



"""simple docstring"""
from math import pow, sqrt

def       _snake_case						(						*UpperCamelCase						:		float						):
 UpperCAmelCase	:					str	=	len(UpperCamelCase						) > 0 and all(value > 0.0 for value in values						)
 return result

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 return (
     round(sqrt(molar_mass_a / molar_mass_a						)			,		6						)
     if validate(UpperCamelCase			,		UpperCamelCase						)
     else ValueError("""Input Error: Molar mass values must greater than 0."""						)
 )

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 return (
     round(effusion_rate * sqrt(molar_mass_a / molar_mass_a						)			,		6						)
     if validate(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""						)
 )

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 return (
     round(effusion_rate / sqrt(molar_mass_a / molar_mass_a						)			,		6						)
     if validate(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""						)
 )

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 return (
     round(molar_mass / pow(effusion_rate_a / effusion_rate_a			,		2						)			,		6						)
     if validate(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""						)
 )

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 return (
     round(pow(effusion_rate_a / effusion_rate_a			,		2						) / molar_mass			,		6						)
     if validate(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""						)
 )






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        lowerCamelCase_							:


 '''simple docstring'''







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



 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_input_mask:
             SCREAMING_SNAKE_CASE_							=			random_attention_mask([self.batch_size, self.seq_length]						)

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_token_type_ids:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size						)

       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       if self.use_labels:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.type_sequence_label_size						)
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels						)
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.num_choices						)

       SCREAMING_SNAKE_CASE_							=			self.get_config()

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



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       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       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			LlamaModel(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



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



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				):
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)						)



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		List[str]				,				):
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()

       # first forward pass
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				use_cache=_lowerCAmelCase				,				)
       SCREAMING_SNAKE_CASE_							=			outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE_							=			ids_tensor((self.batch_size, 3)				,				config.vocab_size						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor((self.batch_size, 3)				,				vocab_size=2						)

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

       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				output_hidden_states=_lowerCAmelCase				,				)['hidden_states'][0]
       SCREAMING_SNAKE_CASE_							=			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
       SCREAMING_SNAKE_CASE_							=			ids_tensor((1,)				,				output_from_past.shape[-1]						).item()
       SCREAMING_SNAKE_CASE_							=			output_from_no_past[:, -3:, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, 
       )							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict





@require_torch
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







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



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			LlamaModelTester(self						)
       SCREAMING_SNAKE_CASE_							=			ConfigTester(self				,				config_class=_lowerCAmelCase				,				hidden_size=37						)



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



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             SCREAMING_SNAKE_CASE_							=			type
             self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.model_tester.batch_size]				,				self.model_tester.type_sequence_label_size						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.assertEqual(result.logits.shape				,				(self.model_tester.batch_size, self.model_tester.num_labels)						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			'single_label_classification'
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.model_tester.batch_size]				,				self.model_tester.type_sequence_label_size						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.assertEqual(result.logits.shape				,				(self.model_tester.batch_size, self.model_tester.num_labels)						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			'multi_label_classification'
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor(
           [self.model_tester.batch_size, config.num_labels]				,				self.model_tester.type_sequence_label_size						).to(torch.float						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		int						):
       pass




 @parameterized.expand([('linear',), ('dynamic',)]						)
 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			ids_tensor([1, 10]				,				config.vocab_size						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([1, int(config.max_position_embeddings * 1.5						)]				,				config.vocab_size						)

       set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       original_model.to(_lowerCAmelCase						)
       original_model.eval()
       SCREAMING_SNAKE_CASE_							=			original_model(_lowerCAmelCase						).last_hidden_state
       SCREAMING_SNAKE_CASE_							=			original_model(_lowerCAmelCase						).last_hidden_state

       set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE_							=			{'type': scaling_type, 'factor': 10.0}
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       scaled_model.to(_lowerCAmelCase						)
       scaled_model.eval()
       SCREAMING_SNAKE_CASE_							=			scaled_model(_lowerCAmelCase						).last_hidden_state
       SCREAMING_SNAKE_CASE_							=			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        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor([input_ids]						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]						)
       # fmt: on
       torch.testing.assert_close(out[0, 0, :30]				,				_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       lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]						)
       # fmt: on
       torch.testing.assert_close(out[0, 0, :30]				,				_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       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]						)
       # 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       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)

       SCREAMING_SNAKE_CASE_							=			torch.tensor(
           [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]				,				dtype=torch.floataa						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]						)
       # fmt: on
       torch.testing.assert_close(out[0, 0, :30]				,				_lowerCAmelCase				,				atol=1E-5				,				rtol=1E-5						)




 @unittest.skip('Model is curently gated'						)
 @slow
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			'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'
       SCREAMING_SNAKE_CASE_							=			'Simply put, the theory of relativity states that '
       SCREAMING_SNAKE_CASE_							=			LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'						)
       SCREAMING_SNAKE_CASE_							=			tokenizer.encode(_lowerCAmelCase				,				return_tensors='pt'						)
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained(
           'meta-llama/Llama-2-13b-chat-hf'				,				device_map='sequential'				,				use_safetensors=_lowerCAmelCase						)

       # greedy generation outputs
       SCREAMING_SNAKE_CASE_							=			model.generate(_lowerCAmelCase				,				max_new_tokens=64				,				top_p=_lowerCAmelCase				,				temperature=1				,				do_sample=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			tokenizer.decode(generated_ids[0]				,				skip_special_tokens=_lowerCAmelCase						)
       self.assertEqual(_lowerCAmelCase				,				_lowerCAmelCase						)