




'''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				)











'''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()





