



'''simple docstring'''





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwiftFormerForImageClassification, SwiftFormerModel
       from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]					,	snake_case_   :	Union[str, Any]=13					,	snake_case_   :	Optional[int]=3					,	snake_case_   :	List[Any]=True					,	snake_case_   :	Dict=True					,	snake_case_   :	Optional[Any]=0.1					,	snake_case_   :	Tuple=0.1					,	snake_case_   :	Dict=224					,	snake_case_   :	Dict=1_000					,	snake_case_   :	Tuple=[3, 3, 6, 4]					,	snake_case_   :	Tuple=[48, 56, 112, 220]					,	):
          snake_case__    :       str       		= parent
          snake_case__    :       Dict       		= batch_size
          snake_case__    :       Dict       		= num_channels
          snake_case__    :       Any       		= is_training
          snake_case__    :       Union[str, Any]       		= use_labels
          snake_case__    :       Tuple       		= hidden_dropout_prob
          snake_case__    :       Optional[int]       		= attention_probs_dropout_prob
          snake_case__    :       List[str]       		= num_labels
          snake_case__    :       Any       		= image_size
          snake_case__    :       str       		= layer_depths
          snake_case__    :       Union[str, Any]       		= embed_dims




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          snake_case__    :       int       		= None
          if self.use_labels:
                snake_case__    :       List[Any]       		= ids_tensor([self.batch_size]					,	self.num_labels		)

          snake_case__    :       List[str]       		= self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase			(       self   :	str		):
          return SwiftFormerConfig(
              depths=self.layer_depths					,	embed_dims=self.embed_dims					,	mlp_ratio=4					,	downsamples=[True, True, True, True]					,	hidden_act="""gelu"""					,	num_labels=self.num_labels					,	down_patch_size=3					,	down_stride=2					,	down_pad=1					,	drop_rate=0.0					,	drop_path_rate=0.0					,	use_layer_scale=snake_case_					,	layer_scale_init_value=1E-5					,	)




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Any					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	List[str]		):
          snake_case__    :       Dict       		= SwiftFormerModel(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       List[str]       		= model(snake_case_		)
          self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.embed_dims[-1], 7, 7)		)




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[int]					,	snake_case_   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= self.num_labels
          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)

          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
          snake_case__    :       Any       		= model(snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)




    def lowerCamelCase			(       self   :	List[Any]		):
          ((snake_case__)		,							(snake_case__)		,							(snake_case__))    :       Union[str, Any]       		= self.prepare_config_and_inputs()
          snake_case__    :       int       		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase   =						(
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= SwiftFormerModelTester(self		)
          snake_case__    :       Union[str, Any]       		= ConfigTester(
              self					,	config_class=snake_case_					,	has_text_modality=snake_case_					,	hidden_size=37					,	num_attention_heads=12					,	num_hidden_layers=12					,	)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.config_tester.run_common_tests()




    @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
    def lowerCamelCase			(       self   :	Optional[Any]		):
          pass




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__		,							snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case_					,	nn.Linear		)		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__		,							snake_case__    :       Dict       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= inspect.signature(model.forward		)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case__    :       List[str]       		= [*signature.parameters.keys()]

                snake_case__    :       Optional[int]       		= ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]					,	snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_		)




    @slow
    def lowerCamelCase			(       self   :	List[str]		):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       Union[str, Any]       		= SwiftFormerModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)




    @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          pass




    def lowerCamelCase			(       self   :	List[Any]		):
          def check_hidden_states_output(snake_case_   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	List[Any]		):
                snake_case__    :       Optional[Any]       		= model_class(snake_case_		)
                model.to(snake_case_		)
                model.eval()

                with torch.no_grad():
                      snake_case__    :       Tuple       		= model(**self._prepare_for_class(snake_case_					,	snake_case_		)		)

                snake_case__    :       Optional[Any]       		= outputs.hidden_states

                snake_case__    :       List[Any]       		= 8
                self.assertEqual(len(snake_case_		)					,	snake_case_		)  # TODO

                # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
                # with the width and height being successively divided by 2, after every 2 blocks
                for i in range(len(snake_case_		)		):
                      self.assertEqual(
                          hidden_states[i].shape					,	torch.Size(
                              [
                                  self.model_tester.batch_size,
                                  self.model_tester.embed_dims[i // 2],
                                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                              ]		)					,	)

          snake_case__		,							snake_case__    :       int       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Any       		= True
                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case__    :       str       		= True

                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          def _config_zero_init(snake_case_   :	Optional[Any]		):
                snake_case__    :       Union[str, Any]       		= copy.deepcopy(snake_case_		)
                for key in configs_no_init.__dict__.keys():
                      if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                            setattr(snake_case_					,	snake_case_					,	1E-1_0		)
                      if isinstance(getattr(snake_case_					,	snake_case_					,	snake_case_		)					,	snake_case_		):
                            snake_case__    :       List[str]       		= _config_zero_init(getattr(snake_case_					,	snake_case_		)		)
                            setattr(snake_case_					,	snake_case_					,	snake_case_		)
                return configs_no_init

          snake_case__		,							snake_case__    :       Tuple       		= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__    :       List[str]       		= _config_zero_init(snake_case_		)
          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(config=snake_case_		)
                for name, param in model.named_parameters():
                      if param.requires_grad:
                            self.assertIn(
                                ((param.data.mean() * 1E9) / 1E9).round().item()					,	[0.0, 1.0]					,	msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,	)




    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def lowerCamelCase			(       self   :	Tuple		):
          pass





def 		__snake_case(							)						->					Optional[int]:
      snake_case__    :       Optional[int]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image







@require_torch
@require_vision
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    @cached_property
    def lowerCamelCase			(       self   :	List[str]		):
          return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None




    @slow
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       str       		= SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(snake_case_		)

          snake_case__    :       Dict       		= self.default_image_processor
          snake_case__    :       Optional[Any]       		= prepare_img()
          snake_case__    :       Union[str, Any]       		= image_processor(images=snake_case_					,	return_tensors="""pt"""		).to(snake_case_		)

          # forward pass
          with torch.no_grad():
                snake_case__    :       Dict       		= model(**snake_case_		)

          # verify the logits
          snake_case__    :       int       		= torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape					,	snake_case_		)

          snake_case__    :       int       		= torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]		).to(snake_case_		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,	snake_case_					,	atol=1E-4		)		)





'''simple docstring'''





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__a  			=						logging.getLogger(__name__)







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						42







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						None
    lowercase   =						None







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"train"
    lowercase   =						"dev"
    lowercase   =						"test"







class        UpperCAmelCase_			:
    """simple docstring"""







    @staticmethod
    def lowerCamelCase			(       snake_case_   :	Optional[Any]					,	snake_case_   :	Union[Split, str]		):
          raise NotImplementedError




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	str		):
          raise NotImplementedError




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	List[InputExample]					,	snake_case_   :	List[str]					,	snake_case_   :	int					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	Optional[int]=False					,	snake_case_   :	Dict="[CLS]"					,	snake_case_   :	str=1					,	snake_case_   :	Dict="[SEP]"					,	snake_case_   :	List[str]=False					,	snake_case_   :	int=False					,	snake_case_   :	Tuple=0					,	snake_case_   :	Union[str, Any]=0					,	snake_case_   :	List[Any]=-100					,	snake_case_   :	Any=0					,	snake_case_   :	Union[str, Any]=True					,	):
          snake_case__    :       int       		= {label: i for i, label in enumerate(snake_case_		)}

          snake_case__    :       List[Any]       		= []
          for ex_index, example in enumerate(snake_case_		):
                if ex_index % 10_000 == 0:
                      logger.info("""Writing example %d of %d"""					,	snake_case_					,	len(snake_case_		)		)

                snake_case__    :       Tuple       		= []
                snake_case__    :       Dict       		= []
                for word, label in zip(example.words					,	example.labels		):
                      snake_case__    :       Any       		= tokenizer.tokenize(snake_case_		)

                      # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                      if len(snake_case_		) > 0:
                            tokens.extend(snake_case_		)
                            # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case_		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                snake_case__    :       Dict       		= tokenizer.num_special_tokens_to_add()
                if len(snake_case_		) > max_seq_length - special_tokens_count:
                      snake_case__    :       Tuple       		= tokens[: (max_seq_length - special_tokens_count)]
                      snake_case__    :       Tuple       		= label_ids[: (max_seq_length - special_tokens_count)]

                # The convention in BERT is:
                # (a) For sequence pairs:
                #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                # (b) For single sequences:
                #  tokens:   [CLS] the dog is hairy . [SEP]
                #  type_ids:   0   0   0   0  0     0   0
                #
                # Where "type_ids" are used to indicate whether this is the first
                # sequence or the second sequence. The embedding vectors for `type=0` and
                # `type=1` were learned during pre-training and are added to the wordpiece
                # embedding vector (and position vector). This is not *strictly* necessary
                # since the [SEP] token unambiguously separates the sequences, but it makes
                # it easier for the model to learn the concept of sequences.
                #
                # For classification tasks, the first vector (corresponding to [CLS]) is
                # used as the "sentence vector". Note that this only makes sense because
                # the entire model is fine-tuned.
                tokens += [sep_token]
                label_ids += [pad_token_label_id]
                if sep_token_extra:
                      # roberta uses an extra separator b/w pairs of sentences
                      tokens += [sep_token]
                      label_ids += [pad_token_label_id]
                snake_case__    :       Dict       		= [sequence_a_segment_id] * len(snake_case_		)

                if cls_token_at_end:
                      tokens += [cls_token]
                      label_ids += [pad_token_label_id]
                      segment_ids += [cls_token_segment_id]
                else:
                      snake_case__    :       str       		= [cls_token] + tokens
                      snake_case__    :       Union[str, Any]       		= [pad_token_label_id] + label_ids
                      snake_case__    :       Any       		= [cls_token_segment_id] + segment_ids

                snake_case__    :       Union[str, Any]       		= tokenizer.convert_tokens_to_ids(snake_case_		)

                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                snake_case__    :       Tuple       		= [1 if mask_padding_with_zero else 0] * len(snake_case_		)

                # Zero-pad up to the sequence length.
                snake_case__    :       Dict       		= max_seq_length - len(snake_case_		)
                if pad_on_left:
                      snake_case__    :       Optional[Any]       		= ([pad_token] * padding_length) + input_ids
                      snake_case__    :       Union[str, Any]       		= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                      snake_case__    :       Union[str, Any]       		= ([pad_token_segment_id] * padding_length) + segment_ids
                      snake_case__    :       Union[str, Any]       		= ([pad_token_label_id] * padding_length) + label_ids
                else:
                      input_ids += [pad_token] * padding_length
                      input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                      segment_ids += [pad_token_segment_id] * padding_length
                      label_ids += [pad_token_label_id] * padding_length

                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length

                if ex_index < 5:
                      logger.info("""*** Example ***"""		)
                      logger.info("""guid: %s"""					,	example.guid		)
                      logger.info("""tokens: %s"""					,	""" """.join([str(snake_case_		) for x in tokens]		)		)
                      logger.info("""input_ids: %s"""					,	""" """.join([str(snake_case_		) for x in input_ids]		)		)
                      logger.info("""input_mask: %s"""					,	""" """.join([str(snake_case_		) for x in input_mask]		)		)
                      logger.info("""segment_ids: %s"""					,	""" """.join([str(snake_case_		) for x in segment_ids]		)		)
                      logger.info("""label_ids: %s"""					,	""" """.join([str(snake_case_		) for x in label_ids]		)		)

                if "token_type_ids" not in tokenizer.model_input_names:
                      snake_case__    :       Tuple       		= None

                features.append(
                    InputFeatures(
                        input_ids=snake_case_					,	attention_mask=snake_case_					,	token_type_ids=snake_case_					,	label_ids=snake_case_		)		)
          return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset







       class        UpperCAmelCase_			(			_a      ):
           """simple docstring"""







           lowercase   =						42
           lowercase   =						nn.CrossEntropyLoss().ignore_index




           def __init__(       self   :	int					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Tuple=False					,	snake_case_   :	Split = Split.train					,	):
                 # Load data features from cache or dataset file
                 snake_case__    :       Tuple       		= os.path.join(
                     snake_case_					,	"""cached_{}_{}_{}""".format(mode.value					,	tokenizer.__class__.__name__					,	str(snake_case_		)		)					,	)

                 # Make sure only the first process in distributed training processes the dataset,
                 # and the others will use the cache.
                 snake_case__    :       Dict       		= cached_features_file + """.lock"""
                 with FileLock(snake_case_		):
                       if os.path.exists(snake_case_		) and not overwrite_cache:
                             logger.info(f"Loading features from cached file {cached_features_file}"		)
                             snake_case__    :       Tuple       		= torch.load(snake_case_		)
                       else:
                             logger.info(f"Creating features from dataset file at {data_dir}"		)
                             snake_case__    :       Any       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                             # TODO clean up all this to leverage built-in features of tokenizers
                             snake_case__    :       Any       		= token_classification_task.convert_examples_to_features(
                                 snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	cls_token_at_end=bool(model_type in ["""xlnet"""]		)					,	cls_token=tokenizer.cls_token					,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0					,	sep_token=tokenizer.sep_token					,	sep_token_extra=snake_case_					,	pad_on_left=bool(tokenizer.padding_side == """left"""		)					,	pad_token=tokenizer.pad_token_id					,	pad_token_segment_id=tokenizer.pad_token_type_id					,	pad_token_label_id=self.pad_token_label_id					,	)
                             logger.info(f"Saving features into cached file {cached_features_file}"		)
                             torch.save(self.features					,	snake_case_		)




           def __len__(       self   :	str		):
                 return len(self.features		)




           def __getitem__(       self   :	int					,	snake_case_   :	Dict		):
                 return self.features[i]


if is_tf_available():
       import tensorflow as tf







       class        UpperCAmelCase_			:
           """simple docstring"""







           lowercase   =						42
           lowercase   =						-1_00




           def __init__(       self   :	List[str]					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Any=False					,	snake_case_   :	Split = Split.train					,	):
                 snake_case__    :       int       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                 # TODO clean up all this to leverage built-in features of tokenizers
                 snake_case__    :       Optional[Any]       		= token_classification_task.convert_examples_to_features(
                     snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	cls_token_at_end=bool(model_type in ["""xlnet"""]		)					,	cls_token=tokenizer.cls_token					,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0					,	sep_token=tokenizer.sep_token					,	sep_token_extra=snake_case_					,	pad_on_left=bool(tokenizer.padding_side == """left"""		)					,	pad_token=tokenizer.pad_token_id					,	pad_token_segment_id=tokenizer.pad_token_type_id					,	pad_token_label_id=self.pad_token_label_id					,	)

                 def gen():
                       for ex in self.features:
                             if ex.token_type_ids is None:
                                   yield (
                                       {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                       ex.label_ids,
                                   )
                             else:
                                   yield (
                                       {
                                           "input_ids": ex.input_ids,
                                           "attention_mask": ex.attention_mask,
                                           "token_type_ids": ex.token_type_ids,
                                       },
                                       ex.label_ids,
                                   )

                 if "token_type_ids" not in tokenizer.model_input_names:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)					,	(
                               {"""input_ids""": tf.TensorShape([None]		), """attention_mask""": tf.TensorShape([None]		)},
                               tf.TensorShape([None]		),
                           )					,	)
                 else:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)					,	(
                               {
                                   """input_ids""": tf.TensorShape([None]		),
                                   """attention_mask""": tf.TensorShape([None]		),
                                   """token_type_ids""": tf.TensorShape([None]		),
                               },
                               tf.TensorShape([None]		),
                           )					,	)




           def lowerCamelCase			(       self   :	Optional[Any]		):
                 snake_case__    :       Optional[Any]       		= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

                 return self.dataset




           def __len__(       self   :	Optional[Any]		):
                 return len(self.features		)




           def __getitem__(       self   :	str					,	snake_case_   :	List[str]		):
                 return self.features[i]
