
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, 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, is_vision_available, logging


if is_vision_available():
						import PIL


snake_case__  							=							logging.get_logger(__name__)







class 							UpperCAmelCase	(   lowerCamelCase__					):
					a__: List[str]			  =				["""pixel_values"""]

					def __init__(			self      :					str ,      lowerCAmelCase      :					bool = True ,      lowerCAmelCase      :					Dict[str, int] = None ,      lowerCAmelCase      :					PILImageResampling = PIL.Image.NEAREST ,      lowerCAmelCase      :					bool = False ,      lowerCAmelCase      :					Dict[str, int] = None ,      lowerCAmelCase      :					Union[int, float] = 1 / 255 ,      lowerCAmelCase      :					bool = False ,      lowerCAmelCase      :					bool = True ,      lowerCAmelCase      :					bool = True ,      lowerCAmelCase      :					Optional[Union[float, List[float]]] = None ,      lowerCAmelCase      :					Optional[Union[float, List[float]]] = None ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					Dict ,      ):
											super().__init__(**SCREAMING_SNAKE_CASE_					)
											lowercase       :    Optional[int]											=  size if size is not None else {'''height''': 346, '''width''': 346}
											lowercase       :    Optional[int]											=  get_size_dict(SCREAMING_SNAKE_CASE_					)
											lowercase       :    Optional[Any]											=  crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
											lowercase       :    Optional[int]											=  get_size_dict(SCREAMING_SNAKE_CASE_ ,      param_name='''crop_size'''					)

											lowercase       :    Optional[Any]											=  do_resize
											lowercase       :    Any											=  size
											lowercase       :    Optional[Any]											=  resample
											lowercase       :    List[str]											=  do_center_crop
											lowercase       :    str											=  crop_size
											lowercase       :    int											=  do_rescale
											lowercase       :    Union[str, Any]											=  rescale_factor
											lowercase       :    Optional[int]											=  rescale_offset
											lowercase       :    List[str]											=  do_normalize
											lowercase       :    int											=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowercase       :    Tuple											=  image_std if image_std is not None else IMAGENET_STANDARD_STD
											lowercase       :    Union[str, Any]											=  include_top

					def        _lowerCAmelCase	(			self      :					Optional[int] ,      lowerCAmelCase      :					np.ndarray ,      lowerCAmelCase      :					Dict[str, int] ,      lowerCAmelCase      :					PILImageResampling = PIL.Image.NEAREST ,      lowerCAmelCase      :					Optional[Union[str, ChannelDimension]] = None ,      **lowerCAmelCase      :					Optional[Any] ,      ):
											lowercase       :    str											=  get_size_dict(SCREAMING_SNAKE_CASE_					)
											if "height" not in size or "width" not in size:
																	raise ValueError(f'''The size dictionary must have 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        _lowerCAmelCase	(			self      :					Optional[int] ,      lowerCAmelCase      :					np.ndarray ,      lowerCAmelCase      :					Dict[str, int] ,      lowerCAmelCase      :					Optional[Union[str, ChannelDimension]] = None ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Dict											=  get_size_dict(SCREAMING_SNAKE_CASE_					)
											if "height" not in size or "width" not in size:
																	raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
											return center_crop(SCREAMING_SNAKE_CASE_ ,      size=(size['''height'''], size['''width''']) ,      data_format=SCREAMING_SNAKE_CASE_ ,      **SCREAMING_SNAKE_CASE_					)

					def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					np.ndarray ,      lowerCAmelCase      :					Union[int, float] ,      lowerCAmelCase      :					bool = True ,      lowerCAmelCase      :					Optional[Union[str, ChannelDimension]] = None ,      **lowerCAmelCase      :					Optional[Any] ,      ):
											if offset:
																	lowercase       :    List[str]											=  (image - 1_27.5) * scale
																	if data_format is not None:
																							lowercase       :    int											=  to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_					)
																	lowercase       :    Optional[int]											=  rescaled_image.astype(np.floataa					)
											else:
																	lowercase       :    Any											=  rescale(SCREAMING_SNAKE_CASE_ ,      scale=SCREAMING_SNAKE_CASE_ ,      data_format=SCREAMING_SNAKE_CASE_ ,      **SCREAMING_SNAKE_CASE_					)
											return rescaled_image

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




					def        _lowerCAmelCase	(			self      :					str ,      lowerCAmelCase      :					ImageInput ,      lowerCAmelCase      :					bool = None ,      lowerCAmelCase      :					Dict[str, int] = None ,      lowerCAmelCase      :					List[str]=None ,      lowerCAmelCase      :					bool = None ,      lowerCAmelCase      :					Dict[str, int] = None ,      lowerCAmelCase      :					bool = None ,      lowerCAmelCase      :					float = None ,      lowerCAmelCase      :					bool = None ,      lowerCAmelCase      :					bool = None ,      lowerCAmelCase      :					Optional[Union[float, List[float]]] = None ,      lowerCAmelCase      :					Optional[Union[float, List[float]]] = None ,      lowerCAmelCase      :					bool = None ,      lowerCAmelCase      :					Optional[Union[str, TensorType]] = None ,      lowerCAmelCase      :					ChannelDimension = ChannelDimension.FIRST ,      **lowerCAmelCase      :					str ,      ):
											lowercase       :    Tuple											=  do_resize if do_resize is not None else self.do_resize
											lowercase       :    str											=  resample if resample is not None else self.resample
											lowercase       :    Union[str, Any]											=  do_center_crop if do_center_crop is not None else self.do_center_crop
											lowercase       :    Optional[Any]											=  do_rescale if do_rescale is not None else self.do_rescale
											lowercase       :    List[str]											=  rescale_factor if rescale_factor is not None else self.rescale_factor
											lowercase       :    List[str]											=  rescale_offset if rescale_offset is not None else self.rescale_offset
											lowercase       :    Union[str, Any]											=  do_normalize if do_normalize is not None else self.do_normalize
											lowercase       :    List[str]											=  image_mean if image_mean is not None else self.image_mean
											lowercase       :    str											=  image_std if image_std is not None else self.image_std
											lowercase       :    Optional[Any]											=  include_top if include_top is not None else self.include_top

											lowercase       :    Optional[Any]											=  size if size is not None else self.size
											lowercase       :    Optional[Any]											=  get_size_dict(SCREAMING_SNAKE_CASE_					)
											lowercase       :    Optional[int]											=  crop_size if crop_size is not None else self.crop_size
											lowercase       :    List[str]											=  get_size_dict(SCREAMING_SNAKE_CASE_ ,      param_name='''crop_size'''					)

											lowercase       :    List[str]											=  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 or resample is None:
																	raise ValueError('''Size and resample 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.'''					)

											# All transformations expect numpy arrays.
											lowercase       :    List[str]											=  [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

											if do_resize:
																	lowercase       :    Union[str, Any]											=  [self.resize(image=SCREAMING_SNAKE_CASE_ ,      size=SCREAMING_SNAKE_CASE_ ,      resample=SCREAMING_SNAKE_CASE_					) for image in images]

											if do_center_crop:
																	lowercase       :    Union[str, Any]											=  [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,      size=SCREAMING_SNAKE_CASE_					) for image in images]

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

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

											if include_top:
																	lowercase       :    Tuple											=  [self.normalize(image=SCREAMING_SNAKE_CASE_ ,      mean=[0, 0, 0] ,      std=SCREAMING_SNAKE_CASE_					) for image in images]

											lowercase       :    List[Any]											=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_					) for image in images]

											lowercase       :    List[Any]											=  {'''pixel_values''': images}
											return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,      tensor_type=SCREAMING_SNAKE_CASE_					)




snake_case__  							=							"""
# Installation de Transformers
! pip install transformers datasets
# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

snake_case__  							=							[{"""type""": """code""", """content""": INSTALL_CONTENT}]
snake_case__  							=							{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


