


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()
SCREAMING_SNAKE_CASE				:						Dict			   = logging.get_logger(__name__)




def 			UpperCamelCase			(					_a						)    ->							Any:
     '''simple docstring'''





     lowercase_				:Union[str, Any]       			=				ConvNextVaConfig()

     if "atto" in checkpoint_url:
          lowercase_				:Dict       			=				[2, 2, 6, 2]
          lowercase_				:List[Any]       			=				[4_0, 8_0, 1_6_0, 3_2_0]
     if "femto" in checkpoint_url:
          lowercase_				:List[str]       			=				[2, 2, 6, 2]
          lowercase_				:Tuple       			=				[4_8, 9_6, 1_9_2, 3_8_4]
     if "pico" in checkpoint_url:
          lowercase_				:Optional[Any]       			=				[2, 2, 6, 2]
          lowercase_				:Dict       			=				[6_4, 1_2_8, 2_5_6, 5_1_2]
     if "nano" in checkpoint_url:
          lowercase_				:Dict       			=				[2, 2, 8, 2]
          lowercase_				:Optional[int]       			=				[8_0, 1_6_0, 3_2_0, 6_4_0]
     if "tiny" in checkpoint_url:
          lowercase_				:Dict       			=				[3, 3, 9, 3]
          lowercase_				:Optional[Any]       			=				[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "base" in checkpoint_url:
          lowercase_				:List[str]       			=				[3, 3, 2_7, 3]
          lowercase_				:int       			=				[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
     if "large" in checkpoint_url:
          lowercase_				:Any       			=				[3, 3, 2_7, 3]
          lowercase_				:Dict       			=				[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
     if "huge" in checkpoint_url:
          lowercase_				:Any       			=				[3, 3, 2_7, 3]
          lowercase_				:List[str]       			=				[3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

     lowercase_				:Union[str, Any]       			=				1_0_0_0
     lowercase_				:List[str]       			=				'''imagenet-1k-id2label.json'''
     lowercase_				:List[str]       			=				(1, 1_0_0_0)

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

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

     return config, expected_shape




def 			UpperCamelCase			(					_a						)    ->							int:
     '''simple docstring'''





     if "downsample_layers.0.0" in name:
          lowercase_				:int       			=				name.replace('''downsample_layers.0.0'''				,						'''embeddings.patch_embeddings'''						)
     if "downsample_layers.0.1" in name:
          lowercase_				:Optional[Any]       			=				name.replace('''downsample_layers.0.1'''				,						'''embeddings.norm'''						)  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          lowercase_				:int       			=				name.replace('''downsample_layers.1.0'''				,						'''stages.1.downsampling_layer.0'''						)
     if "downsample_layers.1.1" in name:
          lowercase_				:int       			=				name.replace('''downsample_layers.1.1'''				,						'''stages.1.downsampling_layer.1'''						)
     if "downsample_layers.2.0" in name:
          lowercase_				:Optional[Any]       			=				name.replace('''downsample_layers.2.0'''				,						'''stages.2.downsampling_layer.0'''						)
     if "downsample_layers.2.1" in name:
          lowercase_				:List[str]       			=				name.replace('''downsample_layers.2.1'''				,						'''stages.2.downsampling_layer.1'''						)
     if "downsample_layers.3.0" in name:
          lowercase_				:str       			=				name.replace('''downsample_layers.3.0'''				,						'''stages.3.downsampling_layer.0'''						)
     if "downsample_layers.3.1" in name:
          lowercase_				:List[str]       			=				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.
          lowercase_				:List[str]       			=				name[: len('''stages.0'''						)] + '''.layers''' + name[len('''stages.0'''						) :]
     if "gamma" in name:
          lowercase_				:List[str]       			=				name.replace('''gamma'''				,						'''weight'''						)
     if "beta" in name:
          lowercase_				:int       			=				name.replace('''beta'''				,						'''bias'''						)
     if "stages" in name:
          lowercase_				:Optional[int]       			=				name.replace('''stages'''				,						'''encoder.stages'''						)
     if "norm" in name:
          lowercase_				:str       			=				name.replace('''norm'''				,						'''layernorm'''						)
     if "head" in name:
          lowercase_				:Tuple       			=				name.replace('''head'''				,						'''classifier'''						)

     return name




def 			UpperCamelCase			(					)    ->							Optional[Any]:
     '''simple docstring'''





     lowercase_				:List[str]       			=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowercase_				:Dict       			=				Image.open(requests.get(lowercase_				,						stream=lowercase_						).raw						)
     return im




def 			UpperCamelCase			(					_a						)    ->							Any:
     '''simple docstring'''





     if "224" in checkpoint_url:
          lowercase_				:Union[str, Any]       			=				2_2_4
          lowercase_				:Tuple       			=				2_2_4 / 2_5_6
     elif "384" in checkpoint_url:
          lowercase_				:Optional[int]       			=				3_8_4
          lowercase_				:str       			=				None
     else:
          lowercase_				:Dict       			=				5_1_2
          lowercase_				:Union[str, 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 			UpperCamelCase			(					_a				,						_a				,						_a				,						_a						)    ->							Optional[int]:
     '''simple docstring'''





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

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

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

     # Check outputs on an image, prepared by ConvNextImageProcessor
     lowercase_				:Optional[int]       			=				convert_preprocessor(lowercase_						)
     lowercase_				:Dict       			=				preprocessor(images=prepare_img()				,						return_tensors='''pt'''						)
     lowercase_				:List[str]       			=				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":
          lowercase_				:Optional[int]       			=				torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
          lowercase_				:List[str]       			=				torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
          lowercase_				:Any       			=				torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
          lowercase_				:Dict       			=				torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
          lowercase_				:Union[str, Any]       			=				torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
          lowercase_				:List[Any]       			=				torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
          lowercase_				:Tuple       			=				torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
          lowercase_				:List[str]       			=				torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
          lowercase_				:Tuple       			=				torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
          lowercase_				:Any       			=				torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
          lowercase_				:Any       			=				torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
          lowercase_				:int       			=				torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
          lowercase_				:Tuple       			=				torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
          lowercase_				:List[Any]       			=				torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
          lowercase_				:List[str]       			=				torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
          lowercase_				:List[str]       			=				torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
          lowercase_				:Dict       			=				torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]						)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
          lowercase_				:str       			=				torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]						)
     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_						)

     lowercase_				:Optional[Any]       			=				'''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__":
   SCREAMING_SNAKE_CASE				:						Tuple			   = 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")

   SCREAMING_SNAKE_CASE				:						List[str]			   = 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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_      						=   logging.get_logger(__name__)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Union[str, Any]			=    ["pixel_values"]







      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       PILImageResampling = PILImageResampling.BICUBIC       ,				snake_case    :       bool = True       ,				snake_case    :       Union[int, float] = 1 / 255       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				**snake_case    :       Union[str, Any]       ,				):
          super().__init__(**snake_case )
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  size if size is not None else {'''height''': 224, '''width''': 224}
          __UpperCamelCase     						=  get_size_dict(snake_case )

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







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  self.size if size is None else size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )

          __UpperCamelCase						,					__UpperCamelCase     						=  get_image_size(snake_case )
          __UpperCamelCase     						=  min(snake_case       ,				snake_case )
          __UpperCamelCase     						=  (size['''height'''] / crop_size['''height''']) * min_dim
          __UpperCamelCase     						=  (size['''width'''] / crop_size['''width''']) * min_dim
          return center_crop(snake_case       ,				size=(cropped_height, cropped_width)       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       PILImageResampling = PIL.Image.BICUBIC       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[str]       ,				):
          __UpperCamelCase     						=  get_size_dict(snake_case )
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
          return resize(
              snake_case       ,				size=(size['''height'''], size['''width'''])       ,				resample=snake_case       ,				data_format=snake_case       ,				**snake_case )







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







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







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

          __UpperCamelCase     						=  make_list_of_images(snake_case )

          if not valid_images(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_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.''' )

          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_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.''' )

          # All transformations expect numpy arrays.
          __UpperCamelCase     						=  [to_numpy_array(snake_case ) for image in images]

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

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

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

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

          __UpperCamelCase     						=  [to_channel_dimension_format(snake_case       ,				snake_case ) for image in images]

          __UpperCamelCase     						=  {'''pixel_values''': images}
          return BatchFeature(data=snake_case       ,				tensor_type=snake_case )






