

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     lowercase_			:




  """simple docstring"""




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






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

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

   __lowercase													=				self.get_config()

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






  def       SCREAMING_SNAKE_CASE   (   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=lowercase__      ,initializer_range=self.initializer_range      ,)






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






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






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






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Dict      ,lowercase__     :  Tuple      ,lowercase__     :  Dict      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Any      ,):
   __lowercase													=				True
   __lowercase													=				True
   __lowercase													=				LlamaForCausalLM(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()

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

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

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

   __lowercase													=				model(
       lowercase__      ,attention_mask=lowercase__      ,encoder_hidden_states=lowercase__      ,encoder_attention_mask=lowercase__      ,output_hidden_states=lowercase__      ,)['''hidden_states'''][0]
   __lowercase													=				model(
       lowercase__      ,attention_mask=lowercase__      ,encoder_hidden_states=lowercase__      ,encoder_attention_mask=lowercase__      ,past_key_values=lowercase__      ,output_hidden_states=lowercase__      ,)['''hidden_states'''][0]

   # select random slice
   __lowercase													=				ids_tensor((1,)      ,output_from_past.shape[-1]    ).item()
   __lowercase													=				output_from_no_past[:, -3:, random_slice_idx].detach()
   __lowercase													=				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(lowercase__      ,lowercase__      ,atol=1e-3    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase  
   )						,			(
       __lowercase  
   )						,			(
       __lowercase  
   )						,			(
       __lowercase  
   )						,			(
       __lowercase  
   )						,			(
       __lowercase  
   )						,			(
       __lowercase  
   )						,			  
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Dict           =    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						List[Any]           =    (LlamaForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						int           =    (
      {
          """feature-extraction""": LlamaModel,
          """text-classification""": LlamaForSequenceClassification,
          """text-generation""": LlamaForCausalLM,
          """zero-shot""": LlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						List[str]           =    False
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				LlamaModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase													=				3
   __lowercase													=				input_dict['''input_ids''']
   __lowercase													=				input_ids.ne(1    ).to(lowercase__    )
   __lowercase													=				ids_tensor([self.model_tester.batch_size]      ,self.model_tester.type_sequence_label_size    )
   __lowercase													=				LlamaForSequenceClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.assertEqual(result.logits.shape      ,(self.model_tester.batch_size, self.model_tester.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase													=				3
   __lowercase													=				'''single_label_classification'''
   __lowercase													=				input_dict['''input_ids''']
   __lowercase													=				input_ids.ne(1    ).to(lowercase__    )
   __lowercase													=				ids_tensor([self.model_tester.batch_size]      ,self.model_tester.type_sequence_label_size    )
   __lowercase													=				LlamaForSequenceClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.assertEqual(result.logits.shape      ,(self.model_tester.batch_size, self.model_tester.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase													=				3
   __lowercase													=				'''multi_label_classification'''
   __lowercase													=				input_dict['''input_ids''']
   __lowercase													=				input_ids.ne(1    ).to(lowercase__    )
   __lowercase													=				ids_tensor(
       [self.model_tester.batch_size, config.num_labels]      ,self.model_tester.type_sequence_label_size    ).to(torch.float    )
   __lowercase													=				LlamaForSequenceClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   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       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   pass






  @parameterized.expand([('''linear''',), ('''dynamic''',)]    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  str    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase													=				ids_tensor([1, 1_0]      ,config.vocab_size    )
   __lowercase													=				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
   __lowercase													=				LlamaModel(lowercase__    )
   original_model.to(lowercase__    )
   original_model.eval()
   __lowercase													=				original_model(lowercase__    ).last_hidden_state
   __lowercase													=				original_model(lowercase__    ).last_hidden_state

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

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






@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




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






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






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

   __lowercase													=				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    )      ,lowercase__      ,atol=1e-2      ,rtol=1e-2    )
   # fmt: off
   __lowercase													=				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]      ,lowercase__      ,atol=1e-5      ,rtol=1e-5    )






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

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




'''simple docstring'''






from scipy.stats import spearmanr

import datasets


lowerCAmelCase__      		=			'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

lowerCAmelCase__      		=			'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

lowerCAmelCase__      		=			R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       , _KWARGS_DESCRIPTION   )
class     lowercase_			(datasets.Metric   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return datasets.MetricInfo(
       description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
           {
               '''predictions''': datasets.Value('''float'''    ),
               '''references''': datasets.Value('''float'''    ),
           }    )      ,reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html''']      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=False    ):
   __lowercase													=				spearmanr(lowercase__      ,lowercase__    )
   if return_pvalue:
    return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
    return {"spearmanr": results[0]}

