





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						Tuple						    =					['pixel_values']



					def __init__(		self     :     Union[str, Any]       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Optional[Dict[str, int]] = None       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BICUBIC       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Union[int, float] = 1 / 2_55       ,      lowerCamelCase     :     Dict[str, int] = None       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      **lowerCamelCase     :     str       ,      ):
							super().__init__(**lowerCamelCase						)
							lowerCamelCase_	:     Union[str, Any] 						=      size if size is not None else {'height': 2_24, 'width': 2_24}
							lowerCamelCase_	:     Optional[int] 						=      get_size_dict(lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
							lowerCamelCase_	:     str 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase       ,      param_name='crop_size'						)

							lowerCamelCase_	:     List[str] 						=      do_resize
							lowerCamelCase_	:     List[Any] 						=      do_rescale
							lowerCamelCase_	:     Any 						=      do_normalize
							lowerCamelCase_	:     List[str] 						=      do_center_crop
							lowerCamelCase_	:     Dict 						=      crop_size
							lowerCamelCase_	:     str 						=      size
							lowerCamelCase_	:     Optional[int] 						=      resample
							lowerCamelCase_	:     Union[str, Any] 						=      rescale_factor
							lowerCamelCase_	:     List[str] 						=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							lowerCamelCase_	:     Union[str, Any] 						=      image_std if image_std is not None else IMAGENET_DEFAULT_STD



					def   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Dict[str, int]       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BILINEAR       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Tuple       ,      ):
							lowerCamelCase_	:     Optional[int] 						=      get_size_dict(lowerCamelCase						)

							if "shortest_edge" in size:
									lowerCamelCase_	:     int 						=      get_resize_output_image_size(lowerCamelCase       ,      size=size['shortest_edge']       ,      default_to_square=lowerCamelCase						)
									# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
									lowerCamelCase_	:     int 						=      (size['height'], size['width'])
							else:
									raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"						)
							return resize(lowerCamelCase       ,      size=lowerCamelCase       ,      resample=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Dict[str, int]       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Optional[Any]       ,      ):
							lowerCamelCase_	:     List[str] 						=      get_size_dict(lowerCamelCase						)
							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(lowerCamelCase       ,      size=(size['height'], size['width'])       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     str       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     float       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Optional[int]						):
							return rescale(lowerCamelCase       ,      scale=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Any       ,      ):
							return normalize(lowerCamelCase       ,      mean=lowerCamelCase       ,      std=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)





					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     ImageInput       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Dict[str, int] = None       ,      lowerCamelCase     :     PILImageResampling = None       ,      lowerCamelCase     :     bool = None       ,      lowerCamelCase     :     int = None       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[float] = None       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[str, TensorType]] = None       ,      lowerCamelCase     :     Union[str, ChannelDimension] = ChannelDimension.FIRST       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							lowerCamelCase_	:     Union[str, Any] 						=      do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_	:     Any 						=      do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_	:     int 						=      do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_	:     Any 						=      do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase_	:     Any 						=      crop_size if crop_size is not None else self.crop_size
							lowerCamelCase_	:     Tuple 						=      get_size_dict(lowerCamelCase       ,      param_name='crop_size'       ,      default_to_square=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      resample if resample is not None else self.resample
							lowerCamelCase_	:     List[str] 						=      rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_	:     Tuple 						=      image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_	:     int 						=      image_std if image_std is not None else self.image_std

							lowerCamelCase_	:     Optional[Any] 						=      size if size is not None else self.size
							lowerCamelCase_	:     Union[str, Any] 						=      get_size_dict(lowerCamelCase						)

							if not is_batched(lowerCamelCase						):
									lowerCamelCase_	:     Union[str, Any] 						=      [images]

							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_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.'						)

							# All transformations expect numpy arrays.
							lowerCamelCase_	:     int 						=      [to_numpy_array(lowerCamelCase						) for image in images]

							if do_resize:
									lowerCamelCase_	:     int 						=      [self.resize(image=lowerCamelCase       ,      size=lowerCamelCase       ,      resample=lowerCamelCase						) for image in images]

							if do_center_crop:
									lowerCamelCase_	:     List[Any] 						=      [self.center_crop(image=lowerCamelCase       ,      size=lowerCamelCase						) for image in images]

							if do_rescale:
									lowerCamelCase_	:     str 						=      [self.rescale(image=lowerCamelCase       ,      scale=lowerCamelCase						) for image in images]

							if do_normalize:
									lowerCamelCase_	:     str 						=      [self.normalize(image=lowerCamelCase       ,      mean=lowerCamelCase       ,      std=lowerCamelCase						) for image in images]

							lowerCamelCase_	:     Dict 						=      [to_channel_dimension_format(lowerCamelCase       ,      lowerCamelCase						) for image in images]

							lowerCamelCase_	:     Dict 						=      {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase       ,      tensor_type=lowerCamelCase						)







_lowercase		:  Optional[Any]			       ="""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowercase		:  Union[str, Any]			       =[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase		:  List[Any]			       ={
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}
