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										=  -1 if torch_device == '''cpu''' else 0
  
  @is_pipeline_test
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __lowerCAmelCase      =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int   ,       snake_case__:	Tuple   ,       snake_case__:	List[str]   ,       snake_case__:	Union[str, Any]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	SummarizationPipeline(model=snake_case__   ,       tokenizer=snake_case__	)
         return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	Any   ,       snake_case__:	List[Any]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	summarizer.model
  
         _lowercase     =	summarizer('(CNN)The Palestinian Authority officially became'	)
         self.assertEqual(snake_case__   ,       [{'summary_text': ANY(snake_case__	)}]	)
  
         _lowercase     =	summarizer(
             '(CNN)The Palestinian Authority officially became '   ,       num_beams=2   ,       min_length=2   ,       max_length=5   ,       )
         self.assertEqual(snake_case__   ,       [{'summary_text': ANY(snake_case__	)}]	)
  
         # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
         _lowercase     =	[
             '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(snake_case__   ,       snake_case__	)
                 and get_gpu_count() > 0
                 and len(summarizer.model.trainable_weights	) > 0
             ):
                 with self.assertRaises(snake_case__	):
                     _lowercase     =	summarizer('This ' * 1_000	)
         _lowercase     =	summarizer('This ' * 1_000   ,       truncation=TruncationStrategy.ONLY_FIRST	)
  
     @require_torch
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	pipeline(task='summarization'   ,       model='sshleifer/tiny-mbart'   ,       framework='pt'	)
         _lowercase     =	summarizer('This is a small test'	)
         self.assertEqual(
             snake_case__   ,       [
                 {
                     'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                 }
             ]   ,       )
  
     @require_tf
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	pipeline(task='summarization'   ,       model='sshleifer/tiny-mbart'   ,       framework='tf'	)
         _lowercase     =	summarizer('This is a small test'	)
         self.assertEqual(
             snake_case__   ,       [
                 {
                     'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                 }
             ]   ,       )
  
     @require_torch
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	pipeline(task='summarization'   ,       device=snake_case__	)
         _lowercase     =	(
             ' (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.'
         )
         _lowercase     =	(
             ' 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 .'
         )
         _lowercase     =	summarizer(snake_case__	)
         self.assertEqual(result[0]['summary_text']   ,       snake_case__	)
  
style_context: 
  
  
  
  
  
  
  import json
  import os
  import shutil
  import tempfile
  import unittest
  
  from transformers import is_speech_available, is_torch_available
  from transformers.models.speechta import SpeechTaTokenizer
  from transformers.testing_utils import get_tests_dir, require_torch
  from transformers.utils import FEATURE_EXTRACTOR_NAME
  
  
  if is_speech_available() and is_torch_available():
     from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor
  
     from .test_feature_extraction_speechta import floats_list
  
  
  lowerCamelCase										=  get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
  
  @require_torch
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	tempfile.mkdtemp()
  
         _lowercase     =	SpeechTaTokenizer(snake_case__	)
         tokenizer.save_pretrained(self.tmpdirname	)
  
         _lowercase     =	{
             'feature_size': 1,
             'padding_value': 0.0,
             'sampling_rate': 16_000,
             'do_normalize': False,
             'num_mel_bins': 80,
             'hop_length': 16,
             'win_length': 64,
             'win_function': 'hann_window',
             'fmin': 80,
             'fmax': 7_600,
             'mel_floor': 1E-10,
             'reduction_factor': 2,
             'return_attention_mask': True,
         }
  
         _lowercase     =	os.path.join(self.tmpdirname   ,       snake_case__	)
         with open(self.feature_extraction_file   ,       'w'   ,       encoding='utf-8'	) as fp:
             fp.write(json.dumps(snake_case__	) + '\n'	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       **snake_case__:	Union[str, Any]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       **snake_case__:	str	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         shutil.rmtree(self.tmpdirname	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.get_tokenizer()
         _lowercase     =	self.get_feature_extractor()
  
         _lowercase     =	SpeechTaProcessor(tokenizer=snake_case__   ,       feature_extractor=snake_case__	)
  
         processor.save_pretrained(self.tmpdirname	)
         _lowercase     =	SpeechTaProcessor.from_pretrained(self.tmpdirname	)
  
         self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer.get_vocab()	)
         self.assertIsInstance(processor.tokenizer   ,       snake_case__	)
  
         self.assertEqual(processor.feature_extractor.to_json_string()   ,       feature_extractor.to_json_string()	)
         self.assertIsInstance(processor.feature_extractor   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,       feature_extractor=self.get_feature_extractor()	)
         processor.save_pretrained(self.tmpdirname	)
  
         _lowercase     =	self.get_tokenizer(bos_token='(BOS)'   ,       eos_token='(EOS)'	)
         _lowercase     =	self.get_feature_extractor(do_normalize=snake_case__   ,       padding_value=1.0	)
  
         _lowercase     =	SpeechTaProcessor.from_pretrained(
             self.tmpdirname   ,       bos_token='(BOS)'   ,       eos_token='(EOS)'   ,       do_normalize=snake_case__   ,       padding_value=1.0	)
  
         self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer_add_kwargs.get_vocab()	)
         self.assertIsInstance(processor.tokenizer   ,       snake_case__	)
  
         self.assertEqual(processor.feature_extractor.to_json_string()   ,       feature_extractor_add_kwargs.to_json_string()	)
         self.assertIsInstance(processor.feature_extractor   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.get_feature_extractor()
         _lowercase     =	self.get_tokenizer()
  
         _lowercase     =	SpeechTaProcessor(tokenizer=snake_case__   ,       feature_extractor=snake_case__	)
  
         _lowercase     =	floats_list((3, 1_000)	)
  
         _lowercase     =	feature_extractor(audio=snake_case__   ,       return_tensors='np'	)
         _lowercase     =	processor(audio=snake_case__   ,       return_tensors='np'	)
  
         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()   ,       input_processor[key].sum()   ,       delta=1E-2	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.get_feature_extractor()
         _lowercase     =	self.get_tokenizer()
  
         _lowercase     =	SpeechTaProcessor(tokenizer=snake_case__   ,       feature_extractor=snake_case__	)
  
         _lowercase     =	floats_list((3, 1_000)	)
  
         _lowercase     =	feature_extractor(audio_target=snake_case__   ,       return_tensors='np'	)
         _lowercase     =	processor(audio_target=snake_case__   ,       return_tensors='np'	)
  
         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()   ,       input_processor[key].sum()   ,       delta=1E-2	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.get_feature_extractor()
         _lowercase     =	self.get_tokenizer()
  
         _lowercase     =	SpeechTaProcessor(tokenizer=snake_case__   ,       feature_extractor=snake_case__	)
  
         _lowercase     =	'This is a test string'
  
         _lowercase     =	processor(text=snake_case__	)
         _lowercase     =	tokenizer(snake_case__	)
  
         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]   ,       encoded_processor[key]	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.get_feature_extractor()
         _lowercase     =	self.get_tokenizer()
  
         _lowercase     =	SpeechTaProcessor(tokenizer=snake_case__   ,       feature_extractor=snake_case__	)
  
         _lowercase     =	'This is a test string'
  
         _lowercase     =	processor(text_target=snake_case__	)
         _lowercase     =	tokenizer(snake_case__	)
  
         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]   ,       encoded_processor[key]	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.get_feature_extractor()
         _lowercase     =	self.get_tokenizer()
  
         _lowercase     =	SpeechTaProcessor(tokenizer=snake_case__   ,       feature_extractor=snake_case__	)
  
         _lowercase     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
         _lowercase     =	processor.batch_decode(snake_case__	)
         _lowercase     =	tokenizer.batch_decode(snake_case__	)
  
         self.assertListEqual(snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.get_feature_extractor()
         _lowercase     =	self.get_tokenizer()
  
         _lowercase     =	SpeechTaProcessor(tokenizer=snake_case__   ,       feature_extractor=snake_case__	)
  
         self.assertListEqual(
             processor.model_input_names   ,       feature_extractor.model_input_names   ,       msg='`processor` and `feature_extractor` model input names do not match'   ,       )
  