
"""simple docstring"""





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()
__magic_name__       =			logging.get_logger()
@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  nn.Module
		__lowercase							:  List[nn.Module]						   =		field(default_factory=__a )
		__lowercase							:  list						   =		field(default_factory=__a )




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = len(list(m.modules())) == 1 or isinstance(lowerCAmelCase__							,  nn.Convad) or isinstance(lowerCAmelCase__							,  nn.BatchNormad)
				if has_not_submodules:
						self.traced.append(lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__):
				for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
				self.module(lowerCAmelCase__)
				[x.remove() for x in self.handles]
				return self




		@property
		def 	snake_case_     (     self):
				# check the len of the state_dict keys to see if we have learnable params
				return list(filter(lambda lowerCAmelCase__: 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=__a )
		__lowercase							:  List						   =		field(default_factory=__a )




		def __call__(     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = Tracker(self.dest)(lowerCAmelCase__).parametrized
				__SCREAMING_SNAKE_CASE            = Tracker(self.src)(lowerCAmelCase__).parametrized

				__SCREAMING_SNAKE_CASE            = list(filter(lambda lowerCAmelCase__: type(lowerCAmelCase__) not in self.src_skip							,  lowerCAmelCase__))
				__SCREAMING_SNAKE_CASE            = list(filter(lambda lowerCAmelCase__: type(lowerCAmelCase__) not in self.dest_skip							,  lowerCAmelCase__))

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

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



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_ = True				):
		print(f"Converting {name}..."				)
		with torch.no_grad():
				__SCREAMING_SNAKE_CASE            = timm.create_model(UpperCamelCase_  ,				pretrained=UpperCamelCase_				).eval()
				__SCREAMING_SNAKE_CASE            = ResNetForImageClassification(UpperCamelCase_				).eval()
				__SCREAMING_SNAKE_CASE            = ModuleTransfer(src=UpperCamelCase_  ,				dest=UpperCamelCase_				)
				__SCREAMING_SNAKE_CASE            = torch.randn((1, 3, 224, 224)				)
				module_transfer(UpperCamelCase_				)

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

		__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = 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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_ = None  ,				UpperCamelCase_ = True				):
		__SCREAMING_SNAKE_CASE            = """imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE            = 1000
		__SCREAMING_SNAKE_CASE            = (1, num_labels)

		__SCREAMING_SNAKE_CASE            = """huggingface/label-files"""
		__SCREAMING_SNAKE_CASE            = num_labels
		__SCREAMING_SNAKE_CASE            = json.load(open(hf_hub_download(UpperCamelCase_  ,				UpperCamelCase_  ,				repo_type="""dataset"""				)  ,				"""r"""				)				)
		__SCREAMING_SNAKE_CASE            = {int(UpperCamelCase_				): v for k, v in idalabel.items()}

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

		__SCREAMING_SNAKE_CASE            = partial(UpperCamelCase_  ,				num_labels=UpperCamelCase_  ,				idalabel=UpperCamelCase_  ,				labelaid=UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = {
		    """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(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__":
							__magic_name__       =			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.",
							)

							__magic_name__       =			parser.parse_args()
							__magic_name__       =			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)








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__:    str					    =       {}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple					    =       ['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpt_swa import GPTSwaTokenizer

else:
   import sys

   A__:    str					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


