






"""simple docstring"""



import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    "files"				,						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]				,						)
def  _snake_case				(     lowerCamelCase__	:    List[Any]				,						lowerCamelCase__	:    Tuple  )				->	Union[str, Any]:
							lowerCamelCase_       :							List[str]									=tmp_path_factory.mktemp("dset_infos_dir"  )
							if "full:README.md" in files:
														with open(dataset_infos_dir / "README.md"				,						"w"  ) as f:
																					f.write("---\ndataset_info:\n  dataset_size: 42\n---"  )
							if "empty:README.md" in files:
														with open(dataset_infos_dir / "README.md"				,						"w"  ) as f:
																					f.write(""  )
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / "dataset_infos.json"				,						"w"  ) as f:
																					f.write("{\"default\": {\"dataset_size\": 42}}"  )
							lowerCamelCase_       :							Any									=DatasetInfosDict.from_directory(UpperCamelCase__  )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    "dataset_info"				,						[
        DatasetInfo(),
        DatasetInfo(
            description="foo"				,						features=Features({"a": Value("int32"  )}  )				,						builder_name="builder"				,						config_name="config"				,						version="1.0.0"				,						splits=[{"name": "train"}]				,						download_size=42				,						),
    ]				,						)
def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Dict  )				->	str:
							lowerCamelCase_       :							Optional[int]									=str(UpperCamelCase__  )
							dataset_info.write_to_directory(UpperCamelCase__  )
							lowerCamelCase_       :							Optional[Any]									=DatasetInfo.from_directory(UpperCamelCase__  )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(UpperCamelCase__				,						"dataset_info.json"  )  )

def  _snake_case				(     )				->	List[str]:
							lowerCamelCase_       :							Tuple									=DatasetInfo(
							    description="foo"				,						citation="bar"				,						homepage="https://foo.bar"				,						license="CC0"				,						features=Features({"a": Value("int32"  )}  )				,						post_processed={}				,						supervised_keys=()				,						task_templates=[]				,						builder_name="builder"				,						config_name="config"				,						version="1.0.0"				,						splits=[{"name": "train", "num_examples": 42}]				,						download_checksums={}				,						download_size=1_337				,						post_processing_size=442				,						dataset_size=1_234				,						size_in_bytes=1_337 + 442 + 1_234				,						)
							lowerCamelCase_       :							Optional[Any]									=dataset_info._to_yaml_dict()
							assert sorted(UpperCamelCase__  ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML  )
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]				,						(list, dict, int, str)  )
							lowerCamelCase_       :							Tuple									=yaml.safe_dump(UpperCamelCase__  )
							lowerCamelCase_       :							Any									=yaml.safe_load(UpperCamelCase__  )
							assert dataset_info_yaml_dict == reloaded

def  _snake_case				(     )				->	Dict:
							lowerCamelCase_       :							Optional[Any]									=DatasetInfo()
							lowerCamelCase_       :							Union[str, Any]									=dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    "dataset_infos_dict"				,						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}  ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}  ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"				,						features=Features({"a": Value("int32"  )}  )				,						builder_name="builder"				,						config_name="config"				,						version="1.0.0"				,						splits=[{"name": "train"}]				,						download_size=42				,						)
            }  ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42  ),
                "v2": DatasetInfo(dataset_size=1_337  ),
            }  ),
    ]				,						)
def  _snake_case				(     lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    Union[str, Any]  )				->	List[Any]:
							lowerCamelCase_       :							Dict									=str(UpperCamelCase__  )
							dataset_infos_dict.write_to_directory(UpperCamelCase__  )
							lowerCamelCase_       :							List[Any]									=DatasetInfosDict.from_directory(UpperCamelCase__  )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														lowerCamelCase_       :							List[str]									=config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														lowerCamelCase_       :							Optional[Any]									=DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()  )
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(UpperCamelCase__				,						"README.md"  )  )






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		:   List[Any]										=       logging.get_logger()







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: List[nn.Module]       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: list       			=			field(default_factory=lowercase_			)




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    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      ,   snake_case       ):
											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     a							(					self       ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case       : len(list(x.state_dict().keys()       )       ) > 0      ,   self.traced       )       )







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: int       			=			0
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)




				def __call__(					self      ,   snake_case       ):
											snake_case_							=    Tracker(self.dest       )(snake_case       ).parametrized
											snake_case_							=    Tracker(self.src       )(snake_case       ).parametrized

											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.src_skip      ,   snake_case       )       )
											snake_case_							=    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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							print(F'''Converting {name}...'''      )
							with torch.no_grad():
														snake_case_							=    timm.create_model(UpperCamelCase__   ,							pretrained=UpperCamelCase__      ).eval()
														snake_case_							=    ResNetForImageClassification(UpperCamelCase__      ).eval()
														snake_case_							=    ModuleTransfer(src=UpperCamelCase__   ,							dest=UpperCamelCase__      )
														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."

							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
														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      ):







							'''simple docstring'''






							snake_case_							=    'imagenet-1k-id2label.json'
							snake_case_							=    1000
							snake_case_							=    (1, num_labels)

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

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

							snake_case_							=    partial(UpperCamelCase__   ,							num_labels=UpperCamelCase__   ,							idalabel=UpperCamelCase__   ,							labelaid=UpperCamelCase__      )

							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__":
					_UpperCAmelCase		:   List[str]										=       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		:   Optional[Any]										=       parser.parse_args()
					_UpperCAmelCase		:   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)




