





import time
from contextlib import contextmanager
from pathlib import Path

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


_UpperCAmelCase        =				'__DUMMY_TRANSFORMERS_USER__'
_UpperCAmelCase        =				'Dummy User'
_UpperCAmelCase        =				'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'

_UpperCAmelCase        =				'https://hub-ci.huggingface.co'
_UpperCAmelCase        =				CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
_UpperCAmelCase        =				CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
_UpperCAmelCase        =				Path('~/.huggingface/hub_ci_token').expanduser()




@pytest.fixture
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Dict:
							monkeypatch.setattr(
							    "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"					,		UpperCamelCase_  )




@pytest.fixture
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Optional[int]:
							monkeypatch.setattr("datasets.config.HF_ENDPOINT"					,		UpperCamelCase_  )
							monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"					,		UpperCamelCase_  )




@pytest.fixture
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, Any]:
							monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"					,		UpperCamelCase_  )




@pytest.fixture
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Dict:
							HfFolder.save_token(UpperCamelCase_  )
							yield
							HfFolder.delete_token()




@pytest.fixture(scope="session"  )
def  lowerCAmelCase_ (							)     ->		Optional[int]:
							return HfApi(endpoint=UpperCamelCase_  )




@pytest.fixture(scope="session"  )
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Tuple:
							UpperCamelCase_          =  HfFolder.get_token()
							HfFolder.save_token(UpperCamelCase_  )
							yield CI_HUB_USER_TOKEN
							if previous_token is not None:
														HfFolder.save_token(UpperCamelCase_  )




@pytest.fixture
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
							def _cleanup_repo(UpperCamelCase_  ):
														hf_api.delete_repo(UpperCamelCase_					,		token=UpperCamelCase_					,		repo_type="dataset"  )

							return _cleanup_repo




@pytest.fixture
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Tuple:
							@contextmanager
							def _temporary_repo(UpperCamelCase_  ):
														try:
																					yield repo_id
														finally:
																					cleanup_repo(UpperCamelCase_  )

							return _temporary_repo




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




@pytest.fixture()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Union[str, Any]:
							return hf_private_dataset_repo_txt_data_




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




@pytest.fixture()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		str:
							return hf_private_dataset_repo_zipped_txt_data_




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







@pytest.fixture()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Optional[int]:
							return hf_private_dataset_repo_zipped_img_data_








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_ (							)     ->		Dict:
       raise RuntimeError("CUDA out of memory."  )



class 							_UpperCamelCase					(	nn.Module     ):

       def __init__(		self: int		)       -> List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_          =  nn.Linear(3					,   4		)
              UpperCamelCase_          =  nn.BatchNormad(4		)
              UpperCamelCase_          =  nn.Linear(4					,   5		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE		)		)		)



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  []

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

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

       def lowercase (		self: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()
                     return batch_size, arga

              UpperCamelCase_       , UpperCamelCase_          =  mock_training_loop_function("hello"		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)
              self.assertListEqual([bs, arga]					,   [8, "hello"]		)

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=0		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     pass

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

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


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

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

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     if batch_size != 8:
                            raise raise_fake_out_of_memory()

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) 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: List[str]		)       -> Optional[Any]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[Any]		):
                     raise ValueError("Oops, we had an error!"		)

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



       @require_cuda
       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  ModelForTest()
              model.cuda()
              self.assertGreater(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  release_memory(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)

