

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     RemBertConfig,
     RemBertForCausalLM,
     RemBertForMaskedLM,
     RemBertForMultipleChoice,
     RemBertForQuestionAnswering,
     RemBertForSequenceClassification,
     RemBertForTokenClassification,
     RemBertModel,
 )
 from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class    __A				:






    def __init__(	self			:    int    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple=13    ,				UpperCAmelCase_			:    Tuple=7    ,				UpperCAmelCase_			:    int=True    ,				UpperCAmelCase_			:    Dict=True    ,				UpperCAmelCase_			:    List[Any]=True    ,				UpperCAmelCase_			:    str=True    ,				UpperCAmelCase_			:    Dict=99    ,				UpperCAmelCase_			:    str=32    ,				UpperCAmelCase_			:    Union[str, Any]=18    ,				UpperCAmelCase_			:    Any=43    ,				UpperCAmelCase_			:    Optional[int]=5    ,				UpperCAmelCase_			:    Tuple=4    ,				UpperCAmelCase_			:    List[str]=37    ,				UpperCAmelCase_			:    Tuple="gelu"    ,				UpperCAmelCase_			:    Optional[Any]=0.1    ,				UpperCAmelCase_			:    int=0.1    ,				UpperCAmelCase_			:    str=512    ,				UpperCAmelCase_			:    Optional[int]=16    ,				UpperCAmelCase_			:    List[Any]=2    ,				UpperCAmelCase_			:    int=0.02    ,				UpperCAmelCase_			:    str=3    ,				UpperCAmelCase_			:    str=4    ,				UpperCAmelCase_			:    Optional[int]=None    ,				):
        lowerCAmelCase				:			Union[str, Any]    			=      parent
        lowerCAmelCase				:			Any    			=      batch_size
        lowerCAmelCase				:			Optional[Any]    			=      seq_length
        lowerCAmelCase				:			str    			=      is_training
        lowerCAmelCase				:			List[str]    			=      use_input_mask
        lowerCAmelCase				:			Any    			=      use_token_type_ids
        lowerCAmelCase				:			Union[str, Any]    			=      use_labels
        lowerCAmelCase				:			Union[str, Any]    			=      vocab_size
        lowerCAmelCase				:			Tuple    			=      hidden_size
        lowerCAmelCase				:			Optional[Any]    			=      input_embedding_size
        lowerCAmelCase				:			Optional[Any]    			=      output_embedding_size
        lowerCAmelCase				:			Tuple    			=      num_hidden_layers
        lowerCAmelCase				:			List[str]    			=      num_attention_heads
        lowerCAmelCase				:			Union[str, Any]    			=      intermediate_size
        lowerCAmelCase				:			Union[str, Any]    			=      hidden_act
        lowerCAmelCase				:			Union[str, Any]    			=      hidden_dropout_prob
        lowerCAmelCase				:			Dict    			=      attention_probs_dropout_prob
        lowerCAmelCase				:			Optional[int]    			=      max_position_embeddings
        lowerCAmelCase				:			Dict    			=      type_vocab_size
        lowerCAmelCase				:			List[Any]    			=      type_sequence_label_size
        lowerCAmelCase				:			Optional[int]    			=      initializer_range
        lowerCAmelCase				:			Optional[Any]    			=      num_labels
        lowerCAmelCase				:			int    			=      num_choices
        lowerCAmelCase				:			Dict    			=      scope






    def 	lowercase__ (	self			:    List[Any]    ):
        lowerCAmelCase				:			int    			=      ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size    )

        lowerCAmelCase				:			List[str]    			=      None
        if self.use_input_mask:
            lowerCAmelCase				:			List[Any]    			=      random_attention_mask([self.batch_size, self.seq_length]    )

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

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

        lowerCAmelCase				:			Optional[Any]    			=      RemBertConfig(
            vocab_size=self.vocab_size    ,				hidden_size=self.hidden_size    ,				input_embedding_size=self.input_embedding_size    ,				output_embedding_size=self.output_embedding_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    ,				is_decoder=UpperCAmelCase_    ,				initializer_range=self.initializer_range    ,				)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def 	lowercase__ (	self			:    str    ):
        (
            (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  
        )				:			Optional[Any]    			=      self.prepare_config_and_inputs()

        lowerCAmelCase				:			Tuple    			=      True
        lowerCAmelCase				:			Optional[Any]    			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
        lowerCAmelCase				:			str    			=      ids_tensor([self.batch_size, self.seq_length]    ,				vocab_size=2    )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    int    ):
        lowerCAmelCase				:			str    			=      RemBertModel(config=UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()
        lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    )
        lowerCAmelCase				:			str    			=      model(UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    )
        lowerCAmelCase				:			Optional[Any]    			=      model(UpperCAmelCase_    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)    )






    def 	lowercase__ (	self			:    str    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				):
        lowerCAmelCase				:			List[Any]    			=      True
        lowerCAmelCase				:			Optional[int]    			=      RemBertModel(UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()
        lowerCAmelCase				:			int    			=      model(
            UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				)
        lowerCAmelCase				:			Optional[int]    			=      model(
            UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				)
        lowerCAmelCase				:			Optional[Any]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)    )






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ,				):
        lowerCAmelCase				:			Any    			=      RemBertForCausalLM(config=UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()
        lowerCAmelCase				:			List[str]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
        self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)    )






    def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ):
        lowerCAmelCase				:			Any    			=      RemBertForMaskedLM(config=UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()
        lowerCAmelCase				:			List[str]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
        self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)    )






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Tuple    ,				):
        lowerCAmelCase				:			Optional[Any]    			=      True
        lowerCAmelCase				:			Dict    			=      True
        lowerCAmelCase				:			List[Any]    			=      RemBertForCausalLM(config=UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()

        # first forward pass
        lowerCAmelCase				:			int    			=      model(
            UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    ,				)
        lowerCAmelCase				:			Optional[Any]    			=      outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        lowerCAmelCase				:			List[Any]    			=      ids_tensor((self.batch_size, 3)    ,				config.vocab_size    )
        lowerCAmelCase				:			Tuple    			=      ids_tensor((self.batch_size, 3)    ,				vocab_size=2    )

        # append to next input_ids and
        lowerCAmelCase				:			int    			=      torch.cat([input_ids, next_tokens]    ,				dim=-1    )
        lowerCAmelCase				:			Any    			=      torch.cat([input_mask, next_mask]    ,				dim=-1    )

        lowerCAmelCase				:			List[Any]    			=      model(
            UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				output_hidden_states=UpperCAmelCase_    ,				)['hidden_states'][0]
        lowerCAmelCase				:			Any    			=      model(
            UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				past_key_values=UpperCAmelCase_    ,				output_hidden_states=UpperCAmelCase_    ,				)['hidden_states'][0]

        # select random slice
        lowerCAmelCase				:			List[Any]    			=      ids_tensor((1,)    ,				output_from_past.shape[-1]    ).item()
        lowerCAmelCase				:			Tuple    			=      output_from_no_past[:, -3:, random_slice_idx].detach()
        lowerCAmelCase				:			Optional[Any]    			=      output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(UpperCAmelCase_    ,				UpperCAmelCase_    ,				atol=1E-3    )    )






    def 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Optional[Any]    ):
        lowerCAmelCase				:			List[str]    			=      RemBertForQuestionAnswering(config=UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()
        lowerCAmelCase				:			Tuple    			=      model(
            UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				start_positions=UpperCAmelCase_    ,				end_positions=UpperCAmelCase_    ,				)
        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 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Any    ):
        lowerCAmelCase				:			Any    			=      self.num_labels
        lowerCAmelCase				:			Tuple    			=      RemBertForSequenceClassification(UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()
        lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
        self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)    )






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






    def 	lowercase__ (	self			:    List[str]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ):
        lowerCAmelCase				:			Optional[Any]    			=      self.num_choices
        lowerCAmelCase				:			int    			=      RemBertForMultipleChoice(config=UpperCAmelCase_    )
        model.to(UpperCAmelCase_    )
        model.eval()
        lowerCAmelCase				:			Tuple    			=      input_ids.unsqueeze(1    ).expand(-1    ,				self.num_choices    ,				-1    ).contiguous()
        lowerCAmelCase				:			Tuple    			=      token_type_ids.unsqueeze(1    ).expand(-1    ,				self.num_choices    ,				-1    ).contiguous()
        lowerCAmelCase				:			List[Any]    			=      input_mask.unsqueeze(1    ).expand(-1    ,				self.num_choices    ,				-1    ).contiguous()
        lowerCAmelCase				:			List[str]    			=      model(
            UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    ,				)
        self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_choices)    )






    def 	lowercase__ (	self			:    Tuple    ):
        lowerCAmelCase				:			Optional[int]    			=      self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  
        )				:			List[Any]    			=      config_and_inputs
        lowerCAmelCase				:			str    			=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict






@require_torch
class    __A				(							lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
    lowerCAmelCase_							:		str						=							(
        (
            RemBertModel,
            RemBertForMaskedLM,
            RemBertForCausalLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase_							:		Any						=							(RemBertForCausalLM,) if is_torch_available() else ()
    lowerCAmelCase_							:		List[Any]						=							(
        {
            "feature-extraction": RemBertModel,
            "fill-mask": RemBertForMaskedLM,
            "question-answering": RemBertForQuestionAnswering,
            "text-classification": RemBertForSequenceClassification,
            "text-generation": RemBertForCausalLM,
            "token-classification": RemBertForTokenClassification,
            "zero-shot": RemBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )






    def 	lowercase__ (	self			:    Optional[int]    ):
        lowerCAmelCase				:			str    			=      RemBertModelTester(self    )
        lowerCAmelCase				:			List[str]    			=      ConfigTester(self    ,				config_class=UpperCAmelCase_    ,				hidden_size=37    )






    def 	lowercase__ (	self			:    Tuple    ):
        self.config_tester.run_common_tests()






    def 	lowercase__ (	self			:    Any    ):
        lowerCAmelCase				:			Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    List[Any]    ):
        lowerCAmelCase				:			List[Any]    			=      self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            lowerCAmelCase				:			str    			=      type
            self.model_tester.create_and_check_model(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    str    ):
        lowerCAmelCase				:			Tuple    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Tuple    ):
        lowerCAmelCase				:			Tuple    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Tuple    ):
        lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase_    )






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






    def 	lowercase__ (	self			:    Any    ):
        lowerCAmelCase				:			List[Any]    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Dict    ):
        lowerCAmelCase				:			Any    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Optional[Any]    ):
        lowerCAmelCase				:			Optional[int]    			=      self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Optional[int]    ):
        # This regression test was failing with PyTorch < 1.3
        (
            (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  (
            lowerCAmelCase
        )			,  
        )				:			Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs_for_decoder()

        lowerCAmelCase				:			List[str]    			=      None

        self.model_tester.create_and_check_model_as_decoder(
            UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				)






    @slow
    def 	lowercase__ (	self			:    Tuple    ):
        for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCAmelCase				:			int    			=      RemBertModel.from_pretrained(UpperCAmelCase_    )
            self.assertIsNotNone(UpperCAmelCase_    )






@require_torch
class    __A				(							unittest.TestCase ):






    @slow
    def 	lowercase__ (	self			:    List[Any]    ):
        # Test exact values at the last hidden layer
        lowerCAmelCase				:			Union[str, Any]    			=      RemBertModel.from_pretrained('google/rembert'    )
        lowerCAmelCase				:			str    			=      torch.tensor([[312, 56498, 313, 2125, 313]]    )
        lowerCAmelCase				:			List[str]    			=      torch.tensor([[0, 0, 0, 1, 1]]    )
        with torch.no_grad():
            lowerCAmelCase				:			str    			=      model(UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				output_hidden_states=UpperCAmelCase_    )

        lowerCAmelCase				:			int    			=      1152

        lowerCAmelCase				:			Union[str, Any]    			=      torch.Size((1, 5, hidden_size)    )
        self.assertEqual(output['last_hidden_state'].shape    ,				UpperCAmelCase_    )

        lowerCAmelCase				:			Dict    			=      torch.tensor(
            [
                [
                    [0.07_54, -0.20_22, 0.19_04],
                    [-0.33_54, -0.36_92, -0.47_91],
                    [-0.23_14, -0.67_29, -0.07_49],
                    [-0.03_96, -0.31_05, -0.42_34],
                    [-0.15_71, -0.05_25, 0.53_53],
                ]
            ]    )

        # Running on the original tf implementation gives slightly different results here.
        # Not clear why this variations is present
        # TODO: Find reason for discrepancy
        # expected_original_implementation = [[
        #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
        #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
        #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
        #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
        #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
        # ]]

        self.assertTrue(torch.allclose(output['last_hidden_state'][:, :, :3]    ,				UpperCAmelCase_    ,				atol=1E-4    )    )








def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:



    '''simple docstring'''






    lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
    lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)

    lowerCAmelCase				:			str    			=      [
        [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]
    lowerCAmelCase				:			str    			=      [
        ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]

    for i in range(1,      len_source_seq + 1				):
        lowerCAmelCase				:			int    			=      i * delete_cost
        lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"

    for i in range(1,      len_destination_seq + 1				):
        lowerCAmelCase				:			Tuple    			=      i * insert_cost
        lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"

    for i in range(1,      len_source_seq + 1				):
        for j in range(1,      len_destination_seq + 1				):
            if source_seq[i - 1] == destination_seq[j - 1]:
                lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
            else:
                lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)

            if costs[i - 1][j] + delete_cost < costs[i][j]:
                lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"

            if costs[i][j - 1] + insert_cost < costs[i][j]:
                lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"

    return costs, ops
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:



    '''simple docstring'''






    if i == 0 and j == 0:
        return []
    else:
        if ops[i][j][0] == "C" or ops[i][j][0] == "R":
            lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
            seq.append(ops[i][j]				)
            return seq
        elif ops[i][j][0] == "D":
            lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
            seq.append(ops[i][j]				)
            return seq
        else:
            lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
            seq.append(ops[i][j]				)
            return seq


if __name__ == "__main__":
 __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)

 __A   :     Optional[int]      =			len(operations)
 __A   :     Optional[Any]      =			len(operations[0])
 __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)

 __A   :     int      =			list('''Python''')
 __A   :     Tuple      =			0
 __A   :     Tuple      =			0

 with open('''min_cost.txt''', '''w''') as file:
  for op in sequence:
   print(''''''.join(string))

   if op[0] == "C":
    file.write('''%-16s''' % '''Copy %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost -= 1
   elif op[0] == "R":
    __A   :     Any      =			op[2]

    file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
    file.write('''\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('''%-16s''' % '''Delete %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('''%-16s''' % '''Insert %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2

   i += 1

  print(''''''.join(string))
  print('''Cost: ''', cost)

  file.write('''\r\nMinimum cost: ''' + str(cost))





