
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     List[Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     float   ,					**__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					feature_size
												lowerCamelCase_							  =					sampling_rate
												lowerCamelCase_							  =					padding_value

												lowerCamelCase_							  =					kwargs.pop("""padding_side"""   ,					"""right"""   )
												lowerCamelCase_							  =					kwargs.pop("""return_attention_mask"""   ,					__UpperCamelCase   )

												super().__init__(**__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = True   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					):
												# If we have a list of dicts, let's convert it in a dict of lists
												# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
												if isinstance(__UpperCamelCase   ,					(list, tuple)   ) and isinstance(processed_features[0]   ,					(dict, BatchFeature)   ):
																			lowerCamelCase_							  =					{
																			    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																			}

												# The model's main input name, usually `input_values`, has be passed for padding
												if self.model_input_names[0] not in processed_features:
																			raise ValueError(
																			    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
																			    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
																			    F''' {list(processed_features.keys()   )}'''   )

												lowerCamelCase_							  =					processed_features[self.model_input_names[0]]
												lowerCamelCase_							  =					(
												    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
												)

												if len(__UpperCamelCase   ) == 0:
																			if return_attention_mask:
																										lowerCamelCase_							  =					[]
																			return processed_features

												# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
												# and rebuild them afterwards if no return_tensors is specified
												# Note that we lose the specific device the tensor may be on for PyTorch

												lowerCamelCase_							  =					required_input[0]
												if isinstance(__UpperCamelCase   ,					(list, tuple)   ):
																			# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																			lowerCamelCase_							  =					0
																			while len(required_input[index]   ) == 0:
																										index += 1
																			if index < len(__UpperCamelCase   ):
																										lowerCamelCase_							  =					required_input[index][0]

												if return_tensors is None:
																			if is_tf_tensor(__UpperCamelCase   ):
																										lowerCamelCase_							  =					"""tf"""
																			elif is_torch_tensor(__UpperCamelCase   ):
																										lowerCamelCase_							  =					"""pt"""
																			elif isinstance(__UpperCamelCase   ,					(int, float, list, tuple, np.ndarray)   ):
																										lowerCamelCase_							  =					"""np"""
																			else:
																										raise ValueError(
																										    F'''type of {first_element} unknown: {type(__UpperCamelCase   )}. '''
																										    """Should be one of a python, numpy, pytorch or tensorflow object."""   )

												for key, value in processed_features.items():
																			if isinstance(value[0]   ,					(int, float)   ):
																										lowerCamelCase_							  =					to_numpy(__UpperCamelCase   )
																			else:
																										lowerCamelCase_							  =					[to_numpy(__UpperCamelCase   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
												lowerCamelCase_							  =					self._get_padding_strategies(padding=__UpperCamelCase   ,					max_length=__UpperCamelCase   )

												lowerCamelCase_							  =					processed_features[self.model_input_names[0]]

												lowerCamelCase_							  =					len(__UpperCamelCase   )
												if not all(len(__UpperCamelCase   ) == batch_size for v in processed_features.values()   ):
																			raise ValueError("""Some items in the output dictionary have a different batch size than others."""   )

												lowerCamelCase_							  =					[]
												for i in range(__UpperCamelCase   ):
																			lowerCamelCase_							  =					{k: v[i] for k, v in processed_features.items()}
																			# truncation
																			lowerCamelCase_							  =					self._truncate(
																			    __UpperCamelCase   ,					max_length=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					)
																			truncated_inputs.append(__UpperCamelCase   )

												if padding_strategy == PaddingStrategy.LONGEST:
																			# make sure that `max_length` cannot be longer than the longest truncated length
																			lowerCamelCase_							  =					max(len(input_slice[self.model_input_names[0]]   ) for input_slice in truncated_inputs   )
																			lowerCamelCase_							  =					PaddingStrategy.MAX_LENGTH

												lowerCamelCase_							  =					{}
												for i in range(__UpperCamelCase   ):
																			# padding
																			lowerCamelCase_							  =					self._pad(
																			    truncated_inputs[i]   ,					max_length=__UpperCamelCase   ,					padding_strategy=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					)

																			for key, value in outputs.items():
																										if key not in batch_outputs:
																																	lowerCamelCase_							  =					[]
																										if value.dtype is np.dtype(np.floataa   ):
																																	lowerCamelCase_							  =					value.astype(np.floataa   )
																										batch_outputs[key].append(__UpperCamelCase   )

												return BatchFeature(__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Union[Dict[str, np.ndarray], BatchFeature]   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					):
												lowerCamelCase_							  =					processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																			lowerCamelCase_							  =					len(__UpperCamelCase   )

												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																			lowerCamelCase_							  =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												lowerCamelCase_							  =					padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCamelCase   ) < max_length

												if return_attention_mask and "attention_mask" not in processed_features:
																			lowerCamelCase_							  =					np.ones(len(__UpperCamelCase   )   ,					dtype=np.intaa   )

												if needs_to_be_padded:
																			lowerCamelCase_							  =					max_length - len(__UpperCamelCase   )
																			if self.padding_side == "right":
																										if return_attention_mask:
																																	lowerCamelCase_							  =					np.pad(
																																	    processed_features["""attention_mask"""]   ,					(0, difference)   )
																										lowerCamelCase_							  =					((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																										lowerCamelCase_							  =					np.pad(
																										    __UpperCamelCase   ,					__UpperCamelCase   ,					"""constant"""   ,					constant_values=self.padding_value   )
																			elif self.padding_side == "left":
																										if return_attention_mask:
																																	lowerCamelCase_							  =					np.pad(
																																	    processed_features["""attention_mask"""]   ,					(difference, 0)   )
																										lowerCamelCase_							  =					((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																										lowerCamelCase_							  =					np.pad(
																										    __UpperCamelCase   ,					__UpperCamelCase   ,					"""constant"""   ,					constant_values=self.padding_value   )
																			else:
																										raise ValueError("""Invalid padding strategy:""" + str(self.padding_side   )   )

												return processed_features

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Union[Dict[str, np.ndarray], BatchFeature]   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					):
												if not truncation:
																			return processed_features
												elif truncation and max_length is None:
																			raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""   )

												lowerCamelCase_							  =					processed_features[self.model_input_names[0]]

												# find `max_length` that fits `pad_to_multiple_of`
												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																			lowerCamelCase_							  =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												lowerCamelCase_							  =					len(__UpperCamelCase   ) > max_length

												if needs_to_be_truncated:
																			lowerCamelCase_							  =					processed_features[self.model_input_names[0]][:max_length]
																			if "attention_mask" in processed_features:
																										lowerCamelCase_							  =					processed_features["""attention_mask"""][:max_length]

												return processed_features



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Tuple=False   ,					__UpperCamelCase					:     Optional[int]=None   ):

												# Get padding strategy
												if padding is not False:
																			if padding is True:
																										lowerCamelCase_							  =					PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																			elif not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																										lowerCamelCase_							  =					PaddingStrategy(__UpperCamelCase   )
																			elif isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																										lowerCamelCase_							  =					padding
												else:
																			lowerCamelCase_							  =					PaddingStrategy.DO_NOT_PAD

												# Set max length if needed
												if max_length is None:
																			if padding_strategy == PaddingStrategy.MAX_LENGTH:
																										raise ValueError(
																										    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''   )

        # Test if we have a padding value
												if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																			raise ValueError(
																			    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
																			    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""   )

												return padding_strategy







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,
    get_resize_output_image_size,
    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


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''pixel_values''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BILINEAR   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[int, float] = 1 / 2_5_5   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(**__UpperCamelCase   )
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_8_4}
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					do_resize
												lowerCamelCase_							  =					size
												# Default value set here for backwards compatibility where the value in config is None
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
												lowerCamelCase_							  =					resample
												lowerCamelCase_							  =					do_rescale
												lowerCamelCase_							  =					rescale_factor
												lowerCamelCase_							  =					do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCamelCase_							  =					image_std if image_std is not None else IMAGENET_STANDARD_STD

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Dict[str, int]   ,					__UpperCamelCase					:     float   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BICUBIC   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
												if "shortest_edge" not in size:
																			raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
												lowerCamelCase_							  =					size["""shortest_edge"""]

												if shortest_edge < 3_8_4:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			lowerCamelCase_							  =					int(shortest_edge / crop_pct   )
																			lowerCamelCase_							  =					get_resize_output_image_size(__UpperCamelCase   ,					size=__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
																			lowerCamelCase_							  =					resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=__UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    __UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

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

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



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     ImageInput   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = 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					:     List[str]   ,					):
												lowerCamelCase_							  =					do_resize if do_resize is not None else self.do_resize
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else self.crop_pct
												lowerCamelCase_							  =					resample if resample is not None else self.resample
												lowerCamelCase_							  =					do_rescale if do_rescale is not None else self.do_rescale
												lowerCamelCase_							  =					rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCamelCase_							  =					do_normalize if do_normalize is not None else self.do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else self.image_mean
												lowerCamelCase_							  =					image_std if image_std is not None else self.image_std

												lowerCamelCase_							  =					size if size is not None else self.size
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																			raise ValueError("""crop_pct must be specified if size < 384."""   )

												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_							  =					[to_numpy_array(__UpperCamelCase   ) for image in images]

												if do_resize:
																			lowerCamelCase_							  =					[self.resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					crop_pct=__UpperCamelCase   ,					resample=__UpperCamelCase   ) for image in images]

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

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

												lowerCamelCase_							  =					[to_channel_dimension_format(__UpperCamelCase   ,					__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )





