code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import unittest
  
  from transformers import (
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      SummarizationPipeline,
      TFPreTrainedModel,
      pipeline,
  )
  from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
  from transformers.tokenization_utils import TruncationStrategy
  
  from .test_pipelines_common import ANY
  
  
  _lowerCamelCase							:		Any										=						-1 if torch_device == 'cpu' else 0
  
  
  
  
  
  @is_pipeline_test
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  					lowercase__					=				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = SummarizationPipeline(model=__UpperCamelCase,						tokenizer=__UpperCamelCase				)
  							return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = summarizer.model
  
  							_snake_case			 = summarizer("(CNN)The Palestinian Authority officially became"				)
  							self.assertEqual(__UpperCamelCase,						[{"summary_text": ANY(__UpperCamelCase				)}]				)
  
  							_snake_case			 = summarizer(
  							    "(CNN)The Palestinian Authority officially became ",						num_beams=2,						min_length=2,						max_length=5,						)
  							self.assertEqual(__UpperCamelCase,						[{"summary_text": ANY(__UpperCamelCase				)}]				)
  
  							# Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
  							_snake_case			 = [
  							    "SwitchTransformersConfig",
  							    "T5Config",
  							    "LongT5Config",
  							    "LEDConfig",
  							    "PegasusXConfig",
  							    "FSMTConfig",
  							    "M2M100Config",
  							    "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
  							]
  							if model.config.__class__.__name__ not in model_can_handle_longer_seq:
  									# Too long and exception is expected.
  									# For TF models, if the weights are initialized in GPU context, we won't get expected index error from
  									# the embedding layer.
  									if not (
  									    isinstance(__UpperCamelCase,						__UpperCamelCase				)
  									    and get_gpu_count() > 0
  									    and len(summarizer.model.trainable_weights				) > 0
  									):
  											with self.assertRaises(__UpperCamelCase				):
  													_snake_case			 = summarizer("This " * 1_000				)
  							_snake_case			 = summarizer("This " * 1_000,						truncation=TruncationStrategy.ONLY_FIRST				)
  
  
  
  
  
  
  					@require_torch
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = pipeline(task="summarization",						model="sshleifer/tiny-mbart",						framework="pt"				)
  							_snake_case			 = summarizer("This is a small test"				)
  							self.assertEqual(
  							    __UpperCamelCase,						[
  							        {
  							            "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
  							        }
  							    ],						)
  
  
  
  
  
  
  					@require_tf
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = pipeline(task="summarization",						model="sshleifer/tiny-mbart",						framework="tf"				)
  							_snake_case			 = summarizer("This is a small test"				)
  							self.assertEqual(
  							    __UpperCamelCase,						[
  							        {
  							            "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
  							        }
  							    ],						)
  
  
  
  
  
  
  					@require_torch
  					@slow
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = pipeline(task="summarization",						device=__UpperCamelCase				)
  							_snake_case			 = (
  							    " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
  							    " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
  							    " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
  							    " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
  							    " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
  							    " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
  							    " situation in Palestinian territories, paving the way for possible war crimes investigations against"
  							    " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
  							    " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
  							    " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
  							    " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
  							    " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
  							    " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
  							    " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
  							    " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
  							    " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
  							    " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
  							    " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
  							    " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
  							    " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
  							    " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
  							    " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
  							    " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
  							    " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
  							    " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
  							    " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
  							    " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
  							    " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
  							    " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
  							    " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
  							    " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
  							    " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
  							    " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
  							    " will include alleged war crimes committed since June. The International Criminal Court was set up in"
  							    " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
  							    " and Faith Karimi contributed to this report."
  							)
  							_snake_case			 = (
  							    " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
  							    " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
  							    " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
  							    " says governments seeking to penalize Palestine should end pressure ."
  							)
  							_snake_case			 = summarizer(__UpperCamelCase				)
  							self.assertEqual(result[0]["summary_text"],						__UpperCamelCase				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''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
  
  						_lowerCamelCase							:		int										=						True
  except ImportError:
  						_lowerCamelCase							:		List[str]										=						False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  _lowerCamelCase							:		List[Any]										=						torch.distributed.is_available()
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = importlib.util.find_spec(lowercase_						) is not None
  		if package_exists:
  				try:
  						_snake_case			 = importlib.metadata.metadata(lowercase_						)
  						return True
  				except importlib.metadata.PackageNotFoundError:
  						return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _torch_distributed_available
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		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 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return importlib.metadata.version("oneccl_bind_pt"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("transformer_engine"						)
  
  
  
  
  
  
  @lru_cache
  def 	_lowercase	(			lowercase_=True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if torch.cuda.is_available():
  				return False
  		if _tpu_available and check_device:
  				try:
  						# Will raise a RuntimeError if no XLA configuration is found
  						_snake_case			 = xm.xla_device()
  						return True
  				except RuntimeError:
  						return False
  		return _tpu_available
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("deepspeed"						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		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 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = _is_package_available("bitsandbytes"						)
  		if package_exists:
  				_snake_case			 = version.parse(importlib.metadata.version("bitsandbytes"						)						)
  				return compare_versions(lowercase_		,							">="		,							"0.39.0"						)
  		return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = _is_package_available("bitsandbytes"						)
  		if package_exists:
  				_snake_case			 = version.parse(importlib.metadata.version("bitsandbytes"						)						)
  				return compare_versions(lowercase_		,							">="		,							"0.37.2"						)
  		return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("bitsandbytes"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if strtobool(os.environ.get("ACCELERATE_USE_MEGATRON_LM"		,							"False"						)						) == 1:
  				_snake_case			 = importlib.util.find_spec("megatron"						) is not None
  				if package_exists:
  						try:
  								_snake_case			 = parse(importlib.metadata.version("megatron-lm"						)						)
  								return compare_versions(lowercase_		,							">="		,							"2.2.0"						)
  						except Exception as e:
  								warnings.warn(f'''Parse Megatron version failed. Exception:{e}'''						)
  								return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("safetensors"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("transformers"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("datasets"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("aim"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("tensorboard"						) or _is_package_available("tensorboardX"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("wandb"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("comet_ml"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("boto3"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		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"		,							lowercase_						)
  				return parse_flag_from_env("ACCELERATE_ENABLE_RICH"		,							lowercase_						)
  		return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("sagemaker"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("tqdm"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("mlflow"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return is_torch_version(">="		,							"1.12"						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def get_major_and_minor_from_version(lowercase_						):
  				return str(version.parse(lowercase_						).major						) + "." + str(version.parse(lowercase_						).minor						)
  
  		_snake_case			 = importlib.metadata.version("torch"						)
  		if importlib.util.find_spec("intel_extension_for_pytorch"						) is None:
  				return False
  		_snake_case			 = "N/A"
  		try:
  				_snake_case			 = importlib.metadata.version("intel_extension_for_pytorch"						)
  		except importlib.metadata.PackageNotFoundError:
  				return False
  		_snake_case			 = get_major_and_minor_from_version(_torch_version						)
  		_snake_case			 = 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 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		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
  						_snake_case			 = torch.npu.device_count()
  						return torch.npu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowercase_		,							"npu"						) and torch.npu.is_available()
  
  
  
  
  
  
  @lru_cache
  def 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if not parse_flag_from_env("ACCELERATE_USE_XPU"		,							default=lowercase_						):
  				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
  						_snake_case			 = torch.xpu.device_count()
  						return torch.xpu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowercase_		,							"xpu"						) and torch.xpu.is_available()
  
  
  
  
  
  