
"""simple docstring"""



import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard






@pytest.mark.parametrize(
    'compression_format, is_archive'     ,  [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]     ,  )
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Any     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			{
							    '7z': (seven_zip_file, SevenZipExtractor),
							    'bz2': (bza_file, BzipaExtractor),
							    'gzip': (gz_file, GzipExtractor),
							    'lz4': (lza_file, LzaExtractor),
							    'tar': (tar_file, TarExtractor),
							    'xz': (xz_file, XzExtractor),
							    'zip': (zip_file, ZipExtractor),
							    'zstd': (zstd_file, ZstdExtractor),
							}
							_lowerCAmelCase	,		_lowerCAmelCase  	=			input_paths_and_base_extractors[compression_format]
							if input_path is None:
														_lowerCAmelCase  	=			f"""for '{compression_format}' compression_format, """
														if compression_format == "7z":
																					reason += require_pyazr.kwargs["reason"]
														elif compression_format == "lz4":
																					reason += require_lza.kwargs["reason"]
														elif compression_format == "zstd":
																					reason += require_zstandard.kwargs["reason"]
														pytest.skip(SCREAMING_SNAKE_CASE						)
							assert base_extractor.is_extractable(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			tmp_path / ('extracted' if is_archive else 'extracted.txt')
							base_extractor.extract(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							if is_archive:
														assert output_path.is_dir()
														for file_path in output_path.iterdir():
																					assert file_path.name == text_file.name
																					_lowerCAmelCase  	=			file_path.read_text(encoding='utf-8'						)
							else:
														_lowerCAmelCase  	=			output_path.read_text(encoding='utf-8'						)
							_lowerCAmelCase  	=			text_file.read_text(encoding='utf-8'						)
							assert extracted_file_content == expected_file_content






@pytest.mark.parametrize(
    'compression_format, is_archive'     ,  [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]     ,  )
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			{
							    '7z': seven_zip_file,
							    'bz2': bza_file,
							    'gzip': gz_file,
							    'lz4': lza_file,
							    'tar': tar_file,
							    'xz': xz_file,
							    'zip': zip_file,
							    'zstd': zstd_file,
							}
							_lowerCAmelCase  	=			input_paths[compression_format]
							if input_path is None:
														_lowerCAmelCase  	=			f"""for '{compression_format}' compression_format, """
														if compression_format == "7z":
																					reason += require_pyazr.kwargs["reason"]
														elif compression_format == "lz4":
																					reason += require_lza.kwargs["reason"]
														elif compression_format == "zstd":
																					reason += require_zstandard.kwargs["reason"]
														pytest.skip(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			Extractor.infer_extractor_format(SCREAMING_SNAKE_CASE						)
							assert extractor_format is not None
							_lowerCAmelCase  	=			tmp_path / ('extracted' if is_archive else 'extracted.txt')
							Extractor.extract(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							if is_archive:
														assert output_path.is_dir()
														for file_path in output_path.iterdir():
																					assert file_path.name == text_file.name
																					_lowerCAmelCase  	=			file_path.read_text(encoding='utf-8'						)
							else:
														_lowerCAmelCase  	=			output_path.read_text(encoding='utf-8'						)
							_lowerCAmelCase  	=			text_file.read_text(encoding='utf-8'						)
							assert extracted_file_content == expected_file_content






@pytest.fixture
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]						):






							"""simple docstring"""



							import tarfile

							_lowerCAmelCase  	=			tmp_path / 'data_dot_dot'
							directory.mkdir()
							_lowerCAmelCase  	=			directory / 'tar_file_with_dot_dot.tar'
							with tarfile.TarFile(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														f.add(SCREAMING_SNAKE_CASE     ,  arcname=os.path.join('..'     ,  text_file.name						)						)
							return path






@pytest.fixture
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							import tarfile

							_lowerCAmelCase  	=			tmp_path / 'data_sym_link'
							directory.mkdir()
							_lowerCAmelCase  	=			directory / 'tar_file_with_sym_link.tar'
							os.symlink('..'     ,  directory / 'subdir'     ,  target_is_directory=SCREAMING_SNAKE_CASE						)
							with tarfile.TarFile(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														f.add(str(directory / 'subdir'						)     ,  arcname='subdir'						)  # str required by os.readlink on Windows and Python < 3.8
							return path






@pytest.mark.parametrize(
    'insecure_tar_file, error_log'     ,  [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')]     ,  )
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			{
							    'tar_file_with_dot_dot': tar_file_with_dot_dot,
							    'tar_file_with_sym_link': tar_file_with_sym_link,
							}
							_lowerCAmelCase  	=			insecure_tar_files[insecure_tar_file]
							_lowerCAmelCase  	=			tmp_path / 'extracted'
							TarExtractor.extract(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							assert caplog.text
							for record in caplog.records:
														assert record.levelname == "ERROR"
														assert error_log in record.msg






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict						):






							"""simple docstring"""



							_lowerCAmelCase  	=			tmpdir / 'not_a_zip_file'
							# From: https://github.com/python/cpython/pull/5053
							_lowerCAmelCase  	=			(
							    B'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
							    B'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
							    B'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
							    B'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
							)
							with not_a_zip_file.open('wb'						) as f:
														f.write(SCREAMING_SNAKE_CASE						)
							assert zipfile.is_zipfile(str(SCREAMING_SNAKE_CASE						)						)  # is a false positive for `zipfile`
							assert not ZipExtractor.is_extractable(SCREAMING_SNAKE_CASE						)  # but we're right








"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')						)
							return token






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			'imagenet-1k-id2label.json'
							_lowerCAmelCase  	=			1000

							_lowerCAmelCase  	=			'huggingface/label-files'
							_lowerCAmelCase  	=			num_labels
							_lowerCAmelCase  	=			json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  repo_type='dataset'						)						)     ,  'r'						)						)
							_lowerCAmelCase  	=			{int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}

							_lowerCAmelCase  	=			idalabel
							_lowerCAmelCase  	=			{v: k for k, v in idalabel.items()}

							_lowerCAmelCase  	=			_lowerCAmelCase  	=			CvtConfig(num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

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

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

							# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
							else:
														_lowerCAmelCase  	=			[2, 2, 20]
														_lowerCAmelCase  	=			[3, 12, 16]
														_lowerCAmelCase  	=			[192, 768, 1024]

							_lowerCAmelCase  	=			CvtForImageClassification(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
							_lowerCAmelCase  	=			image_size
							_lowerCAmelCase  	=			torch.load(SCREAMING_SNAKE_CASE     ,  map_location=torch.device('cpu'						)						)

							_lowerCAmelCase  	=			OrderedDict()
							_lowerCAmelCase  	=			[]

							for idx in range(len(config.depth						)						):
														if config.cls_token[idx]:
																					_lowerCAmelCase  	=			list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE						)
														for cnt in range(config.depth[idx]						):
																					_lowerCAmelCase  	=			list_of_state_dict + attention(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(SCREAMING_SNAKE_CASE						)
							for i in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			original_weights[list_of_state_dict[i][1]]

							model.load_state_dict(SCREAMING_SNAKE_CASE						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


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

if __name__ == "__main__":
	_snake_case          =						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=3_8_4,
	    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          =						parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






