





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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Dict:
    lowerCamelCase__       : Optional[int]   					=						[]
    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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[str]:
    lowerCamelCase__       : Optional[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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Union[str, Any]   					=						[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')					)
    return token


def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    lowerCamelCase__       : Tuple   					=						[]
    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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Optional[int]:
    lowerCamelCase__       : Union[str, Any]   					=						'imagenet-1k-id2label.json'
    lowerCamelCase__       : Tuple   					=						1000

    lowerCamelCase__       : Tuple   					=						'huggingface/label-files'
    lowerCamelCase__       : Optional[int]   					=						num_labels
    lowerCamelCase__       : Optional[Any]   					=						json.load(open(cached_download(hf_hub_url(_UpperCAmelCase		,      _UpperCAmelCase		,      repo_type='dataset'					)					)		,      'r'					)					)
    lowerCamelCase__       : Optional[int]   					=						{int(_UpperCAmelCase					): v for k, v in idalabel.items()}

    lowerCamelCase__       : List[Any]   					=						idalabel
    lowerCamelCase__       : Optional[Any]   					=						{v: k for k, v in idalabel.items()}

    lowerCamelCase__       : int   					=						CvtConfig(num_labels=_UpperCAmelCase		,      idalabel=_UpperCAmelCase		,      labelaid=_UpperCAmelCase					)

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

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

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

    lowerCamelCase__       : Tuple   					=						CvtForImageClassification(_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
    lowerCamelCase__       : Optional[Any]   					=						image_size
    lowerCamelCase__       : int   					=						torch.load(_UpperCAmelCase		,      map_location=torch.device('cpu'					)					)

    lowerCamelCase__       : Dict   					=						OrderedDict()
    lowerCamelCase__       : Union[str, Any]   					=						[]

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

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

    model.load_state_dict(_UpperCAmelCase					)
    model.save_pretrained(_UpperCAmelCase					)
    image_processor.save_pretrained(_UpperCAmelCase					)


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

if __name__ == "__main__":
    _UpperCAmelCase							:			int        =							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."""
    )

    _UpperCAmelCase							:			Optional[int]        =							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)












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


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = 0.9     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : int   					=						size if size is not None else {'shortest_edge': 224}
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__       : str   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Tuple   					=						do_resize
     lowerCamelCase__       : str   					=						size
     lowerCamelCase__       : List[str]   					=						crop_pct
     lowerCamelCase__       : Any   					=						resample
     lowerCamelCase__       : Tuple   					=						do_center_crop
     lowerCamelCase__       : Any   					=						crop_size
     lowerCamelCase__       : Optional[int]   					=						do_rescale
     lowerCamelCase__       : Optional[Any]   					=						rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     lowerCamelCase__       : Optional[Any]   					=						image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[float] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : int     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Any   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     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:
             lowerCamelCase__       : List[Any]   					=						int(size['shortest_edge'] / crop_pct      )
         elif "height" in size and "width" in size:
             if size["height"] == size["width"]:
                 lowerCamelCase__       : int   					=						int(size['height'] / crop_pct      )
             else:
                 lowerCamelCase__       : Any   					=						(int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

         lowerCamelCase__       : Union[str, Any]   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     else:
         if "shortest_edge" in size:
             lowerCamelCase__       : int   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=size['shortest_edge']     ,	default_to_square=UpperCAmelCase      )
         elif "height" in size and "width" in size:
             lowerCamelCase__       : List[Any]   					=						(size['height'], size['width'])
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Union[str, Any]   					=						get_size_dict(UpperCAmelCase      )
     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(UpperCAmelCase     ,	size=(size['height'], size['width'])     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      int:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )







 def 	A_      ( self    : str     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	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    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Any     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : Optional[Any]   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Any   					=						crop_pct if crop_pct is not None else self.crop_pct
     lowerCamelCase__       : Any   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Any   					=						do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : str   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__       : int   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Any   					=						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 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.
     lowerCamelCase__       : Dict   					=						[to_numpy_array(UpperCAmelCase      ) for image in images]

     if do_resize:
         lowerCamelCase__       : Any   					=						[self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	crop_pct=UpperCAmelCase     ,	resample=UpperCAmelCase      ) for image in images]

     if do_center_crop:
         lowerCamelCase__       : Optional[Any]   					=						[self.center_crop(image=UpperCAmelCase     ,	size=UpperCAmelCase      ) for image in images]

     if do_rescale:
         lowerCamelCase__       : int   					=						[self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase      ) for image in images]

     if do_normalize:
         lowerCamelCase__       : List[Any]   					=						[self.normalize(image=UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )





