



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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCAmelCase							=       logging.get_logger(__name__)


if is_vision_available():
	import PIL

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Optional[int]       =       ["""pixel_values"""]


							def __init__(				self	,	a_ = True	,	a_ = None	,	a_ = PILImageResampling.BILINEAR	,	a_ = True	,	a_ = False	,	a_ = True	,	a_ = True	,	a_ = 1 / 255	,	a_ = True	,	a_ = None	,	a_ = None	,	**a_	,	):
											super().__init__(**a_		)

											a__					   =		size if size is not None else {"""height""": 2_560, """width""": 1_920}
											if isinstance(a_	,	(tuple, list)		):
															# The previous feature extractor size parameter was in (width, height) format
															a__					   =		size[::-1]
											a__					   =		get_size_dict(a_		)

											a__					   =		do_resize
											a__					   =		size
											a__					   =		resample
											a__					   =		do_thumbnail
											a__					   =		do_align_long_axis
											a__					   =		do_pad
											a__					   =		do_rescale
											a__					   =		rescale_factor
											a__					   =		do_normalize
											a__					   =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__					   =		image_std if image_std is not None else IMAGENET_STANDARD_STD


							def _a   (				self	,	a_	,	a_	,	a_ = None		):
											a__   ,      a__					   =		get_image_size(a_		)
											a__   ,      a__					   =		size["""height"""], size["""width"""]

											if (output_width < output_height and input_width > input_height) or (
											    output_width > output_height and input_width < input_height
											):
															a__					   =		np.rotaa(a_	,	3		)

											if data_format is not None:
															a__					   =		to_channel_dimension_format(a_	,	a_		)

											return image


							def _a   (				self	,	a_	,	a_	,	a_ = False	,	a_ = None	,	):
											a__   ,      a__					   =		size["""height"""], size["""width"""]
											a__   ,      a__					   =		get_image_size(a_		)

											a__					   =		output_width - input_width
											a__					   =		output_height - input_height

											if random_padding:
															a__					   =		np.random.randint(low=0	,	high=delta_height + 1		)
															a__					   =		np.random.randint(low=0	,	high=delta_width + 1		)
											else:
															a__					   =		delta_height // 2
															a__					   =		delta_width // 2

											a__					   =		delta_height - pad_top
											a__					   =		delta_width - pad_left

											a__					   =		((pad_top, pad_bottom), (pad_left, pad_right))
											return pad(a_	,	a_	,	data_format=a_		)


							def _a   (				self	,	*a_	,	**a_		):
											logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""		)
											return self.pad_image(*a_	,	**a_		)


							def _a   (				self	,	a_	,	a_	,	a_ = PILImageResampling.BICUBIC	,	a_ = None	,	**a_	,	):
											a__   ,      a__					   =		get_image_size(a_		)
											a__   ,      a__					   =		size["""height"""], size["""width"""]

											# We always resize to the smallest of either the input or output size.
											a__					   =		min(a_	,	a_		)
											a__					   =		min(a_	,	a_		)

											if height == input_height and width == input_width:
															return image

											if input_height > input_width:
															a__					   =		int(input_width * height / input_height		)
											elif input_width > input_height:
															a__					   =		int(input_height * width / input_width		)

											return resize(
											    a_	,	size=(height, width)	,	resample=a_	,	reducing_gap=2.0	,	data_format=a_	,	**a_		)


							def _a   (				self	,	a_	,	a_	,	a_ = PILImageResampling.BICUBIC	,	a_ = None	,	**a_	,	):
											a__					   =		get_size_dict(a_		)
											a__					   =		min(size["""height"""]	,	size["""width"""]		)
											a__					   =		get_resize_output_image_size(a_	,	size=a_	,	default_to_square=a_		)
											a__					   =		resize(a_	,	size=a_	,	resample=a_	,	data_format=a_	,	**a_		)
											return resized_image


							def _a   (				self	,	a_	,	a_	,	a_ = None	,	**a_	,	):
											return rescale(a_	,	scale=a_	,	data_format=a_	,	**a_		)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_ = None	,	**a_	,	):
											return normalize(a_	,	mean=a_	,	std=a_	,	data_format=a_	,	**a_		)







							def _a   (				self	,	a_	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = False	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = ChannelDimension.FIRST	,	**a_	,	):
											a__					   =		do_resize if do_resize is not None else self.do_resize
											a__					   =		size if size is not None else self.size
											if isinstance(a_	,	(tuple, list)		):
															# Previous feature extractor had size in (width, height) format
															a__					   =		size[::-1]
											a__					   =		get_size_dict(a_		)
											a__					   =		resample if resample is not None else self.resample
											a__					   =		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
											a__					   =		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
											a__					   =		do_pad if do_pad is not None else self.do_pad
											a__					   =		do_rescale if do_rescale is not None else self.do_rescale
											a__					   =		rescale_factor if rescale_factor is not None else self.rescale_factor
											a__					   =		do_normalize if do_normalize is not None else self.do_normalize
											a__					   =		image_mean if image_mean is not None else self.image_mean
											a__					   =		image_std if image_std is not None else self.image_std

											a__					   =		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:
															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_pad and size is None:
															raise ValueError("""Size must be specified if do_pad 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.
											a__					   =		[to_numpy_array(a_		) for image in images]

											if do_align_long_axis:
															a__					   =		[self.align_long_axis(a_	,	size=a_		) for image in images]

											if do_resize:
															a__					   =		[self.resize(image=a_	,	size=a_	,	resample=a_		) for image in images]

											if do_thumbnail:
															a__					   =		[self.thumbnail(image=a_	,	size=a_		) for image in images]

											if do_pad:
															a__					   =		[self.pad_image(image=a_	,	size=a_	,	random_padding=a_		) for image in images]

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

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

											a__					   =		[to_channel_dimension_format(a_	,	a_		) for image in images]

											a__					   =		{"""pixel_values""": images}
											return BatchFeature(data=a_	,	tensor_type=a_		)









'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A				:				Optional[int]					=		logging.get_logger(__name__)

__A				:				Tuple					=		{
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class 					lowercase							(				_lowerCamelCase			,							_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				"convnext"

    def __init__(       self  :  Any					,  __lowerCamelCase  :  Dict=3					,  __lowerCamelCase  :  int=4					,  __lowerCamelCase  :  Any=4					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  str=None					,  __lowerCamelCase  :  Optional[int]="gelu"					,  __lowerCamelCase  :  List[str]=0.0_2					,  __lowerCamelCase  :  int=1E-12					,  __lowerCamelCase  :  Optional[Any]=1E-6					,  __lowerCamelCase  :  Any=0.0					,  __lowerCamelCase  :  Union[str, Any]=224					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  List[str]=None					,  **__lowerCamelCase  :  Tuple					,  )				->				Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     lowerCamelCase__	=  num_channels
     lowerCamelCase__	=  patch_size
     lowerCamelCase__	=  num_stages
     lowerCamelCase__	=  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     lowerCamelCase__	=  [3, 3, 9, 3] if depths is None else depths
     lowerCamelCase__	=  hidden_act
     lowerCamelCase__	=  initializer_range
     lowerCamelCase__	=  layer_norm_eps
     lowerCamelCase__	=  layer_scale_init_value
     lowerCamelCase__	=  drop_path_rate
     lowerCamelCase__	=  image_size
     lowerCamelCase__	=  ["stem"] + [f'''stage{idx}''' for idx in range(1					,  len(self.depths  ) + 1  )]
     lowerCamelCase__	,						lowerCamelCase__	=  get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase					,  out_indices=__lowerCamelCase					,  stage_names=self.stage_names  )
class 					lowercase							(				_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				version.parse("1.11"   )

    @property
    def  a__					(       self  :  List[Any]  )				->				Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]  )




    @property
    def  a__					(       self  :  Tuple  )				->				float:

     '''simple docstring'''




     return 1E-5

