






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






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



					def 	_lowercase					( self					:  Union[str, Any]	):
												snake_case__			:				str       =  ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size	)

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

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

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

												snake_case__			:				List[Any]       =  self.get_config()

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



					def 	_lowercase					( self					:  Tuple	):
												return LlamaConfig(
												    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=lowerCamelCase_							,		initializer_range=self.initializer_range							,		)



					def 	_lowercase					( self					:  int							,		__A					:  List[str]							,		__A					:  List[Any]							,		__A					:  Optional[int]							,		__A					:  List[str]							,		__A					:  List[str]							,		__A					:  Optional[int]							,		__A					:  Optional[int]	):
												snake_case__			:				List[Any]       =  LlamaModel(config=lowerCamelCase_	)
												model.to(lowerCamelCase_	)
												model.eval()
												snake_case__			:				Dict       =  model(lowerCamelCase_							,		attention_mask=lowerCamelCase_	)
												snake_case__			:				Optional[Any]       =  model(lowerCamelCase_	)
												self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  int							,		__A					:  Tuple							,		__A					:  Optional[Any]							,		__A					:  Dict							,		__A					:  Any							,		__A					:  Optional[Any]							,		__A					:  str							,		__A					:  Optional[Any]							,		__A					:  Tuple							,		):
												snake_case__			:				Any       =  True
												snake_case__			:				List[str]       =  LlamaModel(lowerCamelCase_	)
												model.to(lowerCamelCase_	)
												model.eval()
												snake_case__			:				str       =  model(
												    lowerCamelCase_							,		attention_mask=lowerCamelCase_							,		encoder_hidden_states=lowerCamelCase_							,		encoder_attention_mask=lowerCamelCase_							,		)
												snake_case__			:				Union[str, Any]       =  model(
												    lowerCamelCase_							,		attention_mask=lowerCamelCase_							,		encoder_hidden_states=lowerCamelCase_							,		)
												snake_case__			:				str       =  model(lowerCamelCase_							,		attention_mask=lowerCamelCase_	)
												self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Union[str, Any]							,		__A					:  Any							,		__A					:  Dict							,		__A					:  Union[str, Any]							,		__A					:  List[str]							,		__A					:  Tuple							,		__A					:  int							,		__A					:  Optional[Any]							,		__A					:  List[str]							,		):
												snake_case__			:				Optional[Any]       =  LlamaForCausalLM(config=lowerCamelCase_	)
												model.to(lowerCamelCase_	)
												model.eval()
												snake_case__			:				Optional[Any]       =  model(lowerCamelCase_							,		attention_mask=lowerCamelCase_							,		labels=lowerCamelCase_	)
												self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size)	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  Dict							,		__A					:  str							,		__A					:  Dict							,		__A					:  Tuple							,		__A					:  str							,		__A					:  Optional[Any]							,		__A					:  List[str]							,		__A					:  List[str]							,		__A					:  Any							,		):
												snake_case__			:				str       =  True
												snake_case__			:				str       =  True
												snake_case__			:				Tuple       =  LlamaForCausalLM(config=lowerCamelCase_	)
												model.to(lowerCamelCase_	)
												model.eval()

												# first forward pass
												snake_case__			:				Optional[int]       =  model(
												    lowerCamelCase_							,		attention_mask=lowerCamelCase_							,		encoder_hidden_states=lowerCamelCase_							,		encoder_attention_mask=lowerCamelCase_							,		use_cache=lowerCamelCase_							,		)
												snake_case__			:				Dict       =  outputs.past_key_values

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

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

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

												# select random slice
												snake_case__			:				Union[str, Any]       =  ids_tensor((1,)							,		output_from_past.shape[-1]	).item()
												snake_case__			:				Optional[int]       =  output_from_no_past[:, -3:, random_slice_idx].detach()
												snake_case__			:				Optional[int]       =  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(lowerCamelCase_							,		lowerCamelCase_							,		atol=1e-3	)	)






					def 	_lowercase					( self					:  int	):
												snake_case__			:				Any       =  self.prepare_config_and_inputs()
												(
												    (
												    snake_case__	
												),						(
												    snake_case__	
												),						(
												    snake_case__	
												),						(
												    snake_case__	
												),						(
												    snake_case__	
												),						(
												    snake_case__	
												),						(
												    snake_case__	
												),							
												)			:				Tuple       =  config_and_inputs
												snake_case__			:				int       =  {"input_ids": input_ids, "attention_mask": input_mask}
												return config, inputs_dict



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
					a_     =		(LlamaForCausalLM,) if is_torch_available() else ()
					a_     =		(
					    {
					        "feature-extraction": LlamaModel,
					        "text-classification": LlamaForSequenceClassification,
					        "text-generation": LlamaForCausalLM,
					        "zero-shot": LlamaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a_     =		False
					a_     =		False



					def 	_lowercase					( self					:  Dict	):
												snake_case__			:				str       =  LlamaModelTester(self	)
												snake_case__			:				Union[str, Any]       =  ConfigTester(self							,		config_class=lowerCamelCase_							,		hidden_size=3_7	)



					def 	_lowercase					( self					:  List[Any]	):
												self.config_tester.run_common_tests()



					def 	_lowercase					( self					:  Tuple	):
												snake_case__			:				List[Any]       =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowerCamelCase_	)



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



					def 	_lowercase					( self					:  int	):
												snake_case__,						snake_case__			:				List[str]       =  self.model_tester.prepare_config_and_inputs_for_common()
												snake_case__			:				int       =  3
												snake_case__			:				Optional[int]       =  input_dict["input_ids"]
												snake_case__			:				Tuple       =  input_ids.ne(1	).to(lowerCamelCase_	)
												snake_case__			:				str       =  ids_tensor([self.model_tester.batch_size]							,		self.model_tester.type_sequence_label_size	)
												snake_case__			:				Tuple       =  LlamaForSequenceClassification(lowerCamelCase_	)
												model.to(lowerCamelCase_	)
												model.eval()
												snake_case__			:				List[str]       =  model(lowerCamelCase_							,		attention_mask=lowerCamelCase_							,		labels=lowerCamelCase_	)
												self.assertEqual(result.logits.shape							,		(self.model_tester.batch_size, self.model_tester.num_labels)	)



					def 	_lowercase					( self					:  List[Any]	):
												snake_case__,						snake_case__			:				str       =  self.model_tester.prepare_config_and_inputs_for_common()
												snake_case__			:				Optional[int]       =  3
												snake_case__			:				Any       =  "single_label_classification"
												snake_case__			:				Dict       =  input_dict["input_ids"]
												snake_case__			:				List[str]       =  input_ids.ne(1	).to(lowerCamelCase_	)
												snake_case__			:				Tuple       =  ids_tensor([self.model_tester.batch_size]							,		self.model_tester.type_sequence_label_size	)
												snake_case__			:				List[Any]       =  LlamaForSequenceClassification(lowerCamelCase_	)
												model.to(lowerCamelCase_	)
												model.eval()
												snake_case__			:				Dict       =  model(lowerCamelCase_							,		attention_mask=lowerCamelCase_							,		labels=lowerCamelCase_	)
												self.assertEqual(result.logits.shape							,		(self.model_tester.batch_size, self.model_tester.num_labels)	)



					def 	_lowercase					( self					:  Union[str, Any]	):
												snake_case__,						snake_case__			:				int       =  self.model_tester.prepare_config_and_inputs_for_common()
												snake_case__			:				List[Any]       =  3
												snake_case__			:				Tuple       =  "multi_label_classification"
												snake_case__			:				Any       =  input_dict["input_ids"]
												snake_case__			:				Optional[Any]       =  input_ids.ne(1	).to(lowerCamelCase_	)
												snake_case__			:				List[str]       =  ids_tensor(
												    [self.model_tester.batch_size, config.num_labels]							,		self.model_tester.type_sequence_label_size	).to(torch.float	)
												snake_case__			:				List[str]       =  LlamaForSequenceClassification(lowerCamelCase_	)
												model.to(lowerCamelCase_	)
												model.eval()
												snake_case__			:				str       =  model(lowerCamelCase_							,		attention_mask=lowerCamelCase_							,		labels=lowerCamelCase_	)
												self.assertEqual(result.logits.shape							,		(self.model_tester.batch_size, self.model_tester.num_labels)	)



					@unittest.skip("LLaMA buffers include complex numbers, which breaks this test"	)
					def 	_lowercase					( self					:  Tuple	):
												pass






					@parameterized.expand([("linear",), ("dynamic",)]	)
					def 	_lowercase					( self					:  Any							,		__A					:  str	):
												snake_case__,						snake_case__			:				Any       =  self.model_tester.prepare_config_and_inputs_for_common()
												snake_case__			:				int       =  ids_tensor([1, 1_0]							,		config.vocab_size	)
												snake_case__			:				Dict       =  ids_tensor([1, int(config.max_position_embeddings * 1.5	)]							,		config.vocab_size	)

												set_seed(4_2	)  # Fixed seed at init time so the two models get the same random weights
												snake_case__			:				Optional[Any]       =  LlamaModel(lowerCamelCase_	)
												original_model.to(lowerCamelCase_	)
												original_model.eval()
												snake_case__			:				Dict       =  original_model(lowerCamelCase_	).last_hidden_state
												snake_case__			:				Any       =  original_model(lowerCamelCase_	).last_hidden_state

												set_seed(4_2	)  # Fixed seed at init time so the two models get the same random weights
												snake_case__			:				List[Any]       =  {"type": scaling_type, "factor": 1_0.0}
												snake_case__			:				Optional[Any]       =  LlamaModel(lowerCamelCase_	)
												scaled_model.to(lowerCamelCase_	)
												scaled_model.eval()
												snake_case__			:				Any       =  scaled_model(lowerCamelCase_	).last_hidden_state
												snake_case__			:				Optional[int]       =  scaled_model(lowerCamelCase_	).last_hidden_state

												# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
												# maximum sequence length, so the outputs for the short input should match.
												if scaling_type == "dynamic":
																			self.assertTrue(torch.allclose(lowerCamelCase_							,		lowerCamelCase_							,		atol=1e-5	)	)
												else:
																			self.assertFalse(torch.allclose(lowerCamelCase_							,		lowerCamelCase_							,		atol=1e-5	)	)

												# The output should be different for long inputs
												self.assertFalse(torch.allclose(lowerCamelCase_							,		lowerCamelCase_							,		atol=1e-5	)	)



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"	)
					@slow
					def 	_lowercase					( self					:  Optional[Any]	):
												snake_case__			:				str       =  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
												snake_case__			:				Any       =  LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"							,		device_map="auto"	)
												snake_case__			:				Any       =  model(torch.tensor([input_ids]	)	)
												# Expected mean on dim = -1
												snake_case__			:				Union[str, Any]       =  torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]	)
												torch.testing.assert_close(out.mean(-1	)							,		lowerCamelCase_							,		atol=1e-2							,		rtol=1e-2	)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												snake_case__			:				Union[str, Any]       =  torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]	)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :3_0]							,		lowerCamelCase_							,		atol=1e-5							,		rtol=1e-5	)



					@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"	)
					@slow
					def 	_lowercase					( self					:  List[str]	):
												snake_case__			:				Dict       =  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
												snake_case__			:				Union[str, Any]       =  LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"							,		device_map="auto"	)
												snake_case__			:				Optional[int]       =  model(torch.tensor(lowerCamelCase_	)	)
												# Expected mean on dim = -1
												snake_case__			:				int       =  torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]	)
												torch.testing.assert_close(out.mean(-1	)							,		lowerCamelCase_							,		atol=1e-2							,		rtol=1e-2	)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												snake_case__			:				List[str]       =  torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]	)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :3_0]							,		lowerCamelCase_							,		atol=1e-5							,		rtol=1e-5	)



					@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"	)
					@slow
					def 	_lowercase					( self					:  str	):
												snake_case__			:				int       =  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
												snake_case__			:				List[str]       =  LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"							,		device_map="auto"	)
												snake_case__			:				Dict       =  model(torch.tensor(lowerCamelCase_	)	)
												# Expected mean on dim = -1
												snake_case__			:				Any       =  torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]	)
												torch.testing.assert_close(out.mean(-1	)							,		lowerCamelCase_							,		atol=1e-2							,		rtol=1e-2	)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												snake_case__			:				Optional[int]       =  torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]	)
												# fmt: on
												torch.testing.assert_close(out.mean(-1	)							,		lowerCamelCase_							,		atol=1e-2							,		rtol=1e-2	)



					@unittest.skip(
					    "Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"	)
					@slow
					def 	_lowercase					( self					:  str	):
												snake_case__			:				List[Any]       =  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
												snake_case__			:				Tuple       =  LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"							,		device_map="auto"	)
												snake_case__			:				Union[str, Any]       =  model(torch.tensor(lowerCamelCase_	)	)

												snake_case__			:				Dict       =  torch.tensor(
												    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]							,		dtype=torch.floataa	)
												torch.testing.assert_close(out.mean(-1	)							,		lowerCamelCase_							,		atol=1e-2							,		rtol=1e-2	)
												# fmt: off
												snake_case__			:				Union[str, Any]       =  torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]	)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :3_0]							,		lowerCamelCase_							,		atol=1e-5							,		rtol=1e-5	)






					@unittest.skip("Model is curently gated"	)
					@slow
					def 	_lowercase					( self					:  Optional[Any]	):
												snake_case__			:				Union[str, Any]       =  "Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
												snake_case__			:				Union[str, Any]       =  "Simply put, the theory of relativity states that "
												snake_case__			:				Union[str, Any]       =  LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"	)
												snake_case__			:				Dict       =  tokenizer.encode(lowerCamelCase_							,		return_tensors="pt"	)
												snake_case__			:				List[str]       =  LlamaForCausalLM.from_pretrained(
												    "meta-llama/Llama-2-13b-chat-hf"							,		device_map="sequential"							,		use_safetensors=lowerCamelCase_	)

												# greedy generation outputs
												snake_case__			:				Union[str, Any]       =  model.generate(lowerCamelCase_							,		max_new_tokens=6_4							,		top_p=lowerCamelCase_							,		temperature=1							,		do_sample=lowerCamelCase_	)
												snake_case__			:				int       =  tokenizer.decode(generated_ids[0]							,		skip_special_tokens=lowerCamelCase_	)
												self.assertEqual(lowerCamelCase_							,		lowerCamelCase_	)











import os

import pytest
from attr import dataclass


__lowerCamelCase :      Any										=				"""us-east-1"""  # defaults region



@dataclass
class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					a_     =		42
					a_     =		"arn:aws:iam::558105141721:role/sagemaker_execution_role"
					a_     =		{
					    "task_name": "mnli",
					    "per_device_train_batch_size": 1_6,
					    "per_device_eval_batch_size": 1_6,
					    "do_train": True,
					    "do_eval": True,
					    "do_predict": True,
					    "output_dir": "/opt/ml/model",
					    "overwrite_output_dir": True,
					    "max_steps": 5_0_0,
					    "save_steps": 5_5_0_0,
					}
					a_     =		{**hyperparameters, "max_steps": 1_0_0_0}



					@property
					def 	_lowercase					( self					:  List[Any]	):
						if self.framework == "pytorch":
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
							    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
							]
						else:
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
							    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
							]



					@property
					def 	_lowercase					( self					:  Any	):
						return f'''{self.framework}-transfromers-test'''



					@property
					def 	_lowercase					( self					:  Optional[Any]	):
						return f'''./tests/sagemaker/scripts/{self.framework}'''






					@property
					def 	_lowercase					( self					:  Union[str, Any]	):
						if self.framework == "pytorch":
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
						else:
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"       )
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]       ):
	snake_case__			:				Optional[int]       =  SageMakerTestEnvironment(framework=request.cls.framework       )



