





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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
		import PIL

if is_torch_available():
		import torch


lowercase_		:					Optional[Any]      						=    logging.get_logger(__name__)


class       lowercase					(				a_  ):



						"""simple docstring"""

						_UpperCamelCase	:						str    		=						["pixel_values"]







						def __init__(     self      :		str			,					lowerCamelCase_      :		bool = True			,					lowerCamelCase_      :		Dict[str, int] = None			,					lowerCamelCase_      :		PILImageResampling = PILImageResampling.BILINEAR			,					lowerCamelCase_      :		bool = True			,					lowerCamelCase_      :		Union[int, float] = 1 / 2_55			,					lowerCamelCase_      :		bool = True			,					lowerCamelCase_      :		Dict[str, int] = None			,					lowerCamelCase_      :		bool = True			,					**lowerCamelCase_      :		Dict			,					):





										'''simple docstring'''







										super().__init__(**_lowerCAmelCase       )
										_snake_case			:      Dict	    = size if size is not None else {'shortest_edge': 2_24}
										_snake_case			:      Tuple	    = get_size_dict(_lowerCAmelCase			,					default_to_square=_lowerCAmelCase       )
										_snake_case			:      List[Any]	    = crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
										_snake_case			:      str	    = get_size_dict(_lowerCAmelCase			,					param_name='crop_size'       )

										_snake_case			:      Dict	    = do_resize
										_snake_case			:      List[Any]	    = size
										_snake_case			:      List[Any]	    = resample
										_snake_case			:      str	    = do_rescale
										_snake_case			:      Optional[Any]	    = rescale_factor
										_snake_case			:      Dict	    = do_center_crop
										_snake_case			:      List[Any]	    = crop_size
										_snake_case			:      Optional[Any]	    = do_flip_channel_order







						def 		__UpperCAmelCase							(     self      :		Dict			,					lowerCamelCase_      :		np.ndarray			,					lowerCamelCase_      :		Dict[str, int]			,					lowerCamelCase_      :		PILImageResampling = PIL.Image.BILINEAR			,					lowerCamelCase_      :		Optional[Union[str, ChannelDimension]] = None			,					**lowerCamelCase_      :		List[str]			,					):





										'''simple docstring'''







										_snake_case			:      Optional[int]	    = get_size_dict(_lowerCAmelCase			,					default_to_square=_lowerCAmelCase       )
										if "shortest_edge" not in size:
														raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
										_snake_case			:      Tuple	    = get_resize_output_image_size(_lowerCAmelCase			,					size=size['shortest_edge']			,					default_to_square=_lowerCAmelCase       )
										return resize(_lowerCAmelCase			,					size=_lowerCAmelCase			,					resample=_lowerCAmelCase			,					data_format=_lowerCAmelCase			,					**_lowerCAmelCase       )







						def 		__UpperCAmelCase							(     self      :		Dict			,					lowerCamelCase_      :		np.ndarray			,					lowerCamelCase_      :		Dict[str, int]			,					lowerCamelCase_      :		Optional[Union[str, ChannelDimension]] = None			,					**lowerCamelCase_      :		Tuple			,					):





										'''simple docstring'''







										_snake_case			:      Dict	    = 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()}'''       )
										return center_crop(_lowerCAmelCase			,					size=(size['height'], size['width'])			,					data_format=_lowerCAmelCase			,					**_lowerCAmelCase       )







						def 		__UpperCAmelCase							(     self      :		str			,					lowerCamelCase_      :		np.ndarray			,					lowerCamelCase_      :		Union[int, float]			,					lowerCamelCase_      :		Optional[Union[str, ChannelDimension]] = None			,					**lowerCamelCase_      :		List[Any]			,					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase							(     self      :		int			,					lowerCamelCase_      :		np.ndarray			,					lowerCamelCase_      :		Optional[Union[str, ChannelDimension]] = None       ):





										'''simple docstring'''







										return flip_channel_order(_lowerCAmelCase			,					data_format=_lowerCAmelCase       )







						def 		__UpperCAmelCase							(     self      :		str			,					lowerCamelCase_      :		ImageInput			,					lowerCamelCase_      :		bool = None			,					lowerCamelCase_      :		Dict[str, int] = None			,					lowerCamelCase_      :		PILImageResampling = None			,					lowerCamelCase_      :		bool = None			,					lowerCamelCase_      :		float = None			,					lowerCamelCase_      :		bool = None			,					lowerCamelCase_      :		Dict[str, int] = None			,					lowerCamelCase_      :		bool = None			,					lowerCamelCase_      :		Optional[Union[str, TensorType]] = None			,					lowerCamelCase_      :		ChannelDimension = ChannelDimension.FIRST			,					**lowerCamelCase_      :		List[Any]			,					):





										'''simple docstring'''







										_snake_case			:      int	    = do_resize if do_resize is not None else self.do_resize
										_snake_case			:      List[str]	    = resample if resample is not None else self.resample
										_snake_case			:      Dict	    = do_rescale if do_rescale is not None else self.do_rescale
										_snake_case			:      int	    = rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case			:      List[Any]	    = do_center_crop if do_center_crop is not None else self.do_center_crop
										_snake_case			:      Optional[int]	    = (
										    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
										)

										_snake_case			:      str	    = size if size is not None else self.size
										_snake_case			:      Tuple	    = get_size_dict(_lowerCAmelCase			,					default_to_square=_lowerCAmelCase       )
										_snake_case			:      int	    = crop_size if crop_size is not None else self.crop_size
										_snake_case			:      Optional[Any]	    = get_size_dict(_lowerCAmelCase			,					param_name='crop_size'       )

										_snake_case			:      Union[str, Any]	    = 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:
														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_center_crop and crop_size is None:
														raise ValueError('Crop size must be specified if do_center_crop is True.'       )

										# All transformations expect numpy arrays.
										_snake_case			:      Tuple	    = [to_numpy_array(_lowerCAmelCase       ) for image in images]

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

										if do_center_crop:
														_snake_case			:      str	    = [self.center_crop(image=_lowerCAmelCase			,					size=_lowerCAmelCase       ) for image in images]

										if do_rescale:
														_snake_case			:      Optional[Any]	    = [self.rescale(image=_lowerCAmelCase			,					scale=_lowerCAmelCase       ) for image in images]

										# the pretrained checkpoints assume images are BGR, not RGB
										if do_flip_channel_order:
														_snake_case			:      int	    = [self.flip_channel_order(image=_lowerCAmelCase       ) for image in images]

										_snake_case			:      Tuple	    = [to_channel_dimension_format(_lowerCAmelCase			,					_lowerCAmelCase       ) for image in images]

										_snake_case			:      Dict	    = {'pixel_values': images}
										return BatchFeature(data=_lowerCAmelCase			,					tensor_type=_lowerCAmelCase       )







						def 		__UpperCAmelCase							(     self      :		int			,					lowerCamelCase_      :		str			,					lowerCamelCase_      :		List[Tuple] = None       ):





										'''simple docstring'''







										_snake_case			:      Optional[int]	    = 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       ):
																		_snake_case			:      List[Any]	    = target_sizes.numpy()

														_snake_case			:      List[Any]	    = []

														for idx in range(len(_lowerCAmelCase       )       ):
																		_snake_case			:      Union[str, Any]	    = torch.nn.functional.interpolate(
																		    logits[idx].unsqueeze(dim=0       )			,					size=target_sizes[idx]			,					mode='bilinear'			,					align_corners=_lowerCAmelCase       )
																		_snake_case			:      List[Any]	    = resized_logits[0].argmax(dim=0       )
																		semantic_segmentation.append(_lowerCAmelCase       )
										else:
														_snake_case			:      Optional[int]	    = logits.argmax(dim=1       )
														_snake_case			:      Dict	    = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

										return semantic_segmentation










def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->str:
   '''simple docstring'''






   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      raise ValueError("iterations must be defined as integers"				)
   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				) or not number >= 1:
      raise ValueError(
          "starting number must be\n                         and integer and be more than 0"				)
   if not iterations >= 1:
      raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

   snake_case_            =       ""
   while number <= iterations:
      if number % 3 == 0:
         out += "Fizz"
      if number % 5 == 0:
         out += "Buzz"
      if 0 not in (number % 3, number % 5):
         out += str(lowerCAmelCase_				)

      # print(out)
      number += 1
      out += " "
   return out


if __name__ == "__main__":
   import doctest

   doctest.testmod()




