



'''simple docstring'''




import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a   :			List[Any]					  =    """__DUMMY_TRANSFORMERS_USER__"""
a   :			Tuple					  =    """Dummy User"""
a   :			Optional[Any]					  =    """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"""

a   :			Optional[Any]					  =    """https://hub-ci.huggingface.co"""
a   :			List[Any]					  =    CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
a   :			Tuple					  =    CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
a   :			str					  =    Path("""~/.huggingface/hub_ci_token""").expanduser()



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							monkeypatch.setattr(
							    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""	,				_lowercase		)
							monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[Any]:
							HfFolder.save_token(_lowercase		)
							yield
							HfFolder.delete_token()



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					)      ->			str:
							return HfApi(endpoint=_lowercase		)



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		str							     =		HfFolder.get_token()
							HfFolder.save_token(_lowercase		)
							yield CI_HUB_USER_TOKEN
							if previous_token is not None:
														HfFolder.save_token(_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							def _cleanup_repo(_lowercase		):
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)

							return _cleanup_repo



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							@contextmanager
							def _temporary_repo(_lowercase		):
														try:
																					yield repo_id
														finally:
																					cleanup_repo(_lowercase		)

							return _temporary_repo



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		str							     =		F'''repo_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[Any]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data/text_data.txt"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[int]							     =		F'''repo_zipped_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		Optional[int]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Tuple:
							UpperCAmelCase     :		List[Any]							     =		F'''repo_zipped_img_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[str]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_zipped_img_data_






'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

