



"""simple docstring"""







import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   {'vocab_file': 'spiece.model'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  str			,_snake_case						:  Tuple			,_snake_case						:  int=False			,_snake_case						:  Union[str, Any]=True			,_snake_case						:  List[Any]=False			,_snake_case						:  Dict="<s>"			,_snake_case						:  Optional[Any]="</s>"			,_snake_case						:  List[str]="<unk>"			,_snake_case						:  int="<sep>"			,_snake_case						:  List[Any]="<pad>"			,_snake_case						:  Union[str, Any]="<cls>"			,_snake_case						:  str="<mask>"			,_snake_case						:  int=["<eop>", "<eod>"]			,_snake_case						:  Optional[Dict[str, Any]] = None			,**_snake_case						:  Optional[int]			,)					->		None:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			AddedToken(_snake_case			,lstrip=_snake_case			,rstrip=_snake_case						) if isinstance(_snake_case			,_snake_case						) else mask_token

				lowercase__   :    int				=			{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=_snake_case			,remove_space=_snake_case			,keep_accents=_snake_case			,bos_token=_snake_case			,eos_token=_snake_case			,unk_token=_snake_case			,sep_token=_snake_case			,pad_token=_snake_case			,cls_token=_snake_case			,mask_token=_snake_case			,additional_special_tokens=_snake_case			,sp_model_kwargs=self.sp_model_kwargs			,**_snake_case			,)

				lowercase__   :    Any				=			3

				lowercase__   :    Union[str, Any]				=			do_lower_case
				lowercase__   :    Optional[int]				=			remove_space
				lowercase__   :    Optional[Any]				=			keep_accents
				lowercase__   :    List[str]				=			vocab_file

				lowercase__   :    str				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(_snake_case						)

				try:
						import jieba
				except ModuleNotFoundError as error:
						raise error.__class__(
						    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
						    '''See https://pypi.org/project/jieba/ for installation.'''						)
				lowercase__   :    Union[str, Any]				=			jieba
				lowercase__   :    Tuple				=			str.maketrans(''' \n'''			,'''\u2582\u2583'''						)







		@property
		# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				return len(self.sp_model						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    List[str]				=			{self.convert_ids_to_tokens(_snake_case						): i for i in range(self.vocab_size						)}
				vocab.update(self.added_tokens_encoder						)
				return vocab







		def __getstate__(	self						:  Optional[int]						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    Any				=			self.__dict__.copy()
				lowercase__   :    Optional[int]				=			None
				return state







		def __setstate__(	self						:  List[str]			,_snake_case						:  int						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    str				=			d

				# for backward compatibility
				if not hasattr(self			,'''sp_model_kwargs'''						):
						lowercase__   :    Dict				=			{}

				lowercase__   :    Optional[int]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(self.vocab_file						)







		def 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  Optional[Any]						)					->		Optional[Any]:






				"""simple docstring"""



				if self.remove_space:
						lowercase__   :    str				=			''' '''.join(inputs.strip().split()						)
				else:
						lowercase__   :    List[str]				=			inputs
				lowercase__   :    Tuple				=			outputs.replace('''``'''			,'''"'''						).replace('''\'\''''			,'''"'''						)

				if not self.keep_accents:
						lowercase__   :    Tuple				=			unicodedata.normalize('''NFKD'''			,_snake_case						)
						lowercase__   :    List[str]				=			''''''.join([c for c in outputs if not unicodedata.combining(_snake_case						)]						)
				if self.do_lower_case:
						lowercase__   :    List[str]				=			outputs.lower()

				return outputs







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  str						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			self.preprocess_text(_snake_case						)
				lowercase__   :    int				=			self.sp_model.encode(_snake_case			,out_type=_snake_case						)
				lowercase__   :    List[Any]				=			[]
				for piece in pieces:
						if len(_snake_case						) > 1 and piece[-1] == str(''','''						) and piece[-2].isdigit():
								lowercase__   :    List[str]				=			self.sp_model.EncodeAsPieces(piece[:-1].replace(_snake_case			,''''''						)						)
								if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
										if len(cur_pieces[0]						) == 1:
												lowercase__   :    int				=			cur_pieces[1:]
										else:
												lowercase__   :    Union[str, Any]				=			cur_pieces[0][1:]
								cur_pieces.append(piece[-1]						)
								new_pieces.extend(_snake_case						)
						else:
								new_pieces.append(_snake_case						)

				return new_pieces







		def 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  List[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				return self.sp_model.PieceToId(_snake_case						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  Optional[Any]						)					->		Tuple:






				"""simple docstring"""



				return self.sp_model.IdToPiece(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  int						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			''''''.join(_snake_case						).replace(_snake_case			,''' '''						).strip()
				return out_string







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			[self.sep_token_id]
				lowercase__   :    Optional[Any]				=			[self.cls_token_id]
				if token_ids_a is None:
						return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls







		def 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None			,_snake_case						:  bool = False						)					->		List[int]:






				"""simple docstring"""



				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=_snake_case			,token_ids_a=_snake_case			,already_has_special_tokens=_snake_case						)

				if token_ids_a is not None:
						return ([0] * len(_snake_case						)) + [1] + ([0] * len(_snake_case						)) + [1, 1]
				return ([0] * len(_snake_case						)) + [1, 1]







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			[self.sep_token_id]
				lowercase__   :    Any				=			[2]

				if token_ids_a is None:
						return len(token_ids_a + sep						) * [0] + cls_segment_id
				return len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1] + cls_segment_id







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  str			,_snake_case						:  Optional[str] = None						)					->		Tuple[str]:






				"""simple docstring"""



				if not os.path.isdir(_snake_case						):
						logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
						return
				lowercase__   :    List[Any]				=			os.path.join(
				    _snake_case			,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(_snake_case						) and os.path.isfile(self.vocab_file						):
						copyfile(self.vocab_file			,_snake_case						)
				elif not os.path.isfile(self.vocab_file						):
						with open(_snake_case			,'''wb'''						) as fi:
								lowercase__   :    Optional[Any]				=			self.sp_model.serialized_model_proto()
								fi.write(_snake_case						)

				return (out_vocab_file,)







		def 					UpperCAmelCase    (	self						:  List[Any]			,*_snake_case						:  List[Any]			,**_snake_case						:  List[str]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			super()._decode(*_snake_case			,**_snake_case						)
				lowercase__   :    Tuple				=			text.replace(''' '''			,''''''						).replace('''\u2582'''			,''' '''						).replace('''\u2583'''			,'''\n'''						)
				return text





"""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      __UpperCAmelCase      (    __lowerCamelCase	)							->      Any:
		lowercase__   :    Optional[int]				=			[]
		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      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Dict:
		lowercase__   :    str				=			[]
		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      (    __lowerCamelCase	)							->      Tuple:
		lowercase__   :    List[str]				=			[]
		token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')	)
		return token



def      __UpperCAmelCase      (    )							->      Optional[int]:
		lowercase__   :    List[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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      int:
		lowercase__   :    List[Any]				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Optional[Any]				=			10_00

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

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

		lowercase__   :    Any				=			CvtConfig(num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

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

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

		# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
		else:
				lowercase__   :    List[Any]				=			[2, 2, 20]
				lowercase__   :    Any				=			[3, 12, 16]
				lowercase__   :    Tuple				=			[1_92, 7_68, 10_24]

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

		lowercase__   :    int				=			OrderedDict()
		lowercase__   :    List[Any]				=			[]

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

		lowercase__   :    List[Any]				=			list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(__lowerCamelCase	)
		for i in range(len(__lowerCamelCase	)	):
				lowercase__   :    Optional[Any]				=			original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(__lowerCamelCase	)
		model.save_pretrained(__lowerCamelCase	)
		image_processor.save_pretrained(__lowerCamelCase	)


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

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

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