
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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







class 							__magic_name__     (		__lowercase   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   ['pixel_values']





					def __init__(				self   :							Tuple							,						snake_case_   :							int = True							,						snake_case_   :							Union[str, Any] = None							,						snake_case_   :							Union[str, Any] = 0.9							,						snake_case_   :							List[str] = PILImageResampling.BICUBIC							,						snake_case_   :							Dict = True							,						snake_case_   :							Optional[Any] = None							,						snake_case_   :							Tuple = 1 / 255							,						snake_case_   :							Union[str, Any] = True							,						snake_case_   :							Any = True							,						snake_case_   :							Union[str, Any] = None							,						snake_case_   :							Optional[int] = None							,						**snake_case_   :							Optional[int]							,						):
												super().__init__(**__A    )
												__snake_case     			=       size if size is not None else {"shortest_edge": 224}
												__snake_case     			=       get_size_dict(__A							,						default_to_square=__A    )
												__snake_case     			=       crop_size if crop_size is not None else {"height": 224, "width": 224}
												__snake_case     			=       get_size_dict(__A							,						param_name="crop_size"    )

												__snake_case     			=       do_resize
												__snake_case     			=       size
												__snake_case     			=       crop_pct
												__snake_case     			=       resample
												__snake_case     			=       do_center_crop
												__snake_case     			=       crop_size
												__snake_case     			=       do_rescale
												__snake_case     			=       rescale_factor
												__snake_case     			=       do_normalize
												__snake_case     			=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												__snake_case     			=       image_std if image_std is not None else IMAGENET_DEFAULT_STD





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							str							,						snake_case_   :							List[Any] = None							,						snake_case_   :							Union[str, Any] = PILImageResampling.BICUBIC							,						snake_case_   :							Union[str, Any] = None							,						**snake_case_   :							Tuple							,						):
												__snake_case     			=       get_size_dict(__A							,						default_to_square=__A    )
												if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																			raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''    )
												if crop_pct is not None:
																			if "shortest_edge" in size:
																										__snake_case     			=       int(size["shortest_edge"] / crop_pct    )
																			elif "height" in size and "width" in size:
																										if size["height"] == size["width"]:
																																	__snake_case     			=       int(size["height"] / crop_pct    )
																										else:
																																	__snake_case     			=       (int(size["height"] / crop_pct    ), int(size["width"] / crop_pct    ))
																			else:
																										raise ValueError("Invalid size for resize: {}".format(__A    )    )

																			__snake_case     			=       get_resize_output_image_size(__A							,						size=__A							,						default_to_square=__A    )
												else:
																			if "shortest_edge" in size:
																										__snake_case     			=       get_resize_output_image_size(__A							,						size=size["shortest_edge"]							,						default_to_square=__A    )
																			elif "height" in size and "width" in size:
																										__snake_case     			=       (size["height"], size["width"])
																			else:
																										raise ValueError("Invalid size for resize: {}".format(__A    )    )

												return resize(__A							,						size=__A							,						resample=__A							,						data_format=__A							,						**__A    )





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[int]							,						snake_case_   :							Dict = None							,						**snake_case_   :							Dict							,						):
												__snake_case     			=       get_size_dict(__A    )
												if "height" not in size or "width" not in size:
																			raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''    )
												return center_crop(__A							,						size=(size["height"], size["width"])							,						data_format=__A							,						**__A    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any] = None							,						**snake_case_   :							List[str]							,						):
												return rescale(__A							,						scale=__A							,						data_format=__A							,						**__A    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							List[str]							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Dict = None							,						**snake_case_   :							Optional[Any]							,						):
												return normalize(__A							,						mean=__A							,						std=__A							,						data_format=__A							,						**__A    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Dict							,						snake_case_   :							Tuple = None							,						snake_case_   :							List[str] = None							,						snake_case_   :							Tuple = None							,						snake_case_   :							int = None							,						snake_case_   :							List[str] = None							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							List[Any] = None							,						snake_case_   :							Union[str, Any] = None							,						snake_case_   :							List[str] = None							,						snake_case_   :							Tuple = None							,						snake_case_   :							List[Any] = None							,						snake_case_   :							Optional[Any] = None							,						snake_case_   :							List[str] = ChannelDimension.FIRST							,						**snake_case_   :							Optional[Any]							,						):
												__snake_case     			=       do_resize if do_resize is not None else self.do_resize
												__snake_case     			=       crop_pct if crop_pct is not None else self.crop_pct
												__snake_case     			=       resample if resample is not None else self.resample
												__snake_case     			=       do_center_crop if do_center_crop is not None else self.do_center_crop
												__snake_case     			=       do_rescale if do_rescale is not None else self.do_rescale
												__snake_case     			=       rescale_factor if rescale_factor is not None else self.rescale_factor
												__snake_case     			=       do_normalize if do_normalize is not None else self.do_normalize
												__snake_case     			=       image_mean if image_mean is not None else self.image_mean
												__snake_case     			=       image_std if image_std is not None else self.image_std

												__snake_case     			=       size if size is not None else self.size
												__snake_case     			=       get_size_dict(__A							,						default_to_square=__A    )
												__snake_case     			=       crop_size if crop_size is not None else self.crop_size
												__snake_case     			=       get_size_dict(__A							,						param_name="crop_size"    )

												__snake_case     			=       make_list_of_images(__A    )

												if not valid_images(__A    ):
																			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_pct is None:
																			raise ValueError("Crop_pct 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.
												__snake_case     			=       [to_numpy_array(__A    ) for image in images]

												if do_resize:
																			__snake_case     			=       [self.resize(image=__A							,						size=__A							,						crop_pct=__A							,						resample=__A    ) for image in images]

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

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

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

												__snake_case     			=       [to_channel_dimension_format(__A							,						__A    ) for image in images]

												__snake_case     			=       {"pixel_values": images}
												return BatchFeature(data=__A							,						tensor_type=__A    )


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GroupViTModel""",
		    """GroupViTPreTrainedModel""",
		    """GroupViTTextModel""",
		    """GroupViTVisionModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGroupViTModel""",
		    """TFGroupViTPreTrainedModel""",
		    """TFGroupViTTextModel""",
		    """TFGroupViTVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
