
import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							List[str]=3							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=32							,			SCREAMING_SNAKE_CASE__		:							int=3							,			SCREAMING_SNAKE_CASE__		:							Tuple=10							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=[10, 20, 30, 40]							,			SCREAMING_SNAKE_CASE__		:							Dict=[1, 1, 2, 1]							,			SCREAMING_SNAKE_CASE__		:							int=True							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=True							,			SCREAMING_SNAKE_CASE__		:							List[Any]="relu"							,			SCREAMING_SNAKE_CASE__		:							List[Any]=3							,			SCREAMING_SNAKE_CASE__		:							List[str]=None							,			)     ->							Tuple:
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	image_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	embeddings_size
						__lowerCamelCase    					=	hidden_sizes
						__lowerCamelCase    					=	depths
						__lowerCamelCase    					=	is_training
						__lowerCamelCase    					=	use_labels
						__lowerCamelCase    					=	hidden_act
						__lowerCamelCase    					=	num_labels
						__lowerCamelCase    					=	scope
						__lowerCamelCase    					=	len(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[str]  )     ->							Dict:
						__lowerCamelCase    					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						__lowerCamelCase    					=	self.get_config()

						return config, pixel_values






		def  __A       (		self		:							List[Any]  )     ->							Union[str, Any]:
						return RegNetConfig(
						    num_channels=self.num_channels							,			embeddings_size=self.embeddings_size							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			hidden_act=self.hidden_act							,			num_labels=self.num_labels							,			image_size=self.image_size							,			)






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							int:
						__lowerCamelCase    					=	FlaxRegNetModel(config=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )

						# Output shape (b, c, h, w)
						self.parent.assertEqual(
						    result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,			)






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	self.num_labels
						__lowerCamelCase    					=	FlaxRegNetForImageClassification(config=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)  )







		def  __A       (		self		:							Dict  )     ->							Any:
						__lowerCamelCase    					=	self.prepare_config_and_inputs()
						__lowerCamelCase					,    __lowerCamelCase    					=	config_and_inputs
						__lowerCamelCase    					=	{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_flax
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						str        =		(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

		a__       :						Tuple        =		False
		a__       :						List[str]        =		False
		a__       :						List[str]        =		False






		def  __A       (		self		:							Dict  )     ->							None:
						__lowerCamelCase    					=	FlaxRegNetModelTester(self  )
						__lowerCamelCase    					=	ConfigTester(self							,			config_class=SCREAMING_SNAKE_CASE__							,			has_text_modality=SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							int:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()






		def  __A       (		self		:							Dict  )     ->							Optional[Any]:
						return






		def  __A       (		self		:							Any  )     ->							Any:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Dict  )     ->							List[Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__  )






		@unittest.skip(reason='''RegNet does not use inputs_embeds'''  )
		def  __A       (		self		:							Tuple  )     ->							List[str]:
						pass






		@unittest.skip(reason='''RegNet does not support input and output embeddings'''  )
		def  __A       (		self		:							List[Any]  )     ->							int:
						pass






		def  __A       (		self		:							List[str]  )     ->							int:
						__lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_common()

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

										__lowerCamelCase    					=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[Any]  )     ->							Dict:
						def check_hidden_states_output(SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]  ):
										__lowerCamelCase    					=	model_class(SCREAMING_SNAKE_CASE__  )

										__lowerCamelCase    					=	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )  )

										__lowerCamelCase    					=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										__lowerCamelCase    					=	self.model_tester.num_stages
										self.assertEqual(len(SCREAMING_SNAKE_CASE__  )							,			expected_num_stages + 1  )

						__lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase    					=	True
										check_hidden_states_output(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										__lowerCamelCase    					=	True

										check_hidden_states_output(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )







		def  __A       (		self		:							str  )     ->							Optional[Any]:
						__lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__  ):
														__lowerCamelCase    					=	self._prepare_for_class(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														__lowerCamelCase    					=	model_class(SCREAMING_SNAKE_CASE__  )

														@jax.jit
														def model_jitted(SCREAMING_SNAKE_CASE__		:							int							,			**SCREAMING_SNAKE_CASE__		:							Tuple  ):
																		return model(pixel_values=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )

														with self.subTest('''JIT Enabled'''  ):
																		__lowerCamelCase    					=	model_jitted(**SCREAMING_SNAKE_CASE__  ).to_tuple()

														with self.subTest('''JIT Disabled'''  ):
																		with jax.disable_jit():
																						__lowerCamelCase    					=	model_jitted(**SCREAMING_SNAKE_CASE__  ).to_tuple()

														self.assertEqual(len(SCREAMING_SNAKE_CASE__  )							,			len(SCREAMING_SNAKE_CASE__  )  )
														for jitted_output, output in zip(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
																		self.assertEqual(jitted_output.shape							,			output.shape  )





def  __magic_name__ (   )			->   Union[str, Any]:
				__lowerCamelCase    					=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image







@require_flax
class    lowerCAmelCase__     (	unittest.TestCase			):






		@cached_property
		def  __A       (		self		:							str  )     ->							Dict:
						return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''  ) if is_vision_available() else None







		@slow
		def  __A       (		self		:							Tuple  )     ->							Optional[int]:
						__lowerCamelCase    					=	FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''  )

						__lowerCamelCase    					=	self.default_image_processor
						__lowerCamelCase    					=	prepare_img()
						__lowerCamelCase    					=	image_processor(images=SCREAMING_SNAKE_CASE__							,			return_tensors='''np'''  )

						__lowerCamelCase    					=	model(**SCREAMING_SNAKE_CASE__  )

						# verify the logits
						__lowerCamelCase    					=	(1, 10_00)
						self.assertEqual(outputs.logits.shape							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	jnp.array([-0.4180, -1.5051, -3.4836]  )

						self.assertTrue(jnp.allclose(outputs.logits[0, :3]							,			SCREAMING_SNAKE_CASE__							,			atol=1e-4  )  )





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


SCREAMING_SNAKE_CASE__   :			Optional[int]												=    logging.getLogger(__name__)

@dataclass
class    lowerCAmelCase__     :
		a__       :						str
		a__       :						List[str]
		a__       :						Optional[List[str]]





@dataclass
class    lowerCAmelCase__     :
		a__       :						List[int]
		a__       :						List[int]
		a__       :						Optional[List[int]]        =		None
		a__       :						Optional[List[int]]        =		None





class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Optional[Any]        =		"""train"""
		a__       :						Optional[int]        =		"""dev"""
		a__       :						Dict        =		"""test"""





class    lowerCAmelCase__     :






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[Split, str]  )     ->							List[InputExample]:
						raise NotImplementedError






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							str  )     ->							List[str]:
						raise NotImplementedError







		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							List[InputExample]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							List[str]="[CLS]"							,			SCREAMING_SNAKE_CASE__		:							Tuple=1							,			SCREAMING_SNAKE_CASE__		:							str="[SEP]"							,			SCREAMING_SNAKE_CASE__		:							List[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=False							,			SCREAMING_SNAKE_CASE__		:							Tuple=0							,			SCREAMING_SNAKE_CASE__		:							int=0							,			SCREAMING_SNAKE_CASE__		:							str=-1_00							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=0							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			)     ->							List[InputFeatures]:
						__lowerCamelCase    					=	{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__  )}

						__lowerCamelCase    					=	[]
						for ex_index, example in enumerate(SCREAMING_SNAKE_CASE__  ):
										if ex_index % 1_00_00 == 0:
														logger.info('''Writing example %d of %d'''							,			SCREAMING_SNAKE_CASE__							,			len(SCREAMING_SNAKE_CASE__  )  )

										__lowerCamelCase    					=	[]
										__lowerCamelCase    					=	[]
										for word, label in zip(example.words							,			example.labels  ):
														__lowerCamelCase    					=	tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )

														# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
														if len(SCREAMING_SNAKE_CASE__  ) > 0:
																		tokens.extend(SCREAMING_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(SCREAMING_SNAKE_CASE__  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
										__lowerCamelCase    					=	tokenizer.num_special_tokens_to_add()
										if len(SCREAMING_SNAKE_CASE__  ) > max_seq_length - special_tokens_count:
														__lowerCamelCase    					=	tokens[: (max_seq_length - special_tokens_count)]
														__lowerCamelCase    					=	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]
										__lowerCamelCase    					=	[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE__  )

										if cls_token_at_end:
														tokens += [cls_token]
														label_ids += [pad_token_label_id]
														segment_ids += [cls_token_segment_id]
										else:
														__lowerCamelCase    					=	[cls_token] + tokens
														__lowerCamelCase    					=	[pad_token_label_id] + label_ids
														__lowerCamelCase    					=	[cls_token_segment_id] + segment_ids

										__lowerCamelCase    					=	tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )

										# 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(SCREAMING_SNAKE_CASE__  )

										# Zero-pad up to the sequence length.
										__lowerCamelCase    					=	max_seq_length - len(SCREAMING_SNAKE_CASE__  )
										if pad_on_left:
														__lowerCamelCase    					=	([pad_token] * padding_length) + input_ids
														__lowerCamelCase    					=	([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
														__lowerCamelCase    					=	([pad_token_segment_id] * padding_length) + segment_ids
														__lowerCamelCase    					=	([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(SCREAMING_SNAKE_CASE__  ) == max_seq_length
										assert len(SCREAMING_SNAKE_CASE__  ) == max_seq_length
										assert len(SCREAMING_SNAKE_CASE__  ) == max_seq_length
										assert len(SCREAMING_SNAKE_CASE__  ) == max_seq_length

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

										if "token_type_ids" not in tokenizer.model_input_names:
														__lowerCamelCase    					=	None

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


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

					class    lowerCAmelCase__     (	__lowercase			):
							a__       :						List[InputFeatures]
							a__       :						int        =		nn.CrossEntropyLoss().ignore_index






							def __init__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							TokenClassificationTask							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=False							,			SCREAMING_SNAKE_CASE__		:							Split = Split.train							,			)     ->							Union[str, Any]:
											# Load data features from cache or dataset file
											__lowerCamelCase    					=	os.path.join(
											    SCREAMING_SNAKE_CASE__							,			'''cached_{}_{}_{}'''.format(mode.value							,			tokenizer.__class__.__name__							,			str(SCREAMING_SNAKE_CASE__  )  )							,			)

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											__lowerCamelCase    					=	cached_features_file + '''.lock'''
											with FileLock(SCREAMING_SNAKE_CASE__  ):
															if os.path.exists(SCREAMING_SNAKE_CASE__  ) and not overwrite_cache:
																			logger.info(f'''Loading features from cached file {cached_features_file}'''  )
																			__lowerCamelCase    					=	torch.load(SCREAMING_SNAKE_CASE__  )
															else:
																			logger.info(f'''Creating features from dataset file at {data_dir}'''  )
																			__lowerCamelCase    					=	token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
																			# TODO clean up all this to leverage built-in features of tokenizers
																			__lowerCamelCase    					=	token_classification_task.convert_examples_to_features(
																			    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_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=SCREAMING_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							,			SCREAMING_SNAKE_CASE__  )






							def __len__(		self		:							Dict  )     ->							str:
											return len(self.features  )







							def __getitem__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							InputFeatures:
											return self.features[i]


if is_tf_available():
					import tensorflow as tf

					class    lowerCAmelCase__     :
							a__       :						List[InputFeatures]
							a__       :						int        =		-100






							def __init__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							TokenClassificationTask							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Split = Split.train							,			)     ->							List[Any]:
											__lowerCamelCase    					=	token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
											# TODO clean up all this to leverage built-in features of tokenizers
											__lowerCamelCase    					=	token_classification_task.convert_examples_to_features(
											    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_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=SCREAMING_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:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_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:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_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  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
											__lowerCamelCase    					=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

											return self.dataset






							def __len__(		self		:							List[Any]  )     ->							Any:
											return len(self.features  )







							def __getitem__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							InputFeatures:
											return self.features[i]



