


import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
   from tokenizers import Tokenizer
   from tokenizers.models import WordPiece


class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       str    ,				_lowerCAmelCase       :       str  )   -> int:




     """simple docstring"""
     snake_case_					    =					list(tokenizer_class.max_model_input_sizes.keys()  )
     for model_name in sa_models[:1]:
        snake_case_					    =					tokenizer_class.from_pretrained(_lowerCAmelCase  )
        self.assertIsNotNone(_lowerCAmelCase  )
        self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )
        self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )

        for special_tok in tokenizer.all_special_tokens:
           self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )
           snake_case_					    =					tokenizer.convert_tokens_to_ids(_lowerCAmelCase  )
           self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       List[str]    ,				_lowerCAmelCase       :       BatchEncoding    ,				_lowerCAmelCase       :       Optional[Callable] = None  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					pickle.dumps(_lowerCAmelCase  )
     self.assertIsNotNone(_lowerCAmelCase  )

     snake_case_					    =					pickle.loads(_lowerCAmelCase  )

     # Ensure is_fast is correctly restored
     self.assertEqual(be_restored.is_fast    ,				be_original.is_fast  )

     # Ensure encodings are potentially correctly restored
     if be_original.is_fast:
        self.assertIsNotNone(be_restored.encodings  )
     else:
        self.assertIsNone(be_restored.encodings  )

     # Ensure the keys are the same
     for original_v, restored_v in zip(be_original.values()    ,				be_restored.values()  ):
        if equal_op:
           self.assertTrue(equal_op(_lowerCAmelCase    ,				_lowerCAmelCase  )  )
        else:
           self.assertEqual(_lowerCAmelCase    ,				_lowerCAmelCase  )



  @slow
  def 					lowerCAmelCase__  (     self       :       Any  )   -> Union[str, Any]:




     """simple docstring"""
     self.check_tokenizer_from_pretrained(_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Any  )   -> Tuple:




     """simple docstring"""
     self.assertEqual(TensorType("tf"  )    ,				TensorType.TENSORFLOW  )
     self.assertEqual(TensorType("pt"  )    ,				TensorType.PYTORCH  )
     self.assertEqual(TensorType("np"  )    ,				TensorType.NUMPY  )



  @require_tokenizers
  def 					lowerCAmelCase__  (     self       :       int  )   -> Dict:




     """simple docstring"""
     import numpy as np

     snake_case_					    =					BertTokenizer.from_pretrained("bert-base-cased"  )
     snake_case_					    =					BertTokenizerFast.from_pretrained("bert-base-cased"  )

     # Python no tensor
     with self.subTest("BatchEncoding (Python, return_tensors=None)"  ):
        self.assert_dump_and_restore(tokenizer_p("Small example to encode"  )  )

     with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"  ):
        self.assert_dump_and_restore(
            tokenizer_p("Small example to encode"    ,				return_tensors=TensorType.NUMPY  )    ,				np.array_equal  )

     with self.subTest("BatchEncoding (Rust, return_tensors=None)"  ):
        self.assert_dump_and_restore(tokenizer_r("Small example to encode"  )  )

     with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"  ):
        self.assert_dump_and_restore(
            tokenizer_r("Small example to encode"    ,				return_tensors=TensorType.NUMPY  )    ,				np.array_equal  )



  @require_tf
  @require_tokenizers
  def 					lowerCAmelCase__  (     self       :       str  )   -> Optional[Any]:




     """simple docstring"""
     import tensorflow as tf

     def tf_array_equals(_lowerCAmelCase       :       List[str]    ,				_lowerCAmelCase       :       Optional[int]  ):
        return tf.reduce_all(tf.equal(_lowerCAmelCase    ,				_lowerCAmelCase  )  )

     snake_case_					    =					BertTokenizer.from_pretrained("bert-base-cased"  )
     snake_case_					    =					BertTokenizerFast.from_pretrained("bert-base-cased"  )

     with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"  ):
        self.assert_dump_and_restore(
            tokenizer_p("Small example to encode"    ,				return_tensors=TensorType.TENSORFLOW  )    ,				_lowerCAmelCase  )

     with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"  ):
        self.assert_dump_and_restore(
            tokenizer_r("Small example to encode"    ,				return_tensors=TensorType.TENSORFLOW  )    ,				_lowerCAmelCase  )



  @require_torch
  @require_tokenizers
  def 					lowerCAmelCase__  (     self       :       str  )   -> str:




     """simple docstring"""
     import torch

     snake_case_					    =					BertTokenizer.from_pretrained("bert-base-cased"  )
     snake_case_					    =					BertTokenizerFast.from_pretrained("bert-base-cased"  )

     with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"  ):
        self.assert_dump_and_restore(
            tokenizer_p("Small example to encode"    ,				return_tensors=TensorType.PYTORCH  )    ,				torch.equal  )

     with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"  ):
        self.assert_dump_and_restore(
            tokenizer_r("Small example to encode"    ,				return_tensors=TensorType.PYTORCH  )    ,				torch.equal  )



  @require_tokenizers
  def 					lowerCAmelCase__  (     self       :       Dict  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					BertTokenizer.from_pretrained("bert-base-cased"  )
     snake_case_					    =					BertTokenizerFast.from_pretrained("bert-base-cased"  )

     with self.subTest("Python Tokenizer"  ):
        self.assertFalse(tokenizer_p("Small example to_encode"  ).is_fast  )

     with self.subTest("Rust Tokenizer"  ):
        self.assertTrue(tokenizer_r("Small example to_encode"  ).is_fast  )



  @require_tokenizers
  def 					lowerCAmelCase__  (     self       :       str  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					BertTokenizerFast.from_pretrained("bert-base-cased"  )
     snake_case_					    =					tokenizer_r(["Test", "\xad", "test"]    ,				is_split_into_words=_lowerCAmelCase  )

     self.assertEqual(encoded.word_to_tokens(0  )    ,				TokenSpan(start=1    ,				end=2  )  )
     self.assertEqual(encoded.word_to_tokens(1  )    ,				_lowerCAmelCase  )
     self.assertEqual(encoded.word_to_tokens(2  )    ,				TokenSpan(start=2    ,				end=3  )  )



  def 					lowerCAmelCase__  (     self       :       Dict  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="np"  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(2, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(2,)  )
     # test converting the converted
     with CaptureStderr() as cs:
        snake_case_					    =					batch.convert_to_tensors(tensor_type="np"  )
     self.assertFalse(len(cs.err  )    ,				msg=F'''should have no warning, but got {cs.err}'''  )

     snake_case_					    =					BatchEncoding({"inputs": [1, 2, 3], "labels": 0}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="np"    ,				prepend_batch_axis=_lowerCAmelCase  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(1, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(1,)  )



  @require_torch
  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> int:




     """simple docstring"""
     snake_case_					    =					BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="pt"  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(2, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(2,)  )
     # test converting the converted
     with CaptureStderr() as cs:
        snake_case_					    =					batch.convert_to_tensors(tensor_type="pt"  )
     self.assertFalse(len(cs.err  )    ,				msg=F'''should have no warning, but got {cs.err}'''  )

     snake_case_					    =					BatchEncoding({"inputs": [1, 2, 3], "labels": 0}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="pt"    ,				prepend_batch_axis=_lowerCAmelCase  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(1, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(1,)  )



  @require_tf
  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="tf"  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(2, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(2,)  )
     # test converting the converted
     with CaptureStderr() as cs:
        snake_case_					    =					batch.convert_to_tensors(tensor_type="tf"  )
     self.assertFalse(len(cs.err  )    ,				msg=F'''should have no warning, but got {cs.err}'''  )

     snake_case_					    =					BatchEncoding({"inputs": [1, 2, 3], "labels": 0}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="tf"    ,				prepend_batch_axis=_lowerCAmelCase  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(1, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(1,)  )



  @require_flax
  def 					lowerCAmelCase__  (     self       :       Tuple  )   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="jax"  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(2, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(2,)  )
     # test converting the converted
     with CaptureStderr() as cs:
        snake_case_					    =					batch.convert_to_tensors(tensor_type="jax"  )
     self.assertFalse(len(cs.err  )    ,				msg=F'''should have no warning, but got {cs.err}'''  )

     snake_case_					    =					BatchEncoding({"inputs": [1, 2, 3], "labels": 0}  )
     snake_case_					    =					batch.convert_to_tensors(tensor_type="jax"    ,				prepend_batch_axis=_lowerCAmelCase  )
     self.assertEqual(tensor_batch["inputs"].shape    ,				(1, 3)  )
     self.assertEqual(tensor_batch["labels"].shape    ,				(1,)  )



  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					[{"input_ids": np.array([0, 1, 2]  )}, {"input_ids": np.array([0, 1, 2, 3]  )}]
     snake_case_					    =					BertTokenizer.from_pretrained("bert-base-cased"  )

     snake_case_					    =					tokenizer.pad(_lowerCAmelCase    ,				padding=_lowerCAmelCase  )
     self.assertTrue(isinstance(batch["input_ids"]    ,				np.ndarray  )  )
     self.assertEqual(batch["input_ids"].tolist()    ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
     snake_case_					    =					tokenizer.pad(_lowerCAmelCase    ,				padding=_lowerCAmelCase    ,				return_tensors="np"  )
     self.assertTrue(isinstance(batch["input_ids"]    ,				np.ndarray  )  )
     self.assertEqual(batch["input_ids"].tolist()    ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )



  @require_torch
  def 					lowerCAmelCase__  (     self       :       Union[str, Any]  )   -> Optional[int]:




     """simple docstring"""
     import torch

     snake_case_					    =					[{"input_ids": torch.tensor([0, 1, 2]  )}, {"input_ids": torch.tensor([0, 1, 2, 3]  )}]
     snake_case_					    =					BertTokenizer.from_pretrained("bert-base-cased"  )

     snake_case_					    =					tokenizer.pad(_lowerCAmelCase    ,				padding=_lowerCAmelCase  )
     self.assertTrue(isinstance(batch["input_ids"]    ,				torch.Tensor  )  )
     self.assertEqual(batch["input_ids"].tolist()    ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
     snake_case_					    =					tokenizer.pad(_lowerCAmelCase    ,				padding=_lowerCAmelCase    ,				return_tensors="pt"  )
     self.assertTrue(isinstance(batch["input_ids"]    ,				torch.Tensor  )  )
     self.assertEqual(batch["input_ids"].tolist()    ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )



  @require_tf
  def 					lowerCAmelCase__  (     self       :       str  )   -> List[Any]:




     """simple docstring"""
     import tensorflow as tf

     snake_case_					    =					[{"input_ids": tf.constant([0, 1, 2]  )}, {"input_ids": tf.constant([0, 1, 2, 3]  )}]
     snake_case_					    =					BertTokenizer.from_pretrained("bert-base-cased"  )

     snake_case_					    =					tokenizer.pad(_lowerCAmelCase    ,				padding=_lowerCAmelCase  )
     self.assertTrue(isinstance(batch["input_ids"]    ,				tf.Tensor  )  )
     self.assertEqual(batch["input_ids"].numpy().tolist()    ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
     snake_case_					    =					tokenizer.pad(_lowerCAmelCase    ,				padding=_lowerCAmelCase    ,				return_tensors="tf"  )
     self.assertTrue(isinstance(batch["input_ids"]    ,				tf.Tensor  )  )
     self.assertEqual(batch["input_ids"].numpy().tolist()    ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )



  @require_tokenizers
  def 					lowerCAmelCase__  (     self       :       Any  )   -> Union[str, Any]:




     """simple docstring"""
     snake_case_					    =					Tokenizer(WordPiece(unk_token="[UNK]"  )  )
     PreTrainedTokenizerFast(tokenizer_object=_lowerCAmelCase  )



  @require_tokenizers
  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Any:




     """simple docstring"""
     snake_case_					    =					Tokenizer(WordPiece(unk_token="[UNK]"  )  )
     with tempfile.TemporaryDirectory() as tmpdirname:
        bert_tokenizer.save(os.path.join(_lowerCAmelCase    ,				"tokenizer.json"  )  )
        PreTrainedTokenizerFast(tokenizer_file=os.path.join(_lowerCAmelCase    ,				"tokenizer.json"  )  )








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


SCREAMING_SNAKE_CASE	:Tuple  		=     -1 if torch_device == '''cpu''' else 0


@is_pipeline_test
class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  _SCREAMING_SNAKE_CASE		    =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



  def 					lowerCAmelCase__  (     self       :       List[Any]    ,				_lowerCAmelCase       :       Tuple    ,				_lowerCAmelCase       :       List[str]    ,				_lowerCAmelCase       :       List[str]  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					SummarizationPipeline(model=_lowerCAmelCase    ,				tokenizer=_lowerCAmelCase  )
     return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				_lowerCAmelCase       :       int    ,				_lowerCAmelCase       :       int  )   -> Union[str, Any]:




     """simple docstring"""
     snake_case_					    =					summarizer.model

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

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

     # 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(_lowerCAmelCase    ,				_lowerCAmelCase  )
            and get_gpu_count() > 0
            and len(summarizer.model.trainable_weights  ) > 0
        ):
           with self.assertRaises(_lowerCAmelCase  ):
              snake_case_					    =					summarizer("This " * 1_0_0_0  )
     snake_case_					    =					summarizer("This " * 1_0_0_0    ,				truncation=TruncationStrategy.ONLY_FIRST  )



  @require_torch
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> str:




     """simple docstring"""
     snake_case_					    =					pipeline(task="summarization"    ,				model="sshleifer/tiny-mbart"    ,				framework="pt"  )
     snake_case_					    =					summarizer("This is a small test"  )
     self.assertEqual(
         _lowerCAmelCase    ,				[
             {
                 "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
             }
         ]    ,				)



  @require_tf
  def 					lowerCAmelCase__  (     self       :       Dict  )   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					pipeline(task="summarization"    ,				model="sshleifer/tiny-mbart"    ,				framework="tf"  )
     snake_case_					    =					summarizer("This is a small test"  )
     self.assertEqual(
         _lowerCAmelCase    ,				[
             {
                 "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
             }
         ]    ,				)



  @require_torch
  @slow
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> Union[str, Any]:




     """simple docstring"""
     snake_case_					    =					pipeline(task="summarization"    ,				device=_lowerCAmelCase  )
     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(_lowerCAmelCase  )
     self.assertEqual(result[0]["summary_text"]    ,				_lowerCAmelCase  )




