

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   __UpperCAmelCase					(      __a :     Any      )     ->	Dict:

					"""simple docstring"""

					_a      :       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   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						[]
					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   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	str:

					"""simple docstring"""

					_a      :       Tuple   =						[]
					token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')      )
					return token



def   __UpperCAmelCase					(      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Any   =						[]
					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   __UpperCAmelCase					(      __a :     List[Any]						,__a :     int						,__a :     Any						,__a :     List[str]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						'''imagenet-1k-id2label.json'''
					_a      :       Union[str, Any]   =						1_000

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

					_a      :       Tuple   =						idalabel
					_a      :       Union[str, Any]   =						{v: k for k, v in idalabel.items()}

					_a      :       Any   =						CvtConfig(num_labels=__a						,idalabel=__a						,labelaid=__a      )

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

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

					# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
					else:
										_a      :       Dict   =						[2, 2, 20]
										_a      :       List[Any]   =						[3, 12, 16]
										_a      :       List[str]   =						[192, 768, 1_024]

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

					_a      :       List[Any]   =						OrderedDict()
					_a      :       Tuple   =						[]

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

					_a      :       Union[str, Any]   =						list_of_state_dict + final()
					for gg in list_of_state_dict:
										print(__a      )
					for i in range(len(__a      )      ):
										_a      :       Dict   =						original_weights[list_of_state_dict[i][1]]

					model.load_state_dict(__a      )
					model.save_pretrained(__a      )
					image_processor.save_pretrained(__a      )


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

if __name__ == "__main__":
			a__												=						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.'''
			)

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






a__												=						'''Input must be a string of 8 numbers plus letter'''
a__												=						'''TRWAGMYFPDXBNJZSQVHLCKE'''



def   __UpperCAmelCase					(      __a :     str      )     ->	bool:

					"""simple docstring"""

					if not isinstance(__a						,__a      ):
										_a      :       List[str]   =						F"""Expected string as input, found {type(__a      ).__name__}"""
										raise TypeError(__a      )

					_a      :       List[Any]   =						spanish_id.replace('''-'''						,''''''      ).upper()
					if len(__a      ) != 9:
										raise ValueError(__a      )

					try:
										_a      :       Any   =						int(spanish_id_clean[0:8]      )
										_a      :       str   =						spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__a      ) from ex

					if letter.isdigit():
										raise ValueError(__a      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



