



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


__UpperCAmelCase    	=  -1 if torch_device == '''cpu''' else 0


@is_pipeline_test
class 						lowerCamelCase__			(		unittest.TestCase ):
						_lowerCAmelCase             =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						_lowerCAmelCase             =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	str   ,							_a    :	Optional[int]   ,							_a    :	List[str]      ):
													a__:							Optional[Any]									=SummarizationPipeline(model=_a   ,							tokenizer=_a      )
													return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]


						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	Any   ,							_a    :	Any      ):
													a__:							int									=summarizer.model

													a__:							str									=summarizer("(CNN)The Palestinian Authority officially became"      )
													self.assertEqual(_a   ,							[{"summary_text": ANY(_a      )}]      )

													a__:							str									=summarizer(
													    "(CNN)The Palestinian Authority officially became "   ,							num_beams=2   ,							min_length=2   ,							max_length=5   ,							)
													self.assertEqual(_a   ,							[{"summary_text": ANY(_a      )}]      )

													# Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
													a__:							Dict									=[
													    "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(_a   ,							_a      )
																				    and get_gpu_count() > 0
																				    and len(summarizer.model.trainable_weights      ) > 0
																				):
																											with self.assertRaises(_a      ):
																																		a__:							Dict									=summarizer("This " * 1_0_0_0      )
													a__:							Tuple									=summarizer("This " * 1_0_0_0   ,							truncation=TruncationStrategy.ONLY_FIRST      )


						@require_torch
						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							Tuple									=pipeline(task="summarization"   ,							model="sshleifer/tiny-mbart"   ,							framework="pt"      )
													a__:							Tuple									=summarizer("This is a small test"      )
													self.assertEqual(
													    _a   ,							[
													        {
													            "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
													        }
													    ]   ,							)


						@require_tf
						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							List[Any]									=pipeline(task="summarization"   ,							model="sshleifer/tiny-mbart"   ,							framework="tf"      )
													a__:							List[str]									=summarizer("This is a small test"      )
													self.assertEqual(
													    _a   ,							[
													        {
													            "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
													        }
													    ]   ,							)







						@require_torch
						@slow
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							Any									=pipeline(task="summarization"   ,							device=_a      )
													a__:							int									=(
													    " (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."
													)
													a__:							Any									=(
													    " 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 ."
													)
													a__:							int									=summarizer(_a      )
													self.assertEqual(result[0]["summary_text"]   ,							_a      )










import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





