




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__    			=			logging.get_logger()
@dataclass
class 			a__    :
						_a							:    int					=    4_2
						_a							:    List[str]					=    field(default_factory=snake_case__      )
						_a							:    List[str]					=    field(default_factory=snake_case__      )






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       len(list(m.modules()			)			) == 1 or isinstance(A_		,			nn.Convad			) or isinstance(A_		,			nn.BatchNormad			)
											if has_not_submodules:
																self.traced.append(A_			)






						def __call__(   self		,			_A			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






@dataclass
class 			a__    :
						_a							:    List[str]					=    4_2
						_a							:    str					=    4_2
						_a							:    Dict					=    0
						_a							:    str					=    field(default_factory=snake_case__      )
						_a							:    Any					=    field(default_factory=snake_case__      )






						def __call__(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       Tracker(self.dest			)(A_			).parametrized
											__lowerCAmelCase				   =       Tracker(self.src			)(A_			).parametrized

											__lowerCAmelCase				   =       list(filter(lambda _A			: type(A_			) not in self.src_skip		,			A_			)			)
											__lowerCAmelCase				   =       list(filter(lambda _A			: type(A_			) not in self.dest_skip		,			A_			)			)

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

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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				ResNetConfig      ,      SCREAMING_SNAKE_CASE_							:				Path      ,      SCREAMING_SNAKE_CASE_							:				bool = True     ):
					print(F"""Converting {name}..."""     )
					with torch.no_grad():
										__lowerCAmelCase				   =       timm.create_model(SCREAMING_SNAKE_CASE_      ,      pretrained=SCREAMING_SNAKE_CASE_     ).eval()
										__lowerCAmelCase				   =       ResNetForImageClassification(SCREAMING_SNAKE_CASE_     ).eval()
										__lowerCAmelCase				   =       ModuleTransfer(src=SCREAMING_SNAKE_CASE_      ,      dest=SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       torch.randn((1, 3, 2_24, 2_24)     )
										module_transfer(SCREAMING_SNAKE_CASE_     )

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

					__lowerCAmelCase				   =       F"""resnet{'-'.join(name.split('resnet'     )     )}"""
					print(SCREAMING_SNAKE_CASE_     )

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

										# we can use the convnext one
										__lowerCAmelCase				   =       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=SCREAMING_SNAKE_CASE_      ,      )

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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Path      ,      SCREAMING_SNAKE_CASE_							:				str = None      ,      SCREAMING_SNAKE_CASE_							:				bool = True     ):
					__lowerCAmelCase				   =       "imagenet-1k-id2label.json"
					__lowerCAmelCase				   =       10_00
					__lowerCAmelCase				   =       (1, num_labels)

					__lowerCAmelCase				   =       "huggingface/label-files"
					__lowerCAmelCase				   =       num_labels
					__lowerCAmelCase				   =       json.load(open(hf_hub_download(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				   =       partial(SCREAMING_SNAKE_CASE_      ,      num_labels=SCREAMING_SNAKE_CASE_      ,      idalabel=SCREAMING_SNAKE_CASE_      ,      labelaid=SCREAMING_SNAKE_CASE_     )

					__lowerCAmelCase				   =       {
					    "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(SCREAMING_SNAKE_CASE_      ,      names_to_config[model_name]      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					return config, expected_shape


if __name__ == "__main__":
					UpperCamelCase__    			=			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__    			=			parser.parse_args()
					UpperCamelCase__    			=			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)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Union[str, Any]     =   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Optional[Any]     =   ["ConvBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Dict     =   [
     "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConvBertForMaskedLM",
     "ConvBertForMultipleChoice",
     "ConvBertForQuestionAnswering",
     "ConvBertForSequenceClassification",
     "ConvBertForTokenClassification",
     "ConvBertLayer",
     "ConvBertModel",
     "ConvBertPreTrainedModel",
     "load_tf_weights_in_convbert",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   [
     "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFConvBertForMaskedLM",
     "TFConvBertForMultipleChoice",
     "TFConvBertForQuestionAnswering",
     "TFConvBertForSequenceClassification",
     "TFConvBertForTokenClassification",
     "TFConvBertLayer",
     "TFConvBertModel",
     "TFConvBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase				:		Optional[int]     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



