





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


_UpperCAmelCase				:       int	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple[int, int]:
    def constraint_to_multiple_of(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=0						,	_UpperCAmelCase=None ):
        lowerCamelCase__    :      int					 =		round(val / multiple ) * multiple

        if max_val is not None and x > max_val:
            lowerCamelCase__    :      Tuple					 =		math.floor(val / multiple ) * multiple

        if x < min_val:
            lowerCamelCase__    :      Tuple					 =		math.ceil(val / multiple ) * multiple

        return x

    lowerCamelCase__    :      Any					 =		(output_size, output_size) if isinstance(_UpperCAmelCase						,	_UpperCAmelCase ) else output_size

    lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		get_image_size(_UpperCAmelCase )
    lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		output_size

    # determine new height and width
    lowerCamelCase__    :      Dict					 =		output_height / input_height
    lowerCamelCase__    :      Optional[int]					 =		output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width ) < abs(1 - scale_height ):
            # fit width
            lowerCamelCase__    :      Union[str, Any]					 =		scale_width
        else:
            # fit height
            lowerCamelCase__    :      Tuple					 =		scale_height

    lowerCamelCase__    :      Optional[int]					 =		constraint_to_multiple_of(scale_height * input_height						,	multiple=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		constraint_to_multiple_of(scale_width * input_width						,	multiple=_UpperCAmelCase )

    return (new_height, new_width)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		["""pixel_values"""]
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      Dict[str, int] = None			,     UpperCAmelCase  :      PILImageResampling = PILImageResampling.BILINEAR			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      int = 1			,     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  :      Optional[Any]			,     )			->	None:
     super().__init__(**UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		size if size is not None else {'height': 384, 'width': 384}
     lowerCamelCase__    :      str					 =		get_size_dict(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		do_resize
     lowerCamelCase__    :      List[str]					 =		size
     lowerCamelCase__    :      Optional[int]					 =		keep_aspect_ratio
     lowerCamelCase__    :      Union[str, Any]					 =		ensure_multiple_of
     lowerCamelCase__    :      int					 =		resample
     lowerCamelCase__    :      str					 =		do_rescale
     lowerCamelCase__    :      List[Any]					 =		rescale_factor
     lowerCamelCase__    :      List[Any]					 =		do_normalize
     lowerCamelCase__    :      str					 =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__    :      Tuple					 =		image_std if image_std is not None else IMAGENET_STANDARD_STD
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      np.ndarray			,     UpperCAmelCase  :      Dict[str, int]			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      PILImageResampling = PILImageResampling.BICUBIC			,     UpperCAmelCase  :      Optional[Union[str, ChannelDimension]] = None			,     **UpperCAmelCase  :      str			,     )			->	np.ndarray:
     lowerCamelCase__    :      Optional[int]					 =		get_size_dict(UpperCAmelCase	)
     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()}"""	)
     lowerCamelCase__    :      Union[str, Any]					 =		get_resize_output_image_size(
         UpperCAmelCase			,     output_size=(size['height'], size['width'])			,     keep_aspect_ratio=UpperCAmelCase			,     multiple=UpperCAmelCase			,     )
     return resize(UpperCAmelCase			,     size=UpperCAmelCase			,     resample=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      np.ndarray			,     UpperCAmelCase  :      Union[int, float]			,     UpperCAmelCase  :      Optional[Union[str, ChannelDimension]] = None			,     **UpperCAmelCase  :      int			,     )			->	str:
     return rescale(UpperCAmelCase			,     scale=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      np.ndarray			,     UpperCAmelCase  :      Union[float, List[float]]			,     UpperCAmelCase  :      Union[float, List[float]]			,     UpperCAmelCase  :      Optional[Union[str, ChannelDimension]] = None			,     **UpperCAmelCase  :      str			,     )			->	np.ndarray:
     return normalize(UpperCAmelCase			,     mean=UpperCAmelCase			,     std=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      ImageInput			,     UpperCAmelCase  :      bool = None			,     UpperCAmelCase  :      int = None			,     UpperCAmelCase  :      bool = None			,     UpperCAmelCase  :      int = None			,     UpperCAmelCase  :      PILImageResampling = 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  :      List[str]			,     )			->	PIL.Image.Image:
     lowerCamelCase__    :      Optional[int]					 =		do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__    :      str					 =		size if size is not None else self.size
     lowerCamelCase__    :      int					 =		get_size_dict(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowerCamelCase__    :      Union[str, Any]					 =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowerCamelCase__    :      Any					 =		resample if resample is not None else self.resample
     lowerCamelCase__    :      Optional[Any]					 =		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__    :      Dict					 =		do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__    :      Dict					 =		image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__    :      List[Any]					 =		image_std if image_std is not None else self.image_std

     lowerCamelCase__    :      Optional[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_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__    :      Tuple					 =		[self.resize(image=UpperCAmelCase			,     size=UpperCAmelCase			,     resample=UpperCAmelCase	) for image in images]

     if do_rescale:
         lowerCamelCase__    :      Tuple					 =		[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__    :      List[str]					 =		[to_channel_dimension_format(UpperCAmelCase			,     UpperCAmelCase	) for image in images]

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







 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[Tuple] = None	)			->	List[Any]:
     lowerCamelCase__    :      Dict					 =		outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
         if len(UpperCAmelCase	) != len(UpperCAmelCase	):
             raise ValueError(
                 'Make sure that you pass in as many target sizes as the batch dimension of the logits'	)

         if is_torch_tensor(UpperCAmelCase	):
             lowerCamelCase__    :      Dict					 =		target_sizes.numpy()

         lowerCamelCase__    :      int					 =		[]

         for idx in range(len(UpperCAmelCase	)	):
             lowerCamelCase__    :      Optional[int]					 =		torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0	)			,     size=target_sizes[idx]			,     mode='bilinear'			,     align_corners=UpperCAmelCase	)
             lowerCamelCase__    :      Union[str, Any]					 =		resized_logits[0].argmax(dim=0	)
             semantic_segmentation.append(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      Optional[Any]					 =		logits.argmax(dim=1	)
         lowerCamelCase__    :      Dict					 =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

     return semantic_segmentation












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


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

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

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

    lowerCamelCase__    :      List[str]					 =		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__    :      List[Any]					 =		[1, 2, 10]

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

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

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

    lowerCamelCase__    :      Optional[int]					 =		OrderedDict()
    lowerCamelCase__    :      Tuple					 =		[]

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

    lowerCamelCase__    :      int					 =		list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase )
    for i in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      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				:       List[str]	   =				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				:       List[str]	   =				parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





