from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowercase				   =  '''\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
'''

__lowercase				   =  '''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


__lowercase				   =  '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''






def     snake_case__							(      _A: Any				,       _A: str				,       _A: Optional[int]				,       _A: int			)   ->     Tuple:

		'''simple docstring'''


		lowerCAmelCase        =  [rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCAmelCase        =  Counter(_A			)

		lowerCAmelCase        =  Counter(_A			)
		lowerCAmelCase        =  Counter()
		for sgram, scount in sgramcounter.items():
				lowerCAmelCase        =  scount * numref

		lowerCAmelCase        =  Counter(_A			)
		lowerCAmelCase        =  Counter()
		for cgram, ccount in cgramcounter.items():
				lowerCAmelCase        =  ccount * numref

		# KEEP
		lowerCAmelCase        =  sgramcounter_rep & cgramcounter_rep
		lowerCAmelCase        =  keepgramcounter_rep & rgramcounter
		lowerCAmelCase        =  sgramcounter_rep & rgramcounter

		lowerCAmelCase        =  0
		lowerCAmelCase        =  0
		for keepgram in keepgramcountergood_rep:
				keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
				# Fix an alleged bug [2] in the keep score computation.
				# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
				keeptmpscorea += keepgramcountergood_rep[keepgram]
		# Define 0/0=1 instead of 0 to give higher scores for predictions that match
		#      a target exactly.
		lowerCAmelCase        =  1
		lowerCAmelCase        =  1
		if len(_A			) > 0:
				lowerCAmelCase        =  keeptmpscorea / len(_A			)
		if len(_A			) > 0:
				# Fix an alleged bug [2] in the keep score computation.
				# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
				lowerCAmelCase        =  keeptmpscorea / sum(keepgramcounterall_rep.values()			)
		lowerCAmelCase        =  0
		if keepscore_precision > 0 or keepscore_recall > 0:
				lowerCAmelCase        =  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

		# DELETION
		lowerCAmelCase        =  sgramcounter_rep - cgramcounter_rep
		lowerCAmelCase        =  delgramcounter_rep - rgramcounter
		lowerCAmelCase        =  sgramcounter_rep - rgramcounter
		lowerCAmelCase        =  0
		lowerCAmelCase        =  0
		for delgram in delgramcountergood_rep:
				deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
				deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
		# Define 0/0=1 instead of 0 to give higher scores for predictions that match
		# a target exactly.
		lowerCAmelCase        =  1
		if len(_A			) > 0:
				lowerCAmelCase        =  deltmpscorea / len(_A			)

		# ADDITION
		lowerCAmelCase        =  set(_A			) - set(_A			)
		lowerCAmelCase        =  set(_A			) & set(_A			)
		lowerCAmelCase        =  set(_A			) - set(_A			)

		lowerCAmelCase        =  0
		for addgram in addgramcountergood:
				addtmpscore += 1

		# Define 0/0=1 instead of 0 to give higher scores for predictions that match
		# a target exactly.
		lowerCAmelCase        =  1
		lowerCAmelCase        =  1
		if len(_A			) > 0:
				lowerCAmelCase        =  addtmpscore / len(_A			)
		if len(_A			) > 0:
				lowerCAmelCase        =  addtmpscore / len(_A			)
		lowerCAmelCase        =  0
		if addscore_precision > 0 or addscore_recall > 0:
				lowerCAmelCase        =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

		return (keepscore, delscore_precision, addscore)






def     snake_case__							(      _A: int				,       _A: Optional[Any]				,       _A: str			)   ->     List[str]:

		'''simple docstring'''


		lowerCAmelCase        =  len(_A			)

		lowerCAmelCase        =  ssent.split(""" """			)
		lowerCAmelCase        =  csent.split(""" """			)
		lowerCAmelCase        =  []
		lowerCAmelCase        =  []
		lowerCAmelCase        =  []
		lowerCAmelCase        =  []
		lowerCAmelCase        =  []
		lowerCAmelCase        =  []

		lowerCAmelCase        =  []
		lowerCAmelCase        =  []
		lowerCAmelCase        =  []
		lowerCAmelCase        =  []
		for rsent in rsents:
				lowerCAmelCase        =  rsent.split(""" """			)
				lowerCAmelCase        =  []
				lowerCAmelCase        =  []
				lowerCAmelCase        =  []
				ragramslist.append(_A			)
				for i in range(0				,       len(_A			) - 1			):
						if i < len(_A			) - 1:
								lowerCAmelCase        =  ragrams[i] + """ """ + ragrams[i + 1]
								ragrams.append(_A			)
						if i < len(_A			) - 2:
								lowerCAmelCase        =  ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
								ragrams.append(_A			)
						if i < len(_A			) - 3:
								lowerCAmelCase        =  ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
								ragrams.append(_A			)
				ragramslist.append(_A			)
				ragramslist.append(_A			)
				ragramslist.append(_A			)

		for i in range(0				,       len(_A			) - 1			):
				if i < len(_A			) - 1:
						lowerCAmelCase        =  sagrams[i] + """ """ + sagrams[i + 1]
						sagrams.append(_A			)
				if i < len(_A			) - 2:
						lowerCAmelCase        =  sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
						sagrams.append(_A			)
				if i < len(_A			) - 3:
						lowerCAmelCase        =  sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
						sagrams.append(_A			)

		for i in range(0				,       len(_A			) - 1			):
				if i < len(_A			) - 1:
						lowerCAmelCase        =  cagrams[i] + """ """ + cagrams[i + 1]
						cagrams.append(_A			)
				if i < len(_A			) - 2:
						lowerCAmelCase        =  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
						cagrams.append(_A			)
				if i < len(_A			) - 3:
						lowerCAmelCase        =  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
						cagrams.append(_A			)

		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		lowerCAmelCase        =  sum([keepascore, keepascore, keepascore, keepascore]			) / 4
		lowerCAmelCase        =  sum([delascore, delascore, delascore, delascore]			) / 4
		lowerCAmelCase        =  sum([addascore, addascore, addascore, addascore]			) / 4
		lowerCAmelCase        =  (avgkeepscore + avgdelscore + avgaddscore) / 3
		return finalscore






def     snake_case__							(      _A: Optional[int]				,       _A: bool = True				,       _A: str = "13a"				,       _A: bool = True			)   ->     Union[str, Any]:

		'''simple docstring'''


		if lowercase:
				lowerCAmelCase        =  sentence.lower()

		if tokenizer in ["13a", "intl"]:
				if version.parse(sacrebleu.__version__			).major >= 2:
						lowerCAmelCase        =  sacrebleu.metrics.bleu._get_tokenizer(_A			)()(_A			)
				else:
						lowerCAmelCase        =  sacrebleu.TOKENIZERS[tokenizer]()(_A			)
		elif tokenizer == "moses":
				lowerCAmelCase        =  sacremoses.MosesTokenizer().tokenize(_A				,       return_str=_A				,       escape=_A			)
		elif tokenizer == "penn":
				lowerCAmelCase        =  sacremoses.MosesTokenizer().penn_tokenize(_A				,       return_str=_A			)
		else:
				lowerCAmelCase        =  sentence

		if not return_str:
				lowerCAmelCase        =  normalized_sent.split()

		return normalized_sent






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION     )
class     a__( datasets.Metric     ):


			'''simple docstring'''






			def 							a_	(       self):




					"""simple docstring"""


					return datasets.MetricInfo(
					    description=_DESCRIPTION	,   citation=_CITATION	,   inputs_description=_KWARGS_DESCRIPTION	,   features=datasets.Features(
					        {
					            """sources""": datasets.Value("""string"""	,   id="""sequence"""),
					            """predictions""": datasets.Value("""string"""	,   id="""sequence"""),
					            """references""": datasets.Sequence(datasets.Value("""string"""	,   id="""sequence""")	,   id="""references"""),
					        })	,   codebase_urls=[
					        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
					        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
					    ]	,   reference_urls=["""https://www.aclweb.org/anthology/Q16-1029.pdf"""]	,   )







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					if not (len(__lowerCAmelCase) == len(__lowerCAmelCase) == len(__lowerCAmelCase)):
							raise ValueError("""Sources length must match predictions and references lengths.""")
					lowerCAmelCase        =  0
					for src, pred, refs in zip(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							sari_score += SARIsent(normalize(__lowerCAmelCase)	,   normalize(__lowerCAmelCase)	,   [normalize(__lowerCAmelCase) for sent in refs])
					lowerCAmelCase        =  sari_score / len(__lowerCAmelCase)
					return {"sari": 100 * sari_score}





'''simple docstring'''




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     a__( unittest.TestCase     ):


			'''simple docstring'''






			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  list(tokenizer_class.max_model_input_sizes.keys())
					for model_name in sa_models[:1]:
							lowerCAmelCase        =  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)
									lowerCAmelCase        =  tokenizer.convert_tokens_to_ids(__lowerCAmelCase)
									self.assertIsInstance(__lowerCAmelCase	,   __lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase        =  pickle.dumps(__lowerCAmelCase)
					self.assertIsNotNone(__lowerCAmelCase)

					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					self.check_tokenizer_from_pretrained(__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


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



			@require_tokenizers
			def 							a_	(       self):




					"""simple docstring"""


					import numpy as np

					lowerCAmelCase        =  BertTokenizer.from_pretrained("""bert-base-cased""")
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					import tensorflow as tf

					def tf_array_equals(__lowerCAmelCase	,   __lowerCAmelCase):
							return tf.reduce_all(tf.equal(__lowerCAmelCase	,   __lowerCAmelCase))

					lowerCAmelCase        =  BertTokenizer.from_pretrained("""bert-base-cased""")
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					import torch

					lowerCAmelCase        =  BertTokenizer.from_pretrained("""bert-base-cased""")
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  BertTokenizer.from_pretrained("""bert-base-cased""")
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  BertTokenizerFast.from_pretrained("""bert-base-cased""")
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
					lowerCAmelCase        =  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:
							lowerCAmelCase        =  batch.convert_to_tensors(tensor_type="""np""")
					self.assertFalse(len(cs.err)	,   msg=f"should have no warning, but got {cs.err}")

					lowerCAmelCase        =  BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
					lowerCAmelCase        =  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:
							lowerCAmelCase        =  batch.convert_to_tensors(tensor_type="""pt""")
					self.assertFalse(len(cs.err)	,   msg=f"should have no warning, but got {cs.err}")

					lowerCAmelCase        =  BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
					lowerCAmelCase        =  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:
							lowerCAmelCase        =  batch.convert_to_tensors(tensor_type="""tf""")
					self.assertFalse(len(cs.err)	,   msg=f"should have no warning, but got {cs.err}")

					lowerCAmelCase        =  BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
					lowerCAmelCase        =  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:
							lowerCAmelCase        =  batch.convert_to_tensors(tensor_type="""jax""")
					self.assertFalse(len(cs.err)	,   msg=f"should have no warning, but got {cs.err}")

					lowerCAmelCase        =  BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


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

					lowerCAmelCase        =  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]])
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					import torch

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

					lowerCAmelCase        =  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]])
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					import tensorflow as tf

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

					lowerCAmelCase        =  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]])
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  Tokenizer(WordPiece(unk_token="""[UNK]"""))
					PreTrainedTokenizerFast(tokenizer_object=__lowerCAmelCase)







			@require_tokenizers
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  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"""))




