from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFDebertaForMaskedLM,
				    TFDebertaForQuestionAnswering,
				    TFDebertaForSequenceClassification,
				    TFDebertaForTokenClassification,
				    TFDebertaModel,
				)





class 			__snake_case  :




					def __init__(       self   ,	__UpperCamelCase   ,	__UpperCamelCase=13   ,	__UpperCamelCase=7   ,	__UpperCamelCase=True   ,	__UpperCamelCase=True   ,	__UpperCamelCase=True   ,	__UpperCamelCase=True   ,	__UpperCamelCase=99   ,	__UpperCamelCase=32   ,	__UpperCamelCase=2   ,	__UpperCamelCase=4   ,	__UpperCamelCase=37   ,	__UpperCamelCase="gelu"   ,	__UpperCamelCase=0.1   ,	__UpperCamelCase=0.1   ,	__UpperCamelCase=512   ,	__UpperCamelCase=16   ,	__UpperCamelCase=2   ,	__UpperCamelCase=0.0_2   ,	__UpperCamelCase=3   ,	__UpperCamelCase=4   ,	__UpperCamelCase=None   ,	)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     str 							=				parent
									snake_case__ :     List[str] 							=				13
									snake_case__ :     Optional[Any] 							=				7
									snake_case__ :     Optional[Any] 							=				True
									snake_case__ :     Tuple 							=				True
									snake_case__ :     Dict 							=				True
									snake_case__ :     Dict 							=				True
									snake_case__ :     Union[str, Any] 							=				99
									snake_case__ :     str 							=				32
									snake_case__ :     Union[str, Any] 							=				2
									snake_case__ :     Dict 							=				4
									snake_case__ :     Tuple 							=				37
									snake_case__ :     str 							=				'gelu'
									snake_case__ :     Dict 							=				0.1
									snake_case__ :     Optional[Any] 							=				0.1
									snake_case__ :     Optional[Any] 							=				512
									snake_case__ :     Optional[int] 							=				16
									snake_case__ :     Any 							=				False
									snake_case__ :     Optional[Any] 							=				-1
									snake_case__ :     List[str] 							=				True
									snake_case__ :     Optional[int] 							=				2
									snake_case__ :     Dict 							=				0.0_2
									snake_case__ :     Any 							=				3
									snake_case__ :     List[Any] 							=				4
									snake_case__ :     Union[str, Any] 							=				None




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



									'''simple docstring'''







									snake_case__ :     Any 							=				ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size		)

									snake_case__ :     int 							=				None
									if self.use_input_mask:
													snake_case__ :     Dict 							=				random_attention_mask([self.batch_size, self.seq_length]		)

									snake_case__ :     Optional[int] 							=				None
									if self.use_token_type_ids:
													snake_case__ :     Union[str, Any] 							=				ids_tensor([self.batch_size, self.seq_length]   ,	self.type_vocab_size		)

									snake_case__ :     str 							=				None
									snake_case__ :     Tuple 							=				None
									snake_case__ :     Union[str, Any] 							=				None
									if self.use_labels:
													snake_case__ :     Union[str, Any] 							=				ids_tensor([self.batch_size]   ,	self.type_sequence_label_size		)
													snake_case__ :     Optional[Any] 							=				ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels		)

									snake_case__ :     str 							=				DebertaConfig(
									    vocab_size=self.vocab_size   ,	hidden_size=self.hidden_size   ,	num_hidden_layers=self.num_hidden_layers   ,	num_attention_heads=self.num_attention_heads   ,	intermediate_size=self.intermediate_size   ,	hidden_act=self.hidden_act   ,	hidden_dropout_prob=self.hidden_dropout_prob   ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,	max_position_embeddings=self.max_position_embeddings   ,	type_vocab_size=self.type_vocab_size   ,	relative_attention=self.relative_attention   ,	max_relative_positions=self.max_relative_positions   ,	position_biased_input=self.position_biased_input   ,	initializer_range=self.initializer_range   ,	return_dict=__UpperCamelCase   ,	)

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




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



									'''simple docstring'''







									snake_case__ :     List[str] 							=				TFDebertaModel(config=__UpperCamelCase		)
									snake_case__ :     List[str] 							=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									snake_case__ :     Dict 							=				[input_ids, input_mask]
									snake_case__ :     Dict 							=				model(__UpperCamelCase		)

									snake_case__ :     List[Any] 							=				model(__UpperCamelCase		)

									self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				TFDebertaForMaskedLM(config=__UpperCamelCase		)
									snake_case__ :     int 							=				{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									snake_case__ :     int 							=				model(__UpperCamelCase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  str:



									'''simple docstring'''







									snake_case__ :     str 							=				self.num_labels
									snake_case__ :     str 							=				TFDebertaForSequenceClassification(config=__UpperCamelCase		)
									snake_case__ :     Dict 							=				{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									snake_case__ :     Dict 							=				model(__UpperCamelCase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Any:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				self.num_labels
									snake_case__ :     List[Any] 							=				TFDebertaForTokenClassification(config=__UpperCamelCase		)
									snake_case__ :     List[str] 							=				{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									snake_case__ :     Optional[int] 							=				model(__UpperCamelCase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.num_labels)		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     int 							=				TFDebertaForQuestionAnswering(config=__UpperCamelCase		)
									snake_case__ :     Optional[Any] 							=				{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									snake_case__ :     List[str] 							=				model(__UpperCamelCase		)
									self.parent.assertEqual(result.start_logits.shape   ,	(self.batch_size, self.seq_length)		)
									self.parent.assertEqual(result.end_logits.shape   ,	(self.batch_size, self.seq_length)		)




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



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				self.prepare_config_and_inputs()
									(
									    (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  
									) :     List[Any] 							=				config_and_inputs
									snake_case__ :     Union[str, Any] 							=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict





@require_tf
class 			__snake_case  (      _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       (
					    (
					        TFDebertaModel,
					        TFDebertaForMaskedLM,
					        TFDebertaForQuestionAnswering,
					        TFDebertaForSequenceClassification,
					        TFDebertaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					__lowerCamelCase	 =       (
					    {
					        """feature-extraction""": TFDebertaModel,
					        """fill-mask""": TFDebertaForMaskedLM,
					        """question-answering""": TFDebertaForQuestionAnswering,
					        """text-classification""": TFDebertaForSequenceClassification,
					        """token-classification""": TFDebertaForTokenClassification,
					        """zero-shot""": TFDebertaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					__lowerCamelCase	 =       False
					__lowerCamelCase	 =       False




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				TFDebertaModelTester(self		)
									snake_case__ :     Optional[Any] 							=				ConfigTester(self   ,	config_class=__UpperCamelCase   ,	hidden_size=37		)




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									self.config_tester.run_common_tests()




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase		)




					def       __a      (       self		)					->  str:



									'''simple docstring'''







									snake_case__ :     Any 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase		)




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     str 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase		)




					def       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase		)




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase		)




					@slow
					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     Any 							=				TFDebertaModel.from_pretrained('kamalkraj/deberta-base'		)
									self.assertIsNotNone(__UpperCamelCase		)





@require_tf
class 			__snake_case  (      unittest.TestCase     ):




					@unittest.skip(reason='Model not available yet'		)
					def       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									pass




					@slow
					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				TFDebertaModel.from_pretrained('kamalkraj/deberta-base'		)
									snake_case__ :     Any 							=				tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
									snake_case__ :     str 							=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
									snake_case__ :     Optional[int] 							=				model(__UpperCamelCase   ,	attention_mask=__UpperCamelCase		)[0]

									snake_case__ :     List[Any] 							=				tf.constant(
									    [
									        [
									            [-0.5_9_8_5_5_8_9_6, -0.8_0_5_5_2_5_6_6, -0.8_4_6_2_1_3_5],
									            [1.4_4_8_4_0_2_5, -0.9_3_4_8_3_7_9_4, -0.8_0_5_9_3_0_8_5],
									            [0.3_1_2_2_7_4_1, 0.0_0_3_1_6_0_5_9, -1.4_1_3_1_3_7_7],
									        ]
									    ]		)
									tf.debugging.assert_near(output[:, 1:4, 1:4]   ,	__UpperCamelCase   ,	atol=1E-4		)



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


lowerCAmelCase__					:       Any		=       logging.get_logger(__name__)





@dataclass
class 			__snake_case  :
					__lowerCamelCase	 =       42
					__lowerCamelCase	 =       42
					__lowerCamelCase	 =       None
					__lowerCamelCase	 =       None




					def       __a      (       self		)					->  int:



									'''simple docstring'''







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





@dataclass(frozen=_lowerCamelCase     )
class 			__snake_case  :
					__lowerCamelCase	 =       42
					__lowerCamelCase	 =       None
					__lowerCamelCase	 =       None
					__lowerCamelCase	 =       None




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



									'''simple docstring'''







									return json.dumps(dataclasses.asdict(self		)		) + "\n"





class 			__snake_case  :




					def       __a      (       self   ,	__UpperCamelCase		)					->  Tuple:



									'''simple docstring'''







									raise NotImplementedError()




					def       __a      (       self   ,	__UpperCamelCase		)					->  Any:



									'''simple docstring'''







									raise NotImplementedError()




					def       __a      (       self   ,	__UpperCamelCase		)					->  Any:



									'''simple docstring'''







									raise NotImplementedError()




					def       __a      (       self   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







									raise NotImplementedError()




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



									'''simple docstring'''







									raise NotImplementedError()




					def       __a      (       self   ,	__UpperCamelCase		)					->  Any:



									'''simple docstring'''







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




					@classmethod
					def       __a      (       cls   ,	__UpperCamelCase   ,	__UpperCamelCase=None		)					->  Any:



									'''simple docstring'''







									with open(__UpperCamelCase   ,	'r'   ,	encoding='utf-8-sig'		) as f:
													return list(csv.reader(__UpperCamelCase   ,	delimiter='\t'   ,	quotechar=__UpperCamelCase		)		)





class 			__snake_case  (      _lowerCamelCase     ):




					def __init__(       self   ,	__UpperCamelCase=None   ,	__UpperCamelCase=None   ,	__UpperCamelCase="classification"   ,	__UpperCamelCase=False		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				[] if labels is None else labels
									snake_case__ :     Optional[Any] 							=				[] if examples is None else examples
									snake_case__ :     Any 							=				mode
									snake_case__ :     List[Any] 							=				verbose




					def __len__(       self		)					->  Dict:



									'''simple docstring'''







									return len(self.examples		)




					def __getitem__(       self   ,	__UpperCamelCase		)					->  List[str]:



									'''simple docstring'''







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




					@classmethod
					def       __a      (       cls   ,	__UpperCamelCase   ,	__UpperCamelCase=""   ,	__UpperCamelCase=0   ,	__UpperCamelCase=1   ,	__UpperCamelCase=None   ,	__UpperCamelCase=False   ,	**__UpperCamelCase		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				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       __a      (       cls   ,	__UpperCamelCase   ,	__UpperCamelCase=None   ,	**__UpperCamelCase		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     str 							=				cls(**__UpperCamelCase		)
									processor.add_examples(__UpperCamelCase   ,	labels=__UpperCamelCase		)
									return processor




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase=""   ,	__UpperCamelCase=0   ,	__UpperCamelCase=1   ,	__UpperCamelCase=None   ,	__UpperCamelCase=False   ,	__UpperCamelCase=False   ,	__UpperCamelCase=False   ,	)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     Any 							=				self._read_tsv(__UpperCamelCase		)
									if skip_first_row:
													snake_case__ :     Tuple 							=				lines[1:]
									snake_case__ :     List[Any] 							=				[]
									snake_case__ :     Union[str, Any] 							=				[]
									snake_case__ :     Optional[int] 							=				[]
									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:
																	snake_case__ :     Optional[int] 							=				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       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase=None   ,	__UpperCamelCase=None   ,	__UpperCamelCase=False   ,	__UpperCamelCase=False		)					->  List[str]:



									'''simple docstring'''







									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:
													snake_case__ :     int 							=				[None] * len(__UpperCamelCase		)
									if labels is None:
													snake_case__ :     Any 							=				[None] * len(__UpperCamelCase		)
									snake_case__ :     Optional[int] 							=				[]
									snake_case__ :     str 							=				set()
									for text_or_text_and_label, label, guid in zip(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		):
													if isinstance(__UpperCamelCase   ,	(tuple, list)		) and label is None:
																	snake_case__							,  snake_case__ :     List[str] 							=				text_or_text_and_label
													else:
																	snake_case__ :     Optional[Any] 							=				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:
													snake_case__ :     Dict 							=				examples
									else:
													self.examples.extend(__UpperCamelCase		)

									# Update labels
									if overwrite_labels:
													snake_case__ :     List[Any] 							=				list(__UpperCamelCase		)
									else:
													snake_case__ :     List[str] 							=				list(set(self.labels		).union(__UpperCamelCase		)		)

									return self.examples




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase=None   ,	__UpperCamelCase=False   ,	__UpperCamelCase=0   ,	__UpperCamelCase=True   ,	__UpperCamelCase=None   ,	)					->  Dict:



									'''simple docstring'''







									if max_length is None:
													snake_case__ :     str 							=				tokenizer.max_len

									snake_case__ :     List[str] 							=				{label: i for i, label in enumerate(self.labels		)}

									snake_case__ :     Optional[Any] 							=				[]
									for ex_index, example in enumerate(self.examples		):
													if ex_index % 10000 == 0:
																	logger.info(F"""Tokenizing example {ex_index}"""		)

													snake_case__ :     str 							=				tokenizer.encode(
													    example.text_a   ,	add_special_tokens=__UpperCamelCase   ,	max_length=min(__UpperCamelCase   ,	tokenizer.max_len		)   ,	)
													all_input_ids.append(__UpperCamelCase		)

									snake_case__ :     List[Any] 							=				max(len(__UpperCamelCase		) for input_ids in all_input_ids		)

									snake_case__ :     Union[str, Any] 							=				[]
									for ex_index, (input_ids, example) in enumerate(zip(__UpperCamelCase   ,	self.examples		)		):
													if ex_index % 10000 == 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.
													snake_case__ :     int 							=				[1 if mask_padding_with_zero else 0] * len(__UpperCamelCase		)

													# Zero-pad up to the sequence length.
													snake_case__ :     Optional[Any] 							=				batch_length - len(__UpperCamelCase		)
													if pad_on_left:
																	snake_case__ :     str 							=				([pad_token] * padding_length) + input_ids
																	snake_case__ :     Tuple 							=				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
													else:
																	snake_case__ :     List[str] 							=				input_ids + ([pad_token] * padding_length)
																	snake_case__ :     int 							=				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":
																	snake_case__ :     int 							=				label_map[example.label]
													elif self.mode == "regression":
																	snake_case__ :     int 							=				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)

													snake_case__ :     List[str] 							=				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

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

													snake_case__ :     List[str] 							=				TensorDataset(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)
													return dataset
									else:
													raise ValueError('return_tensors should be one of \'tf\' or \'pt\''		)


