


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        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Tuple											=     []
							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        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    str											=     []
							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        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Any											=     []
							token.append((F'cvt.encoder.stages.{idx}.cls_token', "stage2.cls_token")			)
							return token







def        lowerCAmelCase_  (      ):
							__snake_case	:    Dict											=     []
							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        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Optional[int]											=     "imagenet-1k-id2label.json"
							__snake_case	:    str											=     1_0_0_0

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

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

							__snake_case	:    str											=     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":
														__snake_case	:    Optional[int]											=     [1, 2, 1_0]

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

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

							__snake_case	:    Dict											=     CvtForImageClassification(__lowerCamelCase			)
							__snake_case	:    Any											=     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
							__snake_case	:    int											=     image_size
							__snake_case	:    str											=     torch.load(__lowerCamelCase							,			map_location=torch.device("cpu"			)			)

							__snake_case	:    List[str]											=     OrderedDict()
							__snake_case	:    Dict											=     []

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

							__snake_case	:    Optional[int]											=     list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(__lowerCamelCase			)
							for i in range(len(__lowerCamelCase			)			):
														__snake_case	:    Union[str, Any]											=     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__":
			_snake_case  :	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=384,
			    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."
			)

			_snake_case  :	Tuple			      =       parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_snake_case  :	int			      =       {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_snake_case  :	Dict			      =       logging.WARNING







def        lowerCAmelCase_  (      ):
							__snake_case	:    Union[str, Any]											=     os.getenv("DATASETS_VERBOSITY"							,			__lowerCamelCase			)
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    F'Unknown option DATASETS_VERBOSITY={env_level_str}, '
																					    F'has to be one of: { ", ".join(log_levels.keys()			) }'			)
							return _default_log_level







def        lowerCAmelCase_  (      ):
							return __name__.split("."			)[0]







def        lowerCAmelCase_  (      ):
							return logging.getLogger(_get_library_name()			)







def        lowerCAmelCase_  (      ):
							# Apply our default configuration to the library root logger.
							__snake_case	:    str											=     _get_library_root_logger()
							library_root_logger.setLevel(_get_default_logging_level()			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    Dict											=     _get_library_root_logger()
							library_root_logger.setLevel(logging.NOTSET			)







def        lowerCAmelCase_  (      __lowerCamelCase = None			):
							if name is None:
														__snake_case	:    Tuple											=     _get_library_name()
							return logging.getLogger(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return _get_library_root_logger().getEffectiveLevel()







def        lowerCAmelCase_  (      __lowerCamelCase			):
							_get_library_root_logger().setLevel(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    List[str]											=     False







def        lowerCAmelCase_  (      ):
							__snake_case	:    Tuple											=     True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()

class        a	:




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Optional[Any]					,			**lowerCamelCase :						List[str]       )			->					Optional[int]:  # pylint: disable=unused-argument
														__snake_case	:    int											=     args[0] if args else None

							def __iter__(							self :						Dict       )			->					Optional[int]:
														return iter(self._iterator       )

							def __getattr__(							self :						int					,			lowerCamelCase :						Optional[Any]       )			->					List[Any]:

														def empty_fn(*lowerCamelCase :						Optional[int]					,			**lowerCamelCase :						List[Any]       ):  # pylint: disable=unused-argument
																					return

														return empty_fn

							def __enter__(							self :						List[str]       )			->					int:
														return self






							def __exit__(							self :						Dict					,			lowerCamelCase :						Any					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						str       )			->					Dict:
														return
_snake_case  :	Optional[Any]			      =       True

class        a	:




							"""simple docstring"""


							def __call__(							self :						str					,			*lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple=False					,			**lowerCamelCase :						Union[str, Any]       )			->					Optional[Any]:
														if _tqdm_active and not disable:
																					return tqdm_lib.tqdm(*lowerCamelCase					,			**lowerCamelCase       )
														else:
																					return EmptyTqdm(*lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						str					,			*lowerCamelCase :						str					,			**lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Optional[Any]											=     None
														if _tqdm_active:
																					return tqdm_lib.tqdm.set_lock(*lowerCamelCase					,			**lowerCamelCase       )






							def 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														if _tqdm_active:
																					return tqdm_lib.tqdm.get_lock()
_snake_case  :	int			      =       _tqdm_cls()







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							return bool(_tqdm_active			)







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    Tuple											=     True







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    List[Any]											=     False

