
import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
		import torch

		from transformers import (
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class 						__magic_name__				:





		def __init__(							self      :		List[Any]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		str=1_3  ,					lowerCamelCase__      :		List[Any]=7  ,					lowerCamelCase__      :		Optional[Any]=True  ,					lowerCamelCase__      :		List[Any]=True  ,					lowerCamelCase__      :		Optional[int]=True  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Optional[Any]=9_9  ,					lowerCamelCase__      :		Optional[Any]=3_2  ,					lowerCamelCase__      :		Tuple=5  ,					lowerCamelCase__      :		str=4  ,					lowerCamelCase__      :		str=3_7  ,					lowerCamelCase__      :		Optional[int]="gelu"  ,					lowerCamelCase__      :		Union[str, Any]=0.1  ,					lowerCamelCase__      :		List[Any]=0.1  ,					lowerCamelCase__      :		str=5_1_2  ,					lowerCamelCase__      :		Optional[int]=1_6  ,					lowerCamelCase__      :		Dict=2  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		int=3  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Optional[Any]=2_0  ,					lowerCamelCase__      :		Any=3_0  ,					lowerCamelCase__      :		Optional[Any]=2  ,					lowerCamelCase__      :		List[str]=2  ,					lowerCamelCase__      :		Any=1_0  ,					):
							lowerCAmelCase						:   int							=       parent
							lowerCAmelCase						:   Tuple							=       batch_size
							lowerCAmelCase						:   Optional[Any]							=       seq_length
							lowerCAmelCase						:   List[Any]							=       is_training
							lowerCAmelCase						:   str							=       use_input_mask
							lowerCAmelCase						:   Optional[Any]							=       use_token_type_ids
							lowerCAmelCase						:   List[str]							=       use_labels
							lowerCAmelCase						:   Optional[int]							=       vocab_size
							lowerCAmelCase						:   Optional[int]							=       hidden_size
							lowerCAmelCase						:   Tuple							=       num_hidden_layers
							lowerCAmelCase						:   Any							=       num_attention_heads
							lowerCAmelCase						:   int							=       intermediate_size
							lowerCAmelCase						:   Union[str, Any]							=       hidden_act
							lowerCAmelCase						:   str							=       hidden_dropout_prob
							lowerCAmelCase						:   Tuple							=       attention_probs_dropout_prob
							lowerCAmelCase						:   str							=       max_position_embeddings
							lowerCAmelCase						:   List[Any]							=       type_vocab_size
							lowerCAmelCase						:   Optional[Any]							=       type_sequence_label_size
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   List[Any]							=       num_labels
							lowerCAmelCase						:   Tuple							=       scope
							lowerCAmelCase						:   Any							=       max_xpath_tag_unit_embeddings
							lowerCAmelCase						:   Dict							=       max_xpath_subs_unit_embeddings
							lowerCAmelCase						:   Tuple							=       tag_pad_id
							lowerCAmelCase						:   Any							=       subs_pad_id
							lowerCAmelCase						:   int							=       max_depth





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Tuple							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size			)

							lowerCAmelCase						:   int							=       ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_tag_unit_embeddings			)

							lowerCAmelCase						:   Optional[int]							=       ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_subs_unit_embeddings			)

							lowerCAmelCase						:   str							=       None
							if self.use_input_mask:
												lowerCAmelCase						:   str							=       ids_tensor([self.batch_size, self.seq_length]  ,					vocab_size=2			)

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

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

							lowerCAmelCase						:   Tuple							=       self.get_config()

							return (
							    config,
							    input_ids,
							    xpath_tags_seq,
							    xpath_subs_seq,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							)





		def  _A							(							self      :		Dict			):
							return MarkupLMConfig(
							    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  ,					initializer_range=self.initializer_range  ,					max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings  ,					max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings  ,					tag_pad_id=self.tag_pad_id  ,					subs_pad_id=self.subs_pad_id  ,					max_depth=self.max_depth  ,					)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		List[str]  ,					):
							lowerCAmelCase						:   List[str]							=       MarkupLMModel(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							print('''Configs:'''  ,					model.config.tag_pad_id  ,					model.config.subs_pad_id			)
							lowerCAmelCase						:   Dict							=       model(lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       model(lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)
							lowerCAmelCase						:   Optional[Any]							=       model(lowerCamelCase__			)
							self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)			)
							self.parent.assertEqual(result.pooler_output.shape  ,					(self.batch_size, self.hidden_size)			)





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Dict  ,					):
							lowerCAmelCase						:   str							=       self.num_labels
							lowerCAmelCase						:   Union[str, Any]							=       MarkupLMForSequenceClassification(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[Any]							=       model(
							    lowerCamelCase__  ,					xpath_tags_seq=lowerCamelCase__  ,					xpath_subs_seq=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=lowerCamelCase__  ,					)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)			)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		int  ,					):
							lowerCAmelCase						:   List[str]							=       self.num_labels
							lowerCAmelCase						:   Union[str, Any]							=       MarkupLMForTokenClassification(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   int							=       model(
							    lowerCamelCase__  ,					xpath_tags_seq=lowerCamelCase__  ,					xpath_subs_seq=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=lowerCamelCase__  ,					)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)			)





		def  _A							(							self      :		int  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[int]  ,					):
							lowerCAmelCase						:   Optional[int]							=       MarkupLMForQuestionAnswering(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[Any]							=       model(
							    lowerCamelCase__  ,					xpath_tags_seq=lowerCamelCase__  ,					xpath_subs_seq=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					start_positions=lowerCamelCase__  ,					end_positions=lowerCamelCase__  ,					)
							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      :		Optional[int]			):
							lowerCAmelCase						:   Dict							=       self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							
							)						:   Dict							=       config_and_inputs
							lowerCAmelCase						:   List[Any]							=       {
							    '''input_ids''': input_ids,
							    '''xpath_tags_seq''': xpath_tags_seq,
							    '''xpath_subs_seq''': xpath_subs_seq,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict



@require_torch
class 						__magic_name__				(  snake_case,			snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		(
		    (
		        MarkupLMModel,
		        MarkupLMForSequenceClassification,
		        MarkupLMForTokenClassification,
		        MarkupLMForQuestionAnswering,
		    )
		    if is_torch_available()
		    else None
		)
		_lowerCAmelCase						=		(
		    {
		        "feature-extraction": MarkupLMModel,
		        "question-answering": MarkupLMForQuestionAnswering,
		        "text-classification": MarkupLMForSequenceClassification,
		        "token-classification": MarkupLMForTokenClassification,
		        "zero-shot": MarkupLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)





		def  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Any			):
							# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
							# (batch of pretokenized examples).
							return True





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Tuple							=       MarkupLMModelTester(self			)
							lowerCAmelCase						:   Dict							=       ConfigTester(self  ,					config_class=lowerCamelCase__  ,					hidden_size=3_7			)





		def  _A							(							self      :		Any			):
							self.config_tester.run_common_tests()





		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   Tuple							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__			)





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Any							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__			)





		def  _A							(							self      :		int			):
							lowerCAmelCase						:   Optional[Any]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__			)





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Dict							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__			)



def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       '''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

					return html_string


@require_torch
class 						__magic_name__				(  unittest.TestCase      ):





		@cached_property
		def  _A							(							self      :		Optional[int]			):
							# TODO use from_pretrained here
							lowerCAmelCase						:   List[Any]							=       MarkupLMFeatureExtractor()
							lowerCAmelCase						:   List[str]							=       MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''			)

							return MarkupLMProcessor(lowerCamelCase__  ,					lowerCamelCase__			)





		@slow
		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   str							=       MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''			).to(lowerCamelCase__			)

							lowerCAmelCase						:   List[Any]							=       self.default_processor

							lowerCAmelCase						:   Tuple							=       processor(prepare_html_string()  ,					return_tensors='''pt'''			)
							lowerCAmelCase						:   Optional[Any]							=       inputs.to(lowerCamelCase__			)

							# forward pass
							with torch.no_grad():
												lowerCAmelCase						:   str							=       model(**lowerCamelCase__			)

							# verify the last hidden states
							lowerCAmelCase						:   Dict							=       torch.Size([1, 1_4, 7_6_8]			)
							self.assertEqual(outputs.last_hidden_state.shape  ,					lowerCamelCase__			)

							lowerCAmelCase						:   Dict							=       torch.tensor(
							    [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]			).to(lowerCamelCase__			)

							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,					lowerCamelCase__  ,					atol=1E-4			)			)


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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

__SCREAMING_SNAKE_CASE     :				int             =       {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"gpt2"
		_lowerCAmelCase						=		["past_key_values"]
		_lowerCAmelCase						=		{
		    "hidden_size": "n_embd",
		    "max_position_embeddings": "n_positions",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}





		def __init__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple=5_0_2_5_7  ,					lowerCamelCase__      :		str=1_0_2_4  ,					lowerCamelCase__      :		Union[str, Any]=7_6_8  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Tuple="gelu_new"  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		str=0.1  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		Dict=1E-5  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		Tuple="cls_index"  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Optional[int]=None  ,					lowerCamelCase__      :		int=True  ,					lowerCamelCase__      :		Tuple=0.1  ,					lowerCamelCase__      :		List[str]=True  ,					lowerCamelCase__      :		Dict=True  ,					lowerCamelCase__      :		Tuple=5_0_2_5_6  ,					lowerCamelCase__      :		Optional[int]=5_0_2_5_6  ,					lowerCamelCase__      :		Dict=False  ,					lowerCamelCase__      :		Tuple=False  ,					**lowerCamelCase__      :		List[str]  ,					):
							lowerCAmelCase						:   List[str]							=       vocab_size
							lowerCAmelCase						:   Dict							=       n_positions
							lowerCAmelCase						:   int							=       n_embd
							lowerCAmelCase						:   Union[str, Any]							=       n_layer
							lowerCAmelCase						:   List[str]							=       n_head
							lowerCAmelCase						:   str							=       n_inner
							lowerCAmelCase						:   Tuple							=       activation_function
							lowerCAmelCase						:   List[Any]							=       resid_pdrop
							lowerCAmelCase						:   Any							=       embd_pdrop
							lowerCAmelCase						:   int							=       attn_pdrop
							lowerCAmelCase						:   Dict							=       layer_norm_epsilon
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   List[Any]							=       summary_type
							lowerCAmelCase						:   int							=       summary_use_proj
							lowerCAmelCase						:   List[str]							=       summary_activation
							lowerCAmelCase						:   List[str]							=       summary_first_dropout
							lowerCAmelCase						:   Optional[int]							=       summary_proj_to_labels
							lowerCAmelCase						:   Optional[int]							=       scale_attn_weights
							lowerCAmelCase						:   List[str]							=       use_cache
							lowerCAmelCase						:   List[Any]							=       scale_attn_by_inverse_layer_idx
							lowerCAmelCase						:   int							=       reorder_and_upcast_attn

							lowerCAmelCase						:   List[Any]							=       bos_token_id
							lowerCAmelCase						:   Optional[int]							=       eos_token_id

							super().__init__(bos_token_id=lowerCamelCase__  ,					eos_token_id=lowerCamelCase__  ,					**lowerCamelCase__			)



class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					lowerCamelCase__      :		PretrainedConfig  ,					lowerCamelCase__      :		str = "default"  ,					lowerCamelCase__      :		List[PatchingSpec] = None  ,					lowerCamelCase__      :		bool = False  ,					):
							super().__init__(lowerCamelCase__  ,					task=lowerCamelCase__  ,					patching_specs=lowerCamelCase__  ,					use_past=lowerCamelCase__			)
							if not getattr(self._config  ,					'''pad_token_id'''  ,					lowerCamelCase__			):
												# TODO: how to do that better?
												lowerCAmelCase						:   str							=       0





		@property
		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   List[Any]							=       OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
							if self.use_past:
												self.fill_with_past_key_values_(lowerCamelCase__  ,					direction='''inputs'''			)
												lowerCAmelCase						:   str							=       {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
												lowerCAmelCase						:   int							=       {0: '''batch''', 1: '''sequence'''}

							return common_inputs





		@property
		def  _A							(							self      :		Optional[int]			):
							return self._config.n_layer





		@property
		def  _A							(							self      :		List[str]			):
							return self._config.n_head





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		PreTrainedTokenizer  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		bool = False  ,					lowerCamelCase__      :		Optional[TensorType] = None  ,					):
							lowerCAmelCase						:   Union[str, Any]							=       super(lowerCamelCase__  ,					self			).generate_dummy_inputs(
							    lowerCamelCase__  ,					batch_size=lowerCamelCase__  ,					seq_length=lowerCamelCase__  ,					is_pair=lowerCamelCase__  ,					framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase						:   Tuple							=       OrderedDict({'''input_ids''': common_inputs['''input_ids''']}			)

							# Need to add the past_keys
							if self.use_past:
												if not is_torch_available():
																	raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
												else:
																	import torch

																	lowerCAmelCase		,							lowerCAmelCase						:   Any							=       common_inputs['''input_ids'''].shape
																	# Not using the same length for past_key_values
																	lowerCAmelCase						:   Optional[int]							=       seqlen + 2
																	lowerCAmelCase						:   Any							=       (
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	lowerCAmelCase						:   Any							=       [
																	    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																	]

							lowerCAmelCase						:   List[str]							=       common_inputs['''attention_mask''']
							if self.use_past:
												lowerCAmelCase						:   Optional[Any]							=       ordered_inputs['''attention_mask'''].dtype
												lowerCAmelCase						:   Optional[Any]							=       torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase__  ,					lowerCamelCase__  ,					dtype=lowerCamelCase__			)]  ,					dim=1			)

							return ordered_inputs





		@property
		def  _A							(							self      :		Union[str, Any]			):
							return 1_3
