str, int] = None							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			bool = None							,					UpperCAmelCase      :			Dict[str, int] = None							,					UpperCAmelCase      :			bool = None							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			bool = None							,					UpperCAmelCase      :			Optional[Union[float, List[float]]] = None							,					UpperCAmelCase      :			Optional[Union[float, List[float]]] = None							,					UpperCAmelCase      :			Optional[Union[str, TensorType]] = None							,					UpperCAmelCase      :			ChannelDimension = ChannelDimension.FIRST							,					**UpperCAmelCase      :			str							,					):
													__lowerCamelCase					:		Optional[Any]		   =     do_resize if do_resize is not None else self.do_resize
													__lowerCamelCase					:		Tuple		   =     resample if resample is not None else self.resample
													__lowerCamelCase					:		Any		   =     do_center_crop if do_center_crop is not None else self.do_center_crop
													__lowerCamelCase					:		List[str]		   =     do_rescale if do_rescale is not None else self.do_rescale
													__lowerCamelCase					:		Optional[int]		   =     rescale_factor if rescale_factor is not None else self.rescale_factor
													__lowerCamelCase					:		Optional[int]		   =     do_normalize if do_normalize is not None else self.do_normalize
													__lowerCamelCase					:		List[str]		   =     image_mean if image_mean is not None else self.image_mean
													__lowerCamelCase					:		Optional[int]		   =     image_std if image_std is not None else self.image_std

													__lowerCamelCase					:		Tuple		   =     size if size is not None else self.size
													__lowerCamelCase					:		Union[str, Any]		   =     get_size_dict(UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     crop_size if crop_size is not None else self.crop_size
													__lowerCamelCase					:		Dict		   =     get_size_dict(UpperCAmelCase							,					param_name="crop_size"    )

													__lowerCamelCase					:		Any		   =     make_list_of_images(UpperCAmelCase    )

													if not valid_images(UpperCAmelCase    ):
																				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_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.
													__lowerCamelCase					:		Optional[Any]		   =     [to_numpy_array(UpperCAmelCase    ) for image in images]

													if do_resize:
																				__lowerCamelCase					:		Optional[int]		   =     [self.resize(image=UpperCAmelCase							,					size=UpperCAmelCase							,					resample=UpperCAmelCase    ) for image in images]

													if do_center_crop:
																				__lowerCamelCase					:		Optional[int]		   =     [self.center_crop(image=UpperCAmelCase							,					size=UpperCAmelCase    ) for image in images]

													if do_rescale:
																				__lowerCamelCase					:		Optional[Any]		   =     [self.rescale(image=UpperCAmelCase							,					scale=UpperCAmelCase    ) for image in images]

													if do_normalize:
																				__lowerCamelCase					:		List[Any]		   =     [self.normalize(image=UpperCAmelCase							,					mean=UpperCAmelCase							,					std=UpperCAmelCase    ) for image in images]

													__lowerCamelCase					:		List[Any]		   =     [to_channel_dimension_format(UpperCAmelCase							,					UpperCAmelCase    ) for image in images]

													__lowerCamelCase					:		str		   =     {"pixel_values": images}
													return BatchFeature(data=UpperCAmelCase							,					tensor_type=UpperCAmelCase    )
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


__A  =				{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


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

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