





'''simple docstring'''







import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
					import torch_xla.core.xla_model as xm  # noqa: F401

					snake_case_  :							Union[str, Any]            =	True
except ImportError:
					snake_case_  :							List[str]            =	False


# Cache this result has it's a C FFI call which can be pretty time-consuming
snake_case_  :							Dict            =	torch.distributed.is_available()





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Tuple:
						# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
						UpperCAmelCase_      :   Optional[int]      =	importlib.util.find_spec(SCREAMING_SNAKE_CASE__ ) is not None
						if package_exists:
												try:
																		UpperCAmelCase_      :   Tuple      =	importlib.metadata.metadata(SCREAMING_SNAKE_CASE__ )
																		return True
												except importlib.metadata.PackageNotFoundError:
																		return False





def 							lowerCamelCase_   (	)			->						bool:
						return _torch_distributed_available





def 							lowerCamelCase_   (	)			->						Any:
						try:
												pass
						except ImportError:
												print(
												    '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
												    ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
												    ''' Bindings for PyTorch*.''' )
						return (
						    importlib.util.find_spec('''torch_ccl''' ) is not None
						    or importlib.util.find_spec('''oneccl_bindings_for_pytorch''' ) is not None
						)





def 							lowerCamelCase_   (	)			->						Tuple:
						return importlib.metadata.version('''oneccl_bind_pt''' )





def 							lowerCamelCase_   (	)			->						int:
						return _is_package_available('''transformer_engine''' )





@lru_cache
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int]=True )			->						Dict:
						# Due to bugs on the amp series GPUs, we disable torch-xla on them
						if torch.cuda.is_available():
												return False
						if _tpu_available and check_device:
												try:
																		# Will raise a RuntimeError if no XLA configuration is found
																		UpperCAmelCase_      :   List[str]      =	xm.xla_device()
																		return True
												except RuntimeError:
																		return False
						return _tpu_available





def 							lowerCamelCase_   (	)			->						List[Any]:
						return _is_package_available('''deepspeed''' )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any]=False )			->						Union[str, Any]:
						if is_tpu_available():
												return not ignore_tpu
						if torch.cuda.is_available():
												return torch.cuda.is_bfaa_supported()
						if is_npu_available():
												return False
						return True





def 							lowerCamelCase_   (	)			->						Tuple:
						UpperCAmelCase_      :   List[str]      =	_is_package_available('''bitsandbytes''' )
						if package_exists:
												UpperCAmelCase_      :   Optional[int]      =	version.parse(importlib.metadata.version('''bitsandbytes''' ) )
												return compare_versions(SCREAMING_SNAKE_CASE__,      '''>=''',      '''0.39.0''' )
						return False





def 							lowerCamelCase_   (	)			->						Union[str, Any]:
						UpperCAmelCase_      :   List[Any]      =	_is_package_available('''bitsandbytes''' )
						if package_exists:
												UpperCAmelCase_      :   Any      =	version.parse(importlib.metadata.version('''bitsandbytes''' ) )
												return compare_versions(SCREAMING_SNAKE_CASE__,      '''>=''',      '''0.37.2''' )
						return False





def 							lowerCamelCase_   (	)			->						int:
						return _is_package_available('''bitsandbytes''' )





def 							lowerCamelCase_   (	)			->						Optional[int]:
						if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM''',      '''False''' ) ) == 1:
												UpperCAmelCase_      :   Dict      =	importlib.util.find_spec('''megatron''' ) is not None
												if package_exists:
																		try:
																								UpperCAmelCase_      :   List[str]      =	parse(importlib.metadata.version('''megatron-lm''' ) )
																								return compare_versions(SCREAMING_SNAKE_CASE__,      '''>=''',      '''2.2.0''' )
																		except Exception as e:
																								warnings.warn(F"""Parse Megatron version failed. Exception:{e}""" )
																								return False





def 							lowerCamelCase_   (	)			->						Dict:
						return _is_package_available('''safetensors''' )





def 							lowerCamelCase_   (	)			->						List[str]:
						return _is_package_available('''transformers''' )





def 							lowerCamelCase_   (	)			->						str:
						return _is_package_available('''datasets''' )





def 							lowerCamelCase_   (	)			->						Any:
						return _is_package_available('''aim''' )





def 							lowerCamelCase_   (	)			->						List[Any]:
						return _is_package_available('''tensorboard''' ) or _is_package_available('''tensorboardX''' )





def 							lowerCamelCase_   (	)			->						Tuple:
						return _is_package_available('''wandb''' )





def 							lowerCamelCase_   (	)			->						Any:
						return _is_package_available('''comet_ml''' )





def 							lowerCamelCase_   (	)			->						List[Any]:
						return _is_package_available('''boto3''' )





def 							lowerCamelCase_   (	)			->						Union[str, Any]:
						if _is_package_available('''rich''' ):
												if "ACCELERATE_DISABLE_RICH" in os.environ:
																		warnings.warn(
																		    '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.''' )
																		return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH''',      SCREAMING_SNAKE_CASE__ )
												return parse_flag_from_env('''ACCELERATE_ENABLE_RICH''',      SCREAMING_SNAKE_CASE__ )
						return False





def 							lowerCamelCase_   (	)			->						Union[str, Any]:
						return _is_package_available('''sagemaker''' )





def 							lowerCamelCase_   (	)			->						List[Any]:
						return _is_package_available('''tqdm''' )





def 							lowerCamelCase_   (	)			->						List[Any]:
						return _is_package_available('''mlflow''' )





def 							lowerCamelCase_   (	)			->						Union[str, Any]:
						return is_torch_version('''>=''',      '''1.12''' ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()





def 							lowerCamelCase_   (	)			->						Union[str, Any]:
						def get_major_and_minor_from_version(SCREAMING_SNAKE_CASE__   :      str ):
												return str(version.parse(SCREAMING_SNAKE_CASE__ ).major ) + "." + str(version.parse(SCREAMING_SNAKE_CASE__ ).minor )

						UpperCAmelCase_      :   List[Any]      =	importlib.metadata.version('''torch''' )
						if importlib.util.find_spec('''intel_extension_for_pytorch''' ) is None:
												return False
						UpperCAmelCase_      :   Dict      =	'''N/A'''
						try:
												UpperCAmelCase_      :   Union[str, Any]      =	importlib.metadata.version('''intel_extension_for_pytorch''' )
						except importlib.metadata.PackageNotFoundError:
												return False
						UpperCAmelCase_      :   List[str]      =	get_major_and_minor_from_version(_torch_version )
						UpperCAmelCase_      :   str      =	get_major_and_minor_from_version(_ipex_version )
						if torch_major_and_minor != ipex_major_and_minor:
												warnings.warn(
												    F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
												    F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again.""" )
												return False
						return True





@lru_cache
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict=False )			->						Any:
						if importlib.util.find_spec('''torch''' ) is None or importlib.util.find_spec('''torch_npu''' ) is None:
												return False

						import torch
						import torch_npu  # noqa: F401

						if check_device:
												try:
																		# Will raise a RuntimeError if no NPU is found
																		UpperCAmelCase_      :   List[str]      =	torch.npu.device_count()
																		return torch.npu.is_available()
												except RuntimeError:
																		return False
						return hasattr(SCREAMING_SNAKE_CASE__,      '''npu''' ) and torch.npu.is_available()





@lru_cache
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any]=False )			->						int:
						if not parse_flag_from_env('''ACCELERATE_USE_XPU''',      default=SCREAMING_SNAKE_CASE__ ):
												return False
						if is_ipex_available():
												import torch

												if is_torch_version('''<=''',      '''1.12''' ):
																		return False
						else:
												return False

						import intel_extension_for_pytorch  # noqa: F401

						if check_device:
												try:
																		# Will raise a RuntimeError if no XPU  is found
																		UpperCAmelCase_      :   Optional[int]      =	torch.xpu.device_count()
																		return torch.xpu.is_available()
												except RuntimeError:
																		return False
						return hasattr(SCREAMING_SNAKE_CASE__,      '''xpu''' ) and torch.xpu.is_available()












'''simple docstring'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
					]

					for test_file in test_files:
										snake_case_  :							Any            =	get_tester_classes(test_file)
										for tester_class in tester_classes:
															# A few tester classes don't have `parent` parameter in `__init__`.
															# TODO: deal this better
															try:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	5
																														if target is not None and v > target:
																																			failures.append(
																																			    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																			    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																			    f''' than {target}.'''
																																			)

					if len(failures) > 0:
										raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))





