import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
  import textwrap

  from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
  import torch

  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  UpperCAmelCase_           =				False


UpperCAmelCase_           =				logging.get_logger(__name__)
UpperCAmelCase_           =				"""ybelkada/fonts"""




def    lowerCAmelCase_  (		)     ->					Dict:







 '''simple docstring'''
 if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
  raise ImportError(
      F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
      '''Pix2StructImageProcessor. Please upgrade torch.'''		)




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Dict	,       lowercase:				Optional[int]		)     ->					Any:







 '''simple docstring'''
 requires_backends(lowerCAmelCase_	,       ['''torch''']		)
 _check_torch_version()

 _UpperCamelCase:  int    						=			image_tensor.unsqueeze(0		)
 _UpperCamelCase:  List[Any]    						=			torch.nn.functional.unfold(lowerCAmelCase_	,       (patch_height, patch_width)	,       stride=(patch_height, patch_width)		)
 _UpperCamelCase:  str    						=			patches.reshape(image_tensor.size(0		)	,       image_tensor.size(1		)	,       lowerCAmelCase_	,       lowerCAmelCase_	,       -1		)
 _UpperCamelCase:  Tuple    						=			patches.permute(0	,       4	,       2	,       3	,       1		).reshape(
     image_tensor.size(2		) // patch_height	,       image_tensor.size(3		) // patch_width	,       image_tensor.size(1		) * patch_height * patch_width	,       )
 return patches.unsqueeze(0		)




def    lowerCAmelCase_  (		lowercase:				Union[str, Any]	,       lowercase:				Optional[Any] = 36	,       lowercase:				Tuple = "black"	,       lowercase:				int = "white"	,       lowercase:				Any = 5	,       lowercase:				Optional[int] = 5	,       lowercase:				List[Any] = 5	,       lowercase:				Dict = 5	,       lowercase:				Optional[int] = None	,       lowercase:				str = None	,       )     ->					Image.Image:







 '''simple docstring'''
 requires_backends(lowerCAmelCase_	,       '''vision'''		)
 # Add new lines so that each line is no more than 80 characters.

 _UpperCamelCase:  Dict    						=			textwrap.TextWrapper(width=80		)
 _UpperCamelCase:  Optional[int]    						=			wrapper.wrap(text=lowerCAmelCase_		)
 _UpperCamelCase:  List[str]    						=			'''\n'''.join(lowerCAmelCase_		)

 if font_bytes is not None and font_path is None:
  _UpperCamelCase:  List[str]    						=			io.BytesIO(lowerCAmelCase_		)
 elif font_path is not None:
  _UpperCamelCase:  Tuple    						=			font_path
 else:
  _UpperCamelCase:  Any    						=			hf_hub_download(lowerCAmelCase_	,       '''Arial.TTF'''		)
 _UpperCamelCase:  str    						=			ImageFont.truetype(lowerCAmelCase_	,       encoding='''UTF-8'''	,       size=lowerCAmelCase_		)

 # Use a temporary canvas to determine the width and height in pixels when
 # rendering the text.
 _UpperCamelCase:  Dict    						=			ImageDraw.Draw(Image.new('''RGB'''	,       (1, 1)	,       lowerCAmelCase_		)		)
 _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			temp_draw.textbbox((0, 0)	,       lowerCAmelCase_	,       lowerCAmelCase_		)

 # Create the actual image with a bit of padding around the text.
 _UpperCamelCase:  str    						=			text_width + left_padding + right_padding
 _UpperCamelCase:  List[str]    						=			text_height + top_padding + bottom_padding
 _UpperCamelCase:  List[Any]    						=			Image.new('''RGB'''	,       (image_width, image_height)	,       lowerCAmelCase_		)
 _UpperCamelCase:  Union[str, Any]    						=			ImageDraw.Draw(lowerCAmelCase_		)
 draw.text(xy=(left_padding, top_padding)	,       text=lowerCAmelCase_	,       fill=lowerCAmelCase_	,       font=lowerCAmelCase_		)
 return image




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				Optional[int]	,       **lowercase:				Union[str, Any]		)     ->					int:







 '''simple docstring'''
 requires_backends(lowerCAmelCase_	,       '''vision'''		)

 # Convert to PIL image if necessary
 _UpperCamelCase:  List[str]    						=			to_pil_image(lowerCAmelCase_		)

 _UpperCamelCase:  Tuple    						=			render_text(lowerCAmelCase_	,       **lowerCAmelCase_		)
 _UpperCamelCase:  List[Any]    						=			max(header_image.width	,       image.width		)

 _UpperCamelCase:  List[Any]    						=			int(image.height * (new_width / image.width)		)
 _UpperCamelCase:  Optional[Any]    						=			int(header_image.height * (new_width / header_image.width)		)

 _UpperCamelCase:  Tuple    						=			Image.new('''RGB'''	,       (new_width, new_height + new_header_height)	,       '''white'''		)
 new_image.paste(header_image.resize((new_width, new_header_height)		)	,       (0, 0)		)
 new_image.paste(image.resize((new_width, new_height)		)	,       (0, new_header_height)		)

 # Convert back to the original framework if necessary
 _UpperCamelCase:  Any    						=			to_numpy_array(lowerCAmelCase_		)

 if infer_channel_dimension_format(lowerCAmelCase_		) == ChannelDimension.LAST:
  _UpperCamelCase:  Optional[Any]    						=			to_channel_dimension_format(lowerCAmelCase_	,       ChannelDimension.LAST		)

 return new_image





class 			__magic_name__	(		__SCREAMING_SNAKE_CASE     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = ["""flattened_patches"""]



   def __init__(						self       :	List[Any]		,	_lowercase       :	Optional[int] = True		,	_lowercase       :	List[str] = True		,	_lowercase       :	Tuple = None		,	_lowercase       :	Optional[Any] = 2_048		,	_lowercase       :	Optional[Any] = False		,	**_lowercase       :	Union[str, Any]		,	):



    """simple docstring"""



    super().__init__(**A_				)
    _UpperCamelCase:  Tuple    						=			patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
    _UpperCamelCase:  Optional[int]    						=			do_normalize
    _UpperCamelCase:  Optional[int]    						=			do_convert_rgb
    _UpperCamelCase:  Union[str, Any]    						=			max_patches
    _UpperCamelCase:  Optional[int]    						=			is_vqa



   def 	lowerCAmelCase    (						self       :	List[str]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	int		,	_lowercase       :	int		,	**_lowercase       :	int				):



    """simple docstring"""



    requires_backends(self.extract_flattened_patches		,	'''torch'''				)
    _check_torch_version()

    # convert to torch
    _UpperCamelCase:  str    						=			to_channel_dimension_format(A_		,	ChannelDimension.FIRST				)
    _UpperCamelCase:  Optional[Any]    						=			torch.from_numpy(A_				)

    _UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			patch_size['''height'''], patch_size['''width''']
    _UpperCamelCase						,				_UpperCamelCase:  List[str]    						=			get_image_size(A_				)

    # maximize scale s.t.
    _UpperCamelCase:  List[Any]    						=			math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)				)
    _UpperCamelCase:  List[str]    						=			max(min(math.floor(scale * image_height / patch_height				)		,	A_				)		,	1				)
    _UpperCamelCase:  int    						=			max(min(math.floor(scale * image_width / patch_width				)		,	A_				)		,	1				)
    _UpperCamelCase:  Any    						=			max(num_feasible_rows * patch_height		,	1				)
    _UpperCamelCase:  List[Any]    						=			max(num_feasible_cols * patch_width		,	1				)

    _UpperCamelCase:  List[Any]    						=			torch.nn.functional.interpolate(
        image.unsqueeze(0				)		,	size=(resized_height, resized_width)		,	mode='''bilinear'''		,	align_corners=A_		,	antialias=A_		,	).squeeze(0				)

    # [1, rows, columns, patch_height * patch_width * image_channels]
    _UpperCamelCase:  Dict    						=			torch_extract_patches(A_		,	A_		,	A_				)

    _UpperCamelCase:  Optional[Any]    						=			patches.shape
    _UpperCamelCase:  str    						=			patches_shape[1]
    _UpperCamelCase:  Any    						=			patches_shape[2]
    _UpperCamelCase:  str    						=			patches_shape[3]

    # [rows * columns, patch_height * patch_width * image_channels]
    _UpperCamelCase:  Tuple    						=			patches.reshape([rows * columns, depth]				)

    # [rows * columns, 1]
    _UpperCamelCase:  str    						=			torch.arange(A_				).reshape([rows, 1]				).repeat(1		,	A_				).reshape([rows * columns, 1]				)
    _UpperCamelCase:  Tuple    						=			torch.arange(A_				).reshape([1, columns]				).repeat(A_		,	1				).reshape([rows * columns, 1]				)

    # Offset by 1 so the ids do not contain zeros, which represent padding.
    row_ids += 1
    col_ids += 1

    # Prepare additional patch features.
    # [rows * columns, 1]
    _UpperCamelCase:  Tuple    						=			row_ids.to(torch.floataa				)
    _UpperCamelCase:  List[Any]    						=			col_ids.to(torch.floataa				)

    # [rows * columns, 2 + patch_height * patch_width * image_channels]
    _UpperCamelCase:  Any    						=			torch.cat([row_ids, col_ids, patches]		,	-1				)

    # [max_patches, 2 + patch_height * patch_width * image_channels]
    _UpperCamelCase:  Union[str, Any]    						=			torch.nn.functional.pad(A_		,	[0, 0, 0, max_patches - (rows * columns)]				).float()

    _UpperCamelCase:  Dict    						=			to_numpy_array(A_				)

    return result



   def 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	Optional[int]		,	_lowercase       :	Union[str, Any] = None		,	**_lowercase       :	Optional[Any]				):



    """simple docstring"""



    if image.dtype == np.uinta:
     _UpperCamelCase:  Union[str, Any]    						=			image.astype(np.floataa				)

    # take mean across the whole `image`
    _UpperCamelCase:  int    						=			np.mean(A_				)
    _UpperCamelCase:  List[Any]    						=			np.std(A_				)
    _UpperCamelCase:  Dict    						=			max(A_		,	1.0 / math.sqrt(np.prod(image.shape				)				)				)

    return normalize(A_		,	mean=A_		,	std=A_		,	**A_				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	str		,	_lowercase       :	Optional[Any] = None		,	_lowercase       :	Any = None		,	_lowercase       :	List[str] = None		,	_lowercase       :	int = None		,	_lowercase       :	Union[str, Any] = None		,	_lowercase       :	Optional[Any] = None		,	_lowercase       :	List[Any] = ChannelDimension.FIRST		,	**_lowercase       :	List[Any]		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			do_normalize if do_normalize is not None else self.do_normalize
    _UpperCamelCase:  Optional[int]    						=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
    _UpperCamelCase:  List[str]    						=			patch_size if patch_size is not None else self.patch_size
    _UpperCamelCase:  int    						=			max_patches if max_patches is not None else self.max_patches
    _UpperCamelCase:  Optional[int]    						=			self.is_vqa

    if kwargs.get('''data_format'''		,	A_				) is not None:
     raise ValueError('''data_format is not an accepted input as the outputs are '''				)

    _UpperCamelCase:  Tuple    						=			make_list_of_images(A_				)

    if not valid_images(A_				):
     raise ValueError(
         '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
         '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

    # PIL RGBA images are converted to RGB
    if do_convert_rgb:
     _UpperCamelCase:  int    						=			[convert_to_rgb(A_				) for image in images]

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

    if is_vqa:
     if header_text is None:
      raise ValueError('''A header text must be provided for VQA models.'''				)
     _UpperCamelCase:  Dict    						=			kwargs.pop('''font_bytes'''		,	A_				)
     _UpperCamelCase:  Optional[Any]    						=			kwargs.pop('''font_path'''		,	A_				)

     if isinstance(A_		,	A_				):
      _UpperCamelCase:  List[Any]    						=			[header_text] * len(A_				)

     _UpperCamelCase:  int    						=			[
         render_header(A_		,	header_text[i]		,	font_bytes=A_		,	font_path=A_				)
         for i, image in enumerate(A_				)
     ]

    if do_normalize:
     _UpperCamelCase:  Dict    						=			[self.normalize(image=A_				) for image in images]

    # convert to torch tensor and permute
    _UpperCamelCase:  Dict    						=			[
        self.extract_flattened_patches(image=A_		,	max_patches=A_		,	patch_size=A_				)
        for image in images
    ]

    # create attention mask in numpy
    _UpperCamelCase:  Optional[Any]    						=			[(image.sum(axis=-1				) != 0).astype(np.floataa				) for image in images]

    _UpperCamelCase:  Dict    						=			BatchFeature(
        data={'''flattened_patches''': images, '''attention_mask''': attention_masks}		,	tensor_type=A_				)

    return encoded_outputs






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 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')	)
    return token


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__											=			1_0_0_0

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__											=			num_labels
    SCREAMING_SNAKE_CASE__											=			json.load(open(cached_download(hf_hub_url(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	)	) ,							'''r'''	)	)
    SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE__											=			idalabel
    SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE__											=			SCREAMING_SNAKE_CASE__											=			CvtConfig(num_labels=lowerCAmelCase_ ,							idalabel=lowerCAmelCase_ ,							labelaid=lowerCAmelCase_	)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "13":
        SCREAMING_SNAKE_CASE__											=			[1, 2, 1_0]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "21":
        SCREAMING_SNAKE_CASE__											=			[1, 4, 1_6]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        SCREAMING_SNAKE_CASE__											=			[2, 2, 2_0]
        SCREAMING_SNAKE_CASE__											=			[3, 1_2, 1_6]
        SCREAMING_SNAKE_CASE__											=			[1_9_2, 7_6_8, 1_0_2_4]

    SCREAMING_SNAKE_CASE__											=			CvtForImageClassification(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
    SCREAMING_SNAKE_CASE__											=			image_size
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location=torch.device('''cpu'''	)	)

    SCREAMING_SNAKE_CASE__											=			OrderedDict()
    SCREAMING_SNAKE_CASE__											=			[]

    for idx in range(len(config.depth	)	):
        if config.cls_token[idx]:
            SCREAMING_SNAKE_CASE__											=			list_of_state_dict + cls_token(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			list_of_state_dict + embeddings(lowerCAmelCase_	)
        for cnt in range(config.depth[idx]	):
            SCREAMING_SNAKE_CASE__											=			list_of_state_dict + attention(lowerCAmelCase_ ,							lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(lowerCAmelCase_	)
    for i in range(len(lowerCAmelCase_	)	):
        SCREAMING_SNAKE_CASE__											=			original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(lowerCAmelCase_	)
    model.save_pretrained(lowerCAmelCase_	)
    image_processor.save_pretrained(lowerCAmelCase_	)


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

if __name__ == "__main__":
  _A					:				Union[str, Any]				=			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					:				int				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
