"""simple docstring"""







import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_a					:					Optional[int]=			'http://www.mocksite.com/file1.txt'
_a					:					List[str]=			'"text": ["foo", "foo"]'
_a					:					int=			'6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'

class 		UpperCamelCase      :






				"""simple docstring"""




				UpperCAmelCase    :				Tuple   =		200
				UpperCAmelCase    :				Dict   =		{"""Content-Length""": """100"""}
				UpperCAmelCase    :				List[Any]   =		{}



				def  _lowercase					(self    :				Optional[int]     ,  **_A    :				int)   ->     List[Any]:
					return [bytes(__SCREAMING_SNAKE_CASE     ,  'utf-8')]







def   __UpperCAmelCase    (   *UpperCAmelCase_   :    Dict      ,							**UpperCAmelCase_   :    List[Any]  )							->      Any:

	'''simple docstring'''





	return MockResponse()
@pytest.mark.parametrize('urls_type'      ,							[str, list, dict]  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    Optional[Any]  )							->      Optional[int]:

	'''simple docstring'''





	import requests

	monkeypatch.setattr(lowercase__      ,							'request'      ,							lowercase__  )

	__snake_case      :							Tuple						=			URL
	if issubclass(lowercase__      ,							lowercase__  ):
		__snake_case      :							Optional[int]						=			url
	elif issubclass(lowercase__      ,							lowercase__  ):
		__snake_case      :							Optional[Any]						=			[url]
	elif issubclass(lowercase__      ,							lowercase__  ):
		__snake_case      :							Any						=			{'train': url}
	__snake_case      :							Any						=			'dummy'
	__snake_case      :							Any						=			'downloads'
	__snake_case      :							Any						=			tmp_path
	__snake_case      :							Union[str, Any]						=			DownloadConfig(
	    cache_dir=os.path.join(lowercase__      ,							lowercase__  )      ,							use_etag=lowercase__      ,							)
	__snake_case      :							int						=			DownloadManager(dataset_name=lowercase__      ,							download_config=lowercase__  )
	__snake_case      :							Any						=			dl_manager.download(lowercase__  )
	__snake_case      :							Union[str, Any]						=			urls
	for downloaded_paths in [downloaded_paths]:
		if isinstance(lowercase__      ,							lowercase__  ):
			__snake_case      :							Dict						=			[downloaded_paths]
			__snake_case      :							Any						=			[urls]
		elif isinstance(lowercase__      ,							lowercase__  ):
			assert "train" in downloaded_paths.keys()
			__snake_case      :							int						=			downloaded_paths.values()
			__snake_case      :							Optional[int]						=			urls.values()
		assert downloaded_paths
		for downloaded_path, input_url in zip(lowercase__      ,							lowercase__  ):
			assert downloaded_path == dl_manager.downloaded_paths[input_url]
			__snake_case      :							Optional[Any]						=			Path(lowercase__  )
			__snake_case      :							Any						=			downloaded_path.parts
			assert parts[-1] == HASH
			assert parts[-2] == cache_subdir
			assert downloaded_path.exists()
			__snake_case      :							Any						=			downloaded_path.read_text()
			assert content == CONTENT
			__snake_case      :							int						=			downloaded_path.with_suffix('.json'  )
			assert metadata_downloaded_path.exists()
			__snake_case      :							List[Any]						=			json.loads(metadata_downloaded_path.read_text()  )
			assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('paths_type'      ,							[str, list, dict]  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    List[str]  )							->      List[Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			str(lowercase__  )
	if issubclass(lowercase__      ,							lowercase__  ):
		__snake_case      :							Optional[Any]						=			filename
	elif issubclass(lowercase__      ,							lowercase__  ):
		__snake_case      :							Tuple						=			[filename]
	elif issubclass(lowercase__      ,							lowercase__  ):
		__snake_case      :							Union[str, Any]						=			{'train': filename}
	__snake_case      :							str						=			'dummy'
	__snake_case      :							Dict						=			xz_file.parent
	__snake_case      :							Tuple						=			'extracted'
	__snake_case      :							Optional[int]						=			DownloadConfig(
	    cache_dir=lowercase__      ,							use_etag=lowercase__      ,							)
	__snake_case      :							List[Any]						=			DownloadManager(dataset_name=lowercase__      ,							download_config=lowercase__  )
	__snake_case      :							List[Any]						=			dl_manager.extract(lowercase__  )
	__snake_case      :							str						=			paths
	for extracted_paths in [extracted_paths]:
		if isinstance(lowercase__      ,							lowercase__  ):
			__snake_case      :							Optional[Any]						=			[extracted_paths]
			__snake_case      :							str						=			[paths]
		elif isinstance(lowercase__      ,							lowercase__  ):
			assert "train" in extracted_paths.keys()
			__snake_case      :							Dict						=			extracted_paths.values()
			__snake_case      :							Union[str, Any]						=			paths.values()
		assert extracted_paths
		for extracted_path, input_path in zip(lowercase__      ,							lowercase__  ):
			assert extracted_path == dl_manager.extracted_paths[input_path]
			__snake_case      :							int						=			Path(lowercase__  )
			__snake_case      :							Optional[int]						=			extracted_path.parts
			assert parts[-1] == hash_url_to_filename(lowercase__      ,							etag=lowercase__  )
			assert parts[-2] == extracted_subdir
			assert extracted_path.exists()
			__snake_case      :							List[str]						=			extracted_path.read_text()
			__snake_case      :							Any						=			text_file.read_text()
			assert extracted_file_content == expected_file_content
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    int  )							->      List[Any]:

	'''simple docstring'''





	assert path.endswith('.jsonl'  )
	for num_items, line in enumerate(lowercase__      ,							start=1  ):
		__snake_case      :							Any						=			json.loads(line.decode('utf-8'  )  )
		assert item.keys() == {"col_1", "col_2", "col_3"}
	assert num_items == 4
@pytest.mark.parametrize('archive_jsonl'      ,							['tar_jsonl_path', 'zip_jsonl_path']  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    Union[str, Any]  )							->      List[Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			request.getfixturevalue(lowercase__  )
	__snake_case      :							List[str]						=			DownloadManager()
	for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowercase__  )      ,							start=1  ):
		_test_jsonl(lowercase__      ,							lowercase__  )
	assert num_jsonl == 2
@pytest.mark.parametrize('archive_nested_jsonl'      ,							['tar_nested_jsonl_path', 'zip_nested_jsonl_path']  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    Any  )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			request.getfixturevalue(lowercase__  )
	__snake_case      :							int						=			DownloadManager()
	for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowercase__  )      ,							start=1  ):
		for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowercase__  )      ,							start=1  ):
			_test_jsonl(lowercase__      ,							lowercase__  )
	assert num_tar == 1
	assert num_jsonl == 2

def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]  )							->      str:

	'''simple docstring'''





	__snake_case      :							Any						=			DownloadManager()
	for num_file, file in enumerate(dl_manager.iter_files(lowercase__  )      ,							start=1  ):
		assert os.path.basename(lowercase__  ) == ("test.txt" if num_file == 1 else "train.txt")
	assert num_file == 2






"""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					:					Optional[int]=			logging.get_logger()

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



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]     ,  _A    :				Tensor     ,  _A    :				Tensor)   ->     Any:
					__snake_case      :							str						=			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    :				Dict     ,  _A    :				Tensor)   ->     Optional[Any]:
					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    :				Union[str, Any])   ->     List[str]:
					# 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      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				int   =		0
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)



				def __call__(self    :				List[str]     ,  _A    :				Tensor)   ->     List[Any]:
					__snake_case      :							Any						=			Tracker(self.dest)(_A).parametrized
					__snake_case      :							int						=			Tracker(self.src)(_A).parametrized

					__snake_case      :							List[Any]						=			list(filter(lambda _A: type(_A) not in self.src_skip     ,  _A))
					__snake_case      :							Any						=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    ResNetConfig      ,							UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    bool = True  )							->      List[str]:

	'''simple docstring'''





	print(F"Converting {name}..."  )
	with torch.no_grad():
		__snake_case      :							Dict						=			timm.create_model(UpperCAmelCase_      ,							pretrained=UpperCAmelCase_  ).eval()
		__snake_case      :							List[Any]						=			ResNetForImageClassification(UpperCAmelCase_  ).eval()
		__snake_case      :							int						=			ModuleTransfer(src=UpperCAmelCase_      ,							dest=UpperCAmelCase_  )
		__snake_case      :							Optional[Any]						=			torch.randn((1, 3, 2_24, 2_24)  )
		module_transfer(UpperCAmelCase_  )

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

	__snake_case      :							str						=			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      :							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=UpperCAmelCase_      ,							)

		print(F"Pushed {checkpoint_name}"  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    str = None      ,							UpperCAmelCase_   :    bool = True  )							->      Union[str, Any]:

	'''simple docstring'''





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

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

	__snake_case      :							Optional[Any]						=			idalabel
	__snake_case      :							Optional[Any]						=			{v: k for k, v in idalabel.items()}

	__snake_case      :							Optional[int]						=			partial(UpperCAmelCase_      ,							num_labels=UpperCAmelCase_      ,							idalabel=UpperCAmelCase_      ,							labelaid=UpperCAmelCase_  )

	__snake_case      :							str						=			{
	    '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(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__":
	_a					:					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.",
	)

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





