






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 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, logging


__snake_case							      =						logging.get_logger(__name__)







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      UpperCamelCase_       :      List[str]     =['pixel_values']
      def __init__(       self					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = 1 / 255					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   None:
          super().__init__(**SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :List[str]										=     size if size is not None else {'''height''': 224, '''width''': 224}
          UpperCamelCase :List[Any]										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Optional[Any]										=     do_resize
          UpperCamelCase :Dict										=     do_rescale
          UpperCamelCase :Optional[int]										=     do_normalize
          UpperCamelCase :Any										=     size
          UpperCamelCase :Tuple										=     resample
          UpperCamelCase :Union[str, Any]										=     rescale_factor
          UpperCamelCase :List[str]										=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase :str										=     image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   np.ndarray:
          UpperCamelCase :str										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
          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 resize(
              SCREAMING_SNAKE_CASE_					,       size=(size['''height'''], size['''width'''])					,       resample=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_				)       ->   np.ndarray:
          return rescale(SCREAMING_SNAKE_CASE_					,       scale=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   np.ndarray:
          return normalize(SCREAMING_SNAKE_CASE_					,       mean=SCREAMING_SNAKE_CASE_					,       std=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)




      def 					UpperCAmelCase		(       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_ = ChannelDimension.FIRST					,       **SCREAMING_SNAKE_CASE_					,       )       ->   int:
          UpperCamelCase :Any										=     do_resize if do_resize is not None else self.do_resize
          UpperCamelCase :Tuple										=     do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase :List[Any]										=     do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase :Union[str, Any]										=     resample if resample is not None else self.resample
          UpperCamelCase :List[str]										=     rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase :Tuple										=     image_mean if image_mean is not None else self.image_mean
          UpperCamelCase :Any										=     image_std if image_std is not None else self.image_std

          UpperCamelCase :int										=     size if size is not None else self.size
          UpperCamelCase :str										=     get_size_dict(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :Union[str, Any]										=     make_list_of_images(SCREAMING_SNAKE_CASE_				)

          if not valid_images(SCREAMING_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_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

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

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

          if do_rescale:
              UpperCamelCase :int										=     [self.rescale(image=SCREAMING_SNAKE_CASE_					,       scale=SCREAMING_SNAKE_CASE_				) for image in images]

          if do_normalize:
              UpperCamelCase :Optional[Any]										=     [self.normalize(image=SCREAMING_SNAKE_CASE_					,       mean=SCREAMING_SNAKE_CASE_					,       std=SCREAMING_SNAKE_CASE_				) for image in images]

          UpperCamelCase :List[str]										=     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) for image in images]

          UpperCamelCase :Dict										=     {'''pixel_values''': images}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_					,       tensor_type=SCREAMING_SNAKE_CASE_				)













from __future__ import annotations

from typing import Generic, TypeVar

__snake_case							      =						TypeVar("""T""")







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      pass







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      pass







class     UpperCAmelCase_		(      Generic[T]				):



      """simple docstring"""
      def __init__(       self					,       SCREAMING_SNAKE_CASE_ = 10				)       ->   List[str]:
          UpperCamelCase :list[T]										=     []
          UpperCamelCase :Any										=     limit
      def __bool__(       self				)       ->   bool:
          return bool(self.stack				)
      def __str__(       self				)       ->   str:
          return str(self.stack				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
          if len(self.stack				) >= self.limit:
              raise StackOverflowError
          self.stack.append(SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   T:
          if not self.stack:
              raise StackUnderflowError
          return self.stack.pop()
      def 					UpperCAmelCase		(       self				)       ->   T:
          if not self.stack:
              raise StackUnderflowError
          return self.stack[-1]
      def 					UpperCAmelCase		(       self				)       ->   bool:
          return not bool(self.stack				)
      def 					UpperCAmelCase		(       self				)       ->   bool:
          return self.size() == self.limit
      def 					UpperCAmelCase		(       self				)       ->   int:
          return len(self.stack				)




      def __contains__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   bool:
          return item in self.stack







def 						_A     (       ):
    UpperCamelCase :Stack[int]										=     Stack(10 )
    assert bool(SCREAMING_SNAKE_CASE__ ) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(SCREAMING_SNAKE_CASE__ ) == "[]"

    try:
        UpperCamelCase :Optional[int]										=     stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        UpperCamelCase :Union[str, Any]										=     stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10 ):
        assert stack.size() == i
        stack.push(SCREAMING_SNAKE_CASE__ )

    assert bool(SCREAMING_SNAKE_CASE__ )
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(SCREAMING_SNAKE_CASE__ ) == str(list(range(10 ) ) )
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100 )
    assert str(SCREAMING_SNAKE_CASE__ ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )

    try:
        stack.push(200 )
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()





