
"""simple docstring"""




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


a		:       Any     						=       logging.get_logger(__name__)



def     __magic_name__		(					UpperCamelCase     :		np.ndarray							,		UpperCamelCase     :		Union[int, Iterable[int]]							,		UpperCamelCase     :		bool							,		UpperCamelCase     :		int     )     ->						Union[str, Any]:




    def constraint_to_multiple_of(UpperCamelCase     :		str							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		List[str]=0							,		UpperCamelCase     :		List[str]=None     ):
        a__		=			round(val / multiple     ) * multiple

        if max_val is not None and x > max_val:
            a__		=			math.floor(val / multiple     ) * multiple

        if x < min_val:
            a__		=			math.ceil(val / multiple     ) * multiple

        return x

    a__		=			(output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE     ) else output_size

    a__	,     a__		=			get_image_size(_SCREAMING_SNAKE_CASE     )
    a__	,     a__		=			output_size

    # determine new height and width
    a__		=			output_height / input_height
    a__		=			output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width     ) < abs(1 - scale_height     ):
            # fit width
            a__		=			scale_width
        else:
            # fit height
            a__		=			scale_height

    a__		=			constraint_to_multiple_of(scale_height * input_height							,		multiple=_SCREAMING_SNAKE_CASE     )
    a__		=			constraint_to_multiple_of(scale_width * input_width							,		multiple=_SCREAMING_SNAKE_CASE     )

    return (new_height, new_width)







class     lowercase(a_       ):
     __snake_case:	Optional[Any]						=     ['''pixel_values''']



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







         """simple docstring"""



         super().__init__(**lowerCAmelCase_   )
         a__		=			size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
         a__		=			get_size_dict(lowerCAmelCase_   )
         a__		=			do_resize
         a__		=			size
         a__		=			keep_aspect_ratio
         a__		=			ensure_multiple_of
         a__		=			resample
         a__		=			do_rescale
         a__		=			rescale_factor
         a__		=			do_normalize
         a__		=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         a__		=			image_std if image_std is not None else IMAGENET_STANDARD_STD



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = 1     ,				__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     np.ndarray:







         """simple docstring"""



         a__		=			get_size_dict(lowerCAmelCase_   )
         if "height" not in size or "width" not in size:
             raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'   )
         a__		=			get_resize_output_image_size(
             lowerCAmelCase_     ,				output_size=(size['height'], size['width'])     ,				keep_aspect_ratio=lowerCAmelCase_     ,				multiple=lowerCAmelCase_     ,				)
         return resize(lowerCAmelCase_     ,				size=lowerCAmelCase_     ,				resample=lowerCAmelCase_     ,				data_format=lowerCAmelCase_     ,				**lowerCAmelCase_   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     List[Any]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     np.ndarray:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     PIL.Image.Image:







         """simple docstring"""



         a__		=			do_resize if do_resize is not None else self.do_resize
         a__		=			size if size is not None else self.size
         a__		=			get_size_dict(lowerCAmelCase_   )
         a__		=			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         a__		=			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         a__		=			resample if resample is not None else self.resample
         a__		=			do_rescale if do_rescale is not None else self.do_rescale
         a__		=			rescale_factor if rescale_factor is not None else self.rescale_factor
         a__		=			do_normalize if do_normalize is not None else self.do_normalize
         a__		=			image_mean if image_mean is not None else self.image_mean
         a__		=			image_std if image_std is not None else self.image_std

         a__		=			make_list_of_images(lowerCAmelCase_   )

         if not valid_images(lowerCAmelCase_   ):
             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_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.
         a__		=			[to_numpy_array(lowerCAmelCase_   ) for image in images]

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

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

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

         a__		=			[to_channel_dimension_format(lowerCAmelCase_     ,				lowerCAmelCase_   ) for image in images]

         a__		=			{'pixel_values': images}
         return BatchFeature(data=lowerCAmelCase_     ,				tensor_type=lowerCAmelCase_   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     List[str]:







         """simple docstring"""



         a__		=			outputs.logits

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

             if is_torch_tensor(lowerCAmelCase_   ):
                 a__		=			target_sizes.numpy()

             a__		=			[]

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

         return semantic_segmentation






'''simple docstring'''





# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  			=				re.compile(r'''([A-Z]+)([A-Z][a-z])''')
UpperCAmelCase__  			=				re.compile(r'''([a-z\d])([A-Z])''')

UpperCAmelCase__  			=				re.compile(r'''(?<!_)_(?!_)''')
UpperCAmelCase__  			=				re.compile(r'''(_{2,})''')

UpperCAmelCase__  			=				r'''^\w+(\.\w+)*$'''

UpperCAmelCase__  			=				r'''<>:/\|?*'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A=	_uppercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			__A=	_lowercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			return name.lower()






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	_single_underscore_re.split(_SCREAMING_SNAKE_CASE )
			__A=	[_multiple_underscores_re.split(_SCREAMING_SNAKE_CASE ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) if n != '' )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			return camelcase_to_snakecase(_SCREAMING_SNAKE_CASE )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			if not re.match(_split_re,_SCREAMING_SNAKE_CASE ):
						raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
			return f"""{filename_prefix_for_name(_SCREAMING_SNAKE_CASE )}-{split}"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Union[str, Any]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			if filetype_suffix:
						prefix += f""".{filetype_suffix}"""
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			return f"""{filepath}*"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[Any]=None,_SCREAMING_SNAKE_CASE					: List[str]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			if shard_lengths:
						__A=	len(_SCREAMING_SNAKE_CASE )
						__A=	[f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(_SCREAMING_SNAKE_CASE )]
						if filetype_suffix:
									__A=	[filename + f""".{filetype_suffix}""" for filename in filenames]
						return filenames
			else:
						__A=	prefix
						if filetype_suffix:
									filename += f""".{filetype_suffix}"""
						return [filename]



