


'''simple docstring'''
def      a (     __a = 1000000  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					set(range(3					,   __a					,   2  )  )
					primes.add(2  )
					for p in range(3					,   __a					,   2  ):
										if p not in primes:
															continue
										primes.difference_update(set(range(p * p					,   __a					,   __a  )  )  )

					UpperCamelCase__  :Any				   =					[float(__a  ) for n in range(limit + 1  )]

					for p in primes:
										for n in range(__a					,   limit + 1					,   __a  ):
															phi[n] *= 1 - 1 / p

					return int(sum(phi[2:]  )  )


if __name__ == "__main__":
		print(F"""{solution() = }""")



'''simple docstring'''
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    FalconForCausalLM,
		    FalconForQuestionAnswering,
		    FalconForSequenceClassification,
		    FalconForTokenClassification,
		    FalconModel,
		)
class   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=3       ,  UpperCamelCase_=7       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=False       ,  UpperCamelCase_=True       ,  UpperCamelCase_=99       ,  UpperCamelCase_=32       ,  UpperCamelCase_=5       ,  UpperCamelCase_=4       ,  UpperCamelCase_=37       ,  UpperCamelCase_="gelu"       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=512       ,  UpperCamelCase_=16       ,  UpperCamelCase_=2       ,  UpperCamelCase_=0.02       ,  UpperCamelCase_=3       ,  UpperCamelCase_=4       ,  UpperCamelCase_=None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					parent
							UpperCamelCase__  :Any				   =					batch_size
							UpperCamelCase__  :Tuple				   =					seq_length
							UpperCamelCase__  :str				   =					is_training
							UpperCamelCase__  :str				   =					use_input_mask
							UpperCamelCase__  :Union[str, Any]				   =					use_token_type_ids
							UpperCamelCase__  :int				   =					use_labels
							UpperCamelCase__  :Union[str, Any]				   =					vocab_size
							UpperCamelCase__  :int				   =					hidden_size
							UpperCamelCase__  :Union[str, Any]				   =					num_hidden_layers
							UpperCamelCase__  :List[Any]				   =					num_attention_heads
							UpperCamelCase__  :int				   =					intermediate_size
							UpperCamelCase__  :str				   =					hidden_act
							UpperCamelCase__  :List[str]				   =					hidden_dropout_prob
							UpperCamelCase__  :Tuple				   =					attention_probs_dropout_prob
							UpperCamelCase__  :str				   =					max_position_embeddings
							UpperCamelCase__  :List[Any]				   =					type_vocab_size
							UpperCamelCase__  :Any				   =					type_sequence_label_size
							UpperCamelCase__  :Dict				   =					initializer_range
							UpperCamelCase__  :List[str]				   =					num_labels
							UpperCamelCase__  :Tuple				   =					num_choices
							UpperCamelCase__  :List[Any]				   =					scope




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)

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

							UpperCamelCase__  :Optional[Any]				   =					None

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

							UpperCamelCase__  :Any				   =					self.get_config()

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return FalconConfig(
							    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       ,  is_decoder=UpperCamelCase_       ,  initializer_range=self.initializer_range       ,  pad_token_id=1       ,  new_decoder_architecture=UpperCamelCase_       ,  )




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					FalconModel(config=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :Dict				   =					model(UpperCamelCase_       ,  attention_mask=UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					model(UpperCamelCase_				)
							self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					True
							UpperCamelCase__  :Dict				   =					FalconModel(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :Optional[Any]				   =					model(
							    UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  encoder_attention_mask=UpperCamelCase_       ,  )
							UpperCamelCase__  :List[str]				   =					model(
							    UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  )
							UpperCamelCase__  :str				   =					model(UpperCamelCase_       ,  attention_mask=UpperCamelCase_				)
							self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					FalconForCausalLM(config=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :Any				   =					model(UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  labels=UpperCamelCase_				)
							self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					True
							UpperCamelCase__  :Tuple				   =					True
							UpperCamelCase__  :Any				   =					FalconForCausalLM(config=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							# first forward pass
							UpperCamelCase__  :Dict				   =					model(
							    UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  encoder_attention_mask=UpperCamelCase_       ,  use_cache=UpperCamelCase_       ,  )
							UpperCamelCase__  :List[Any]				   =					outputs.past_key_values

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

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

							UpperCamelCase__  :Any				   =					model(
							    UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  encoder_attention_mask=UpperCamelCase_       ,  output_hidden_states=UpperCamelCase_       ,  )['''hidden_states'''][0]
							UpperCamelCase__  :Optional[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
							UpperCamelCase__  :List[Any]				   =					ids_tensor((1,)       ,  output_from_past.shape[-1]				).item()
							UpperCamelCase__  :Optional[int]				   =					output_from_no_past[:, -3:, random_slice_idx].detach()
							UpperCamelCase__  :Tuple				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					self.prepare_config_and_inputs()
							(
							    (
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							
							)  :Optional[int]				   =					config_and_inputs
							UpperCamelCase__  :Optional[int]				   =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict



@require_torch
class   lowercase      (				A__      ,						A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							(
		    (
		        FalconModel,
		        FalconForCausalLM,
		        FalconForSequenceClassification,
		        FalconForTokenClassification,
		        FalconForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		_a       					=							(FalconForCausalLM,) if is_torch_available() else ()
		_a       					=							(
		    {
		        'feature-extraction': FalconModel,
		        'text-classification': FalconForSequenceClassification,
		        'text-generation': FalconForCausalLM,
		        'question-answering': FalconForQuestionAnswering,
		        'token-classification': FalconForTokenClassification,
		        'zero-shot': FalconForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_a       					=							False
		_a       					=							False




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					FalconModelTester(self				)
							UpperCamelCase__  :List[Any]				   =					ConfigTester(self       ,  config_class=UpperCamelCase_       ,  hidden_size=37				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.config_tester.run_common_tests()




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							*UpperCamelCase__  :Union[str, Any]				   =					self.model_tester.prepare_config_and_inputs()
							for alibi in [True, False]:
												UpperCamelCase__  :Any				   =					alibi
												self.model_tester.create_and_check_model(UpperCamelCase_       ,  *UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :List[str]				   =					self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__  :str				   =					3
							UpperCamelCase__  :Any				   =					input_dict['''input_ids''']
							UpperCamelCase__  :Tuple				   =					input_ids.ne(1				).to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size				)
							UpperCamelCase__  :List[Any]				   =					FalconForSequenceClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :Dict				   =					model(UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  labels=UpperCamelCase_				)
							self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__  :List[str]				   =					3
							UpperCamelCase__  :str				   =					'''single_label_classification'''
							UpperCamelCase__  :Union[str, Any]				   =					input_dict['''input_ids''']
							UpperCamelCase__  :List[str]				   =					input_ids.ne(1				).to(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size				)
							UpperCamelCase__  :Dict				   =					FalconForSequenceClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :str				   =					model(UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  labels=UpperCamelCase_				)
							self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__  :int				   =					input_dict['''input_ids''']
							UpperCamelCase__  :List[str]				   =					FalconForCausalLM(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_       ,  use_cache=UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					input_ids.shape[0]
							UpperCamelCase__  :Optional[int]				   =					model._convert_to_rw_cache(result.past_key_values				)
							UpperCamelCase__  :List[Any]				   =					model._convert_cache_to_standard_format(UpperCamelCase_       ,  UpperCamelCase_				)
							for layer in range(len(UpperCamelCase_				)				):
												for tensor_idx in range(2				):
																	self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3				)
																	self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4				)
																	self.assertTrue(
																	    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]				)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__  :Optional[int]				   =					3
							UpperCamelCase__  :Union[str, Any]				   =					'''multi_label_classification'''
							UpperCamelCase__  :Dict				   =					input_dict['''input_ids''']
							UpperCamelCase__  :Tuple				   =					input_ids.ne(1				).to(UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]       ,  self.model_tester.type_sequence_label_size				).to(torch.float				)
							UpperCamelCase__  :Optional[Any]				   =					FalconForSequenceClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :str				   =					model(UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  labels=UpperCamelCase_				)
							self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for model_class in self.all_generative_model_classes:
												UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					self.model_tester.prepare_config_and_inputs_for_common()

												# If it doesn't support cache, pass the test
												if not hasattr(UpperCamelCase_       ,  '''use_cache'''				):
																	return

												UpperCamelCase__  :Tuple				   =					model_class(UpperCamelCase_				).to(UpperCamelCase_				)
												if "use_cache" not in inputs:
																	UpperCamelCase__  :Tuple				   =					True
												UpperCamelCase__  :List[str]				   =					model(**UpperCamelCase_				)

												# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
												if "past_key_values" not in outputs:
																	return

												UpperCamelCase__  :str				   =					(
												    getattr(UpperCamelCase_       ,  '''decoder_layers'''       ,  UpperCamelCase_				)
												    or getattr(UpperCamelCase_       ,  '''num_decoder_layers'''       ,  UpperCamelCase_				)
												    or config.num_hidden_layers
												)
												UpperCamelCase__  :List[Any]				   =					getattr(UpperCamelCase_       ,  '''num_kv_heads'''       ,  config.num_attention_heads				)
												UpperCamelCase__  :str				   =					getattr(UpperCamelCase_       ,  '''d_model'''       ,  config.hidden_size				)
												UpperCamelCase__  :Optional[Any]				   =					embed_dim // num_attention_heads

												UpperCamelCase__  :Optional[Any]				   =					outputs['''past_key_values''']
												self.assertEqual(len(UpperCamelCase_				)       ,  UpperCamelCase_				)

												UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					inputs['''input_ids'''].shape
												for i in range(UpperCamelCase_				):
																	if config.new_decoder_architecture:
																						UpperCamelCase__  :List[Any]				   =					config.num_attention_heads
																	elif config.multi_query:
																						UpperCamelCase__  :str				   =					1
																	self.assertEqual(len(past_kv[0]				)       ,  2				)  # K V for the decoder = 2
																	self.assertEqual(
																	    past_kv[i][0].shape       ,  (batch_size, num_attention_heads, seq_length, per_head_embed_dim)				)
																	self.assertEqual(
																	    past_kv[i][1].shape       ,  (batch_size, num_attention_heads, seq_length, per_head_embed_dim)				)



@require_torch
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b'''				)
							UpperCamelCase__  :List[Any]				   =					FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b'''				)
							model.eval()
							model.to(UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					tokenizer('''My favorite food is'''       ,  return_tensors='''pt'''				).to(UpperCamelCase_				)

							UpperCamelCase__  :Optional[Any]				   =					(
							    '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
							)

							UpperCamelCase__  :int				   =					model.generate(**UpperCamelCase_       ,  do_sample=UpperCamelCase_       ,  max_new_tokens=19				)
							UpperCamelCase__  :str				   =					tokenizer.batch_decode(UpperCamelCase_				)[0]

							self.assertEqual(UpperCamelCase_       ,  UpperCamelCase_				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
												UpperCamelCase__  :Any				   =					AutoTokenizer.from_pretrained(UpperCamelCase_				)
												UpperCamelCase__  :Dict				   =					FalconForCausalLM.from_pretrained(UpperCamelCase_				)
												model.eval()
												model.to(UpperCamelCase_				)
												UpperCamelCase__  :str				   =					tokenizer('''My favorite food is'''       ,  return_tensors='''pt'''				).to(UpperCamelCase_				)

												# We just test that these run without errors - the models are randomly initialized
												# and so the actual text outputs will be garbage
												model.generate(**UpperCamelCase_       ,  do_sample=UpperCamelCase_       ,  max_new_tokens=4				)
												model.generate(**UpperCamelCase_       ,  do_sample=UpperCamelCase_       ,  max_new_tokens=4				)
												model.generate(**UpperCamelCase_       ,  num_beams=2       ,  max_new_tokens=4				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							with torch.no_grad():
												for repo in [
												    "Rocketknight1/falcon-rw-1b",
												    "Rocketknight1/tiny-random-falcon-7b",
												    "Rocketknight1/tiny-random-falcon-40b",
												]:
																	UpperCamelCase__  :List[Any]				   =					AutoTokenizer.from_pretrained(UpperCamelCase_				)
																	UpperCamelCase__  :Union[str, Any]				   =					FalconForCausalLM.from_pretrained(UpperCamelCase_				)
																	model.eval()
																	model.to(device=UpperCamelCase_				)
																	UpperCamelCase__  :Tuple				   =					tokenizer('''My favorite food is'''       ,  return_tensors='''pt'''				).to(UpperCamelCase_				)

																	# Test results are the same with and without cache
																	UpperCamelCase__  :Union[str, Any]				   =					model.generate(**UpperCamelCase_       ,  do_sample=UpperCamelCase_       ,  max_new_tokens=20       ,  use_cache=UpperCamelCase_				)
																	UpperCamelCase__  :Any				   =					model.generate(**UpperCamelCase_       ,  do_sample=UpperCamelCase_       ,  max_new_tokens=20       ,  use_cache=UpperCamelCase_				)
																	self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0				)