

'''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()
lowerCAmelCase__    :							int					    =    logging.get_logger()




@dataclass
class      a      :






						"""simple docstring"""




						__UpperCAmelCase        =     42
						__UpperCAmelCase        =     field(default_factory=_a							)
						__UpperCAmelCase        =     field(default_factory=_a							)







						def    __magic_name__				(				self       :   Optional[Any]      ,      snake_case_       :   str      ,      snake_case_       :   Tensor      ,      snake_case_       :   Tensor	):



								'''simple docstring'''



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







						def __call__(				self       :   int      ,      snake_case_       :   Tensor	):



								'''simple docstring'''



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







						@property
						def    __magic_name__				(				self       :   Any	):



								'''simple docstring'''



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





@dataclass
class      a      :






						"""simple docstring"""




						__UpperCAmelCase        =     42
						__UpperCAmelCase        =     42
						__UpperCAmelCase        =     0
						__UpperCAmelCase        =     field(default_factory=_a							)
						__UpperCAmelCase        =     field(default_factory=_a							)







						def __call__(				self       :   List[Any]      ,      snake_case_       :   Tensor	):



								'''simple docstring'''



								snake_case__    :							Tuple							    =	Tracker(self.dest	)(snake_case_	).parametrized
								snake_case__    :							Optional[Any]							    =	Tracker(self.src	)(snake_case_	).parametrized

								snake_case__    :							Optional[int]							    =	list(filter(lambda snake_case_	: type(snake_case_	) not in self.src_skip      ,      snake_case_	)	)
								snake_case__    :							int							    =	list(filter(lambda snake_case_	: type(snake_case_	) not in self.dest_skip      ,      snake_case_	)	)

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

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





def 						_a							(					__lowerCAmelCase		:   Optional[Any] ,				__lowerCAmelCase		:   Optional[int] ,				__lowerCAmelCase		:   Tuple ,				__lowerCAmelCase		:   Dict = True  ):

		"""simple docstring"""

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

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

		snake_case__    :							List[Any]							    =	F"""resnet{'-'.join(name.split('resnet'  )  )}"""
		print(_lowerCAmelCase  )

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

				# we can use the convnext one
				snake_case__    :							List[str]							    =	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=_lowerCAmelCase ,				)

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




def 						_a							(					__lowerCAmelCase		:   List[str] ,				__lowerCAmelCase		:   Tuple = None ,				__lowerCAmelCase		:   Tuple = True  ):

		"""simple docstring"""

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

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

		snake_case__    :							Dict							    =	idalabel
		snake_case__    :							Optional[int]							    =	{v: k for k, v in idalabel.items()}

		snake_case__    :							Dict							    =	partial(_lowerCAmelCase ,				num_labels=_lowerCAmelCase ,				idalabel=_lowerCAmelCase ,				labelaid=_lowerCAmelCase  )

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


if __name__ == "__main__":
					lowerCAmelCase__    :							Optional[Any]					    =    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.""",
					)

					lowerCAmelCase__    :							Union[str, Any]					    =    parser.parse_args()
					lowerCAmelCase__    :							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 os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


A				:       List[str]      =  pytest.mark.integration





@pytest.mark.parametrize("path"   ,						["paws", "csv"]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						inspect_dataset(_lowerCAmelCase   ,						_lowerCAmelCase			)
						__snake_case				=		path + ".py"
						assert script_name in os.listdir(_lowerCAmelCase			)
						assert "__pycache__" not in os.listdir(_lowerCAmelCase			)





@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"   ,						["accuracy"]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						inspect_metric(_lowerCAmelCase   ,						_lowerCAmelCase			)
						__snake_case				=		path + ".py"
						assert script_name in os.listdir(_lowerCAmelCase			)
						assert "__pycache__" not in os.listdir(_lowerCAmelCase			)





@pytest.mark.parametrize(
    "path, config_name, expected_splits"   ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		get_dataset_config_info(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)
						assert info.config_name == config_name
						assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"   ,						[
        ("paws", None, ValueError),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						with pytest.raises(_lowerCAmelCase			):
												get_dataset_config_info(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)





@pytest.mark.parametrize(
    "path, expected"   ,						[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						__snake_case				=		get_dataset_config_names(_lowerCAmelCase			)
						assert expected in config_names





@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"   ,						[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		get_dataset_infos(_lowerCAmelCase			)
						assert list(infos.keys()			) == expected_configs
						__snake_case				=		expected_configs[0]
						assert expected_config in infos
						__snake_case				=		infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()			) == expected_splits_in_first_config





@pytest.mark.parametrize(
    "path, expected_config, expected_splits"   ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						__snake_case				=		get_dataset_infos(_lowerCAmelCase			)
						assert expected_config in infos
						__snake_case				=		infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"   ,						[
        ("paws", None, ValueError),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						with pytest.raises(_lowerCAmelCase			):
												get_dataset_split_names(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)





