

'''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	(       __snake_case					:		Any	)	->							List[Any]:






						"""simple docstring"""





						A__					:       Optional[Any]     =[]
						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	(       __snake_case					:		Union[str, Any],		__snake_case					:		Tuple	)	->							List[Any]:






						"""simple docstring"""





						A__					:       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 						__lowerCamelCase	(       __snake_case					:		str	)	->							Tuple:






						"""simple docstring"""





						A__					:       List[str]     =[]
						token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")	)
						return token
def 						__lowerCamelCase	(       )	->							str:






						"""simple docstring"""





						A__					:       Union[str, 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 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		Dict,		__snake_case					:		str,		__snake_case					:		List[str]	)	->							List[str]:






						"""simple docstring"""





						A__					:       Any     ="""imagenet-1k-id2label.json"""
						A__					:       Tuple     =1_000

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

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

						A__					:       str     =CvtConfig(num_labels=__snake_case,		idalabel=__snake_case,		labelaid=__snake_case	)

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("""/""",		1	)[-1][4:6] == "13":
												A__					:       List[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__					:       Tuple     =[2, 2, 20]
												A__					:       Optional[int]     =[3, 12, 16]
												A__					:       Union[str, Any]     =[192, 768, 1_024]

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

						A__					:       Optional[Any]     =OrderedDict()
						A__					:       List[Any]     =[]

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

						A__					:       Dict     =list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(__snake_case	)
						for i in range(len(__snake_case	)	):
												A__					:       List[str]     =original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(__snake_case	)
						model.save_pretrained(__snake_case	)
						image_processor.save_pretrained(__snake_case	)


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

if __name__ == "__main__":
		__snake_case			:     Dict										=      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			:     List[str]										=      parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	['image_processor', 'tokenizer']
			__snake_case        =	'FlavaImageProcessor'
			__snake_case        =	('BertTokenizer', 'BertTokenizerFast')


			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""		,      lowerCAmelCase_		,      )
															A__					:       Optional[Any]     =kwargs.pop("""feature_extractor"""			)

									A__					:       List[Any]     =image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""			)
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""			)

									super().__init__(lowerCAmelCase_		,      lowerCAmelCase_			)
									A__					:       int     =self.image_processor


			def __call__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[ImageInput] = None		,      lowerCAmelCase_	:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Union[bool, str, PaddingStrategy] = False		,      lowerCAmelCase_	:						Union[bool, str, TruncationStrategy] = False		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						int = 0		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		str:





									'''simple docstring'''

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

									if text is not None:
															A__					:       int     =self.tokenizer(
															    text=lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_		,      padding=lowerCAmelCase_		,      truncation=lowerCAmelCase_		,      max_length=lowerCAmelCase_		,      stride=lowerCAmelCase_		,      pad_to_multiple_of=lowerCAmelCase_		,      return_token_type_ids=lowerCAmelCase_		,      return_attention_mask=lowerCAmelCase_		,      return_overflowing_tokens=lowerCAmelCase_		,      return_special_tokens_mask=lowerCAmelCase_		,      return_offsets_mapping=lowerCAmelCase_		,      return_length=lowerCAmelCase_		,      verbose=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									if images is not None:
															A__					:       List[str]     =self.image_processor(
															    lowerCAmelCase_		,      return_image_mask=lowerCAmelCase_		,      return_codebook_pixels=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									if text is not None and images is not None:
															encoding.update(lowerCAmelCase_			)
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**lowerCAmelCase_			)		,      tensor_type=lowerCAmelCase_			)


			def lowercase__						(				self	:						Any		,      *lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									return self.tokenizer.batch_decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						Any			)      ->		Tuple:





									'''simple docstring'''
									return self.tokenizer.decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Any     =self.tokenizer.model_input_names
									A__					:       Optional[Any]     =self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		int:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor_class



			@property
			def lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor

