



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						: int     						=logging.get_logger()

@dataclass
class   snake_case__							:







						"""simple docstring"""




						__lowerCAmelCase	:nn.Module
						__lowerCAmelCase	:List[nn.Module]         =	field(default_factory=_UpperCamelCase						)
						__lowerCAmelCase	:list         =	field(default_factory=_UpperCamelCase						)

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase    )   -> Tuple:
									"""simple docstring"""




									a__  :		Optional[Any]					=    len(list(m.modules()    )    ) == 1 or isinstance(__lowercase   ,  nn.Convad    ) or isinstance(__lowercase   ,  nn.BatchNormad    )
									if has_not_submodules:
												self.traced.append(__lowercase    )

						def __call__(  self   ,  __lowercase    )   -> str:
									"""simple docstring"""




									for m in self.module.modules():
												self.handles.append(m.register_forward_hook(self._forward_hook    )    )
									self.module(__lowercase    )
									[x.remove() for x in self.handles]
									return self

						@property
						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Any:
									"""simple docstring"""




									return list(filter(lambda __lowercase    : len(list(x.state_dict().keys()    )    ) > 0   ,  self.traced    )    )




@dataclass
class   snake_case__							:







						"""simple docstring"""




						__lowerCAmelCase	:nn.Module
						__lowerCAmelCase	:nn.Module
						__lowerCAmelCase	:int         =	0
						__lowerCAmelCase	:List         =	field(default_factory=_UpperCamelCase						)
						__lowerCAmelCase	:List         =	field(default_factory=_UpperCamelCase						)

						def __call__(  self   ,  __lowercase    )   -> Optional[int]:
									"""simple docstring"""




									a__  :		Any					=    Tracker(self.dest    )(__lowercase    ).parametrized
									a__  :		Any					=    Tracker(self.src    )(__lowercase    ).parametrized

									a__  :		Dict					=    list(filter(lambda __lowercase    : type(__lowercase    ) not in self.src_skip   ,  __lowercase    )    )
									a__  :		Dict					=    list(filter(lambda __lowercase    : type(__lowercase    ) not in self.dest_skip   ,  __lowercase    )    )

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

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




def       lowerCAmelCase_	(    _lowercase							:  List[str]		,  _lowercase							:  Any		,  _lowercase							:  List[str]		,  _lowercase							:  Dict = True)				->			List[str]:







			"""simple docstring"""
			print(F'''Converting {name}...''')
			with torch.no_grad():
						a__  :		Optional[Any]					=    timm.create_model(_lowercase		,  pretrained=_lowercase).eval()
						a__  :		str					=    ResNetForImageClassification(_lowercase).eval()
						a__  :		List[str]					=    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__  :		List[Any]					=    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__  :		List[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       lowerCAmelCase_	(    _lowercase							:  Tuple		,  _lowercase							:  Tuple = None		,  _lowercase							:  Any = True)				->			List[Any]:







			"""simple docstring"""
			a__  :		Dict					=    "imagenet-1k-id2label.json"
			a__  :		int					=    1000
			a__  :		Any					=    (1, num_labels)

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

			a__  :		Optional[Any]					=    idalabel
			a__  :		Tuple					=    {v: k for k, v in idalabel.items()}

			a__  :		List[str]					=    partial(_lowercase		,  num_labels=_lowercase		,  idalabel=_lowercase		,  labelaid=_lowercase)

			a__  :		Tuple					=    {
			    "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, 1024, 2048]		,  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, 1024, 2048]		,  layer_type="""bottleneck"""),
			    "resnet101": ImageNetPreTrainedConfig(
			        depths=[3, 4, 23, 3]		,  hidden_sizes=[256, 512, 1024, 2048]		,  layer_type="""bottleneck"""),
			    "resnet152": ImageNetPreTrainedConfig(
			        depths=[3, 8, 36, 3]		,  hidden_sizes=[256, 512, 1024, 2048]		,  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						: Optional[int]     						=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						: Tuple     						=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 argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

