






import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case			 =						logging.get_logger(__name__)


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			ConvNextVaConfig()

  if "atto" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					Dict		  =			[2, 2, 6, 2]
    SCREAMING_SNAKE_CASE				:					Dict		  =			[40, 80, 160, 320]
  if "femto" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					List[Any]		  =			[2, 2, 6, 2]
    SCREAMING_SNAKE_CASE				:					List[str]		  =			[48, 96, 192, 384]
  if "pico" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					int		  =			[2, 2, 6, 2]
    SCREAMING_SNAKE_CASE				:					Tuple		  =			[64, 128, 256, 512]
  if "nano" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[2, 2, 8, 2]
    SCREAMING_SNAKE_CASE				:					List[str]		  =			[80, 160, 320, 640]
  if "tiny" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[3, 3, 9, 3]
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[96, 192, 384, 768]
  if "base" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					List[str]		  =			[3, 3, 27, 3]
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[128, 256, 512, 1024]
  if "large" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					List[str]		  =			[3, 3, 27, 3]
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[192, 384, 768, 1536]
  if "huge" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					List[Any]		  =			[3, 3, 27, 3]
    SCREAMING_SNAKE_CASE				:					Dict		  =			[352, 704, 1408, 2816]

  SCREAMING_SNAKE_CASE				:					List[str]		  =			1000
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			"imagenet-1k-id2label.json"
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(1, 1000)

  SCREAMING_SNAKE_CASE				:					List[str]		  =			"huggingface/label-files"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			num_labels
  SCREAMING_SNAKE_CASE				:					int		  =			json.load(open(hf_hub_download(lowercase  ,   lowercase  ,   repo_type="dataset" )  ,   "r" ) )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			{int(lowercase ): v for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE				:					int		  =			idalabel
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{v: k for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			hidden_sizes
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			depths

  return config, expected_shape


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  if "downsample_layers.0.0" in name:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			name.replace("downsample_layers.0.0"  ,   "embeddings.patch_embeddings" )
  if "downsample_layers.0.1" in name:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			name.replace("downsample_layers.0.1"  ,   "embeddings.norm" )  # we rename to layernorm later on
  if "downsample_layers.1.0" in name:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			name.replace("downsample_layers.1.0"  ,   "stages.1.downsampling_layer.0" )
  if "downsample_layers.1.1" in name:
    SCREAMING_SNAKE_CASE				:					int		  =			name.replace("downsample_layers.1.1"  ,   "stages.1.downsampling_layer.1" )
  if "downsample_layers.2.0" in name:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			name.replace("downsample_layers.2.0"  ,   "stages.2.downsampling_layer.0" )
  if "downsample_layers.2.1" in name:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			name.replace("downsample_layers.2.1"  ,   "stages.2.downsampling_layer.1" )
  if "downsample_layers.3.0" in name:
    SCREAMING_SNAKE_CASE				:					List[str]		  =			name.replace("downsample_layers.3.0"  ,   "stages.3.downsampling_layer.0" )
  if "downsample_layers.3.1" in name:
    SCREAMING_SNAKE_CASE				:					Dict		  =			name.replace("downsample_layers.3.1"  ,   "stages.3.downsampling_layer.1" )
  if "stages" in name and "downsampling_layer" not in name:
    # stages.0.0. for instance should be renamed to stages.0.layers.0.
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			name[: len("stages.0" )] + ".layers" + name[len("stages.0" ) :]
  if "gamma" in name:
    SCREAMING_SNAKE_CASE				:					Dict		  =			name.replace("gamma"  ,   "weight" )
  if "beta" in name:
    SCREAMING_SNAKE_CASE				:					str		  =			name.replace("beta"  ,   "bias" )
  if "stages" in name:
    SCREAMING_SNAKE_CASE				:					List[Any]		  =			name.replace("stages"  ,   "encoder.stages" )
  if "norm" in name:
    SCREAMING_SNAKE_CASE				:					str		  =			name.replace("norm"  ,   "layernorm" )
  if "head" in name:
    SCREAMING_SNAKE_CASE				:					Tuple		  =			name.replace("head"  ,   "classifier" )

  return name


def      lowerCamelCase__	(   ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE				:					str		  =			Image.open(requests.get(lowercase  ,   stream=lowercase ).raw )
  return im


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  if "224" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			224
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			224 / 256
  elif "384" in checkpoint_url:
    SCREAMING_SNAKE_CASE				:					Tuple		  =			384
    SCREAMING_SNAKE_CASE				:					List[str]		  =			None
  else:
    SCREAMING_SNAKE_CASE				:					Dict		  =			512
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			None

  return ConvNextImageProcessor(
      size=lowercase  ,   crop_pct=lowercase  ,   image_mean=[0.485, 0.456, 0.406]  ,   image_std=[0.229, 0.224, 0.225]  ,   resample=PILImageResampling.BICUBIC  ,   )


@torch.no_grad()
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  print("Downloading original model from checkpoint..." )
  # define ConvNeXTV2 configuration based on URL
  SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_convnextva_config(lowercase )
  # load original state_dict from URL
  SCREAMING_SNAKE_CASE				:					Any		  =			torch.hub.load_state_dict_from_url(lowercase )["model"]

  print("Converting model parameters..." )
  # rename keys
  for key in state_dict.copy().keys():
    SCREAMING_SNAKE_CASE				:					List[str]		  =			state_dict.pop(lowercase )
    SCREAMING_SNAKE_CASE				:					Any		  =			val
  # add prefix to all keys expect classifier head
  for key in state_dict.copy().keys():
    SCREAMING_SNAKE_CASE				:					Tuple		  =			state_dict.pop(lowercase )
    if not key.startswith("classifier" ):
      SCREAMING_SNAKE_CASE				:					List[Any]		  =			"convnextv2." + key
    SCREAMING_SNAKE_CASE				:					List[str]		  =			val

  # load HuggingFace model
  SCREAMING_SNAKE_CASE				:					int		  =			ConvNextVaForImageClassification(lowercase )
  model.load_state_dict(lowercase )
  model.eval()

  # Check outputs on an image, prepared by ConvNextImageProcessor
  SCREAMING_SNAKE_CASE				:					Any		  =			convert_preprocessor(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			preprocessor(images=prepare_img()  ,   return_tensors="pt" )
  SCREAMING_SNAKE_CASE				:					Any		  =			model(**lowercase ).logits

  # note: the logits below were obtained without center cropping
  if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					int		  =			torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					List[str]		  =			torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Dict		  =			torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Any		  =			torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					str		  =			torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
    SCREAMING_SNAKE_CASE				:					Any		  =			torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
    SCREAMING_SNAKE_CASE				:					Dict		  =			torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					str		  =			torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
    SCREAMING_SNAKE_CASE				:					Any		  =			torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
    SCREAMING_SNAKE_CASE				:					Any		  =			torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
    SCREAMING_SNAKE_CASE				:					str		  =			torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
    SCREAMING_SNAKE_CASE				:					Dict		  =			torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826] )
  else:
    raise ValueError(F'''Unknown URL: {checkpoint_url}''' )

  assert torch.allclose(logits[0, :5]  ,   lowercase  ,   atol=1E-3 )
  assert logits.shape == expected_shape
  print("Model outputs match the original results!" )

  if save_model:
    print("Saving model to local..." )
    # Create folder to save model
    if not os.path.isdir(lowercase ):
      os.mkdir(lowercase )

    model.save_pretrained(lowercase )
    preprocessor.save_pretrained(lowercase )

  SCREAMING_SNAKE_CASE				:					Dict		  =			"convnextv2"
  if "atto" in checkpoint_url:
    model_name += "-atto"
  if "femto" in checkpoint_url:
    model_name += "-femto"
  if "pico" in checkpoint_url:
    model_name += "-pico"
  if "nano" in checkpoint_url:
    model_name += "-nano"
  elif "tiny" in checkpoint_url:
    model_name += "-tiny"
  elif "base" in checkpoint_url:
    model_name += "-base"
  elif "large" in checkpoint_url:
    model_name += "-large"
  elif "huge" in checkpoint_url:
    model_name += "-huge"
  if "22k" in checkpoint_url and "1k" not in checkpoint_url:
    model_name += "-22k"
  elif "22k" in checkpoint_url and "1k" in checkpoint_url:
    model_name += "-22k-1k"
  elif "1k" in checkpoint_url:
    model_name += "-1k"
  if "224" in checkpoint_url:
    model_name += "-224"
  elif "384" in checkpoint_url:
    model_name += "-384"
  elif "512" in checkpoint_url:
    model_name += "-512"

  if push_to_hub:
    print(F'''Pushing {model_name} to the hub...''' )
    model.push_to_hub(lowercase )
    preprocessor.push_to_hub(lowercase )


if __name__ == "__main__":
   snake_case			 =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt""",
       type=str,
       help="""URL of the original ConvNeXTV2 checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""model""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
   parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image preprocessor to the hub""")

   snake_case			 =						parser.parse_args()
   convert_convnextva_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
   )














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


snake_case			 =						logging.get_logger(__name__)


if is_vision_available():
   import PIL


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						List[Any]								=		['''pixel_values''']


     def __init__( self				:		Tuple    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Dict[str, int] = None    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BILINEAR    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Union[int, float] = 1 / 255    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       **UpperCAmelCase_				:		List[str]    ,       ):
       super().__init__(**UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size if size is not None else {"height": 2560, "width": 1920}
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # The previous feature extractor size parameter was in (width, height) format
         SCREAMING_SNAKE_CASE				:					List[str]		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_size_dict(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					int		  =			size
       SCREAMING_SNAKE_CASE				:					str		  =			resample
       SCREAMING_SNAKE_CASE				:					List[str]		  =			do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			do_pad
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_rescale
       SCREAMING_SNAKE_CASE				:					Tuple		  =			rescale_factor
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE				:					Any		  =			image_std if image_std is not None else IMAGENET_STANDARD_STD


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			size["height"], size["width"]

       if (output_width < output_height and input_width > input_height) or (
           output_width > output_height and input_width < input_height
       ):
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			np.rotaa(UpperCAmelCase_    ,       3					)

       if data_format is not None:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					)

       return image


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			size["height"], size["width"]
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_image_size(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_width - input_width
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_height - input_height

       if random_padding:
         SCREAMING_SNAKE_CASE				:					int		  =			np.random.randint(low=0    ,       high=delta_height + 1					)
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			np.random.randint(low=0    ,       high=delta_width + 1					)
       else:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			delta_height // 2
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delta_width // 2

       SCREAMING_SNAKE_CASE				:					Any		  =			delta_height - pad_top
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			delta_width - pad_left

       SCREAMING_SNAKE_CASE				:					Dict		  =			((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(UpperCAmelCase_    ,       UpperCAmelCase_    ,       data_format=UpperCAmelCase_					)


     def  _A				( self				:		Optional[Any]    ,       *UpperCAmelCase_				:		int    ,       **UpperCAmelCase_				:		Union[str, Any]					):
       logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."					)
       return self.pad_image(*UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		int    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					int		  =			size["height"], size["width"]

       # We always resize to the smallest of either the input or output size.
       SCREAMING_SNAKE_CASE				:					Any		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)

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

       if input_height > input_width:
         SCREAMING_SNAKE_CASE				:					Tuple		  =			int(input_width * height / input_height					)
       elif input_width > input_height:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(input_height * width / input_width					)

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


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		Optional[int]    ,       ):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			min(size["height"]    ,       size["width"]					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_resize_output_image_size(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       default_to_square=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			resize(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       resample=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)
       return resized_image


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


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






     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		ImageInput    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Dict[str, int] = None    ,       UpperCAmelCase_				:		PILImageResampling = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		float = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[str, TensorType]] = None    ,       UpperCAmelCase_				:		Optional[ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCAmelCase_				:		Union[str, Any]    ,       ):
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE				:					str		  =			size if size is not None else self.size
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # Previous feature extractor had size in (width, height) format
         SCREAMING_SNAKE_CASE				:					str		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					int		  =			resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
       SCREAMING_SNAKE_CASE				:					int		  =			do_pad if do_pad is not None else self.do_pad
       SCREAMING_SNAKE_CASE				:					str		  =			do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE				:					int		  =			rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE				:					Tuple		  =			do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE				:					Dict		  =			make_list_of_images(UpperCAmelCase_					)

       if not valid_images(UpperCAmelCase_					):
         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.
       SCREAMING_SNAKE_CASE				:					int		  =			[to_numpy_array(UpperCAmelCase_					) for image in images]

       if do_align_long_axis:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.align_long_axis(UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

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

       if do_thumbnail:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[self.thumbnail(image=UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

       if do_pad:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[self.pad_image(image=UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       random_padding=UpperCAmelCase_					) for image in images]

       if do_rescale:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.rescale(image=UpperCAmelCase_    ,       scale=UpperCAmelCase_					) for image in images]

       if do_normalize:
         SCREAMING_SNAKE_CASE				:					Any		  =			[self.normalize(image=UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_					) for image in images]

       SCREAMING_SNAKE_CASE				:					str		  =			[to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					) for image in images]

       SCREAMING_SNAKE_CASE				:					Tuple		  =			{"pixel_values": images}
       return BatchFeature(data=UpperCAmelCase_    ,       tensor_type=UpperCAmelCase_					)






