


from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


a			:			Any												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 2_5_6}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								__lowercase			       = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								__lowercase			       = get_size_dict(snake_case_			,							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 IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								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(snake_case_			,							size=size['''shortest_edge''']			,							default_to_square=snake_case_    )
								return resize(snake_case_			,							size=snake_case_			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_    )
								if "height" not in size or "width" not in size:
											raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'    )
								return center_crop(snake_case_			,							size=(size['''height'''], size['''width'''])			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_    ) ->      np.ndarray:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      int:







								'''simple docstring'''

								__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(snake_case_			,							default_to_square=snake_case_    )
								__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(snake_case_			,							param_name='''crop_size'''    )
								__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			       = 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			       = [to_numpy_array(snake_case_    ) for image in images]

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

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

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

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

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

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






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = outputs.logits

								# Resize logits and compute semantic segmentation maps
								if target_sizes is not None:
											if len(snake_case_    ) != len(snake_case_    ):
														raise ValueError(
														    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''    )

											if is_torch_tensor(snake_case_    ):
														__lowercase			       = target_sizes.numpy()

											__lowercase			       = []

											for idx in range(len(snake_case_    )    ):
														__lowercase			       = torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0    )			,							size=target_sizes[idx]			,							mode='''bilinear'''			,							align_corners=snake_case_    )
														__lowercase			       = resized_logits[0].argmax(dim=0    )
														semantic_segmentation.append(snake_case_    )
								else:
											__lowercase			       = logits.argmax(dim=1    )
											__lowercase			       = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

								return semantic_segmentation






a			:			Any												=					[
    '''Audio''',
    '''Array2D''',
    '''Array3D''',
    '''Array4D''',
    '''Array5D''',
    '''ClassLabel''',
    '''Features''',
    '''Sequence''',
    '''Value''',
    '''Image''',
    '''Translation''',
    '''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages


