



"""simple docstring"""







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        __A       (	A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Any   	=				CLIPTokenizer
		lowerCAmelCase			:					Union[str, Any]   	=				CLIPTokenizerFast
		lowerCAmelCase			:					List[Any]   	=				True
		lowerCAmelCase			:					Optional[int]   	=				{}
		lowerCAmelCase			:					str   	=				False







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






				"""simple docstring"""



				super().setUp()

				# fmt: off
				lowercase__   :    Dict				=			['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
				# fmt: on
				lowercase__   :    Union[str, Any]				=			dict(zip(_snake_case			,range(len(_snake_case						)						)						)						)
				lowercase__   :    Union[str, Any]				=			['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
				lowercase__   :    List[str]				=			{'''unk_token''': '''<unk>'''}

				lowercase__   :    List[Any]				=			os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['''vocab_file''']						)
				lowercase__   :    List[str]				=			os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['''merges_file''']						)
				with open(self.vocab_file			,'''w'''			,encoding='''utf-8'''						) as fp:
						fp.write(json.dumps(_snake_case						) + '''\n'''						)
				with open(self.merges_file			,'''w'''			,encoding='''utf-8'''						) as fp:
						fp.write('''\n'''.join(_snake_case						)						)







		def 					UpperCAmelCase    (	self						:  Any			,**_snake_case						:  Any						)					->		str:






				"""simple docstring"""



				kwargs.update(self.special_tokens_map						)
				return CLIPTokenizer.from_pretrained(self.tmpdirname			,**_snake_case						)







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






				"""simple docstring"""



				kwargs.update(self.special_tokens_map						)
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,**_snake_case						)







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






				"""simple docstring"""



				lowercase__   :    Dict				=			'''lower newer'''
				lowercase__   :    Optional[Any]				=			'''lower newer'''
				return input_text, output_text







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






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			CLIPTokenizer(self.vocab_file			,self.merges_file			,**self.special_tokens_map						)
				lowercase__   :    Dict				=			'''lower newer'''
				lowercase__   :    Union[str, Any]				=			['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
				lowercase__   :    Tuple				=			tokenizer.tokenize(_snake_case						)
				self.assertListEqual(_snake_case			,_snake_case						)

				lowercase__   :    List[str]				=			tokens + [tokenizer.unk_token]
				lowercase__   :    Optional[Any]				=			[10, 2, 16, 9, 3, 2, 16, 20]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case						)			,_snake_case						)







		@require_ftfy
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		List[Any]:






				"""simple docstring"""



				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
								lowercase__   :    List[str]				=			self.tokenizer_class.from_pretrained(_snake_case			,**_snake_case						)
								lowercase__   :    Dict				=			self.rust_tokenizer_class.from_pretrained(_snake_case			,**_snake_case						)

								lowercase__   :    Union[str, Any]				=			'''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
								lowercase__   :    str				=			tokenizer_s.tokenize(_snake_case						)
								lowercase__   :    Optional[Any]				=			tokenizer_r.tokenize(_snake_case						)

								self.assertListEqual(_snake_case			,_snake_case						)

								# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
								# with Tilde) encoded in 2 different ways
								lowercase__   :    Optional[Any]				=			'''xa\u0303y''' + ''' ''' + '''x\xe3y'''
								lowercase__   :    int				=			tokenizer_s.tokenize(_snake_case						)
								lowercase__   :    Union[str, Any]				=			tokenizer_r.tokenize(_snake_case						)

								self.assertListEqual(_snake_case			,_snake_case						)

								# Test that the tokenization is identical on unicode of space type
								lowercase__   :    Optional[int]				=			[
								    '''\u0009''',  # (horizontal tab, '\t')
								    '''\u000B''',  # (vertical tab)
								    '''\u000C''',  # (form feed)
								    '''\u0020''',  # (space, ' ')
								    '''\u200E''',  # (left-to-right mark):w
								    '''\u200F''',  # (right-to-left mark)
								]
								for unicode_seq in spaces_unicodes:
										lowercase__   :    Tuple				=			tokenizer_s.tokenize(_snake_case						)
										lowercase__   :    Optional[int]				=			tokenizer_r.tokenize(_snake_case						)

										self.assertListEqual(_snake_case			,_snake_case						)

								# Test that the tokenization is identical on unicode of line break type
								lowercase__   :    Tuple				=			[
								    '''\u000A''',  # (line feed, '\n')
								    '''\r\n''',  # (carriage return and line feed, '\r\n')
								    '''\u000D''',  # (carriage return, '\r')
								    '''\r''',  # (carriage return, '\r')
								    '''\u000D''',  # (carriage return, '\r')
								    '''\u2028''',  # (line separator)
								    '''\u2029''',  # (paragraph separator)
								    # "\u0085", # (next line)
								]

								# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
								# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
								# space (and thus into an empty list).

								for unicode_seq in line_break_unicodes:
										lowercase__   :    Dict				=			tokenizer_s.tokenize(_snake_case						)
										lowercase__   :    Tuple				=			tokenizer_r.tokenize(_snake_case						)

										self.assertListEqual(_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Optional[int]:






				"""simple docstring"""



				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
								lowercase__   :    Union[str, Any]				=			'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
								lowercase__   :    List[Any]				=			f"""{text_of_1_token} {text_of_1_token}"""

								lowercase__   :    Dict				=			self.rust_tokenizer_class.from_pretrained(
								    _snake_case			,use_fast=_snake_case			,)
								lowercase__   :    Optional[int]				=			tokenizer_r(_snake_case			,return_offsets_mapping=_snake_case			,add_special_tokens=_snake_case						)
								self.assertEqual(encoding.offset_mapping[0]			,(0, len(_snake_case						))						)
								self.assertEqual(
								    encoding.offset_mapping[1]			,(len(_snake_case						) + 1, len(_snake_case						) + 1 + len(_snake_case						))			,)

								lowercase__   :    Tuple				=			f""" {text}"""

								lowercase__   :    Any				=			self.rust_tokenizer_class.from_pretrained(
								    _snake_case			,use_fast=_snake_case			,)
								lowercase__   :    Dict				=			tokenizer_r(_snake_case			,return_offsets_mapping=_snake_case			,add_special_tokens=_snake_case						)
								self.assertEqual(encoding.offset_mapping[0]			,(1, 1 + len(_snake_case						))						)
								self.assertEqual(
								    encoding.offset_mapping[1]			,(1 + len(_snake_case						) + 1, 1 + len(_snake_case						) + 1 + len(_snake_case						))			,)







		def 					UpperCAmelCase    (	self						:  Any						)					->		Tuple:






				"""simple docstring"""



				with self.assertRaises(_snake_case						) as context:
						self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''						)

				self.assertTrue(
				    context.exception.args[0].startswith(
				        '''The `backend_tokenizer` provided does not match the expected format.'''						)						)







		@require_ftfy
		def 					UpperCAmelCase    (	self						:  Any						)					->		Union[str, Any]:






				"""simple docstring"""



				super().test_tokenization_python_rust_equals()







		def 					UpperCAmelCase    (	self						:  str						)					->		Optional[int]:






				"""simple docstring"""



				pass





"""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)
