



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    :						Dict  =						logging.get_logger()




@dataclass
class 							lowerCAmelCase__  :
					__A     :    nn.Module
					__A     :    List[nn.Module]           =						field(default_factory=UpperCamelCase						)
					__A     :    list           =						field(default_factory=UpperCamelCase						)







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     Optional[Any]			,		_A      :     Tensor			,		_A      :     Tensor):
											A__	:							List[Any]						    = 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      :     Optional[Any]			,		_A      :     Tensor):
											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  _lowercase			(       self      :     Any):
											# 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 							lowerCAmelCase__  :
					__A     :    nn.Module
					__A     :    nn.Module
					__A     :    int           =						0
					__A     :    List           =						field(default_factory=UpperCamelCase						)
					__A     :    List           =						field(default_factory=UpperCamelCase						)







					def __call__(       self      :     Optional[Any]			,		_A      :     Tensor):
											A__	:							List[str]						    = Tracker(self.dest)(_A).parametrized
											A__	:							Union[str, Any]						    = Tracker(self.src)(_A).parametrized

											A__	:							Dict						    = list(filter(lambda _A: type(_A) not in self.src_skip			,		_A))
											A__	:							Optional[int]						    = 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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase = True       )		->		List[Any]:







						"""simple docstring"""

						print(F'Converting {name}...'       )
						with torch.no_grad():
												A__	:							Dict						    = timm.create_model(__lowercase							,     pretrained=__lowercase       ).eval()
												A__	:							str						    = ResNetForImageClassification(__lowercase       ).eval()
												A__	:							Any						    = ModuleTransfer(src=__lowercase							,     dest=__lowercase       )
												A__	:							List[Any]						    = torch.randn((1, 3, 2_2_4, 2_2_4)       )
												module_transfer(__lowercase       )

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

						A__	:							Optional[int]						    = 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
												A__	:							int						    = 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 	snake_case__       (					__lowercase							,     __lowercase = None							,     __lowercase = True       )		->		int:







						"""simple docstring"""

						A__	:							int						    = "imagenet-1k-id2label.json"
						A__	:							str						    = 1_0_0_0
						A__	:							Tuple						    = (1, num_labels)

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

						A__	:							List[str]						    = idalabel
						A__	:							int						    = {v: k for k, v in idalabel.items()}

						A__	:							Dict						    = partial(__lowercase							,     num_labels=__lowercase							,     idalabel=__lowercase							,     labelaid=__lowercase       )

						A__	:							List[str]						    = {
						    "resnet18": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]							,     hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]							,     layer_type="basic"       ),
						    "resnet26": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]							,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]							,     layer_type="bottleneck"       ),
						    "resnet34": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]							,     hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]							,     layer_type="basic"       ),
						    "resnet50": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]							,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]							,     layer_type="bottleneck"       ),
						    "resnet101": ImageNetPreTrainedConfig(
						        depths=[3, 4, 2_3, 3]							,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]							,     layer_type="bottleneck"       ),
						    "resnet152": ImageNetPreTrainedConfig(
						        depths=[3, 8, 3_6, 3]							,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]							,     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    :						int  =						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    :						Optional[Any]  =						parser.parse_args()
		snake_case    :						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 multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Dict			,		_A      :     NestedDataStructureLike[PathLike]			,		_A      :     Optional[NamedSplit] = None			,		_A      :     Optional[Features] = None			,		_A      :     str = None			,		_A      :     bool = False			,		_A      :     bool = False			,		_A      :     Optional[str] = None			,		_A      :     Optional[int] = None			,		**_A      :     Dict			,		):
											super().__init__(
											    _A			,		split=_A			,		features=_A			,		cache_dir=_A			,		keep_in_memory=_A			,		streaming=_A			,		num_proc=_A			,		**_A			,		)
											A__	:							Optional[int]						    = field
											A__	:							Dict						    = path_or_paths if isinstance(_A			,		_A) else {self.split: path_or_paths}
											A__	:							int						    = Json(
											    cache_dir=_A			,		data_files=_A			,		features=_A			,		field=_A			,		**_A			,		)







					def  _lowercase			(       self      :     Dict):
											# Build iterable dataset
											if self.streaming:
																	A__	:							Union[str, Any]						    = self.builder.as_streaming_dataset(split=self.split)
											# Build regular (map-style) dataset
											else:
																	A__	:							Optional[Any]						    = None
																	A__	:							Any						    = None
																	A__	:							Tuple						    = None
																	A__	:							Tuple						    = None

																	self.builder.download_and_prepare(
																	    download_config=_A			,		download_mode=_A			,		verification_mode=_A			,		base_path=_A			,		num_proc=self.num_proc			,		)
																	A__	:							Any						    = self.builder.as_dataset(
																	    split=self.split			,		verification_mode=_A			,		in_memory=self.keep_in_memory)
											return dataset




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]			,		_A      :     Dataset			,		_A      :     Union[PathLike, BinaryIO]			,		_A      :     Optional[int] = None			,		_A      :     Optional[int] = None			,		**_A      :     Any			,		):
											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

											A__	:							Tuple						    = dataset
											A__	:							Tuple						    = path_or_buf
											A__	:							int						    = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											A__	:							Tuple						    = num_proc
											A__	:							Any						    = "utf-8"
											A__	:							Optional[int]						    = to_json_kwargs







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Any						    = self.to_json_kwargs.pop("path_or_buf"			,		_A)
											A__	:							Union[str, Any]						    = self.to_json_kwargs.pop("orient"			,		"records")
											A__	:							List[Any]						    = self.to_json_kwargs.pop("lines"			,		True if orient == "records" else False)
											A__	:							List[Any]						    = self.to_json_kwargs.pop("index"			,		False if orient in ["split", "table"] else True)
											A__	:							int						    = self.to_json_kwargs.pop("compression"			,		_A)

											if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																	raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

											if isinstance(self.path_or_buf			,		(str, bytes, os.PathLike)):
																	with fsspec.open(self.path_or_buf			,		"wb"			,		compression=_A) as buffer:
																							A__	:							int						    = self._write(file_obj=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											else:
																	if compression:
																							raise NotImplementedError(
																							    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																							    " was passed. Please provide a local path instead.")
																	A__	:							Optional[Any]						    = self._write(
																	    file_obj=self.path_or_buf			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											return written







					def  _lowercase			(       self      :     List[Any]			,		_A      :     int):
											A__    ,       A__    ,       A__    ,       A__    ,       A__	:							Union[str, Any]						    = args

											A__	:							Dict						    = query_table(
											    table=self.dataset.data			,		key=slice(_A			,		offset + self.batch_size)			,		indices=self.dataset._indices			,		)
											A__	:							Dict						    = batch.to_pandas().to_json(
											    path_or_buf=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**_A)
											if not json_str.endswith("\n"):
																	json_str += "\n"
											return json_str.encode(self.encoding)







					def  _lowercase			(       self      :     List[str]			,		_A      :     BinaryIO			,		_A      :     str			,		_A      :     List[Any]			,		_A      :     Optional[Any]			,		**_A      :     Optional[Any]			,		):
											A__	:							Any						    = 0

											if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0			,		len(self.dataset)			,		self.batch_size)			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																							A__	:							List[str]						    = self._batch_json((offset, orient, lines, index, to_json_kwargs))
																							written += file_obj.write(_A)
											else:
																	A__    ,       A__	:							Union[str, Any]						    = len(self.dataset), self.batch_size
																	with multiprocessing.Pool(self.num_proc) as pool:
																							for json_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_json			,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,		_A			,		_A)]			,		)			,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																													written += file_obj.write(_A)

											return written