import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase							:      Optional[int]      							=logging.get_logger()



@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     nn.Module
  lowercase		:     List[nn.Module]            =				field(default_factory=lowerCAmelCase_			)
  lowercase		:     list            =				field(default_factory=lowerCAmelCase_			)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tensor      ,	SCREAMING_SNAKE_CASE__    :					Tensor     )    ->				List[Any]:
         A						:     Optional[Any]         =len(list(m.modules()     )     ) == 1 or isinstance(SCREAMING_SNAKE_CASE__      ,	nn.Convad     ) or isinstance(SCREAMING_SNAKE_CASE__      ,	nn.BatchNormad     )
         if has_not_submodules:
                self.traced.append(SCREAMING_SNAKE_CASE__     )







  def __call__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Tensor     )    ->				Dict:
         for m in self.module.modules():
                self.handles.append(m.register_forward_hook(self._forward_hook     )     )
         self.module(SCREAMING_SNAKE_CASE__     )
         [x.remove() for x in self.handles]
         return self







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Union[str, Any]:
         # check the len of the state_dict keys to see if we have learnable params
         return list(filter(lambda SCREAMING_SNAKE_CASE__     : len(list(x.state_dict().keys()     )     ) > 0      ,	self.traced     )     )







@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     nn.Module
  lowercase		:     nn.Module
  lowercase		:     int            =				0
  lowercase		:     List            =				field(default_factory=lowerCAmelCase_			)
  lowercase		:     List            =				field(default_factory=lowerCAmelCase_			)







  def __call__(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tensor     )    ->				str:
         A						:     Tuple         =Tracker(self.dest     )(SCREAMING_SNAKE_CASE__     ).parametrized
         A						:     int         =Tracker(self.src     )(SCREAMING_SNAKE_CASE__     ).parametrized

         A						:     List[Any]         =list(filter(lambda SCREAMING_SNAKE_CASE__     : type(SCREAMING_SNAKE_CASE__     ) not in self.src_skip      ,	SCREAMING_SNAKE_CASE__     )     )
         A						:     int         =list(filter(lambda SCREAMING_SNAKE_CASE__     : type(SCREAMING_SNAKE_CASE__     ) not in self.dest_skip      ,	SCREAMING_SNAKE_CASE__     )     )

         if len(SCREAMING_SNAKE_CASE__     ) != len(SCREAMING_SNAKE_CASE__     ):
                raise Exception(
                    f'Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE__     )} operations while'
                    f' destination module has {len(SCREAMING_SNAKE_CASE__     )}.'     )

         for dest_m, src_m in zip(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                dest_m.load_state_dict(src_m.state_dict()     )
                if self.verbose == 1:
                       print(f'Transfered from={src_m} to={dest_m}'     )







def A__  (			lowercase:     str,		lowercase:     ResNetConfig,		lowercase:     Path,		lowercase:     bool = True					)				->		Optional[int]:
       print(F'Converting {name}...'					)
       with torch.no_grad():
              A						:     Dict         =timm.create_model(lowercase,		pretrained=lowercase					).eval()
              A						:     Any         =ResNetForImageClassification(lowercase					).eval()
              A						:     Dict         =ModuleTransfer(src=lowercase,		dest=lowercase					)
              A						:     Optional[int]         =torch.randn((1, 3, 224, 224)					)
              module_transfer(lowercase					)

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

       A						:     int         =F'resnet{"-".join(name.split("resnet"					)					)}'
       print(lowercase					)

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name,		commit_message='Add model',		use_temp_dir=lowercase,		)

              # we can use the convnext one
              A						:     Union[str, Any]         =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name,		commit_message='Add image processor',		use_temp_dir=lowercase,		)

              print(F'Pushed {checkpoint_name}'					)



def A__  (			lowercase:     Path,		lowercase:     str = None,		lowercase:     bool = True					)				->		Any:
       A						:     Optional[int]         ='imagenet-1k-id2label.json'
       A						:     str         =1_000
       A						:     Optional[Any]         =(1, num_labels)

       A						:     Dict         ='huggingface/label-files'
       A						:     int         =num_labels
       A						:     str         =json.load(open(hf_hub_download(lowercase,		lowercase,		repo_type='dataset'					),		'r'					)					)
       A						:     Dict         ={int(lowercase					): v for k, v in idalabel.items()}

       A						:     List[Any]         =idalabel
       A						:     int         ={v: k for k, v in idalabel.items()}

       A						:     Union[str, Any]         =partial(lowercase,		num_labels=lowercase,		idalabel=lowercase,		labelaid=lowercase					)

       A						:     List[Any]         ={
           'resnet18': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2],		hidden_sizes=[64, 128, 256, 512],		layer_type='basic'					),
           'resnet26': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2],		hidden_sizes=[256, 512, 1_024, 2_048],		layer_type='bottleneck'					),
           'resnet34': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3],		hidden_sizes=[64, 128, 256, 512],		layer_type='basic'					),
           'resnet50': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3],		hidden_sizes=[256, 512, 1_024, 2_048],		layer_type='bottleneck'					),
           'resnet101': ImageNetPreTrainedConfig(
               depths=[3, 4, 23, 3],		hidden_sizes=[256, 512, 1_024, 2_048],		layer_type='bottleneck'					),
           'resnet152': ImageNetPreTrainedConfig(
               depths=[3, 8, 36, 3],		hidden_sizes=[256, 512, 1_024, 2_048],		layer_type='bottleneck'					),
       }

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


if __name__ == "__main__":
    _lowercase							:      str      							=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 resnet* architecture,'''
            ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default=None,
        type=Path,
        required=True,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument(
        '''--push_to_hub''',
        default=True,
        type=bool,
        required=False,
        help='''If True, push model and image processor to the hub.''',
    )

    _lowercase							:      List[str]      							=parser.parse_args()
    _lowercase							:      Path      							=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 collections
import importlib.util
import os
import re
from pathlib import Path


_lowercase							:      List[str]      							='''src/transformers'''


# Matches is_xxx_available()
_lowercase							:      Dict      							=re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
_lowercase							:      List[Any]      							=re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_lowercase							:      Tuple      							=re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
_lowercase							:      Dict      							=re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
_lowercase							:      List[Any]      							=re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_lowercase							:      str      							=re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
_lowercase							:      Optional[int]      							=re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
_lowercase							:      Any      							=re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
_lowercase							:      List[Any]      							=re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
_lowercase							:      Optional[Any]      							=re.compile(R'''^\s*try:''')
# Catches a line with else:
_lowercase							:      List[Any]      							=re.compile(R'''^\s*else:''')



def A__  (			lowercase:     Dict					)				->		int:
       if _re_test_backend.search(lowercase					) is None:
              return None
       A						:     Any         =[b[0] for b in _re_backend.findall(lowercase					)]
       backends.sort()
       return "_and_".join(lowercase					)



def A__  (			lowercase:     Any					)				->		List[Any]:
       with open(lowercase,		'r',		encoding='utf-8',		newline='\n'					) as f:
              A						:     Optional[Any]         =f.readlines()

       A						:     Dict         =0
       while line_index < len(lowercase					) and not lines[line_index].startswith('_import_structure = {'					):
              line_index += 1

       # If this is a traditional init, just return.
       if line_index >= len(lowercase					):
              return None

       # First grab the objects without a specific backend in _import_structure
       A						:     Optional[int]         =[]
       while not lines[line_index].startswith('if TYPE_CHECKING'					) and find_backend(lines[line_index]					) is None:
              A						:     int         =lines[line_index]
              # If we have everything on a single line, let's deal with it.
              if _re_one_line_import_struct.search(lowercase					):
                     A						:     int         =_re_one_line_import_struct.search(lowercase					).groups()[0]
                     A						:     int         =re.findall('\[([^\]]+)\]',		lowercase					)
                     for imp in imports:
                            objects.extend([obj[1:-1] for obj in imp.split(', '					)]					)
                     line_index += 1
                     continue
              A						:     Optional[int]         =_re_import_struct_key_value.search(lowercase					)
              if single_line_import_search is not None:
                     A						:     Dict         =[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '					) if len(lowercase					) > 0]
                     objects.extend(lowercase					)
              elif line.startswith(' ' * 8 + '"'					):
                     objects.append(line[9:-3]					)
              line_index += 1

       A						:     str         ={'none': objects}
       # Let's continue with backend-specific objects in _import_structure
       while not lines[line_index].startswith('if TYPE_CHECKING'					):
              # If the line is an if not is_backend_available, we grab all objects associated.
              A						:     Optional[int]         =find_backend(lines[line_index]					)
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]					) is None:
                     A						:     str         =None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]					) is None:
                            line_index += 1

                     line_index += 1

                     A						:     List[str]         =[]
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]					) <= 1 or lines[line_index].startswith(' ' * 4					):
                            A						:     Optional[Any]         =lines[line_index]
                            if _re_import_struct_add_one.search(lowercase					) is not None:
                                   objects.append(_re_import_struct_add_one.search(lowercase					).groups()[0]					)
                            elif _re_import_struct_add_many.search(lowercase					) is not None:
                                   A						:     Optional[Any]         =_re_import_struct_add_many.search(lowercase					).groups()[0].split(', '					)
                                   A						:     int         =[obj[1:-1] for obj in imports if len(lowercase					) > 0]
                                   objects.extend(lowercase					)
                            elif _re_between_brackets.search(lowercase					) is not None:
                                   A						:     Optional[int]         =_re_between_brackets.search(lowercase					).groups()[0].split(', '					)
                                   A						:     Optional[int]         =[obj[1:-1] for obj in imports if len(lowercase					) > 0]
                                   objects.extend(lowercase					)
                            elif _re_quote_object.search(lowercase					) is not None:
                                   objects.append(_re_quote_object.search(lowercase					).groups()[0]					)
                            elif line.startswith(' ' * 8 + '"'					):
                                   objects.append(line[9:-3]					)
                            elif line.startswith(' ' * 12 + '"'					):
                                   objects.append(line[13:-3]					)
                            line_index += 1

                     A						:     Optional[Any]         =objects
              else:
                     line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
       A						:     Optional[Any]         =[]
       while (
           line_index < len(lowercase					)
           and find_backend(lines[line_index]					) is None
           and not lines[line_index].startswith('else'					)
       ):
              A						:     Any         =lines[line_index]
              A						:     Optional[int]         =_re_import.search(lowercase					)
              if single_line_import_search is not None:
                     objects.extend(single_line_import_search.groups()[0].split(', '					)					)
              elif line.startswith(' ' * 8					):
                     objects.append(line[8:-2]					)
              line_index += 1

       A						:     Optional[Any]         ={'none': objects}
       # Let's continue with backend-specific objects
       while line_index < len(lowercase					):
              # If the line is an if is_backend_available, we grab all objects associated.
              A						:     str         =find_backend(lines[line_index]					)
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]					) is None:
                     A						:     Optional[Any]         =None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]					) is None:
                            line_index += 1

                     line_index += 1

                     A						:     List[str]         =[]
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]					) <= 1 or lines[line_index].startswith(' ' * 8					):
                            A						:     Any         =lines[line_index]
                            A						:     Any         =_re_import.search(lowercase					)
                            if single_line_import_search is not None:
                                   objects.extend(single_line_import_search.groups()[0].split(', '					)					)
                            elif line.startswith(' ' * 12					):
                                   objects.append(line[12:-2]					)
                            line_index += 1

                     A						:     Dict         =objects
              else:
                     line_index += 1

       return import_dict_objects, type_hint_objects



def A__  (			lowercase:     Any,		lowercase:     int					)				->		Dict:

       def find_duplicates(lowercase:     List[str]					):
              return [k for k, v in collections.Counter(lowercase					).items() if v > 1]

       if list(import_dict_objects.keys()					) != list(type_hint_objects.keys()					):
              return ["Both sides of the init do not have the same backends!"]

       A						:     List[Any]         =[]
       for key in import_dict_objects.keys():
              A						:     List[Any]         =find_duplicates(import_dict_objects[key]					)
              if duplicate_imports:
                     errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}'					)
              A						:     Tuple         =find_duplicates(type_hint_objects[key]					)
              if duplicate_type_hints:
                     errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'					)

              if sorted(set(import_dict_objects[key]					)					) != sorted(set(type_hint_objects[key]					)					):
                     A						:     Tuple         ='base imports' if key == 'none' else F'{key} backend'
                     errors.append(F'Differences for {name}:'					)
                     for a in type_hint_objects[key]:
                            if a not in import_dict_objects[key]:
                                   errors.append(F'  {a} in TYPE_HINT but not in _import_structure.'					)
                     for a in import_dict_objects[key]:
                            if a not in type_hint_objects[key]:
                                   errors.append(F'  {a} in _import_structure but not in TYPE_HINT.'					)
       return errors



def A__  (			)				->		List[str]:
       A						:     Dict         =[]
       for root, _, files in os.walk(lowercase					):
              if "__init__.py" in files:
                     A						:     Any         =os.path.join(lowercase,		'__init__.py'					)
                     A						:     Union[str, Any]         =parse_init(lowercase					)
                     if objects is not None:
                            A						:     str         =analyze_results(*lowercase					)
                            if len(lowercase					) > 0:
                                   A						:     Any         =F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
                                   failures.append('\n'.join(lowercase					)					)
       if len(lowercase					) > 0:
              raise ValueError('\n\n'.join(lowercase					)					)



def A__  (			)				->		int:
       A						:     List[str]         =[]
       for path, directories, files in os.walk(lowercase					):
              for folder in directories:
                     # Ignore private modules
                     if folder.startswith('_'					):
                            directories.remove(lowercase					)
                            continue
                     # Ignore leftovers from branches (empty folders apart from pycache)
                     if len(list((Path(lowercase					) / folder).glob('*.py'					)					)					) == 0:
                            continue
                     A						:     Any         =str((Path(lowercase					) / folder).relative_to(lowercase					)					)
                     A						:     List[str]         =short_path.replace(os.path.sep,		'.'					)
                     submodules.append(lowercase					)
              for fname in files:
                     if fname == "__init__.py":
                            continue
                     A						:     Optional[Any]         =str((Path(lowercase					) / fname).relative_to(lowercase					)					)
                     A						:     Dict         =short_path.replace('.py',		''					).replace(os.path.sep,		'.'					)
                     if len(submodule.split('.'					)					) == 1:
                            submodules.append(lowercase					)
       return submodules


_lowercase							:      Tuple      							=[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]



def A__  (			)				->		Tuple:
       # This is to make sure the transformers module imported is the one in the repo.
       A						:     str         =importlib.util.spec_from_file_location(
           'transformers',		os.path.join(lowercase,		'__init__.py'					),		submodule_search_locations=[PATH_TO_TRANSFORMERS],		)
       A						:     Any         =spec.loader.load_module()

       A						:     Any         =[
           module
           for module in get_transformers_submodules()
           if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
       ]
       if len(lowercase					) > 0:
              A						:     Dict         ='\n'.join(F'- {module}' for module in module_not_registered					)
              raise ValueError(
                  'The following submodules are not properly registered in the main init of Transformers:\n'
                  F'{list_of_modules}\n'
                  'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'					)


if __name__ == "__main__":
    check_all_inits()
    check_submodules()






