
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Any			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Any=13			,							UpperCamelCase__			: str=7			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Optional[Any]=True			,							UpperCamelCase__			: Tuple=False			,							UpperCamelCase__			: int=True			,							UpperCamelCase__			: int=99			,							UpperCamelCase__			: List[Any]=32			,							UpperCamelCase__			: Tuple=5			,							UpperCamelCase__			: Optional[int]=4			,							UpperCamelCase__			: str=37			,							UpperCamelCase__			: Optional[Any]="gelu"			,							UpperCamelCase__			: Union[str, Any]=0.1			,							UpperCamelCase__			: Union[str, Any]=0.1			,							UpperCamelCase__			: str=512			,							UpperCamelCase__			: Optional[Any]=16			,							UpperCamelCase__			: str=2			,							UpperCamelCase__			: Optional[int]=0.02			,							UpperCamelCase__			: int=3			,							UpperCamelCase__			: Optional[int]=4			,							UpperCamelCase__			: str=None			,							)			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       seq_length
    __magic_name__				     =       is_training
    __magic_name__				     =       use_input_mask
    __magic_name__				     =       use_token_type_ids
    __magic_name__				     =       use_labels
    __magic_name__				     =       vocab_size
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       type_vocab_size
    __magic_name__				     =       type_sequence_label_size
    __magic_name__				     =       initializer_range
    __magic_name__				     =       num_labels
    __magic_name__				     =       num_choices
    __magic_name__				     =       scope
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size    )

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

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

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

    __magic_name__				     =       self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  def 							_lowercase							( self			: List[str]    )			->	str:







    """simple docstring"""






    return OpenLlamaConfig(
        vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							type_vocab_size=self.type_vocab_size			,							is_decoder=UpperCamelCase__			,							initializer_range=self.initializer_range			,							use_stable_embedding=UpperCamelCase__			,							)
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: List[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       OpenLlamaModel(config=UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: int			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: List[str]			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       True
    __magic_name__				     =       OpenLlamaModel(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							encoder_attention_mask=UpperCamelCase__			,							)
    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							)
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__    )
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Tuple			,							)			->	Dict:







    """simple docstring"""






    __magic_name__				     =       OpenLlamaForCausalLM(config=UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.vocab_size)    )
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: List[str]			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       True
    __magic_name__				     =       True
    __magic_name__				     =       OpenLlamaForCausalLM(config=UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()

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

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

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

    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							encoder_attention_mask=UpperCamelCase__			,							output_hidden_states=UpperCamelCase__			,							)["""hidden_states"""][0]
    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							encoder_attention_mask=UpperCamelCase__			,							past_key_values=UpperCamelCase__			,							output_hidden_states=UpperCamelCase__			,							)["""hidden_states"""][0]

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

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-3    )    )





  def 							_lowercase							( self			: Optional[Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       self.prepare_config_and_inputs()
    (
        (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    
    )				     =       config_and_inputs
    __magic_name__				     =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
    return config, inputs_dict







@require_torch
class        UpperCAmelCase_			( _A  ,       _A  ,       _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		(
      (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
  )
  a__           =		(OpenLlamaForCausalLM,) if is_torch_available() else ()
  a__           =		(
      {
          """feature-extraction""": OpenLlamaModel,
          """text-classification""": OpenLlamaForSequenceClassification,
          """text-generation""": OpenLlamaForCausalLM,
          """zero-shot""": OpenLlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  a__           =		False
  a__           =		False
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       OpenLlamaModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							hidden_size=37    )
  def 							_lowercase							( self			: List[Any]    )			->	Tuple:







    """simple docstring"""






    self.config_tester.run_common_tests()
  def 							_lowercase							( self			: str    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
      __magic_name__				     =       type
      self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       3
    __magic_name__				     =       input_dict["""input_ids"""]
    __magic_name__				     =       input_ids.ne(1    ).to(UpperCamelCase__    )
    __magic_name__				     =       ids_tensor([self.model_tester.batch_size]			,							self.model_tester.type_sequence_label_size    )
    __magic_name__				     =       OpenLlamaForSequenceClassification(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.assertEqual(result.logits.shape			,							(self.model_tester.batch_size, self.model_tester.num_labels)    )
  def 							_lowercase							( self			: List[str]    )			->	Tuple:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       3
    __magic_name__				     =       """single_label_classification"""
    __magic_name__				     =       input_dict["""input_ids"""]
    __magic_name__				     =       input_ids.ne(1    ).to(UpperCamelCase__    )
    __magic_name__				     =       ids_tensor([self.model_tester.batch_size]			,							self.model_tester.type_sequence_label_size    )
    __magic_name__				     =       OpenLlamaForSequenceClassification(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.assertEqual(result.logits.shape			,							(self.model_tester.batch_size, self.model_tester.num_labels)    )
  def 							_lowercase							( self			: str    )			->	Dict:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       3
    __magic_name__				     =       """multi_label_classification"""
    __magic_name__				     =       input_dict["""input_ids"""]
    __magic_name__				     =       input_ids.ne(1    ).to(UpperCamelCase__    )
    __magic_name__				     =       ids_tensor(
        [self.model_tester.batch_size, config.num_labels]			,							self.model_tester.type_sequence_label_size    ).to(torch.float    )
    __magic_name__				     =       OpenLlamaForSequenceClassification(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.assertEqual(result.logits.shape			,							(self.model_tester.batch_size, self.model_tester.num_labels)    )
  @unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""    )
  def 							_lowercase							( self			: str    )			->	int:







    """simple docstring"""






    pass





  @parameterized.expand([("""linear""",), ("""dynamic""",)]    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: List[Any]    )			->	str:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       ids_tensor([1, 10]			,							config.vocab_size    )
    __magic_name__				     =       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
    __magic_name__				     =       OpenLlamaModel(UpperCamelCase__    )
    original_model.to(UpperCamelCase__    )
    original_model.eval()
    __magic_name__				     =       original_model(UpperCamelCase__    ).last_hidden_state
    __magic_name__				     =       original_model(UpperCamelCase__    ).last_hidden_state

    set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
    __magic_name__				     =       {"""type""": scaling_type, """factor""": 10.0}
    __magic_name__				     =       OpenLlamaModel(UpperCamelCase__    )
    scaled_model.to(UpperCamelCase__    )
    scaled_model.eval()
    __magic_name__				     =       scaled_model(UpperCamelCase__    ).last_hidden_state
    __magic_name__				     =       scaled_model(UpperCamelCase__    ).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(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-5    )    )
    else:
      self.assertFalse(torch.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-5    )    )

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









class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a					)       ->    None:
								snake_case_				=							set_counts
								snake_case_				=							max(a					)
								snake_case_				=							len(a					)
								snake_case_				=							[1] * num_sets
								snake_case_				=							list(range(a					)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a					)       ->    bool:
								snake_case_				=							self.get_parent(a					)
								snake_case_				=							self.get_parent(a					)

								if src_parent == dst_parent:
															return False

								if self.ranks[dst_parent] >= self.ranks[src_parent]:
															self.set_counts[dst_parent] += self.set_counts[src_parent]
															snake_case_				=							0
															snake_case_				=							dst_parent
															if self.ranks[dst_parent] == self.ranks[src_parent]:
																						self.ranks[dst_parent] += 1
															snake_case_				=							self.set_counts[dst_parent]
								else:
															self.set_counts[src_parent] += self.set_counts[dst_parent]
															snake_case_				=							0
															snake_case_				=							src_parent
															snake_case_				=							self.set_counts[src_parent]

								snake_case_				=							max(self.max_set       ,			a					)
								return True






	def 							_UpperCamelCase			(       self       ,			a					)       ->    int:
								if self.parents[disj_set] == disj_set:
															return disj_set
								snake_case_				=							self.get_parent(self.parents[disj_set]					)
								return self.parents[disj_set]





