
"""simple docstring"""




import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import (
	    MODEL_MAPPING,
	    DataaVecVisionForImageClassification,
	    DataaVecVisionForSemanticSegmentation,
	    DataaVecVisionModel,
	)
	from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import BeitImageProcessor




class 					UpperCamelCase     :

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__=100		,     lowercase__=13		,     lowercase__=30		,     lowercase__=2		,     lowercase__=3		,     lowercase__=True		,     lowercase__=True		,     lowercase__=32		,     lowercase__=4		,     lowercase__=4		,     lowercase__=37		,     lowercase__="gelu"		,     lowercase__=0.1		,     lowercase__=0.1		,     lowercase__=10		,     lowercase__=0.02		,     lowercase__=3		,     lowercase__=None		,     lowercase__=[0, 1, 2, 3]		,     )   ->   Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     parent
									SCREAMING_SNAKE_CASE           =     100
									SCREAMING_SNAKE_CASE           =     batch_size
									SCREAMING_SNAKE_CASE           =     image_size
									SCREAMING_SNAKE_CASE           =     patch_size
									SCREAMING_SNAKE_CASE           =     num_channels
									SCREAMING_SNAKE_CASE           =     is_training
									SCREAMING_SNAKE_CASE           =     use_labels
									SCREAMING_SNAKE_CASE           =     hidden_size
									SCREAMING_SNAKE_CASE           =     num_hidden_layers
									SCREAMING_SNAKE_CASE           =     num_attention_heads
									SCREAMING_SNAKE_CASE           =     intermediate_size
									SCREAMING_SNAKE_CASE           =     hidden_act
									SCREAMING_SNAKE_CASE           =     hidden_dropout_prob
									SCREAMING_SNAKE_CASE           =     attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE           =     type_sequence_label_size
									SCREAMING_SNAKE_CASE           =     initializer_range
									SCREAMING_SNAKE_CASE           =     scope
									SCREAMING_SNAKE_CASE           =     out_indices
									SCREAMING_SNAKE_CASE           =     num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									SCREAMING_SNAKE_CASE           =     (image_size // patch_size) ** 2
									SCREAMING_SNAKE_CASE           =     num_patches + 1


				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									SCREAMING_SNAKE_CASE           =     None
									SCREAMING_SNAKE_CASE           =     None
									if self.use_labels:
														SCREAMING_SNAKE_CASE           =     ids_tensor([self.batch_size]		,     self.type_sequence_label_size      )
														SCREAMING_SNAKE_CASE           =     ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels      )

									SCREAMING_SNAKE_CASE           =     self.get_config()

									return config, pixel_values, labels, pixel_labels


				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									return DataaVecVisionConfig(
									    vocab_size=self.vocab_size		,     image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     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		,     is_decoder=lowercase__		,     initializer_range=self.initializer_range		,     out_indices=self.out_indices		,     )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     DataaVecVisionModel(config=lowercase__      )
									model.to(lowercase__      )
									model.eval()
									SCREAMING_SNAKE_CASE           =     model(lowercase__      )
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									SCREAMING_SNAKE_CASE           =     (self.image_size // self.patch_size) ** 2
									self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, num_patches + 1, self.hidden_size)      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.type_sequence_label_size
									SCREAMING_SNAKE_CASE           =     DataaVecVisionForImageClassification(lowercase__      )
									model.to(lowercase__      )
									model.eval()
									SCREAMING_SNAKE_CASE           =     model(lowercase__		,     labels=lowercase__      )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.num_labels
									SCREAMING_SNAKE_CASE           =     DataaVecVisionForSemanticSegmentation(lowercase__      )
									model.to(lowercase__      )
									model.eval()
									SCREAMING_SNAKE_CASE           =     model(lowercase__      )
									self.parent.assertEqual(
									    result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
									SCREAMING_SNAKE_CASE           =     model(lowercase__		,     labels=lowercase__      )
									self.parent.assertEqual(
									    result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )




				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     config_and_inputs
									SCREAMING_SNAKE_CASE           =     {'pixel_values': pixel_values}
									return config, inputs_dict






@require_torch
class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__  ,				unittest.TestCase				):

				"""simple docstring"""



				UpperCAmelCase_    :				Any						  =    (
				    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase_    :				Union[str, Any]						  =    (
				    {
				        "feature-extraction": DataaVecVisionModel,
				        "image-classification": DataaVecVisionForImageClassification,
				        "image-segmentation": DataaVecVisionForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase_    :				List[str]						  =    False
				UpperCAmelCase_    :				List[str]						  =    False
				UpperCAmelCase_    :				str						  =    False


				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     DataaVecVisionModelTester(self      )
									SCREAMING_SNAKE_CASE           =     ConfigTester(
									    self		,     config_class=lowercase__		,     has_text_modality=lowercase__		,     hidden_size=37      )


				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									self.config_tester.run_common_tests()


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									pass


				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'      )
				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									pass


				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =     model_class(lowercase__      )
														self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)      )
														SCREAMING_SNAKE_CASE           =     model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(lowercase__		,     nn.Linear      )      )


				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =     model_class(lowercase__      )
														SCREAMING_SNAKE_CASE           =     inspect.signature(model.forward      )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														SCREAMING_SNAKE_CASE           =     [*signature.parameters.keys()]

														SCREAMING_SNAKE_CASE           =     ['pixel_values']
														self.assertListEqual(arg_names[:1]		,     lowercase__      )


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase__      )


				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*lowercase__      )


				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									if not self.model_tester.is_training:
														return

									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE           =     True

									for model_class in self.all_model_classes:
														if model_class in [*get_values(lowercase__      )]:
																			continue

														SCREAMING_SNAKE_CASE           =     model_class(lowercase__      )
														model.to(lowercase__      )
														model.train()
														SCREAMING_SNAKE_CASE           =     self._prepare_for_class(lowercase__		,     lowercase__		,     return_labels=lowercase__      )
														SCREAMING_SNAKE_CASE           =     model(**lowercase__      ).loss
														loss.backward()


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
														return

									SCREAMING_SNAKE_CASE           =     False
									SCREAMING_SNAKE_CASE           =     True

									for model_class in self.all_model_classes:
														if model_class in [*get_values(lowercase__      )] or not model_class.supports_gradient_checkpointing:
																			continue
														# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
														# this can then be incorporated into _prepare_for_class in test_modeling_common.py
														elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																			SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     inputs_dict['pixel_values'].shape
																			SCREAMING_SNAKE_CASE           =     torch.zeros(
																			    [self.model_tester.batch_size, height, width]		,     device=lowercase__      ).long()
														SCREAMING_SNAKE_CASE           =     model_class(lowercase__      )
														model.gradient_checkpointing_enable()
														model.to(lowercase__      )
														model.train()
														SCREAMING_SNAKE_CASE           =     self._prepare_for_class(lowercase__		,     lowercase__		,     return_labels=lowercase__      )
														SCREAMING_SNAKE_CASE           =     model(**lowercase__      ).loss
														loss.backward()


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE           =     _config_zero_init(lowercase__      )
									for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =     model_class(config=lowercase__      )
														for name, param in model.named_parameters():
																			# we skip lambda parameters as these require special initial values
																			# determined by config.layer_scale_init_value
																			if "lambda" in name:
																								continue
																			if param.requires_grad:
																								self.assertIn(
																								    ((param.data.mean() * 1E9).round() / 1E9).item()		,     [0.0, 1.0]		,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''		,     )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__=2E-4		,     lowercase__="outputs"		,     lowercase__=None      )   ->   List[Any]:
									"""simple docstring"""







									super().check_pt_tf_outputs(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase__      )




				@slow
				def 							A (    self      )   ->   int:
									"""simple docstring"""







									for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE           =     DataaVecVisionModel.from_pretrained(lowercase__      )
														self.assertIsNotNone(lowercase__      )






def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
					return image




@require_torch
@require_vision
class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				@cached_property
				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									return (
									    BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'      ) if is_vision_available() else None
									)




				@slow
				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'      ).to(
									    lowercase__      )

									SCREAMING_SNAKE_CASE           =     self.default_image_processor
									SCREAMING_SNAKE_CASE           =     prepare_img()
									SCREAMING_SNAKE_CASE           =     image_processor(images=lowercase__		,     return_tensors='pt'      ).to(lowercase__      )

									# forward pass
									with torch.no_grad():
														SCREAMING_SNAKE_CASE           =     model(**lowercase__      )
									SCREAMING_SNAKE_CASE           =     outputs.logits

									# verify the logits
									SCREAMING_SNAKE_CASE           =     torch.Size((1, 1000)      )
									self.assertEqual(logits.shape		,     lowercase__      )

									SCREAMING_SNAKE_CASE           =     torch.tensor([0.3_277, -0.1_395, 0.0_911]      ).to(lowercase__      )

									self.assertTrue(torch.allclose(logits[0, :3]		,     lowercase__		,     atol=1E-4      )      )

									SCREAMING_SNAKE_CASE           =     [model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
									self.assertEqual(logits[0].topk(2      ).indices.cpu().tolist()		,     lowercase__      )


"""simple docstring"""




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


snake_case			       =		logging.get_logger(__name__)




@dataclass
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				Optional[str]						  =    None
				UpperCAmelCase_    :				Optional[str]						  =    None


				def 							A (    self      )   ->   int:
									"""simple docstring"""







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






@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				List[int]
				UpperCAmelCase_    :				Optional[List[int]]						  =    None
				UpperCAmelCase_    :				Optional[List[int]]						  =    None
				UpperCAmelCase_    :				Optional[Union[int, float]]						  =    None


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







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






class 					UpperCamelCase     :

				"""simple docstring"""



				def 							A (    self		,     lowercase__      )   ->   Union[str, Any]:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   int:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







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




				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=None      )   ->   Dict:
									"""simple docstring"""







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






class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				def __init__(    self		,     lowercase__=None		,     lowercase__=None		,     lowercase__="classification"		,     lowercase__=False      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     [] if labels is None else labels
									SCREAMING_SNAKE_CASE           =     [] if examples is None else examples
									SCREAMING_SNAKE_CASE           =     mode
									SCREAMING_SNAKE_CASE           =     verbose


				def __len__(    self      )   ->   Optional[int]:
									"""simple docstring"""







									return len(self.examples      )


				def __getitem__(    self		,     lowercase__      )   ->   List[Any]:
									"""simple docstring"""







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


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=""		,     lowercase__=0		,     lowercase__=1		,     lowercase__=None		,     lowercase__=False		,     **lowercase__      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     cls(**lowercase__      )
									processor.add_examples_from_csv(
									    lowercase__		,     split_name=lowercase__		,     column_label=lowercase__		,     column_text=lowercase__		,     column_id=lowercase__		,     skip_first_row=lowercase__		,     overwrite_labels=lowercase__		,     overwrite_examples=lowercase__		,     )
									return processor


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=None		,     **lowercase__      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     cls(**lowercase__      )
									processor.add_examples(lowercase__		,     labels=lowercase__      )
									return processor


				def 							A (    self		,     lowercase__		,     lowercase__=""		,     lowercase__=0		,     lowercase__=1		,     lowercase__=None		,     lowercase__=False		,     lowercase__=False		,     lowercase__=False		,     )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._read_tsv(lowercase__      )
									if skip_first_row:
														SCREAMING_SNAKE_CASE           =     lines[1:]
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									for i, line in enumerate(lowercase__      ):
														texts.append(line[column_text]      )
														labels.append(line[column_label]      )
														if column_id is not None:
																			ids.append(line[column_id]      )
														else:
																			SCREAMING_SNAKE_CASE           =     f'''{split_name}-{i}''' if split_name else str(lowercase__      )
																			ids.append(lowercase__      )

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


				def 							A (    self		,     lowercase__		,     lowercase__=None		,     lowercase__=None		,     lowercase__=False		,     lowercase__=False      )   ->   str:
									"""simple docstring"""







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

									# Update examples
									if overwrite_examples:
														SCREAMING_SNAKE_CASE           =     examples
									else:
														self.examples.extend(lowercase__      )

									# Update labels
									if overwrite_labels:
														SCREAMING_SNAKE_CASE           =     list(lowercase__      )
									else:
														SCREAMING_SNAKE_CASE           =     list(set(self.labels      ).union(lowercase__      )      )

									return self.examples




				def 							A (    self		,     lowercase__		,     lowercase__=None		,     lowercase__=False		,     lowercase__=0		,     lowercase__=True		,     lowercase__=None		,     )   ->   Optional[int]:
									"""simple docstring"""







									if max_length is None:
														SCREAMING_SNAKE_CASE           =     tokenizer.max_len

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

									SCREAMING_SNAKE_CASE           =     []
									for ex_index, example in enumerate(self.examples      ):
														if ex_index % 10000 == 0:
																			logger.info(f'''Tokenizing example {ex_index}'''      )

														SCREAMING_SNAKE_CASE           =     tokenizer.encode(
														    example.text_a		,     add_special_tokens=lowercase__		,     max_length=min(lowercase__		,     tokenizer.max_len      )		,     )
														all_input_ids.append(lowercase__      )

									SCREAMING_SNAKE_CASE           =     max(len(lowercase__      ) for input_ids in all_input_ids      )

									SCREAMING_SNAKE_CASE           =     []
									for ex_index, (input_ids, example) in enumerate(zip(lowercase__		,     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.
														SCREAMING_SNAKE_CASE           =     [1 if mask_padding_with_zero else 0] * len(lowercase__      )

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

														if len(lowercase__      ) != batch_length:
																			raise ValueError(f'''Error with input length {len(lowercase__      )} vs {batch_length}'''      )
														if len(lowercase__      ) != batch_length:
																			raise ValueError(f'''Error with input length {len(lowercase__      )} vs {batch_length}'''      )

														if self.mode == "classification":
																			SCREAMING_SNAKE_CASE           =     label_map[example.label]
														elif self.mode == "regression":
																			SCREAMING_SNAKE_CASE           =     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(lowercase__      ) for x in input_ids]      )}'''      )
																			logger.info(f'''attention_mask: {' '.join([str(lowercase__      ) for x in attention_mask]      )}'''      )
																			logger.info(f'''label: {example.label} (id = {label})'''      )

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

									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)

														SCREAMING_SNAKE_CASE           =     tf.data.Dataset.from_generator(
														    lowercase__		,     ({'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

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

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