




"""simple docstring"""





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  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Tuple=1_3 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=7 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=True ,SCREAMING_SNAKE_CASE_				:       Dict=True ,SCREAMING_SNAKE_CASE_				:       Tuple=True ,SCREAMING_SNAKE_CASE_				:       Dict=True ,SCREAMING_SNAKE_CASE_				:       List[str]=9_9 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=3_2 ,SCREAMING_SNAKE_CASE_				:       Tuple=1_8 ,SCREAMING_SNAKE_CASE_				:       List[str]=4_3 ,SCREAMING_SNAKE_CASE_				:       Dict=5 ,SCREAMING_SNAKE_CASE_				:       int=4 ,SCREAMING_SNAKE_CASE_				:       List[str]=3_7 ,SCREAMING_SNAKE_CASE_				:       Any="gelu" ,SCREAMING_SNAKE_CASE_				:       Any=0.1 ,SCREAMING_SNAKE_CASE_				:       Any=0.1 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=5_1_2 ,SCREAMING_SNAKE_CASE_				:       Any=1_6 ,SCREAMING_SNAKE_CASE_				:       int=2 ,SCREAMING_SNAKE_CASE_				:       List[str]=0.02 ,SCREAMING_SNAKE_CASE_				:       Dict=3 ,SCREAMING_SNAKE_CASE_				:       Any=4 ,SCREAMING_SNAKE_CASE_				:       List[Any]=None ,):
					snake_case__    :   List[str]							     =   parent
					snake_case__    :   List[Any]							     =   batch_size
					snake_case__    :   Tuple							     =   seq_length
					snake_case__    :   Any							     =   is_training
					snake_case__    :   str							     =   use_input_mask
					snake_case__    :   Union[str, Any]							     =   use_token_type_ids
					snake_case__    :   Optional[int]							     =   use_labels
					snake_case__    :   List[Any]							     =   vocab_size
					snake_case__    :   Any							     =   hidden_size
					snake_case__    :   Dict							     =   input_embedding_size
					snake_case__    :   Tuple							     =   output_embedding_size
					snake_case__    :   Any							     =   num_hidden_layers
					snake_case__    :   str							     =   num_attention_heads
					snake_case__    :   Optional[Any]							     =   intermediate_size
					snake_case__    :   List[Any]							     =   hidden_act
					snake_case__    :   Optional[Any]							     =   hidden_dropout_prob
					snake_case__    :   List[str]							     =   attention_probs_dropout_prob
					snake_case__    :   Tuple							     =   max_position_embeddings
					snake_case__    :   Optional[Any]							     =   type_vocab_size
					snake_case__    :   List[Any]							     =   type_sequence_label_size
					snake_case__    :   Optional[Any]							     =   initializer_range
					snake_case__    :   List[str]							     =   num_labels
					snake_case__    :   List[Any]							     =   num_choices
					snake_case__    :   Dict							     =   scope

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

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

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

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

					snake_case__    :   Union[str, 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=SCREAMING_SNAKE_CASE__ ,initializer_range=self.initializer_range ,)

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

				def  _lowerCamelCase   (				self				:       List[Any] ):
					(
					    snake_case__      
					)    :   List[str]							     =   self.prepare_config_and_inputs()

					snake_case__    :   Union[str, Any]							     =   True
					snake_case__    :   List[Any]							     =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					snake_case__    :   int							     =   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  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       Dict ):
					snake_case__    :   List[Any]							     =   RemBertModel(config=SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()
					snake_case__    :   Optional[int]							     =   model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ )
					snake_case__    :   List[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) )

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

				def  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Any ,):
					snake_case__    :   List[str]							     =   RemBertForCausalLM(config=SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()
					snake_case__    :   Tuple							     =   model(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.vocab_size) )

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ):
					snake_case__    :   Dict							     =   RemBertForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()
					snake_case__    :   Optional[int]							     =   model(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.vocab_size) )

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,):
					snake_case__    :   Union[str, Any]							     =   True
					snake_case__    :   Dict							     =   True
					snake_case__    :   Optional[int]							     =   RemBertForCausalLM(config=SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()

					# first forward pass
					snake_case__    :   List[str]							     =   model(
					    SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,encoder_hidden_states=SCREAMING_SNAKE_CASE__ ,encoder_attention_mask=SCREAMING_SNAKE_CASE__ ,use_cache=SCREAMING_SNAKE_CASE__ ,)
					snake_case__    :   int							     =   outputs.past_key_values

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

					# append to next input_ids and
					snake_case__    :   List[str]							     =   torch.cat([input_ids, next_tokens] ,dim=-1 )
					snake_case__    :   Union[str, Any]							     =   torch.cat([input_mask, next_mask] ,dim=-1 )

					snake_case__    :   str							     =   model(
					    SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,encoder_hidden_states=SCREAMING_SNAKE_CASE__ ,encoder_attention_mask=SCREAMING_SNAKE_CASE__ ,output_hidden_states=SCREAMING_SNAKE_CASE__ ,)["hidden_states"][0]
					snake_case__    :   Tuple							     =   model(
					    SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,encoder_hidden_states=SCREAMING_SNAKE_CASE__ ,encoder_attention_mask=SCREAMING_SNAKE_CASE__ ,past_key_values=SCREAMING_SNAKE_CASE__ ,output_hidden_states=SCREAMING_SNAKE_CASE__ ,)["hidden_states"][0]

					# select random slice
					snake_case__    :   Dict							     =   ids_tensor((1,) ,output_from_past.shape[-1] ).item()
					snake_case__    :   int							     =   output_from_no_past[:, -3:, random_slice_idx].detach()
					snake_case__    :   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(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1E-3 ) )

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Tuple ):
					snake_case__    :   Dict							     =   RemBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()
					snake_case__    :   Tuple							     =   model(
					    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  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       List[str] ):
					snake_case__    :   Any							     =   self.num_labels
					snake_case__    :   Tuple							     =   RemBertForSequenceClassification(SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()
					snake_case__    :   Any							     =   model(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  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ):
					snake_case__    :   Optional[int]							     =   self.num_labels
					snake_case__    :   int							     =   RemBertForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()
					snake_case__    :   List[Any]							     =   model(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  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       List[str] ):
					snake_case__    :   List[str]							     =   self.num_choices
					snake_case__    :   List[Any]							     =   RemBertForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
					model.to(SCREAMING_SNAKE_CASE__ )
					model.eval()
					snake_case__    :   str							     =   input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
					snake_case__    :   Optional[Any]							     =   token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
					snake_case__    :   Any							     =   input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
					snake_case__    :   Optional[int]							     =   model(
					    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_choices) )





				def  _lowerCamelCase   (				self				:       Any ):
					snake_case__    :   List[Any]							     =   self.prepare_config_and_inputs()
					(
					    snake_case__      
					)    :   Any							     =   config_and_inputs
					snake_case__    :   List[str]							     =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
					return config, inputs_dict







@require_torch
class  __lowercase    (     _a  ,				_a  ,				unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase		     =(
				    (
				        RemBertModel,
				        RemBertForMaskedLM,
				        RemBertForCausalLM,
				        RemBertForMultipleChoice,
				        RemBertForQuestionAnswering,
				        RemBertForSequenceClassification,
				        RemBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase		     =(RemBertForCausalLM,) if is_torch_available() else ()
				_lowerCAmelCase		     =(
				    {
				        '''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  _lowerCamelCase   (				self				:       Tuple ):
					snake_case__    :   Dict							     =   RemBertModelTester(self )
					snake_case__    :   Union[str, Any]							     =   ConfigTester(self ,config_class=SCREAMING_SNAKE_CASE__ ,hidden_size=3_7 )

				def  _lowerCamelCase   (				self				:       str ):
					self.config_tester.run_common_tests()

				def  _lowerCamelCase   (				self				:       Any ):
					snake_case__    :   Optional[int]							     =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
					snake_case__    :   List[str]							     =   self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						snake_case__    :   str							     =   type
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
					snake_case__    :   str							     =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       Tuple ):
					snake_case__    :   List[Any]							     =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       Optional[int] ):
					snake_case__    :   List[str]							     =   self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       Any ):
					snake_case__    :   Optional[Any]							     =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       int ):
					snake_case__    :   int							     =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
					snake_case__    :   List[Any]							     =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       Dict ):
					snake_case__    :   Union[str, Any]							     =   self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE__ )

				def  _lowerCamelCase   (				self				:       str ):
					# This regression test was failing with PyTorch < 1.3
					(
					    snake_case__      
					)    :   str							     =   self.model_tester.prepare_config_and_inputs_for_decoder()

					snake_case__    :   Optional[int]							     =   None

					self.model_tester.create_and_check_model_as_decoder(
					    SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,)





				@slow
				def  _lowerCamelCase   (				self				:       Any ):
					for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case__    :   Tuple							     =   RemBertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
						self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )







@require_torch
class  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


				@slow
				def  _lowerCamelCase   (				self				:       Tuple ):
					# Test exact values at the last hidden layer
					snake_case__    :   Optional[int]							     =   RemBertModel.from_pretrained('''google/rembert''' )
					snake_case__    :   Any							     =   torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]] )
					snake_case__    :   int							     =   torch.tensor([[0, 0, 0, 1, 1]] )
					with torch.no_grad():
						snake_case__    :   str							     =   model(SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,output_hidden_states=SCREAMING_SNAKE_CASE__ )

					snake_case__    :   Dict							     =   1_1_5_2

					snake_case__    :   List[str]							     =   torch.Size((1, 5, hidden_size) )
					self.assertEqual(output['''last_hidden_state'''].shape ,SCREAMING_SNAKE_CASE__ )

					snake_case__    :   List[str]							     =   torch.tensor(
					    [
					        [
					            [0.0754, -0.2022, 0.1904],
					            [-0.3354, -0.3692, -0.4791],
					            [-0.2314, -0.6729, -0.0749],
					            [-0.0396, -0.3105, -0.4234],
					            [-0.1571, -0.0525, 0.5353],
					        ]
					    ] )

					# 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] ,SCREAMING_SNAKE_CASE__ ,atol=1E-4 ) )








"""simple docstring"""





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase=0.999 ,     lowerCamelCase="cosine" ,     ):
						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowerCamelCase						):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowerCamelCase						):
																		return math.exp(t * -12.0						)

						else:
												raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

						snake_case__    :   Tuple							     =   []
						for i in range(lowerCamelCase						):
												snake_case__    :   int							     =   i / num_diffusion_timesteps
												snake_case__    :   Tuple							     =   (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowerCamelCase						) / alpha_bar_fn(lowerCamelCase						) ,     lowerCamelCase						)						)
						return torch.tensor(lowerCamelCase ,     dtype=torch.floataa						)

class  __lowercase    (     snake_case_  ,				snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =[e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase		     =2

				@register_to_config
				def __init__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       int = 1_0_0_0 ,SCREAMING_SNAKE_CASE_				:       float = 0.00085 ,SCREAMING_SNAKE_CASE_				:       float = 0.012 ,SCREAMING_SNAKE_CASE_				:       str = "linear" ,SCREAMING_SNAKE_CASE_				:       Optional[Union[np.ndarray, List[float]]] = None ,SCREAMING_SNAKE_CASE_				:       str = "epsilon" ,SCREAMING_SNAKE_CASE_				:       str = "linspace" ,SCREAMING_SNAKE_CASE_				:       int = 0 ,):
										if trained_betas is not None:
																snake_case__    :   Union[str, Any]							     =   torch.tensor(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										elif beta_schedule == "linear":
																snake_case__    :   List[str]							     =   torch.linspace(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																snake_case__    :   Optional[Any]							     =   (
																    torch.linspace(beta_start**0.5 ,beta_end**0.5 ,SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa ) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																snake_case__    :   str							     =   betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )
										else:
																raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

										snake_case__    :   Tuple							     =   1.0 - self.betas
										snake_case__    :   List[Any]							     =   torch.cumprod(self.alphas ,dim=0 )

										#  set all values
										self.set_timesteps(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       List[Any]=None ):
										if schedule_timesteps is None:
																snake_case__    :   Union[str, Any]							     =   self.timesteps

										snake_case__    :   Optional[int]							     =   (schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter ) == 0:
																snake_case__    :   List[str]							     =   1 if len(SCREAMING_SNAKE_CASE_ ) > 1 else 0
										else:
																snake_case__    :   Any							     =   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
																snake_case__    :   int							     =   self._index_counter[timestep_int]

										return indices[pos].item()

				@property
				def  _lowerCamelCase   (				self				:       List[Any] ):
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5

				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       Union[float, torch.FloatTensor] ,):
										snake_case__    :   Optional[int]							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										if self.state_in_first_order:
																snake_case__    :   Optional[Any]							     =   self.sigmas[step_index]
										else:
																snake_case__    :   str							     =   self.sigmas_interpol[step_index - 1]

										snake_case__    :   Dict							     =   sample / ((sigma**2 + 1) ** 0.5)
										return sample

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Union[str, torch.device] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,):
										snake_case__    :   str							     =   num_inference_steps

										snake_case__    :   Union[str, Any]							     =   num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																snake_case__    :   Optional[Any]							     =   np.linspace(0 ,num_train_timesteps - 1 ,SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																snake_case__    :   Optional[Any]							     =   num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																snake_case__    :   Dict							     =   (np.arange(0 ,SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_ )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																snake_case__    :   Union[str, Any]							     =   num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																snake_case__    :   List[Any]							     =   (np.arange(SCREAMING_SNAKE_CASE_ ,0 ,-step_ratio )).round().copy().astype(SCREAMING_SNAKE_CASE_ )
																timesteps -= 1
										else:
																raise ValueError(
																    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

										snake_case__    :   List[str]							     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
										snake_case__    :   List[str]							     =   torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   List[str]							     =   np.interp(SCREAMING_SNAKE_CASE_ ,np.arange(0 ,len(SCREAMING_SNAKE_CASE_ ) ) ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   int							     =   np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
										snake_case__    :   Dict							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )

										# compute up and down sigmas
										snake_case__    :   str							     =   sigmas.roll(-1 )
										snake_case__    :   str							     =   0.0
										snake_case__    :   List[Any]							     =   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
										snake_case__    :   List[str]							     =   (sigmas_next**2 - sigmas_up**2) ** 0.5
										snake_case__    :   List[Any]							     =   0.0

										# compute interpolated sigmas
										snake_case__    :   Tuple							     =   sigmas.log().lerp(sigmas_down.log() ,0.5 ).exp()
										snake_case__    :   Optional[Any]							     =   0.0

										# set sigmas
										snake_case__    :   List[str]							     =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
										snake_case__    :   Optional[int]							     =   torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
										snake_case__    :   str							     =   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
										snake_case__    :   Optional[Any]							     =   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

										if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
																# mps does not support float64
																snake_case__    :   Tuple							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										else:
																snake_case__    :   int							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Tuple							     =   self.sigma_to_t(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=timesteps.dtype )
										snake_case__    :   int							     =   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,dim=-1 ).flatten()

										snake_case__    :   Dict							     =   torch.cat([timesteps[:1], interleaved_timesteps] )

										snake_case__    :   Optional[int]							     =   None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										snake_case__    :   Optional[int]							     =   defaultdict(SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										# get log sigma
										snake_case__    :   Dict							     =   sigma.log()

										# get distribution
										snake_case__    :   Union[str, Any]							     =   log_sigma - self.log_sigmas[:, None]

										# get sigmas range
										snake_case__    :   Optional[Any]							     =   dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
										snake_case__    :   Any							     =   low_idx + 1

										snake_case__    :   Any							     =   self.log_sigmas[low_idx]
										snake_case__    :   str							     =   self.log_sigmas[high_idx]

										# interpolate sigmas
										snake_case__    :   int							     =   (low - log_sigma) / (low - high)
										snake_case__    :   List[Any]							     =   w.clamp(0 ,1 )

										# transform interpolation to time range
										snake_case__    :   Optional[int]							     =   (1 - w) * low_idx + w * high_idx
										snake_case__    :   int							     =   t.view(sigma.shape )
										return t

				@property
				def  _lowerCamelCase   (				self				:       List[Any] ):
										return self.sample is None

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Union[torch.FloatTensor, np.ndarray] ,SCREAMING_SNAKE_CASE_				:       Union[float, torch.FloatTensor] ,SCREAMING_SNAKE_CASE_				:       Union[torch.FloatTensor, np.ndarray] ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										snake_case__    :   str							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										# advance index counter by 1
										snake_case__    :   Optional[int]							     =   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
																snake_case__    :   List[Any]							     =   self.sigmas[step_index]
																snake_case__    :   str							     =   self.sigmas_interpol[step_index]
																snake_case__    :   str							     =   self.sigmas_up[step_index]
																snake_case__    :   Optional[int]							     =   self.sigmas_down[step_index - 1]
										else:
																# 2nd order / KPDM2's method
																snake_case__    :   int							     =   self.sigmas[step_index - 1]
																snake_case__    :   Tuple							     =   self.sigmas_interpol[step_index - 1]
																snake_case__    :   Optional[Any]							     =   self.sigmas_up[step_index - 1]
																snake_case__    :   Dict							     =   self.sigmas_down[step_index - 1]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										snake_case__    :   Optional[int]							     =   0
										snake_case__    :   List[Any]							     =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										snake_case__    :   Dict							     =   model_output.device
										snake_case__    :   Tuple							     =   randn_tensor(model_output.shape ,dtype=model_output.dtype ,device=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ )

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																snake_case__    :   List[str]							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   Any							     =   sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																snake_case__    :   Tuple							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   List[Any]							     =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
										elif self.config.prediction_type == "sample":
																raise NotImplementedError('''prediction_type not implemented yet: sample''' )
										else:
																raise ValueError(
																    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

										if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																snake_case__    :   List[str]							     =   (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																snake_case__    :   Tuple							     =   sigma_interpol - sigma_hat

																# store for 2nd order step
																snake_case__    :   int							     =   sample
																snake_case__    :   Tuple							     =   dt
																snake_case__    :   Tuple							     =   sample + derivative * dt
										else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																snake_case__    :   Tuple							     =   (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																snake_case__    :   Union[str, Any]							     =   sigma_down - sigma_hat

																snake_case__    :   int							     =   self.sample
																snake_case__    :   Tuple							     =   None

																snake_case__    :   Dict							     =   sample + derivative * dt
																snake_case__    :   Any							     =   prev_sample + noise * sigma_up

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										snake_case__    :   Optional[int]							     =   self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
										if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_ ):
																# mps does not support float64
																snake_case__    :   List[str]							     =   self.timesteps.to(original_samples.device ,dtype=torch.floataa )
																snake_case__    :   int							     =   timesteps.to(original_samples.device ,dtype=torch.floataa )
										else:
																snake_case__    :   Any							     =   self.timesteps.to(original_samples.device )
																snake_case__    :   List[str]							     =   timesteps.to(original_samples.device )

										snake_case__    :   List[Any]							     =   [self.index_for_timestep(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for t in timesteps]

										snake_case__    :   str							     =   sigmas[step_indices].flatten()
										while len(sigma.shape ) < len(original_samples.shape ):
																snake_case__    :   int							     =   sigma.unsqueeze(-1 )

										snake_case__    :   List[str]							     =   original_samples + noise * sigma
										return noisy_samples





				def __len__(				self				:       int ):
										return self.config.num_train_timesteps


