


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__SCREAMING_SNAKE_CASE	:							List[Any]           =  logging.get_logger(__name__)


@dataclass
class        __lowerCamelCase      :


				"""simple docstring"""







				a_:		Dict					      =	42
				a_:		Dict					      =	42
				a_:		Optional[int]					      =	None
				a_:		Optional[int]					      =	None






				def        lowerCAmelCase__			(				self     :    int					):
								return json.dumps(dataclasses.asdict(self					)	,							indent=2					) + "\n"


@dataclass(frozen=_lowerCAmelCase )
class        __lowerCamelCase      :


				"""simple docstring"""







				a_:		Optional[int]					      =	42
				a_:		int					      =	None
				a_:		int					      =	None
				a_:		int					      =	None






				def        lowerCAmelCase__			(				self     :    List[Any]					):
								return json.dumps(dataclasses.asdict(self					)					) + "\n"


class        __lowerCamelCase      :


				"""simple docstring"""







				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    List[str]					):
								raise NotImplementedError()






				def        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    List[Any]					):
								raise NotImplementedError()






				def        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    Optional[int]					):
								raise NotImplementedError()






				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    Tuple					):
								raise NotImplementedError()






				def        lowerCAmelCase__			(				self     :    str					):
								raise NotImplementedError()






				def        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    List[Any]					):



								if len(self.get_labels()					) > 1:
												_lowerCAmelCase  						=self.get_labels()[int(example.label					)]
								return example






				@classmethod
				def        lowerCAmelCase__			(				cls     :    Optional[Any]	,							lowerCamelCase_     :    int	,							lowerCamelCase_     :    Union[str, Any]=None					):



								with open(__UpperCAmelCase	,							"""r"""	,							encoding="""utf-8-sig"""					) as f:
												return list(csv.reader(__UpperCAmelCase	,							delimiter="""\t"""	,							quotechar=__UpperCAmelCase					)					)


class        __lowerCamelCase      (				_lowerCAmelCase ):


				"""simple docstring"""







				def __init__(				self     :    Any	,							lowerCamelCase_     :    Optional[int]=None	,							lowerCamelCase_     :    Any=None	,							lowerCamelCase_     :    int="classification"	,							lowerCamelCase_     :    int=False					):
								_lowerCAmelCase  						=[] if labels is None else labels
								_lowerCAmelCase  						=[] if examples is None else examples
								_lowerCAmelCase  						=mode
								_lowerCAmelCase  						=verbose






				def __len__(				self     :    Union[str, Any]					):
								return len(self.examples					)






				def __getitem__(				self     :    Tuple	,							lowerCamelCase_     :    Optional[int]					):



								if isinstance(__UpperCAmelCase	,							__UpperCAmelCase					):
												return SingleSentenceClassificationProcessor(labels=self.labels	,							examples=self.examples[idx]					)
								return self.examples[idx]






				@classmethod
				def        lowerCAmelCase__			(				cls     :    Optional[int]	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    int=""	,							lowerCamelCase_     :    Any=0	,							lowerCamelCase_     :    Union[str, Any]=1	,							lowerCamelCase_     :    Tuple=None	,							lowerCamelCase_     :    Dict=False	,							**lowerCamelCase_     :    Tuple					):
								_lowerCAmelCase  						=cls(**__UpperCAmelCase					)
								processor.add_examples_from_csv(
								    __UpperCAmelCase	,							split_name=__UpperCAmelCase	,							column_label=__UpperCAmelCase	,							column_text=__UpperCAmelCase	,							column_id=__UpperCAmelCase	,							skip_first_row=__UpperCAmelCase	,							overwrite_labels=__UpperCAmelCase	,							overwrite_examples=__UpperCAmelCase	,							)
								return processor






				@classmethod
				def        lowerCAmelCase__			(				cls     :    Dict	,							lowerCamelCase_     :    Union[str, Any]	,							lowerCamelCase_     :    Any=None	,							**lowerCamelCase_     :    int					):
								_lowerCAmelCase  						=cls(**__UpperCAmelCase					)
								processor.add_examples(__UpperCAmelCase	,							labels=__UpperCAmelCase					)
								return processor






				def        lowerCAmelCase__			(				self     :    str	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    Union[str, Any]=""	,							lowerCamelCase_     :    Optional[int]=0	,							lowerCamelCase_     :    Tuple=1	,							lowerCamelCase_     :    Any=None	,							lowerCamelCase_     :    Tuple=False	,							lowerCamelCase_     :    List[str]=False	,							lowerCamelCase_     :    Optional[int]=False	,							):
								_lowerCAmelCase  						=self._read_tsv(__UpperCAmelCase					)
								if skip_first_row:
												_lowerCAmelCase  						=lines[1:]
								_lowerCAmelCase  						=[]
								_lowerCAmelCase  						=[]
								_lowerCAmelCase  						=[]
								for i, line in enumerate(__UpperCAmelCase					):
												texts.append(line[column_text]					)
												labels.append(line[column_label]					)
												if column_id is not None:
																ids.append(line[column_id]					)
												else:
																_lowerCAmelCase  						=F"{split_name}-{i}" if split_name else str(__UpperCAmelCase					)
																ids.append(__UpperCAmelCase					)

								return self.add_examples(
								    __UpperCAmelCase	,							__UpperCAmelCase	,							__UpperCAmelCase	,							overwrite_labels=__UpperCAmelCase	,							overwrite_examples=__UpperCAmelCase					)






				def        lowerCAmelCase__			(				self     :    List[Any]	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    List[str]=None	,							lowerCamelCase_     :    Optional[Any]=None	,							lowerCamelCase_     :    List[str]=False	,							lowerCamelCase_     :    Optional[int]=False					):



								if labels is not None and len(__UpperCAmelCase					) != len(__UpperCAmelCase					):
												raise ValueError(
												    F"Text and labels have mismatched lengths {len(__UpperCAmelCase					)} and {len(__UpperCAmelCase					)}"					)
								if ids is not None and len(__UpperCAmelCase					) != len(__UpperCAmelCase					):
												raise ValueError(F"Text and ids have mismatched lengths {len(__UpperCAmelCase					)} and {len(__UpperCAmelCase					)}"					)
								if ids is None:
												_lowerCAmelCase  						=[None] * len(__UpperCAmelCase					)
								if labels is None:
												_lowerCAmelCase  						=[None] * len(__UpperCAmelCase					)
								_lowerCAmelCase  						=[]
								_lowerCAmelCase  						=set()
								for text_or_text_and_label, label, guid in zip(__UpperCAmelCase	,							__UpperCAmelCase	,							__UpperCAmelCase					):
												if isinstance(__UpperCAmelCase	,							(tuple, list)					) and label is None:
																_lowerCAmelCase  						=text_or_text_and_label
												else:
																_lowerCAmelCase  						=text_or_text_and_label
												added_labels.add(__UpperCAmelCase					)
												examples.append(InputExample(guid=__UpperCAmelCase	,							text_a=__UpperCAmelCase	,							text_b=__UpperCAmelCase	,							label=__UpperCAmelCase					)					)

								# Update examples
								if overwrite_examples:
												_lowerCAmelCase  						=examples
								else:
												self.examples.extend(__UpperCAmelCase					)

								# Update labels
								if overwrite_labels:
												_lowerCAmelCase  						=list(__UpperCAmelCase					)
								else:
												_lowerCAmelCase  						=list(set(self.labels					).union(__UpperCAmelCase					)					)

								return self.examples






				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    int=None	,							lowerCamelCase_     :    str=False	,							lowerCamelCase_     :    str=0	,							lowerCamelCase_     :    int=True	,							lowerCamelCase_     :    int=None	,							):



								if max_length is None:
												_lowerCAmelCase  						=tokenizer.max_len

								_lowerCAmelCase  						={label: i for i, label in enumerate(self.labels					)}

								_lowerCAmelCase  						=[]
								for ex_index, example in enumerate(self.examples					):
												if ex_index % 1_0000 == 0:
																logger.info(F"Tokenizing example {ex_index}"					)

												_lowerCAmelCase  						=tokenizer.encode(
												    example.text_a	,							add_special_tokens=__UpperCAmelCase	,							max_length=min(__UpperCAmelCase	,							tokenizer.max_len					)	,							)
												all_input_ids.append(__UpperCAmelCase					)

								_lowerCAmelCase  						=max(len(__UpperCAmelCase					) for input_ids in all_input_ids					)

								_lowerCAmelCase  						=[]
								for ex_index, (input_ids, example) in enumerate(zip(__UpperCAmelCase	,							self.examples					)					):
												if ex_index % 1_0000 == 0:
																logger.info(F"Writing example {ex_index}/{len(self.examples					)}"					)
												# The mask has 1 for real tokens and 0 for padding tokens. Only real
												# tokens are attended to.
												_lowerCAmelCase  						=[1 if mask_padding_with_zero else 0] * len(__UpperCAmelCase					)

												# Zero-pad up to the sequence length.
												_lowerCAmelCase  						=batch_length - len(__UpperCAmelCase					)
												if pad_on_left:
																_lowerCAmelCase  						=([pad_token] * padding_length) + input_ids
																_lowerCAmelCase  						=([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
												else:
																_lowerCAmelCase  						=input_ids + ([pad_token] * padding_length)
																_lowerCAmelCase  						=attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

												if len(__UpperCAmelCase					) != batch_length:
																raise ValueError(F"Error with input length {len(__UpperCAmelCase					)} vs {batch_length}"					)
												if len(__UpperCAmelCase					) != batch_length:
																raise ValueError(F"Error with input length {len(__UpperCAmelCase					)} vs {batch_length}"					)

												if self.mode == "classification":
																_lowerCAmelCase  						=label_map[example.label]
												elif self.mode == "regression":
																_lowerCAmelCase  						=float(example.label					)
												else:
																raise ValueError(self.mode					)

												if ex_index < 5 and self.verbose:
																logger.info("""*** Example ***"""					)
																logger.info(F"guid: {example.guid}"					)
																logger.info(F"input_ids: {' '.join([str(__UpperCAmelCase					) for x in input_ids]					)}"					)
																logger.info(F"attention_mask: {' '.join([str(__UpperCAmelCase					) for x in attention_mask]					)}"					)
																logger.info(F"label: {example.label} (id = {label})"					)

												features.append(InputFeatures(input_ids=__UpperCAmelCase	,							attention_mask=__UpperCAmelCase	,							label=__UpperCAmelCase					)					)

								if return_tensors is None:
												return features
								elif return_tensors == "tf":
												if not is_tf_available():
																raise RuntimeError("""return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported"""					)
												import tensorflow as tf

												def gen():
																for ex in features:
																				yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

												_lowerCAmelCase  						=tf.data.Dataset.from_generator(
												    __UpperCAmelCase	,							({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)	,							({"""input_ids""": tf.TensorShape([None]					), """attention_mask""": tf.TensorShape([None]					)}, tf.TensorShape([]					))	,							)
												return dataset
								elif return_tensors == "pt":
												if not is_torch_available():
																raise RuntimeError("""return_tensors set to \'pt\' but PyTorch can\'t be imported"""					)
												import torch
												from torch.utils.data import TensorDataset

												_lowerCAmelCase  						=torch.tensor([f.input_ids for f in features]	,							dtype=torch.long					)
												_lowerCAmelCase  						=torch.tensor([f.attention_mask for f in features]	,							dtype=torch.long					)
												if self.mode == "classification":
																_lowerCAmelCase  						=torch.tensor([f.label for f in features]	,							dtype=torch.long					)
												elif self.mode == "regression":
																_lowerCAmelCase  						=torch.tensor([f.label for f in features]	,							dtype=torch.float					)

												_lowerCAmelCase  						=TensorDataset(__UpperCAmelCase	,							__UpperCAmelCase	,							__UpperCAmelCase					)
												return dataset
								else:
												raise ValueError("""return_tensors should be one of \'tf\' or \'pt\'"""					)


"""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        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					list(tokenizer_class.max_model_input_sizes.keys()  )
									for model_name in sa_models[:1]:
														lowerCamelCase__		:   Tuple  				=					tokenizer_class.from_pretrained(__UpperCAmelCase  )
														self.assertIsNotNone(__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )

														for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
																			lowerCamelCase__		:   str  				=					tokenizer.convert_tokens_to_ids(__UpperCAmelCase  )
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :BatchEncoding      ,__UpperCAmelCase    :Optional[Callable] = None  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					pickle.dumps(__UpperCAmelCase  )
									self.assertIsNotNone(__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					pickle.loads(__UpperCAmelCase  )

									# 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(__UpperCAmelCase      ,__UpperCAmelCase  )  )
														else:
																			self.assertEqual(__UpperCAmelCase      ,__UpperCAmelCase  )







				@slow
				def   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									self.check_tokenizer_from_pretrained(__UpperCAmelCase  )







				def   lowercase_		(    self    :Optional[int]  )      ->					int:




									"""simple docstring"""



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







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




									"""simple docstring"""



									import numpy as np

									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Optional[int]  				=					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   lowercase_		(    self    :Optional[int]  )      ->					int:




									"""simple docstring"""



									import tensorflow as tf

									def tf_array_equals(__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :List[Any]  ):
														return tf.reduce_all(tf.equal(__UpperCAmelCase      ,__UpperCAmelCase  )  )

									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[Any]  				=					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  )      ,__UpperCAmelCase  )

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







				@require_torch
				@require_tokenizers
				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Optional[int]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[str]  				=					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   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Any  				=					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   lowercase_		(    self    :List[str]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   str  				=					tokenizer_r(['''Test''', '''\xad''', '''test''']      ,is_split_into_words=__UpperCAmelCase  )

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







				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Tuple  				=					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__		:   List[str]  				=					batch.convert_to_tensors(tensor_type='''np'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Union[str, Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''np'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_torch
				def   lowercase_		(    self    :Optional[int]  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Any  				=					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__		:   Optional[int]  				=					batch.convert_to_tensors(tensor_type='''pt'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''pt'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_tf
				def   lowercase_		(    self    :Optional[Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Union[str, Any]  				=					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__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''tf'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   int  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''tf'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_flax
				def   lowercase_		(    self    :Optional[int]  )      ->					List[str]:




									"""simple docstring"""



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

									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Tuple  				=					batch.convert_to_tensors(tensor_type='''jax'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				def   lowercase_		(    self    :str  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					[{'''input_ids''': np.array([0, 1, 2]  )}, {'''input_ids''': np.array([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Dict  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									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__		:   Optional[Any]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,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   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Union[str, Any]  				=					[{'''input_ids''': torch.tensor([0, 1, 2]  )}, {'''input_ids''': torch.tensor([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									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__		:   Optional[int]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,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   lowercase_		(    self    :int  )      ->					Optional[int]:




									"""simple docstring"""



									import tensorflow as tf

									lowerCamelCase__		:   List[Any]  				=					[{'''input_ids''': tf.constant([0, 1, 2]  )}, {'''input_ids''': tf.constant([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									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__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,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   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase  )







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




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									with tempfile.TemporaryDirectory() as tmpdirname:
														bert_tokenizer.save(os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )
														PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )






