



"""simple docstring"""






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					[]
				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    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Dict					=					[]
				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    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					[]
				token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')	)
				return token







def   _snake_case    (       ):
				_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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Any					=					'imagenet-1k-id2label.json'
				_lowerCamelCase  :    List[str]					=					1000

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

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

				_lowerCamelCase  :    Tuple					=					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  :    str					=					[1, 2, 10]

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

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

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

				_lowerCamelCase  :    List[str]					=					OrderedDict()
				_lowerCamelCase  :    Union[str, Any]					=					[]

				for idx in range(len(config.depth	)	):
								if config.cls_token[idx]:
												_lowerCamelCase  :    Dict					=					list_of_state_dict + cls_token(_UpperCamelCase	)
								_lowerCamelCase  :    str					=					list_of_state_dict + embeddings(_UpperCamelCase	)
								for cnt in range(config.depth[idx]	):
												_lowerCamelCase  :    Optional[int]					=					list_of_state_dict + attention(_UpperCamelCase							,					_UpperCamelCase	)

				_lowerCamelCase  :    List[str]					=					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__":
			lowercase__  		=		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."""
			)

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





"""simple docstring"""






lowercase__  		=		[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

lowercase__  		=		[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

lowercase__  		=		[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

lowercase__  		=		[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

lowercase__  		=		[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

lowercase__  		=		[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

lowercase__  		=		[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

lowercase__  		=		[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
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