






"""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        _lowerCAmelCase						(							UpperCAmelCase__    :   List[Any] )						->Tuple:
						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        _lowerCAmelCase						(							UpperCAmelCase__    :   str,  UpperCAmelCase__    :   Tuple )						->str:
						A__  :					Optional[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        _lowerCAmelCase						(							UpperCAmelCase__    :   Any )						->List[str]:
						A__  :					List[str]											=					[]
						token.append((f'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""") )
						return token




def        _lowerCAmelCase						(							)						->Tuple:
						A__  :					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        _lowerCAmelCase						(							UpperCAmelCase__    :   str,  UpperCAmelCase__    :   Union[str, Any],  UpperCAmelCase__    :   Optional[Any],  UpperCAmelCase__    :   str )						->Any:
						A__  :					Any											=					"""imagenet-1k-id2label.json"""
						A__  :					Optional[int]											=					1_0_0_0

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

						A__  :					str											=					idalabel
						A__  :					Dict											=					{v: k for k, v in idalabel.items()}

						A__  :					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":
												A__  :					List[Any]											=					[1, 2, 1_0]

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

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

						A__  :					Optional[int]											=					CvtForImageClassification(UpperCAmelCase__ )
						A__  :					str											=					AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
						A__  :					str											=					image_size
						A__  :					int											=					torch.load(UpperCAmelCase__,  map_location=torch.device("""cpu""" ) )

						A__  :					Dict											=					OrderedDict()
						A__  :					Dict											=					[]

						for idx in range(len(config.depth ) ):
												if config.cls_token[idx]:
																		A__  :					int											=					list_of_state_dict + cls_token(UpperCAmelCase__ )
												A__  :					Dict											=					list_of_state_dict + embeddings(UpperCAmelCase__ )
												for cnt in range(config.depth[idx] ):
																		A__  :					Any											=					list_of_state_dict + attention(UpperCAmelCase__,  UpperCAmelCase__ )

						A__  :					str											=					list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(UpperCAmelCase__ )
						for i in range(len(UpperCAmelCase__ ) ):
												A__  :					Dict											=					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__":
						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)














"""simple docstring"""



import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
A_      =							object()

# For specifying empty leaf dict `{}`
A_      =							object()




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Tuple,  UpperCAmelCase__    :   List[Any] )						->Dict:
						A__  :					Union[str, Any]											=					tuple((re.compile(x + """$""" ) for x in qs) )
						for i in range(len(UpperCAmelCase__ ) - len(UpperCAmelCase__ ) + 1 ):
												A__  :					Optional[Any]											=					[x.match(UpperCAmelCase__ ) for x, y in zip(UpperCAmelCase__,  ks[i:] )]
												if matches and all(UpperCAmelCase__ ):
																		return True
						return False




def        _lowerCAmelCase						(							UpperCAmelCase__    :   List[Any] )						->Dict:
						def replace(UpperCAmelCase__    :   int,  UpperCAmelCase__    :   List[str] ):
												for rule, replacement in rules:
																		if _match(UpperCAmelCase__,  UpperCAmelCase__ ):
																								return replacement
												return val

						return replace




def        _lowerCAmelCase						(							)						->Tuple:
						return [
						    # embeddings
						    (("transformer", "wpe", "embedding"), P("""mp""",  UpperCAmelCase__ )),
						    (("transformer", "wte", "embedding"), P("""mp""",  UpperCAmelCase__ )),
						    # atention
						    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCAmelCase__,  """mp""" )),
						    (("attention", "out_proj", "kernel"), P("""mp""",  UpperCAmelCase__ )),
						    (("attention", "out_proj", "bias"), None),
						    # mlp
						    (("mlp", "c_fc", "kernel"), P(UpperCAmelCase__,  """mp""" )),
						    (("mlp", "c_fc", "bias"), P("""mp""" )),
						    (("mlp", "c_proj", "kernel"), P("""mp""",  UpperCAmelCase__ )),
						    (("mlp", "c_proj", "bias"), None),
						    # layer norms
						    ((r"ln_\d+", "bias"), None),
						    ((r"\d+", r"ln_\d+", "scale"), None),
						    (("ln_f", "bias"), None),
						    (("ln_f", "scale"), None),
						]




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Tuple )						->Any:
						A__  :					Union[str, Any]											=					_get_partition_rules()
						A__  :					int											=					_replacement_rules(UpperCAmelCase__ )
						A__  :					Tuple											=					{k: _unmatched for k in flatten_dict(UpperCAmelCase__ )}
						A__  :					Optional[int]											=					{k: replace(UpperCAmelCase__,  UpperCAmelCase__ ) for k, v in initd.items()}
						assert _unmatched not in result.values(), "Incomplete partition spec."
						return freeze(unflatten_dict(UpperCAmelCase__ ) )






