



import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


A__       =			logging.get_logger(__name__)

A__       =			[
    """yjernite/retribert-base-uncased""",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							RetriBertConfig
				__lowerCamelCase					=							None
				__lowerCamelCase					=							'''retribert'''




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									if isinstance(SCREAMING_SNAKE_CASE_		,							nn.Linear						):
														module.weight.data.normal_(mean=0.0		,							std=self.config.initializer_range						)
														if module.bias is not None:
																			module.bias.data.zero_()
									elif isinstance(SCREAMING_SNAKE_CASE_		,							nn.Embedding						):
														module.weight.data.normal_(mean=0.0		,							std=self.config.initializer_range						)
														if module.padding_idx is not None:
																			module.weight.data[module.padding_idx].zero_()
									elif isinstance(SCREAMING_SNAKE_CASE_		,							nn.LayerNorm						):
														module.bias.data.zero_()
														module.weight.data.fill_(1.0						)

A__       =			R"""\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"""





@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''			,		lowerCamelCase__			,		)
class    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case						):



									"""simple docstring"""





									super().__init__(SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       config.projection_dim

									_lowerCAmelCase         =       BertModel(SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       None if config.share_encoders else BertModel(SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       nn.Dropout(config.hidden_dropout_prob						)
									_lowerCAmelCase         =       nn.Linear(config.hidden_size		,							config.projection_dim		,							bias=SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       nn.Linear(config.hidden_size		,							config.projection_dim		,							bias=SCREAMING_SNAKE_CASE_						)

									_lowerCAmelCase         =       nn.CrossEntropyLoss(reduction="""mean"""						)

									# Initialize weights and apply final processing
									self.post_init()




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case=-1		,							):



									"""simple docstring"""





									if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
														return sent_encoder(SCREAMING_SNAKE_CASE_		,							attention_mask=SCREAMING_SNAKE_CASE_						)[1]
									else:
														# prepare implicit variables
														_lowerCAmelCase         =       input_ids.device
														_lowerCAmelCase         =       input_ids.size()
														_lowerCAmelCase         =       torch.zeros(SCREAMING_SNAKE_CASE_		,							dtype=torch.long		,							device=SCREAMING_SNAKE_CASE_						)
														_lowerCAmelCase         =       [None] * sent_encoder.config.num_hidden_layers
														_lowerCAmelCase         =       sent_encoder.get_extended_attention_mask(
														    SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_						)

														# define function for checkpointing
														def partial_encode(*_snake_case						):
																			_lowerCAmelCase         =       sent_encoder.encoder(
																			    inputs[0]		,							attention_mask=inputs[1]		,							head_mask=SCREAMING_SNAKE_CASE_		,							)
																			_lowerCAmelCase         =       encoder_outputs[0]
																			_lowerCAmelCase         =       sent_encoder.pooler(SCREAMING_SNAKE_CASE_						)
																			return pooled_output

														# run embedding layer on everything at once
														_lowerCAmelCase         =       sent_encoder.embeddings(
														    input_ids=SCREAMING_SNAKE_CASE_		,							position_ids=SCREAMING_SNAKE_CASE_		,							token_type_ids=SCREAMING_SNAKE_CASE_		,							inputs_embeds=SCREAMING_SNAKE_CASE_						)
														# run encoding and pooling on one mini-batch at a time
														_lowerCAmelCase         =       []
														for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size						)						):
																			_lowerCAmelCase         =       embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																			_lowerCAmelCase         =       extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																			_lowerCAmelCase         =       checkpoint.checkpoint(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_						)
																			pooled_output_list.append(SCREAMING_SNAKE_CASE_						)
														return torch.cat(SCREAMING_SNAKE_CASE_		,							dim=0						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case=None		,							_snake_case=-1		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       self.embed_sentences_checkpointed(
									    SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							self.bert_query		,							SCREAMING_SNAKE_CASE_		,							)
									return self.project_query(SCREAMING_SNAKE_CASE_						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case=None		,							_snake_case=-1		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       self.embed_sentences_checkpointed(
									    SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							self.bert_query if self.bert_doc is None else self.bert_doc		,							SCREAMING_SNAKE_CASE_		,							)
									return self.project_doc(SCREAMING_SNAKE_CASE_						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case = -1		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       input_ids_query.device
									_lowerCAmelCase         =       self.embed_questions(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       self.embed_answers(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       torch.mm(SCREAMING_SNAKE_CASE_		,							a_reps.t()						)
									_lowerCAmelCase         =       self.ce_loss(SCREAMING_SNAKE_CASE_		,							torch.arange(compare_scores.shape[1]						).to(SCREAMING_SNAKE_CASE_						)						)
									_lowerCAmelCase         =       self.ce_loss(compare_scores.t()		,							torch.arange(compare_scores.shape[0]						).to(SCREAMING_SNAKE_CASE_						)						)
									_lowerCAmelCase         =       (loss_qa + loss_aq) / 2
									return loss





'''simple docstring'''





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     a :




				def __init__(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Any=13,     SCREAMING_SNAKE_CASE_						:					Any=7,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=True,     SCREAMING_SNAKE_CASE_						:					List[str]=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=True,     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					str=99,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=32,     SCREAMING_SNAKE_CASE_						:					Optional[int]=5,     SCREAMING_SNAKE_CASE_						:					str=4,     SCREAMING_SNAKE_CASE_						:					Dict=37,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					List[str]=0.1,     SCREAMING_SNAKE_CASE_						:					Tuple=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					Dict=16,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Tuple=0.02,     SCREAMING_SNAKE_CASE_						:					int=3,     SCREAMING_SNAKE_CASE_						:					Dict=None,     SCREAMING_SNAKE_CASE_						:					List[str]=20,     SCREAMING_SNAKE_CASE_						:					List[Any]=30,     SCREAMING_SNAKE_CASE_						:					Optional[int]=2,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=10,     ):
										snake_case							:  Union[str, Any]			=     parent
										snake_case							:  List[str]			=     batch_size
										snake_case							:  List[str]			=     seq_length
										snake_case							:  Optional[int]			=     is_training
										snake_case							:  Optional[Any]			=     use_input_mask
										snake_case							:  List[Any]			=     use_token_type_ids
										snake_case							:  int			=     use_labels
										snake_case							:  List[Any]			=     vocab_size
										snake_case							:  Tuple			=     hidden_size
										snake_case							:  List[str]			=     num_hidden_layers
										snake_case							:  List[Any]			=     num_attention_heads
										snake_case							:  int			=     intermediate_size
										snake_case							:  List[str]			=     hidden_act
										snake_case							:  int			=     hidden_dropout_prob
										snake_case							:  Dict			=     attention_probs_dropout_prob
										snake_case							:  Tuple			=     max_position_embeddings
										snake_case							:  List[str]			=     type_vocab_size
										snake_case							:  str			=     type_sequence_label_size
										snake_case							:  Optional[Any]			=     initializer_range
										snake_case							:  List[str]			=     num_labels
										snake_case							:  Union[str, Any]			=     scope
										snake_case							:  int			=     max_xpath_tag_unit_embeddings
										snake_case							:  Optional[Any]			=     max_xpath_subs_unit_embeddings
										snake_case							:  str			=     tag_pad_id
										snake_case							:  Dict			=     subs_pad_id
										snake_case							:  List[Any]			=     max_depth




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Tuple			=     ids_tensor([self.batch_size, self.seq_length],     self.vocab_size   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_tag_unit_embeddings   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_subs_unit_embeddings   )

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

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

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

										snake_case							:  Union[str, Any]			=     self.get_config()

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




				def 						__snake_case				(				self						:					Tuple   ):
										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 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Optional[Any]			=     MarkupLMModel(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										print('''Configs:''',     model.config.tag_pad_id,     model.config.subs_pad_id   )
										snake_case							:  Optional[Any]			=     model(SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     model(SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     model(SCREAMING_SNAKE_CASE_   )
										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 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  int			=     self.num_labels
										snake_case							:  Union[str, Any]			=     MarkupLMForSequenceClassification(SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  str			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     labels=SCREAMING_SNAKE_CASE_,     )
										self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels)   )




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Any			=     self.num_labels
										snake_case							:  List[str]			=     MarkupLMForTokenClassification(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Dict			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     labels=SCREAMING_SNAKE_CASE_,     )
										self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.num_labels)   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  List[Any]			=     MarkupLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Union[str, Any]			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     start_positions=SCREAMING_SNAKE_CASE_,     end_positions=SCREAMING_SNAKE_CASE_,     )
										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 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  int			=     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       
										)							:  Any			=     config_and_inputs
										snake_case							:  str			=     {
										    '''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     a (					__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				_snake_case		     =			(
				    {
				        '''feature-extraction''': MarkupLMModel,
				        '''question-answering''': MarkupLMForQuestionAnswering,
				        '''text-classification''': MarkupLMForSequenceClassification,
				        '''token-classification''': MarkupLMForTokenClassification,
				        '''zero-shot''': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




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




				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Tuple			=     MarkupLMModelTester(self   )
										snake_case							:  List[Any]			=     ConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     hidden_size=37   )




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




				def 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  List[Any]			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_   )




def   A			(							):
						snake_case							:  Optional[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     a (					unittest.TestCase     ):




				@cached_property
				def 						__snake_case				(				self						:					Tuple   ):
										# TODO use from_pretrained here
										snake_case							:  Optional[int]			=     MarkupLMFeatureExtractor()
										snake_case							:  Tuple			=     MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''   )

										return MarkupLMProcessor(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				@slow
				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Dict			=     MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''   ).to(SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     self.default_processor

										snake_case							:  str			=     processor(prepare_html_string(),     return_tensors='''pt'''   )
										snake_case							:  List[Any]			=     inputs.to(SCREAMING_SNAKE_CASE_   )

										# forward pass
										with torch.no_grad():
																snake_case							:  Any			=     model(**SCREAMING_SNAKE_CASE_   )

										# verify the last hidden states
										snake_case							:  Tuple			=     torch.Size([1, 14, 7_68]   )
										self.assertEqual(outputs.last_hidden_state.shape,     SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     torch.tensor(
										    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]   ).to(SCREAMING_SNAKE_CASE_   )

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

