

'''simple docstring'''

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					[]
  embed.append(
      (
          F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
          F'''stage{idx}.patch_embed.proj.weight''',
      ) )
  embed.append(
      (
          F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
          F'''stage{idx}.patch_embed.proj.bias''',
      ) )
  embed.append(
      (
          F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
          F'''stage{idx}.patch_embed.norm.weight''',
      ) )
  embed.append(
      (
          F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
          F'''stage{idx}.patch_embed.norm.bias''',
      ) )
  return embed


def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:int						      =					[]
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
          F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
          F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
          F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
          F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
          F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
      ) )
  attention_weights.append(
      (
          F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
          F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
      ) )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''') )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''') )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''') )
  attention_weights.append(
      (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''') )
  return attention_weights


def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					[]
  token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token") )
  return token


def    A_  ( ):
  SCREAMING_SNAKE_CASE:str						      =					[]
  head.append(("layernorm.weight", "norm.weight") )
  head.append(("layernorm.bias", "norm.bias") )
  head.append(("classifier.weight", "head.weight") )
  head.append(("classifier.bias", "head.bias") )
  return head


def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					"imagenet-1k-id2label.json"
  SCREAMING_SNAKE_CASE:Optional[int]						      =					1000

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

  SCREAMING_SNAKE_CASE:str						      =					idalabel
  SCREAMING_SNAKE_CASE:List[str]						      =					{v: k for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					CvtConfig(num_labels=_A      ,       idalabel=_A      ,       labelaid=_A )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit("/"      ,       1 )[-1][4:6] == "13":
    SCREAMING_SNAKE_CASE:Any						      =					[1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit("/"      ,       1 )[-1][4:6] == "21":
    SCREAMING_SNAKE_CASE:List[str]						      =					[1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    SCREAMING_SNAKE_CASE:List[Any]						      =					[2, 2, 20]
    SCREAMING_SNAKE_CASE:str						      =					[3, 12, 16]
    SCREAMING_SNAKE_CASE:Tuple						      =					[192, 768, 1024]

  SCREAMING_SNAKE_CASE:Optional[Any]						      =					CvtForImageClassification(_A )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_size
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.load(_A      ,       map_location=torch.device("cpu" ) )

  SCREAMING_SNAKE_CASE:Any						      =					OrderedDict()
  SCREAMING_SNAKE_CASE:Dict						      =					[]

  for idx in range(len(config.depth ) ):
    if config.cls_token[idx]:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					list_of_state_dict + cls_token(_A )
    SCREAMING_SNAKE_CASE:Tuple						      =					list_of_state_dict + embeddings(_A )
    for cnt in range(config.depth[idx] ):
      SCREAMING_SNAKE_CASE:List[Any]						      =					list_of_state_dict + attention(_A      ,       _A )

  SCREAMING_SNAKE_CASE:List[str]						      =					list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(_A )
  for i in range(len(_A ) ):
    SCREAMING_SNAKE_CASE:Any						      =					original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(_A )
  model.save_pretrained(_A )
  image_processor.save_pretrained(_A )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    parser.add_argument(
        "--cvt_model",
        default="cvt-w24",
        type=str,
        help="Name of the cvt model you'd like to convert.",
    )
    parser.add_argument(
        "--image_size",
        default=3_84,
        type=int,
        help="Input Image Size",
    )
    parser.add_argument(
        "--cvt_file_name",
        default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
        type=str,
        help="Input Image Size",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )

    A_       						=							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









'''simple docstring'''

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A_       						=							logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 							_snake_case (       _a						):
   _A       :     int       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					size if size is not None else {"shortest_edge": 224}
     SCREAMING_SNAKE_CASE:int						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:str						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__     ,param_name="crop_size"							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					do_resize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					size
     SCREAMING_SNAKE_CASE:List[Any]						      =					resample
     SCREAMING_SNAKE_CASE:Any						      =					do_center_crop
     SCREAMING_SNAKE_CASE:List[Any]						      =					crop_size
     SCREAMING_SNAKE_CASE:Tuple						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Dict						      =					do_normalize
     SCREAMING_SNAKE_CASE:int						      =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_convert_rgb
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Any     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Dict						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=size["shortest_edge"]     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     return resize(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__							)
     if "height" not in size or "width" not in size:
       raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
     return center_crop(SCREAMING_SNAKE_CASE__     ,size=(size["height"], size["width"])     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Tuple     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







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

     SCREAMING_SNAKE_CASE:Any						      =					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:
       raise ValueError("Size 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."							)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
       SCREAMING_SNAKE_CASE:List[Any]						      =					[convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:Optional[int]						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE: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:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.center_crop(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__							) for image in images]

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

     if do_normalize:
       SCREAMING_SNAKE_CASE:List[str]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:List[Any]						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Optional[int]						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






