
"""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()
_a														=		logging.get_logger()






@dataclass
class    _UpperCAmelCase:
		lowercase__      =				42
		lowercase__      =				field(default_factory=lowerCamelCase     )
		lowercase__      =				field(default_factory=lowerCamelCase     )



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  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)	->	Union[str, Any]:






							'''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 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							# check the len of the state_dict keys to see if we have learnable params
							return list(filter(lambda __a: len(list(x.state_dict().keys())) > 0    ,		self.traced))






@dataclass
class    _UpperCAmelCase:
		lowercase__      =				42
		lowercase__      =				42
		lowercase__      =				0
		lowercase__      =				field(default_factory=lowerCamelCase     )
		lowercase__      =				field(default_factory=lowerCamelCase     )



		def __call__( self    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  Tracker(self.dest)(__a).parametrized
							_UpperCamelCase										=  Tracker(self.src)(__a).parametrized

							_UpperCamelCase										=  list(filter(lambda __a: type(__a) not in self.src_skip    ,		__a))
							_UpperCamelCase										=  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 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case = True     )							->		Any:


					"""simple docstring"""





					print(F'''Converting {name}...'''     )
					with torch.no_grad():
										_UpperCamelCase										=  timm.create_model(__snake_case,					pretrained=__snake_case     ).eval()
										_UpperCamelCase										=  ResNetForImageClassification(__snake_case     ).eval()
										_UpperCamelCase										=  ModuleTransfer(src=__snake_case,					dest=__snake_case     )
										_UpperCamelCase										=  torch.randn((1, 3, 2_24, 2_24)     )
										module_transfer(__snake_case     )

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

					_UpperCamelCase										=  F'''resnet{"-".join(name.split("resnet"     )     )}'''
					print(__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=__snake_case,					)

										# we can use the convnext one
										_UpperCamelCase										=  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=__snake_case,					)

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



def 				lowerCamelCase__		(      __snake_case,					__snake_case = None,					__snake_case = True     )							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase										=  '''imagenet-1k-id2label.json'''
					_UpperCamelCase										=  10_00
					_UpperCamelCase										=  (1, num_labels)

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

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

					_UpperCamelCase										=  partial(__snake_case,					num_labels=__snake_case,					idalabel=__snake_case,					labelaid=__snake_case     )

					_UpperCamelCase										=  {
					    '''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(__snake_case,					names_to_config[model_name],					__snake_case,					__snake_case     )
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(__snake_case,					__snake_case,					__snake_case,					__snake_case     )
					return config, expected_shape


if __name__ == "__main__":
				_a														=		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.""",
				)

				_a														=		parser.parse_args()
				_a														=		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"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a														=		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					return (abs(source - target     ) / target) < 0.01



@pytest.mark.integration
def 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  _TestCommandArgs(dataset=__snake_case,					all_configs=__snake_case,					save_infos=__snake_case     )
					_UpperCamelCase										=  TestCommand(*__snake_case     )
					test_command.run()
					_UpperCamelCase										=  os.path.join(__snake_case,					'''README.md'''     )
					assert os.path.exists(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict.from_directory(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict(
					    {
					        '''default''': DatasetInfo(
					            features=Features(
					                {
					                    '''tokens''': Sequence(Value('''string'''     )     ),
					                    '''ner_tags''': Sequence(
					                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']     )     ),
					                    '''langs''': Sequence(Value('''string'''     )     ),
					                    '''spans''': Sequence(Value('''string'''     )     ),
					                }     ),					splits=[
					                {
					                    '''name''': '''train''',
					                    '''num_bytes''': 2_35_15_63,
					                    '''num_examples''': 1_00_00,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 23_84_18,
					                    '''num_examples''': 10_00,
					                },
					            ],					download_size=3_94_06_80,					dataset_size=2_58_99_81,					)
					    }     )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_UpperCamelCase			,  _UpperCamelCase										=  getattr(dataset_infos['''default'''],					__snake_case     ), getattr(expected_dataset_infos['''default'''],					__snake_case     )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case,					__snake_case     )
										elif key == "splits":
															assert list(__snake_case     ) == list(__snake_case     )
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes,					expected[split].num_bytes     )
										else:
															result == expected






