






"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase							=  logging.get_logger()



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    LevitConfig      ,   __UpperCAmelCase      :    Path      ,   __UpperCAmelCase      :    bool = True  )    ->							Dict:
						print(f'Converting {name}...'  )

						with torch.no_grad():
												if hidden_sizes == 1_2_8:
																		if name[-1] == "S":
																								__magic_name__:	Dict							=       timm.create_model("""levit_128s"""      ,   pretrained=__UpperCAmelCase  )
																		else:
																								__magic_name__:	int							=       timm.create_model("""levit_128"""      ,   pretrained=__UpperCAmelCase  )
												if hidden_sizes == 1_9_2:
																		__magic_name__:	Union[str, Any]							=       timm.create_model("""levit_192"""      ,   pretrained=__UpperCAmelCase  )
												if hidden_sizes == 2_5_6:
																		__magic_name__:	List[Any]							=       timm.create_model("""levit_256"""      ,   pretrained=__UpperCAmelCase  )
												if hidden_sizes == 3_8_4:
																		__magic_name__:	Optional[int]							=       timm.create_model("""levit_384"""      ,   pretrained=__UpperCAmelCase  )

												from_model.eval()
												__magic_name__:	Dict							=       LevitForImageClassificationWithTeacher(__UpperCAmelCase  ).eval()
												__magic_name__:	str							=       OrderedDict()

												__magic_name__:	Any							=       from_model.state_dict()
												__magic_name__:	Dict							=       list(from_model.state_dict().keys()  )
												__magic_name__:	int							=       list(our_model.state_dict().keys()  )
												print(len(__UpperCAmelCase  )      ,   len(__UpperCAmelCase  )  )
												for i in range(len(__UpperCAmelCase  )  ):
																		__magic_name__:	int							=       weights[og_keys[i]]
												our_model.load_state_dict(__UpperCAmelCase  )

												__magic_name__:	List[str]							=       torch.randn((2, 3, 2_2_4, 2_2_4)  )
												__magic_name__:	Dict							=       from_model(__UpperCAmelCase  )
												__magic_name__:	List[str]							=       our_model(__UpperCAmelCase  ).logits

						assert torch.allclose(__UpperCAmelCase      ,   __UpperCAmelCase  ), "The model logits don't match the original one."

						__magic_name__:	List[Any]							=       name
						print(__UpperCAmelCase  )

						if push_to_hub:
												our_model.save_pretrained(save_directory / checkpoint_name  )
												__magic_name__:	Union[str, Any]							=       LevitImageProcessor()
												image_processor.save_pretrained(save_directory / checkpoint_name  )

												print(f'Pushed {checkpoint_name}'  )



def   a    (     __UpperCAmelCase      :    Path      ,   __UpperCAmelCase      :    str = None      ,   __UpperCAmelCase      :    bool = True  )    ->							List[Any]:
						__magic_name__:	str							=       """imagenet-1k-id2label.json"""
						__magic_name__:	Dict							=       1_0_0_0
						__magic_name__:	Optional[Any]							=       (1, num_labels)

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

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

						__magic_name__:	List[Any]							=       partial(__UpperCAmelCase      ,   num_labels=__UpperCAmelCase      ,   idalabel=__UpperCAmelCase      ,   labelaid=__UpperCAmelCase  )

						__magic_name__:	List[Any]							=       {
						    """levit-128S""": 1_2_8,
						    """levit-128""": 1_2_8,
						    """levit-192""": 1_9_2,
						    """levit-256""": 2_5_6,
						    """levit-384""": 3_8_4,
						}

						__magic_name__:	List[Any]							=       {
						    """levit-128S""": ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]      ,   num_attention_heads=[4, 6, 8]      ,   depths=[2, 3, 4]      ,   key_dim=[1_6, 1_6, 1_6]      ,   drop_path_rate=0      ,   ),
						    """levit-128""": ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]      ,   num_attention_heads=[4, 8, 1_2]      ,   depths=[4, 4, 4]      ,   key_dim=[1_6, 1_6, 1_6]      ,   drop_path_rate=0      ,   ),
						    """levit-192""": ImageNetPreTrainedConfig(
						        hidden_sizes=[1_9_2, 2_8_8, 3_8_4]      ,   num_attention_heads=[3, 5, 6]      ,   depths=[4, 4, 4]      ,   key_dim=[3_2, 3_2, 3_2]      ,   drop_path_rate=0      ,   ),
						    """levit-256""": ImageNetPreTrainedConfig(
						        hidden_sizes=[2_5_6, 3_8_4, 5_1_2]      ,   num_attention_heads=[4, 6, 8]      ,   depths=[4, 4, 4]      ,   key_dim=[3_2, 3_2, 3_2]      ,   drop_path_rate=0      ,   ),
						    """levit-384""": ImageNetPreTrainedConfig(
						        hidden_sizes=[3_8_4, 5_1_2, 7_6_8]      ,   num_attention_heads=[6, 9, 1_2]      ,   depths=[4, 4, 4]      ,   key_dim=[3_2, 3_2, 3_2]      ,   drop_path_rate=0.1      ,   ),
						}

						if model_name:
												convert_weight_and_push(
												    names_to_hidden_sizes[model_name]      ,   __UpperCAmelCase      ,   names_to_config[model_name]      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name]      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
						return config, expected_shape


if __name__ == "__main__":
							__lowerCamelCase							=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default=None,
							    type=str,
							    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='levit-dump-folder/',
							    type=Path,
							    required=False,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)

							__lowerCamelCase							=  parser.parse_args()
							__lowerCamelCase							=  args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import argparse
import os
import re

import packaging.version


__a	 =      """examples/"""
__a	 =      {
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
__a	 =      {
    """init""": """src/diffusers/__init__.py""",
    """setup""": """setup.py""",
}
__a	 =      """README.md"""
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   with open(lowerCAmelCase_	,    """r"""	,    encoding="""utf-8"""	,    newline="""\n""" ) as f:
      UpperCAmelCase      					=  f.read()
   UpperCAmelCase					,	UpperCAmelCase      					=  REPLACE_PATTERNS[pattern]
   UpperCAmelCase      					=  replace.replace("""VERSION"""	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  re_pattern.sub(lowerCAmelCase_	,    lowerCAmelCase_ )
   with open(lowerCAmelCase_	,    """w"""	,    encoding="""utf-8"""	,    newline="""\n""" ) as f:
      f.write(lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   for folder, directories, fnames in os.walk(lowerCAmelCase_ ):
      # Removing some of the folders with non-actively maintained examples from the walk
      if "research_projects" in directories:
         directories.remove("""research_projects""" )
      if "legacy" in directories:
         directories.remove("""legacy""" )
      for fname in fnames:
         if fname.endswith(""".py""" ):
            update_version_in_file(os.path.join(lowerCAmelCase_	,    lowerCAmelCase_ )	,    lowerCAmelCase_	,    pattern="""examples""" )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_=False )    ->Optional[int]:
   for pattern, fname in REPLACE_FILES.items():
      update_version_in_file(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )
   if not patch:
      update_version_in_examples(lowerCAmelCase_ )
def 	_UpperCamelCase					(    )    ->List[str]:
   UpperCAmelCase      					=  """🤗 Transformers currently provides the following architectures"""
   UpperCAmelCase      					=  """1. Want to contribute a new model?"""
   with open(lowerCAmelCase_	,    """r"""	,    encoding="""utf-8"""	,    newline="""\n""" ) as f:
      UpperCAmelCase      					=  f.readlines()

   # Find the start of the list.
   UpperCAmelCase      					=  0
   while not lines[start_index].startswith(_start_prompt ):
      start_index += 1
   start_index += 1

   UpperCAmelCase      					=  start_index
   # Update the lines in the model list.
   while not lines[index].startswith(_end_prompt ):
      if lines[index].startswith("""1.""" ):
         UpperCAmelCase      					=  lines[index].replace(
             """https://huggingface.co/docs/diffusers/main/model_doc"""	,    """https://huggingface.co/docs/diffusers/model_doc"""	,    )
      index += 1

   with open(lowerCAmelCase_	,    """w"""	,    encoding="""utf-8"""	,    newline="""\n""" ) as f:
      f.writelines(lowerCAmelCase_ )
def 	_UpperCamelCase					(    )    ->List[str]:
   with open(REPLACE_FILES["""init"""]	,    """r""" ) as f:
      UpperCAmelCase      					=  f.read()
   UpperCAmelCase      					=  REPLACE_PATTERNS["""init"""][0].search(lowerCAmelCase_ ).groups()[0]
   return packaging.version.parse(lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_=False )    ->int:
   UpperCAmelCase      					=  get_version()
   if patch and default_version.is_devrelease:
      raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
   if default_version.is_devrelease:
      UpperCAmelCase      					=  default_version.base_version
   elif patch:
      UpperCAmelCase      					=  F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
   else:
      UpperCAmelCase      					=  F"""{default_version.major}.{default_version.minor + 1}.0"""

   # Now let's ask nicely if that's the right one.
   UpperCAmelCase      					=  input(F"""Which version are you releasing? [{default_version}]""" )
   if len(lowerCAmelCase_ ) == 0:
      UpperCAmelCase      					=  default_version

   print(F"""Updating version to {version}.""" )
   global_version_update(lowerCAmelCase_	,    patch=lowerCAmelCase_ )
def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  get_version()
   UpperCAmelCase      					=  F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
   UpperCAmelCase      					=  current_version.base_version

   # Check with the user we got that right.
   UpperCAmelCase      					=  input(F"""Which version are we developing now? [{dev_version}]""" )
   if len(lowerCAmelCase_ ) == 0:
      UpperCAmelCase      					=  dev_version

   print(F"""Updating version to {version}.""" )
   global_version_update(lowerCAmelCase_ )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
 __a	 =      argparse.ArgumentParser()
 parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
 parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
 __a	 =      parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print("""Nothing to do after a patch :-)""")
 else:
  post_release_work()



