






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()
UpperCAmelCase_   :				int						      = logging.get_logger()
@dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  Optional[int]       =		42
      snake_case__       :  List[Any]       =		field(default_factory=SCREAMING_SNAKE_CASE_    )
      snake_case__       :  List[str]       =		field(default_factory=SCREAMING_SNAKE_CASE_    )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : str  )	->    Dict:
             a_      :	List[str]							=						len(list(m.modules()  )  ) == 1 or isinstance(UpperCamelCase__  ,							nn.Convad  ) or isinstance(UpperCamelCase__  ,							nn.BatchNormad  )
             if has_not_submodules:
                    self.traced.append(UpperCamelCase__  )
      def __call__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : int  )	->    Any:
             for m in self.module.modules():
                    self.handles.append(m.register_forward_hook(self._forward_hook  )  )
             self.module(UpperCamelCase__  )
             [x.remove() for x in self.handles]
             return self

      @property
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             # 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__						:
      snake_case__       :  List[Any]       =		42
      snake_case__       :  Dict       =		42
      snake_case__       :  Optional[Any]       =		0
      snake_case__       :  List[str]       =		field(default_factory=SCREAMING_SNAKE_CASE_    )
      snake_case__       :  int       =		field(default_factory=SCREAMING_SNAKE_CASE_    )
      def __call__( self       : str  ,							SCREAMING_SNAKE_CASE__       : int  )	->    Tuple:
             a_      :	Any							=						Tracker(self.dest  )(UpperCamelCase__  ).parametrized
             a_      :	Optional[Any]							=						Tracker(self.src  )(UpperCamelCase__  ).parametrized

             a_      :	Tuple							=						list(filter(lambda SCREAMING_SNAKE_CASE__  : type(UpperCamelCase__  ) not in self.src_skip  ,							UpperCamelCase__  )  )
             a_      :	Union[str, Any]							=						list(filter(lambda SCREAMING_SNAKE_CASE__  : type(UpperCamelCase__  ) not in self.dest_skip  ,							UpperCamelCase__  )  )

             if len(UpperCamelCase__  ) != len(UpperCamelCase__  ):
                    raise Exception(
                        F"""Numbers of operations are different. Source module has {len(UpperCamelCase__  )} operations while"""
                        F""" destination module has {len(UpperCamelCase__  )}."""  )

             for dest_m, src_m in zip(UpperCamelCase__  ,							UpperCamelCase__  ):
                    dest_m.load_state_dict(src_m.state_dict()  )
                    if self.verbose == 1:
                           print(F"""Transfered from={src_m} to={dest_m}"""  )







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    ResNetConfig						,					__A				:    Path						,					__A				:    bool = True       )			->	Union[str, Any]:

       """simple docstring"""

       print(F"""Converting {name}..."""       )
       with torch.no_grad():
              a_      :	List[str]							=						timm.create_model(__UpperCamelCase						,					pretrained=__UpperCamelCase       ).eval()
              a_      :	List[str]							=						ResNetForImageClassification(__UpperCamelCase       ).eval()
              a_      :	List[Any]							=						ModuleTransfer(src=__UpperCamelCase						,					dest=__UpperCamelCase       )
              a_      :	Any							=						torch.randn((1, 3, 2_24, 2_24)       )
              module_transfer(__UpperCamelCase       )

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

       a_      :	Union[str, Any]							=						F"""resnet{"-".join(name.split("resnet"       )       )}"""
       print(__UpperCamelCase       )

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

              # we can use the convnext one
              a_      :	int							=						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=__UpperCamelCase						,					)

              print(F"""Pushed {checkpoint_name}"""       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Path						,					__A				:    str = None						,					__A				:    bool = True       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[int]							=						'''imagenet-1k-id2label.json'''
       a_      :	Optional[int]							=						10_00
       a_      :	Optional[int]							=						(1, num_labels)

       a_      :	Dict							=						'''huggingface/label-files'''
       a_      :	List[Any]							=						num_labels
       a_      :	str							=						json.load(open(hf_hub_download(__UpperCamelCase						,					__UpperCamelCase						,					repo_type='dataset'       )						,					'r'       )       )
       a_      :	Any							=						{int(__UpperCamelCase       ): v for k, v in idalabel.items()}

       a_      :	str							=						idalabel
       a_      :	List[str]							=						{v: k for k, v in idalabel.items()}

       a_      :	Dict							=						partial(__UpperCamelCase						,					num_labels=__UpperCamelCase						,					idalabel=__UpperCamelCase						,					labelaid=__UpperCamelCase       )

       a_      :	str							=						{
           '''resnet18''': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]						,					hidden_sizes=[64, 1_28, 2_56, 5_12]						,					layer_type='basic'       ),
           '''resnet26''': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]						,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]						,					layer_type='bottleneck'       ),
           '''resnet34''': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]						,					hidden_sizes=[64, 1_28, 2_56, 5_12]						,					layer_type='basic'       ),
           '''resnet50''': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]						,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]						,					layer_type='bottleneck'       ),
           '''resnet101''': ImageNetPreTrainedConfig(
               depths=[3, 4, 23, 3]						,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]						,					layer_type='bottleneck'       ),
           '''resnet152''': ImageNetPreTrainedConfig(
               depths=[3, 8, 36, 3]						,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]						,					layer_type='bottleneck'       ),
       }

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


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = 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.',
   )

   UpperCAmelCase_   :				Optional[Any]						      = parser.parse_args()
   UpperCAmelCase_   :				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 argparse
import os
import re


UpperCAmelCase_   :				List[Any]						      = 'src/transformers'

# Pattern that looks at the indentation in a line.
UpperCAmelCase_   :				Any						      = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_   :				str						      = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase_   :				Dict						      = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase_   :				int						      = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase_   :				List[str]						      = re.compile(R'\[([^\]]+)\]')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]       )			->	Optional[Any]:

       """simple docstring"""

       a_      :	List[Any]							=						_re_indent.search(__A       )
       return "" if search is None else search.groups()[0]







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Union[str, Any]=""						,					__A				:    Dict=None						,					__A				:    Dict=None       )			->	int:

       """simple docstring"""

       a_      :	Tuple							=						0
       a_      :	Dict							=						code.split('\n'       )
       if start_prompt is not None:
              while not lines[index].startswith(__A       ):
                     index += 1
              a_      :	List[Any]							=						['\n'.join(lines[:index]       )]
       else:
              a_      :	List[str]							=						[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_      :	Tuple							=						[lines[index]]
       index += 1
       while index < len(__A       ) and (end_prompt is None or not lines[index].startswith(__A       )):
              if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
                     if len(__A       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ' '       ):
                            current_block.append(lines[index]       )
                            blocks.append('\n'.join(__A       )       )
                            if index < len(__A       ) - 1:
                                   a_      :	Dict							=						[lines[index + 1]]
                                   index += 1
                            else:
                                   a_      :	Dict							=						[]
                     else:
                            blocks.append('\n'.join(__A       )       )
                            a_      :	int							=						[lines[index]]
              else:
                     current_block.append(lines[index]       )
              index += 1

       # Adds current block if it's nonempty.
       if len(__A       ) > 0:
              blocks.append('\n'.join(__A       )       )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(__A       ):
              blocks.append('\n'.join(lines[index:]       )       )

       return blocks







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict       )			->	Any:

       """simple docstring"""

       def _inner(__A				:    Tuple       ):
              return key(__A       ).lower().replace('_'						,					''       )

       return _inner







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    List[Any]=None       )			->	List[str]:

       """simple docstring"""

       def noop(__A				:    Tuple       ):
              return x

       if key is None:
              a_      :	Optional[Any]							=						noop
       # Constants are all uppercase, they go first.
       a_      :	List[Any]							=						[obj for obj in objects if key(__A       ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_      :	Dict							=						[obj for obj in objects if key(__A       )[0].isupper() and not key(__A       ).isupper()]
       # Functions begin with a lowercase, they go last.
       a_      :	Optional[int]							=						[obj for obj in objects if not key(__A       )[0].isupper()]

       a_      :	Optional[Any]							=						ignore_underscore(__A       )
       return sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Optional[int]:

       """simple docstring"""

       def _replace(__A				:    List[Any]       ):
              a_      :	Tuple							=						match.groups()[0]
              if "," not in imports:
                     return F"""[{imports}]"""
              a_      :	int							=						[part.strip().replace('"'						,					''       ) for part in imports.split(','       )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]       ) == 0:
                     a_      :	str							=						keys[:-1]
              return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(__A       )]       ) + "]"

       a_      :	Optional[int]							=						import_statement.split('\n'       )
       if len(__A       ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a_      :	int							=						2 if lines[1].strip() == '[' else 1
              a_      :	int							=						[(i, _re_strip_line.search(__A       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
              a_      :	str							=						sort_objects(__A						,					key=lambda __A       : x[1]       )
              a_      :	int							=						[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
       elif len(__A       ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]       ) is not None:
                     a_      :	str							=						_re_bracket_content.sub(_replace						,					lines[1]       )
              else:
                     a_      :	List[str]							=						[part.strip().replace('"'						,					''       ) for part in lines[1].split(','       )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]       ) == 0:
                            a_      :	Optional[int]							=						keys[:-1]
                     a_      :	Any							=						get_indent(lines[1]       ) + ', '.join([F"""\"{k}\"""" for k in sort_objects(__A       )]       )
              return "\n".join(__A       )
       else:
              # Finally we have to deal with imports fitting on one line
              a_      :	Union[str, Any]							=						_re_bracket_content.sub(_replace						,					__A       )
              return import_statement







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any=True       )			->	Union[str, Any]:

       """simple docstring"""

       with open(__A						,					encoding='utf-8'       ) as f:
              a_      :	Union[str, Any]							=						f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_      :	Optional[Any]							=						split_code_in_indented_blocks(
           __A						,					start_prompt='_import_structure = {'						,					end_prompt='if TYPE_CHECKING:'       )

       # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1						,					len(__A       ) - 1       ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a_      :	Any							=						main_blocks[block_idx]
              a_      :	Any							=						block.split('\n'       )

              # Get to the start of the imports.
              a_      :	Any							=						0
              while line_idx < len(__A       ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_      :	Tuple							=						len(__A       )
                     else:
                            line_idx += 1
              if line_idx >= len(__A       ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_      :	List[str]							=						'\n'.join(block_lines[line_idx:-1]       )
              a_      :	List[Any]							=						get_indent(block_lines[1]       )
              # Slit the internal block into blocks of indent level 1.
              a_      :	Optional[int]							=						split_code_in_indented_blocks(__A						,					indent_level=__A       )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_      :	Tuple							=						_re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a_      :	Union[str, Any]							=						[(pattern.search(__A       ).groups()[0] if pattern.search(__A       ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_      :	str							=						[(i, key) for i, key in enumerate(__A       ) if key is not None]
              a_      :	Optional[int]							=						[x[0] for x in sorted(__A						,					key=lambda __A       : x[1]       )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_      :	int							=						0
              a_      :	int							=						[]
              for i in range(len(__A       )       ):
                     if keys[i] is None:
                            reorderded_blocks.append(internal_blocks[i]       )
                     else:
                            a_      :	List[str]							=						sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
                            reorderded_blocks.append(__A       )
                            count += 1

        # And we put our main block back together with its first and last line.
              a_      :	Any							=						'\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]       )

       if code != "\n".join(__A       ):
              if check_only:
                     return True
              else:
                     print(F"""Overwriting {file}."""       )
                     with open(__A						,					'w'						,					encoding='utf-8'       ) as f:
                            f.write('\n'.join(__A       )       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]=True       )			->	List[Any]:

       """simple docstring"""

       a_      :	Dict							=						[]
       for root, _, files in os.walk(__A       ):
              if "__init__.py" in files:
                     a_      :	Dict							=						sort_imports(os.path.join(__A						,					'__init__.py'       )						,					check_only=__A       )
                     if result:
                            a_      :	Tuple							=						[os.path.join(__A						,					'__init__.py'       )]
       if len(__A       ) > 0:
              raise ValueError(F"""Would overwrite {len(__A       )} files, run `make style`."""       )


if __name__ == "__main__":
   UpperCAmelCase_   :				int						      = argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   UpperCAmelCase_   :				Tuple						      = parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)


