


import time
from contextlib import contextmanager
from pathlib import Path

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


_UpperCAmelCase			:  Any     ='''__DUMMY_TRANSFORMERS_USER__'''
_UpperCAmelCase			:  Optional[int]     ='''Dummy User'''
_UpperCAmelCase			:  str     ='''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'''

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



@pytest.fixture
def 			lowerCAmelCase (		lowerCAmelCase_ )-> Optional[Any]:
				monkeypatch.setattr(
				    '''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE''' ,   _UpperCAmelCase )



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



@pytest.fixture
def 			lowerCAmelCase (		lowerCAmelCase_ )-> List[str]:
				monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token''' ,   _UpperCAmelCase )



@pytest.fixture
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[int]:
				HfFolder.save_token(_UpperCAmelCase )
				yield
				HfFolder.delete_token()



@pytest.fixture(scope='''session''' )
def 			lowerCAmelCase (		)-> Dict:
				return HfApi(endpoint=_UpperCAmelCase )



@pytest.fixture(scope='''session''' )
def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      Optional[int]    				=				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 (		lowerCAmelCase_ )-> Optional[int]:
				def _cleanup_repo(lowerCAmelCase_ ):
								hf_api.delete_repo(_UpperCAmelCase ,   token=_UpperCAmelCase ,   repo_type='''dataset''' )

				return _cleanup_repo



@pytest.fixture
def 			lowerCAmelCase (		lowerCAmelCase_ )-> Union[str, Any]:
				@contextmanager
				def _temporary_repo(lowerCAmelCase_ ):
								try:
												yield repo_id
								finally:
												cleanup_repo(_UpperCAmelCase )

				return _temporary_repo



@pytest.fixture(scope='''session''' )
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[int]:
				lowerCAmelCase_ :      List[str]    				=				f"""repo_txt_data-{int(time.time() * 1_0e3 )}"""
				lowerCAmelCase_ :      List[str]    				=				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 (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope='''session''' )
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Any:
				lowerCAmelCase_ :      Any    				=				f"""repo_zipped_txt_data-{int(time.time() * 1_0e3 )}"""
				lowerCAmelCase_ :      int    				=				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 (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[int]:
				return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope='''session''' )
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> List[Any]:
				lowerCAmelCase_ :      str    				=				f"""repo_zipped_img_data-{int(time.time() * 1_0e3 )}"""
				lowerCAmelCase_ :      List[str]    				=				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 (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				return hf_private_dataset_repo_zipped_img_data_








# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						__lowercase = False							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				scheduler
										lowerCAmelCase_ :      Dict    				=				optimizers if isinstance(__lowercase   ,						(list, tuple)							) else [optimizers]
										lowerCAmelCase_ :      str    				=				split_batches
										lowerCAmelCase_ :      Any    				=				step_with_optimizer
										lowerCAmelCase_ :      Optional[Any]    				=				GradientState()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    Any:
										if not self.step_with_optimizer:
														# No link between scheduler and optimizer -> just step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
														return

										# Otherwise, first make sure the optimizer was stepped.
										if not self.gradient_state.sync_gradients:
														if self.gradient_state.adjust_scheduler:
																		self.scheduler._step_count += 1
														return

										for opt in self.optimizers:
														if opt.step_was_skipped:
																		return
										if self.split_batches:
														# Split batches -> the training dataloader batch size is not changed so one step per training step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
										else:
														# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
														# num_processes steps per training step
														lowerCAmelCase_ :      Optional[Any]    				=				AcceleratorState().num_processes
														for _ in range(__lowercase							):
																		# Special case when using OneCycle and `drop_last` was not used
																		if hasattr(self.scheduler   ,						'''total_steps'''							):
																						if self.scheduler._step_count <= self.scheduler.total_steps:
																										self.scheduler.step(*__lowercase   ,						**__lowercase							)
																		else:
																						self.scheduler.step(*__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.scheduler.get_last_lr()




						def 						lowercase_ (				self							)					->    List[str]:
										return self.scheduler.state_dict()




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										self.scheduler.load_state_dict(__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										return self.scheduler.get_lr()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    int:
										return self.scheduler.print_lr(*__lowercase   ,						**__lowercase							)