


"""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()
__snake_case             =   logging.get_logger()







@dataclass
class _lowerCAmelCase    :
			__UpperCAmelCase			:	nn.Module
			__UpperCAmelCase			:	List[nn.Module]							  =  field(default_factory=snake_case_     )
			__UpperCAmelCase			:	list							  =  field(default_factory=snake_case_     )







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

							'''simple docstring'''




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







			def __call__(     self						,     UpperCamelCase__						)     ->		Any:

							'''simple docstring'''




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







			@property
			def 			lowerCamelCase		(     self						)     ->		Any:

							'''simple docstring'''




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







@dataclass
class _lowerCAmelCase    :
			__UpperCAmelCase			:	nn.Module
			__UpperCAmelCase			:	nn.Module
			__UpperCAmelCase			:	int							  =  0
			__UpperCAmelCase			:	List							  =  field(default_factory=snake_case_     )
			__UpperCAmelCase			:	List							  =  field(default_factory=snake_case_     )







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

							'''simple docstring'''




							snake_case   :				int				 =   Tracker(self.dest						)(UpperCamelCase__						).parametrized
							snake_case   :				Union[str, Any]				 =   Tracker(self.src						)(UpperCamelCase__						).parametrized

							snake_case   :				List[str]				 =   list(filter(lambda UpperCamelCase__						: type(UpperCamelCase__						) not in self.src_skip						,     UpperCamelCase__						)						)
							snake_case   :				List[str]				 =   list(filter(lambda UpperCamelCase__						: type(UpperCamelCase__						) not in self.dest_skip						,     UpperCamelCase__						)						)

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

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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  ResNetConfig						,							lowercase  :  Path						,							lowercase  :  bool = True			)     ->						Dict:


				"""simple docstring"""





				print(F'Converting {name}...'			)
				with torch.no_grad():
								snake_case   :				Union[str, Any]				 =   timm.create_model(lowercase						,							pretrained=lowercase			).eval()
								snake_case   :				str				 =   ResNetForImageClassification(lowercase			).eval()
								snake_case   :				Union[str, Any]				 =   ModuleTransfer(src=lowercase						,							dest=lowercase			)
								snake_case   :				Optional[int]				 =   torch.randn((1, 3, 224, 224)			)
								module_transfer(lowercase			)

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

				snake_case   :				Tuple				 =   F'resnet{"-".join(name.split("resnet"			)			)}'
				print(lowercase			)

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

								# we can use the convnext one
								snake_case   :				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=lowercase						,							)

								print(F'Pushed {checkpoint_name}'			)







def   __lowerCAmelCase						(						lowercase  :  Path						,							lowercase  :  str = None						,							lowercase  :  bool = True			)     ->						int:


				"""simple docstring"""





				snake_case   :				List[str]				 =   "imagenet-1k-id2label.json"
				snake_case   :				List[str]				 =   1000
				snake_case   :				Tuple				 =   (1, num_labels)

				snake_case   :				List[str]				 =   "huggingface/label-files"
				snake_case   :				Dict				 =   num_labels
				snake_case   :				int				 =   json.load(open(hf_hub_download(lowercase						,							lowercase						,							repo_type="dataset"			)						,							"r"			)			)
				snake_case   :				str				 =   {int(lowercase			): v for k, v in idalabel.items()}

				snake_case   :				Dict				 =   idalabel
				snake_case   :				Any				 =   {v: k for k, v in idalabel.items()}

				snake_case   :				Union[str, Any]				 =   partial(lowercase						,							num_labels=lowercase						,							idalabel=lowercase						,							labelaid=lowercase			)

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


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

		__snake_case             =   parser.parse_args()
		__snake_case             =   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 pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    for split in ds.keys():
        print(F'Splitting {split} with {ds[split].num_rows} records'			)

        # to save to val.source, val.target like summary datasets
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   tgt_path.open("w+"			)

        # reader is the bottleneck so writing one record at a time doesn't slow things down
        for x in tqdm(ds[split]			):
            snake_case   :				int				 =   x["translation"]
            src_fp.write(ex[src_lang] + "\n"			)
            tgt_fp.write(ex[tgt_lang] + "\n"			)

    print(F'Saved {dataset} dataset to {save_dir}'			)


if __name__ == "__main__":
  fire.Fire(download_wmt_dataset)





