'''simple docstring'''


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     A :





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





							def    snake_case__    (   self					:				Optional[int]						)				->						Optional[Any]:
								__UpperCAmelCase      					=      ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size						)

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

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

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

								__UpperCAmelCase      					=      self.get_config()

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





							def    snake_case__    (   self					:				Optional[Any]						)				->						Any:
								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=__SCREAMING_SNAKE_CASE  ,		initializer_range=self.initializer_range  ,		)





							def    snake_case__    (   self					:				Tuple  ,		__a					:				Union[str, Any]  ,		__a					:				Union[str, Any]  ,		__a					:				Optional[int]  ,		__a					:				str  ,		__a					:				Any  ,		__a					:				Union[str, Any]  ,		__a					:				int						)				->						Dict:
								__UpperCAmelCase      					=      LlamaModel(config=__SCREAMING_SNAKE_CASE						)
								model.to(__SCREAMING_SNAKE_CASE						)
								model.eval()
								__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE						)
								self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)						)





							def    snake_case__    (   self					:				str  ,		__a					:				Any  ,		__a					:				Optional[Any]  ,		__a					:				Optional[int]  ,		__a					:				Dict  ,		__a					:				Optional[Any]  ,		__a					:				Optional[Any]  ,		__a					:				Optional[int]  ,		__a					:				Tuple  ,		__a					:				Tuple  ,		)				->						Any:
								__UpperCAmelCase      					=      True
								__UpperCAmelCase      					=      LlamaModel(__SCREAMING_SNAKE_CASE						)
								model.to(__SCREAMING_SNAKE_CASE						)
								model.eval()
								__UpperCAmelCase      					=      model(
								    __SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE  ,		encoder_hidden_states=__SCREAMING_SNAKE_CASE  ,		encoder_attention_mask=__SCREAMING_SNAKE_CASE  ,		)
								__UpperCAmelCase      					=      model(
								    __SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE  ,		encoder_hidden_states=__SCREAMING_SNAKE_CASE  ,		)
								__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE						)
								self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)						)





							def    snake_case__    (   self					:				Any  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]  ,		__a					:				str  ,		__a					:				Any  ,		__a					:				Dict  ,		__a					:				Optional[Any]  ,		__a					:				Optional[int]  ,		__a					:				str  ,		__a					:				Dict  ,		)				->						List[str]:
								__UpperCAmelCase      					=      LlamaForCausalLM(config=__SCREAMING_SNAKE_CASE						)
								model.to(__SCREAMING_SNAKE_CASE						)
								model.eval()
								__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE  ,		labels=__SCREAMING_SNAKE_CASE						)
								self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size)						)





							def    snake_case__    (   self					:				Dict  ,		__a					:				List[str]  ,		__a					:				Union[str, Any]  ,		__a					:				Optional[int]  ,		__a					:				Tuple  ,		__a					:				Optional[Any]  ,		__a					:				str  ,		__a					:				Union[str, Any]  ,		__a					:				str  ,		__a					:				Union[str, Any]  ,		)				->						Optional[int]:
								__UpperCAmelCase      					=      True
								__UpperCAmelCase      					=      True
								__UpperCAmelCase      					=      LlamaForCausalLM(config=__SCREAMING_SNAKE_CASE						)
								model.to(__SCREAMING_SNAKE_CASE						)
								model.eval()

								# first forward pass
								__UpperCAmelCase      					=      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  ,		)
								__UpperCAmelCase      					=      outputs.past_key_values

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

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

								__UpperCAmelCase      					=      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]
								__UpperCAmelCase      					=      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
								__UpperCAmelCase      					=      ids_tensor((1,)  ,		output_from_past.shape[-1]						).item()
								__UpperCAmelCase      					=      output_from_no_past[:, -3:, random_slice_idx].detach()
								__UpperCAmelCase      					=      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    snake_case__    (   self					:				Tuple						)				->						Dict:
								__UpperCAmelCase      					=      self.prepare_config_and_inputs()
								(
								    (
								    __UpperCAmelCase     
								)					,      (
								    __UpperCAmelCase     
								)					,      (
								    __UpperCAmelCase     
								)					,      (
								    __UpperCAmelCase     
								)					,      (
								    __UpperCAmelCase     
								)					,      (
								    __UpperCAmelCase     
								)					,      (
								    __UpperCAmelCase     
								)					,           
								)      					=      config_and_inputs
								__UpperCAmelCase      					=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict






@require_torch
class     A ( _lowercase							,						_lowercase							,						_lowercase							,						unittest.TestCase ):
							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    snake_case__    (   self					:				Dict						)				->						Any:
								__UpperCAmelCase      					=      LlamaModelTester(self						)
								__UpperCAmelCase      					=      ConfigTester(self  ,		config_class=__SCREAMING_SNAKE_CASE  ,		hidden_size=3_7						)





							def    snake_case__    (   self					:				Tuple						)				->						List[Any]:
								self.config_tester.run_common_tests()





							def    snake_case__    (   self					:				Dict						)				->						Optional[Any]:
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)





							def    snake_case__    (   self					:				Tuple						)				->						Any:
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									__UpperCAmelCase      					=      type
									self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)





							def    snake_case__    (   self					:				Tuple						)				->						int:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCAmelCase      					=      3
								__UpperCAmelCase      					=      input_dict['''input_ids''']
								__UpperCAmelCase      					=      input_ids.ne(1						).to(__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      ids_tensor([self.model_tester.batch_size]  ,		self.model_tester.type_sequence_label_size						)
								__UpperCAmelCase      					=      LlamaForSequenceClassification(__SCREAMING_SNAKE_CASE						)
								model.to(__SCREAMING_SNAKE_CASE						)
								model.eval()
								__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE  ,		labels=__SCREAMING_SNAKE_CASE						)
								self.assertEqual(result.logits.shape  ,		(self.model_tester.batch_size, self.model_tester.num_labels)						)





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





							def    snake_case__    (   self					:				int						)				->						int:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCAmelCase      					=      3
								__UpperCAmelCase      					=      '''multi_label_classification'''
								__UpperCAmelCase      					=      input_dict['''input_ids''']
								__UpperCAmelCase      					=      input_ids.ne(1						).to(__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]  ,		self.model_tester.type_sequence_label_size						).to(torch.float						)
								__UpperCAmelCase      					=      LlamaForSequenceClassification(__SCREAMING_SNAKE_CASE						)
								model.to(__SCREAMING_SNAKE_CASE						)
								model.eval()
								__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE  ,		labels=__SCREAMING_SNAKE_CASE						)
								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    snake_case__    (   self					:				List[str]						)				->						List[str]:
								pass






							@parameterized.expand([('''linear''',), ('''dynamic''',)]						)
							def    snake_case__    (   self					:				str  ,		__a					:				Union[str, Any]						)				->						Optional[int]:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCAmelCase      					=      ids_tensor([1, 1_0]  ,		config.vocab_size						)
								__UpperCAmelCase      					=      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
								__UpperCAmelCase      					=      LlamaModel(__SCREAMING_SNAKE_CASE						)
								original_model.to(__SCREAMING_SNAKE_CASE						)
								original_model.eval()
								__UpperCAmelCase      					=      original_model(__SCREAMING_SNAKE_CASE						).last_hidden_state
								__UpperCAmelCase      					=      original_model(__SCREAMING_SNAKE_CASE						).last_hidden_state

								set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
								__UpperCAmelCase      					=      {'''type''': scaling_type, '''factor''': 1_0.0}
								__UpperCAmelCase      					=      LlamaModel(__SCREAMING_SNAKE_CASE						)
								scaled_model.to(__SCREAMING_SNAKE_CASE						)
								scaled_model.eval()
								__UpperCAmelCase      					=      scaled_model(__SCREAMING_SNAKE_CASE						).last_hidden_state
								__UpperCAmelCase      					=      scaled_model(__SCREAMING_SNAKE_CASE						).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(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-5						)						)
								else:
									self.assertFalse(torch.allclose(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-5						)						)

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






@require_torch
class     A ( unittest.TestCase ):





							@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''						)
							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						Any:
								__UpperCAmelCase      					=      [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]
								__UpperCAmelCase      					=      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''  ,		device_map='''auto'''						)
								__UpperCAmelCase      					=      model(torch.tensor([input_ids]						)						)
								# Expected mean on dim = -1
								__UpperCAmelCase      					=      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						)  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-2  ,		rtol=1e-2						)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								__UpperCAmelCase      					=      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]  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-5  ,		rtol=1e-5						)





							@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''						)
							@slow
							def    snake_case__    (   self					:				Any						)				->						Optional[int]:
								__UpperCAmelCase      					=      [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]
								__UpperCAmelCase      					=      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''  ,		device_map='''auto'''						)
								__UpperCAmelCase      					=      model(torch.tensor(__SCREAMING_SNAKE_CASE						)						)
								# Expected mean on dim = -1
								__UpperCAmelCase      					=      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						)  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-2  ,		rtol=1e-2						)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								__UpperCAmelCase      					=      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]  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-5  ,		rtol=1e-5						)





							@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''						)
							@slow
							def    snake_case__    (   self					:				Optional[int]						)				->						str:
								__UpperCAmelCase      					=      [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]
								__UpperCAmelCase      					=      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''  ,		device_map='''auto'''						)
								__UpperCAmelCase      					=      model(torch.tensor(__SCREAMING_SNAKE_CASE						)						)
								# Expected mean on dim = -1
								__UpperCAmelCase      					=      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						)  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-2  ,		rtol=1e-2						)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								__UpperCAmelCase      					=      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						)  ,		__SCREAMING_SNAKE_CASE  ,		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    snake_case__    (   self					:				int						)				->						Tuple:
								__UpperCAmelCase      					=      [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]
								__UpperCAmelCase      					=      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''  ,		device_map='''auto'''						)
								__UpperCAmelCase      					=      model(torch.tensor(__SCREAMING_SNAKE_CASE						)						)

								__UpperCAmelCase      					=      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						)  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-2  ,		rtol=1e-2						)
								# fmt: off
								__UpperCAmelCase      					=      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]  ,		__SCREAMING_SNAKE_CASE  ,		atol=1e-5  ,		rtol=1e-5						)






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

								# greedy generation outputs
								__UpperCAmelCase      					=      model.generate(__SCREAMING_SNAKE_CASE  ,		max_new_tokens=6_4  ,		top_p=__SCREAMING_SNAKE_CASE  ,		temperature=1  ,		do_sample=__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      tokenizer.decode(generated_ids[0]  ,		skip_special_tokens=__SCREAMING_SNAKE_CASE						)
								self.assertEqual(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE						)


"""simple docstring"""




from __future__ import annotations

import os
from collections.abc import Mapping

a		:       Optional[Any]     						=       tuple[int, int]







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			vertices
         a__		=			{
             (min(__SCREAMING_SNAKE_CASE   ), max(__SCREAMING_SNAKE_CASE   )): weight for edge, weight in edges.items()
         }



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         self.vertices.add(edge[0]   )
         self.vertices.add(edge[1]   )
         a__		=			weight




     def 			lowercase__  (							self   )		->     Graph:







         """simple docstring"""



         a__		=			Graph({min(self.vertices   )}     ,				{}   )
         a__		=			42
         a__		=			42
         a__		=			42
         a__		=			42

         while len(subgraph.vertices   ) < len(self.vertices   ):
             a__		=			max(self.edges.values()   ) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         a__		=			edge
                         a__		=			weight

             subgraph.add_edge(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         return subgraph




def     __magic_name__		(					UpperCamelCase     :		str = "p107_network.txt"     )     ->						int:
    a__		=			os.path.abspath(os.path.dirname(UpperCamelCase     )     )
    a__		=			os.path.join(UpperCamelCase							,		UpperCamelCase     )
    a__		=			{}
    a__		=			42
    a__		=			42
    a__		=			42

    with open(UpperCamelCase     ) as f:
        a__		=			f.read().strip().split('\n'     )

    a__		=			[line.split(','     ) for line in data]

    for edgea in range(1							,		len(UpperCamelCase     )     ):
        for edgea in range(UpperCamelCase     ):
            if adjaceny_matrix[edgea][edgea] != "-":
                a__		=			int(adjaceny_matrix[edgea][edgea]     )

    a__		=			Graph(set(range(len(UpperCamelCase     )     )     )							,		UpperCamelCase     )

    a__		=			graph.prims_algorithm()

    a__		=			sum(graph.edges.values()     )
    a__		=			sum(subgraph.edges.values()     )

    return initial_total - optimal_total


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


