

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)






import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase  :      int			 ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase  :      Dict			 =direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCAmelCase  :      List[str]			 =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCAmelCase  :      int			 =re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_lowerCAmelCase  :      Union[str, Any]			 ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= None

	# source code of `config_class`
	UpperCAmelCase__:	Optional[int]									= inspect.getsource(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[Any]									= _re_checkpoint.findall(SCREAMING_SNAKE_CASE      )

	# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
	# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
	for ckpt_name, ckpt_link in checkpoints:
		# allow the link to end with `/`
		if ckpt_link.endswith("/"      ):
			UpperCAmelCase__:	Any									= ckpt_link[:-1]

		# verify the checkpoint name corresponds to the checkpoint link
		UpperCAmelCase__:	int									= f"https://huggingface.co/{ckpt_name}"
		if ckpt_link == ckpt_link_from_name:
			UpperCAmelCase__:	Optional[int]									= ckpt_name
			break

	return checkpoint



def 			_A	(      ):
	UpperCAmelCase__:	List[str]									= []

	for config_class in list(CONFIG_MAPPING.values()      ):
		# Skip deprecated models
		if "models.deprecated" in config_class.__module__:
			continue
		UpperCAmelCase__:	Optional[Any]									= get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	List[str]									= config_class.__name__
		if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
			configs_without_checkpoint.append(SCREAMING_SNAKE_CASE      )

	if len(SCREAMING_SNAKE_CASE      ) > 0:
		UpperCAmelCase__:	Dict									= "\n".join(sorted(SCREAMING_SNAKE_CASE      )      )
		raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"      )


if __name__ == "__main__":
					check_config_docstrings_have_checkpoints()