
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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase       =					logging.get_logger(__name__)


if is_vision_available():
	import PIL







class 			A_  (     lowerCAmelCase__  ):




	'''simple docstring'''


	_UpperCamelCase	:	Optional[Any]									=			["""pixel_values"""]





	def __init__(			self						,       snake_case = True						,       snake_case = None						,       snake_case = PILImageResampling.BICUBIC						,       snake_case = True						,       snake_case = None						,       snake_case = True						,       snake_case = 1 / 255						,       snake_case = True						,       snake_case = None						,       snake_case = None						,       snake_case = True						,       **snake_case						,       ):
		super().__init__(**__UpperCAmelCase			)
		lowercase	    =       size if size is not None else {'shortest_edge': 224}
		lowercase	    =       get_size_dict(__UpperCAmelCase						,       default_to_square=__UpperCAmelCase			)
		lowercase	    =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
		lowercase	    =       get_size_dict(__UpperCAmelCase						,       default_to_square=__UpperCAmelCase						,       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 OPENAI_CLIP_MEAN
		lowercase	    =       image_std if image_std is not None else OPENAI_CLIP_STD
		lowercase	    =       do_convert_rgb





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case = PILImageResampling.BICUBIC						,       snake_case = None						,       **snake_case						,       ):
		lowercase	    =       get_size_dict(__UpperCAmelCase						,       default_to_square=__UpperCAmelCase			)
		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(__UpperCAmelCase						,       size=size['shortest_edge']						,       default_to_square=__UpperCAmelCase			)
		return resize(__UpperCAmelCase						,       size=__UpperCAmelCase						,       resample=__UpperCAmelCase						,       data_format=__UpperCAmelCase						,       **__UpperCAmelCase			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case = None						,       **snake_case						,       ):
		lowercase	    =       get_size_dict(__UpperCAmelCase			)
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''			)
		return center_crop(__UpperCAmelCase						,       size=(size['height'], size['width'])						,       data_format=__UpperCAmelCase						,       **__UpperCAmelCase			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case = None						,       **snake_case						,       ):
		return rescale(__UpperCAmelCase						,       scale=__UpperCAmelCase						,       data_format=__UpperCAmelCase						,       **__UpperCAmelCase			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case = None						,       **snake_case						,       ):
		return normalize(__UpperCAmelCase						,       mean=__UpperCAmelCase						,       std=__UpperCAmelCase						,       data_format=__UpperCAmelCase						,       **__UpperCAmelCase			)





	def       SCREAMING_SNAKE_CASE__		(			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 = None						,       snake_case = ChannelDimension.FIRST						,       **snake_case						,       ):
		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(__UpperCAmelCase						,       param_name='size'						,       default_to_square=__UpperCAmelCase			)
		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(__UpperCAmelCase						,       param_name='crop_size'						,       default_to_square=__UpperCAmelCase			)
		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	    =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

		lowercase	    =       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:
			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.'			)

		# PIL RGBA images are converted to RGB
		if do_convert_rgb:
			lowercase	    =       [convert_to_rgb(__UpperCAmelCase			) for image in images]

		# All transformations expect numpy arrays.
		lowercase	    =       [to_numpy_array(__UpperCAmelCase			) for image in images]

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

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

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

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

		lowercase	    =       [to_channel_dimension_format(__UpperCAmelCase						,       __UpperCAmelCase			) for image in images]

		lowercase	    =       {'pixel_values': images}
		return BatchFeature(data=__UpperCAmelCase						,       tensor_type=__UpperCAmelCase			)






def 		a__      (		_UpperCamelCase      :			list[int]				):
   if not numbers:
      return 0

   if not isinstance(_UpperCamelCase			,(list, tuple)				) or not all(
       isinstance(_UpperCamelCase			,_UpperCamelCase				) for number in numbers				):
      raise ValueError('''numbers must be an iterable of integers'''				)

   __lowerCamelCase    				=     __lowerCamelCase    				=     __lowerCamelCase    				=     numbers[0]

   for i in range(1			,len(_UpperCamelCase				)				):
      # update the maximum and minimum subarray products
      __lowerCamelCase    				=     numbers[i]
      if number < 0:
         __lowerCamelCase				,__lowerCamelCase    				=     min_till_now, max_till_now
      __lowerCamelCase    				=     max(_UpperCamelCase			,max_till_now * number				)
      __lowerCamelCase    				=     min(_UpperCamelCase			,min_till_now * number				)

      # update the maximum product found till now
      __lowerCamelCase    				=     max(_UpperCamelCase			,_UpperCamelCase				)

   return max_prod






