
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[Any]    					=       ["""input_values""", """attention_mask"""]

			def __init__(				self       :				Tuple  ,		snake_case_       :				Any=1  ,		snake_case_       :				str=1_6000  ,		snake_case_       :				Optional[Any]=0.0  ,		snake_case_       :				str=False  ,		snake_case_       :				List[str]=True  ,		**snake_case_       :				List[Any]  ,		):
							super().__init__(feature_size=snake_case_  ,		sampling_rate=snake_case_  ,		padding_value=snake_case_  ,		**snake_case_					)
							UpperCamelCase_:							Optional[Any]		=  return_attention_mask
							UpperCamelCase_:							Tuple		=  do_normalize

			@staticmethod
			def 					lowerCAmelCase__		(				snake_case_       :				List[np.ndarray]  ,		snake_case_       :				List[np.ndarray]  ,		snake_case_       :				float = 0.0					):
							if attention_mask is not None:
											UpperCamelCase_:							Tuple		=  np.array(snake_case_  ,		np.intaa					)
											UpperCamelCase_:							Tuple		=  []

											for vector, length in zip(snake_case_  ,		attention_mask.sum(-1					)					):
															UpperCamelCase_:							List[str]		=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
															if length < normed_slice.shape[0]:
																			UpperCamelCase_:							str		=  padding_value

															normed_input_values.append(snake_case_					)
							else:
											UpperCamelCase_:							str		=  [(x - x.mean()) / np.sqrt(x.var() + 1e-7					) for x in input_values]

							return normed_input_values




			def __call__(				self       :				Union[str, Any]  ,		snake_case_       :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,		snake_case_       :				Union[bool, str, PaddingStrategy] = False  ,		snake_case_       :				Optional[int] = None  ,		snake_case_       :				bool = False  ,		snake_case_       :				Optional[int] = None  ,		snake_case_       :				Optional[bool] = None  ,		snake_case_       :				Optional[Union[str, TensorType]] = None  ,		snake_case_       :				Optional[int] = None  ,		**snake_case_       :				int  ,		):

							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
															    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
															    f''' {self.sampling_rate} and not {sampling_rate}.'''					)
							else:
											logger.warning(
											    """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
											    """Failing to do so can result in silent errors that might be hard to debug."""					)

							UpperCamelCase_:							Optional[Any]		=  isinstance(snake_case_  ,		np.ndarray					) and len(raw_speech.shape					) > 1
							if is_batched_numpy and len(raw_speech.shape					) > 2:
											raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''					)
							UpperCamelCase_:							Optional[int]		=  is_batched_numpy or (
							    isinstance(snake_case_  ,		(list, tuple)					) and (isinstance(raw_speech[0]  ,		(np.ndarray, tuple, list)					))
							)

							# always return batch
							if not is_batched:
											UpperCamelCase_:							List[str]		=  [raw_speech]

							# convert into correct format for padding
							UpperCamelCase_:							List[Any]		=  BatchFeature({"""input_values""": raw_speech}					)

							UpperCamelCase_:							Optional[int]		=  self.pad(
							    snake_case_  ,		padding=snake_case_  ,		max_length=snake_case_  ,		truncation=snake_case_  ,		pad_to_multiple_of=snake_case_  ,		return_attention_mask=snake_case_  ,		)

							# convert input values to correct format
							UpperCamelCase_:							Union[str, Any]		=  padded_inputs["""input_values"""]
							if not isinstance(input_values[0]  ,		np.ndarray					):
											UpperCamelCase_:							Dict		=  [np.asarray(snake_case_  ,		dtype=np.floataa					) for array in input_values]
							elif (
							    not isinstance(snake_case_  ,		np.ndarray					)
							    and isinstance(input_values[0]  ,		np.ndarray					)
							    and input_values[0].dtype is np.dtype(np.floataa					)
							):
											UpperCamelCase_:							List[str]		=  [array.astype(np.floataa					) for array in input_values]
							elif isinstance(snake_case_  ,		np.ndarray					) and input_values.dtype is np.dtype(np.floataa					):
											UpperCamelCase_:							Optional[int]		=  input_values.astype(np.floataa					)

							# convert attention_mask to correct format
							UpperCamelCase_:							Union[str, Any]		=  padded_inputs.get("""attention_mask"""					)
							if attention_mask is not None:
											UpperCamelCase_:							Dict		=  [np.asarray(snake_case_  ,		dtype=np.intaa					) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if self.do_normalize:
											UpperCamelCase_:							Dict		=  (
											    attention_mask
											    if self._get_padding_strategies(snake_case_  ,		max_length=snake_case_					) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											UpperCamelCase_:							str		=  self.zero_mean_unit_var_norm(
											    padded_inputs["""input_values"""]  ,		attention_mask=snake_case_  ,		padding_value=self.padding_value					)

							if return_tensors is not None:
											UpperCamelCase_:							int		=  padded_inputs.convert_to_tensors(snake_case_					)

							return padded_inputs









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


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)


if is_vision_available():
					import PIL

class  a     (		UpperCAmelCase ):
						_lowercase			  =	["pixel_values"]


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







										'''simple docstring'''


										super().__init__(**A_		)

										_UpperCAmelCase				:						str       						=			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(A_  ,    (tuple, list)		):
														# The previous feature extractor size parameter was in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Dict       						=			get_size_dict(A_		)

										_UpperCAmelCase				:						int       						=			do_resize
										_UpperCAmelCase				:						Dict       						=			size
										_UpperCAmelCase				:						List[Any]       						=			resample
										_UpperCAmelCase				:						Union[str, Any]       						=			do_thumbnail
										_UpperCAmelCase				:						Optional[int]       						=			do_align_long_axis
										_UpperCAmelCase				:						Optional[Any]       						=			do_pad
										_UpperCAmelCase				:						int       						=			do_rescale
										_UpperCAmelCase				:						List[Any]       						=			rescale_factor
										_UpperCAmelCase				:						Union[str, Any]       						=			do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase				:						Dict       						=			image_std if image_std is not None else IMAGENET_STANDARD_STD


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







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						List[str]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Any       						=			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														_UpperCAmelCase				:						Optional[int]       						=			np.rotaa(A_  ,    3		)

										if data_format is not None:
														_UpperCAmelCase				:						List[str]       						=			to_channel_dimension_format(A_  ,    A_		)

										return image


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = False  ,    A_ = None  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			size["height"], size["width"]
										_UpperCAmelCase						,							_UpperCAmelCase				:						List[Any]       						=			get_image_size(A_		)

										_UpperCAmelCase				:						Optional[Any]       						=			output_width - input_width
										_UpperCAmelCase				:						Any       						=			output_height - input_height

										if random_padding:
														_UpperCAmelCase				:						List[str]       						=			np.random.randint(low=0  ,    high=delta_height + 1		)
														_UpperCAmelCase				:						Optional[int]       						=			np.random.randint(low=0  ,    high=delta_width + 1		)
										else:
														_UpperCAmelCase				:						Union[str, Any]       						=			delta_height // 2
														_UpperCAmelCase				:						str       						=			delta_width // 2

										_UpperCAmelCase				:						Optional[Any]       						=			delta_height - pad_top
										_UpperCAmelCase				:						List[Any]       						=			delta_width - pad_left

										_UpperCAmelCase				:						Optional[Any]       						=			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(A_  ,    A_  ,    data_format=A_		)


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."		)
										return self.pad_image(*A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										_UpperCAmelCase				:						Dict       						=			min(A_  ,    A_		)
										_UpperCAmelCase				:						Tuple       						=			min(A_  ,    A_		)

										if height == input_height and width == input_width:
														return image

										if input_height > input_width:
														_UpperCAmelCase				:						Any       						=			int(input_width * height / input_height		)
										elif input_width > input_height:
														_UpperCAmelCase				:						Any       						=			int(input_height * width / input_width		)

										return resize(
										    A_  ,    size=(height, width)  ,    resample=A_  ,    reducing_gap=2.0  ,    data_format=A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						List[str]       						=			min(size["height"]  ,    size["width"]		)
										_UpperCAmelCase				:						Optional[Any]       						=			get_resize_output_image_size(A_  ,    size=A_  ,    default_to_square=A_		)
										_UpperCAmelCase				:						str       						=			resize(A_  ,    size=A_  ,    resample=A_  ,    data_format=A_  ,    **A_		)
										return resized_image


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







										'''simple docstring'''


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


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







										'''simple docstring'''


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





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







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase				:						str       						=			size if size is not None else self.size
										if isinstance(A_  ,    (tuple, list)		):
														# Previous feature extractor had size in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Union[str, Any]       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						Dict       						=			resample if resample is not None else self.resample
										_UpperCAmelCase				:						str       						=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										_UpperCAmelCase				:						Optional[Any]       						=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										_UpperCAmelCase				:						List[str]       						=			do_pad if do_pad is not None else self.do_pad
										_UpperCAmelCase				:						Optional[Any]       						=			do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase				:						int       						=			rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase				:						List[str]       						=			do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase				:						List[Any]       						=			image_std if image_std is not None else self.image_std

										_UpperCAmelCase				:						str       						=			make_list_of_images(A_		)

										if not valid_images(A_		):
														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_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."		)

										if do_pad and size is None:
														raise ValueError("Size must be specified if do_pad 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.
										_UpperCAmelCase				:						Union[str, Any]       						=			[to_numpy_array(A_		) for image in images]

										if do_align_long_axis:
														_UpperCAmelCase				:						Tuple       						=			[self.align_long_axis(A_  ,    size=A_		) for image in images]

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

										if do_thumbnail:
														_UpperCAmelCase				:						Optional[Any]       						=			[self.thumbnail(image=A_  ,    size=A_		) for image in images]

										if do_pad:
														_UpperCAmelCase				:						List[Any]       						=			[self.pad_image(image=A_  ,    size=A_  ,    random_padding=A_		) for image in images]

										if do_rescale:
														_UpperCAmelCase				:						Tuple       						=			[self.rescale(image=A_  ,    scale=A_		) for image in images]

										if do_normalize:
														_UpperCAmelCase				:						int       						=			[self.normalize(image=A_  ,    mean=A_  ,    std=A_		) for image in images]

										_UpperCAmelCase				:						str       						=			[to_channel_dimension_format(A_  ,    A_		) for image in images]

										_UpperCAmelCase				:						Dict       						=			{"pixel_values": images}
										return BatchFeature(data=A_  ,    tensor_type=A_		)


