

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   int=13	,							lowerCAmelCase			:   Dict=7	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   Any=False	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   Dict=99	,							lowerCAmelCase			:   Union[str, Any]=32	,							lowerCAmelCase			:   Union[str, Any]=5	,							lowerCAmelCase			:   int=4	,							lowerCAmelCase			:   Union[str, Any]=37	,							lowerCAmelCase			:   str="gelu"	,							lowerCAmelCase			:   Any=0.1	,							lowerCAmelCase			:   Tuple=0.1	,							lowerCAmelCase			:   List[str]=5_12	,							lowerCAmelCase			:   Tuple=16	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   str=0.02	,							lowerCAmelCase			:   Optional[Any]=3	,							lowerCAmelCase			:   Union[str, Any]=4	,							lowerCAmelCase			:   Tuple=None	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						parent
     __lowerCAmelCase  :      List[str]    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      Any    =						is_training
     __lowerCAmelCase  :      Dict    =						use_input_mask
     __lowerCAmelCase  :      Dict    =						use_token_type_ids
     __lowerCAmelCase  :      Optional[Any]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      List[Any]    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      List[str]    =						num_attention_heads
     __lowerCAmelCase  :      List[Any]    =						intermediate_size
     __lowerCAmelCase  :      Dict    =						hidden_act
     __lowerCAmelCase  :      Optional[Any]    =						hidden_dropout_prob
     __lowerCAmelCase  :      str    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Any    =						max_position_embeddings
     __lowerCAmelCase  :      Tuple    =						type_vocab_size
     __lowerCAmelCase  :      Any    =						type_sequence_label_size
     __lowerCAmelCase  :      Optional[int]    =						initializer_range
     __lowerCAmelCase  :      str    =						num_labels
     __lowerCAmelCase  :      List[str]    =						num_choices
     __lowerCAmelCase  :      Optional[int]    =						scope







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      int    =						None
     if self.use_input_mask:
        __lowerCAmelCase  :      str    =						random_attention_mask([self.batch_size, self.seq_length]   )

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

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

     __lowerCAmelCase  :      str    =						self.get_config()

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     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=_a	,							initializer_range=self.initializer_range	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[int]   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						LlamaModel(config=_a   )
     model.to(_a   )
     model.eval()
     __lowerCAmelCase  :      List[str]    =						model(_a	,							attention_mask=_a   )
     __lowerCAmelCase  :      Optional[int]    =						model(_a   )
     self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[Any]	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						True
     __lowerCAmelCase  :      Tuple    =						LlamaModel(_a   )
     model.to(_a   )
     model.eval()
     __lowerCAmelCase  :      int    =						model(
         _a	,							attention_mask=_a	,							encoder_hidden_states=_a	,							encoder_attention_mask=_a	,							)
     __lowerCAmelCase  :      str    =						model(
         _a	,							attention_mask=_a	,							encoder_hidden_states=_a	,							)
     __lowerCAmelCase  :      List[str]    =						model(_a	,							attention_mask=_a   )
     self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Any	,							)							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						LlamaForCausalLM(config=_a   )
     model.to(_a   )
     model.eval()
     __lowerCAmelCase  :      List[Any]    =						model(_a	,							attention_mask=_a	,							labels=_a   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int	,							)							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						True
     __lowerCAmelCase  :      Optional[int]    =						True
     __lowerCAmelCase  :      Dict    =						LlamaForCausalLM(config=_a   )
     model.to(_a   )
     model.eval()

     # first forward pass
     __lowerCAmelCase  :      List[Any]    =						model(
         _a	,							attention_mask=_a	,							encoder_hidden_states=_a	,							encoder_attention_mask=_a	,							use_cache=_a	,							)
     __lowerCAmelCase  :      Any    =						outputs.past_key_values

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

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

     __lowerCAmelCase  :      Optional[Any]    =						model(
         _a	,							attention_mask=_a	,							encoder_hidden_states=_a	,							encoder_attention_mask=_a	,							output_hidden_states=_a	,							)["""hidden_states"""][0]
     __lowerCAmelCase  :      str    =						model(
         _a	,							attention_mask=_a	,							encoder_hidden_states=_a	,							encoder_attention_mask=_a	,							past_key_values=_a	,							output_hidden_states=_a	,							)["""hidden_states"""][0]

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase       
     )   ,(
         __lowerCAmelCase       
     )   ,(
         __lowerCAmelCase       
     )   ,(
         __lowerCAmelCase       
     )   ,(
         __lowerCAmelCase       
     )   ,(
         __lowerCAmelCase       
     )   ,(
         __lowerCAmelCase       
     )   ,       
     )  :      List[Any]    =						config_and_inputs
     __lowerCAmelCase  :      Any    =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
     return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE     (  __lowercase       ,							__lowercase       ,							__lowercase       ,							unittest.TestCase			):
  """simple docstring"""






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







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						LlamaModelTester(self   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConfigTester(self	,							config_class=_a	,							hidden_size=37   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_a   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
        __lowerCAmelCase  :      Optional[Any]    =						type
        self.model_tester.create_and_check_model(*_a   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase  :      List[str]    =						3
     __lowerCAmelCase  :      str    =						input_dict["""input_ids"""]
     __lowerCAmelCase  :      List[Any]    =						input_ids.ne(1   ).to(_a   )
     __lowerCAmelCase  :      List[str]    =						ids_tensor([self.model_tester.batch_size]	,							self.model_tester.type_sequence_label_size   )
     __lowerCAmelCase  :      Any    =						LlamaForSequenceClassification(_a   )
     model.to(_a   )
     model.eval()
     __lowerCAmelCase  :      int    =						model(_a	,							attention_mask=_a	,							labels=_a   )
     self.assertEqual(result.logits.shape	,							(self.model_tester.batch_size, self.model_tester.num_labels)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Any:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase  :      Any    =						3
     __lowerCAmelCase  :      Any    =						"""multi_label_classification"""
     __lowerCAmelCase  :      int    =						input_dict["""input_ids"""]
     __lowerCAmelCase  :      Optional[int]    =						input_ids.ne(1   ).to(_a   )
     __lowerCAmelCase  :      Union[str, Any]    =						ids_tensor(
         [self.model_tester.batch_size, config.num_labels]	,							self.model_tester.type_sequence_label_size   ).to(torch.float   )
     __lowerCAmelCase  :      List[Any]    =						LlamaForSequenceClassification(_a   )
     model.to(_a   )
     model.eval()
     __lowerCAmelCase  :      Union[str, Any]    =						model(_a	,							attention_mask=_a	,							labels=_a   )
     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			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     pass







  @parameterized.expand([("""linear""",), ("""dynamic""",)]   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase  :      Tuple    =						ids_tensor([1, 10]	,							config.vocab_size   )
     __lowerCAmelCase  :      Optional[int]    =						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
     __lowerCAmelCase  :      Optional[int]    =						LlamaModel(_a   )
     original_model.to(_a   )
     original_model.eval()
     __lowerCAmelCase  :      Optional[Any]    =						original_model(_a   ).last_hidden_state
     __lowerCAmelCase  :      str    =						original_model(_a   ).last_hidden_state

     set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
     __lowerCAmelCase  :      str    =						{"""type""": scaling_type, """factor""": 10.0}
     __lowerCAmelCase  :      List[Any]    =						LlamaModel(_a   )
     scaled_model.to(_a   )
     scaled_model.eval()
     __lowerCAmelCase  :      Optional[Any]    =						scaled_model(_a   ).last_hidden_state
     __lowerCAmelCase  :      Optional[Any]    =						scaled_model(_a   ).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(_a	,							_a	,							atol=1e-5   )   )
     else:
        self.assertFalse(torch.allclose(_a	,							_a	,							atol=1e-5   )   )

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






@require_torch
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""   )
  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     __lowerCAmelCase  :      str    =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""	,							device_map="""auto"""   )
     __lowerCAmelCase  :      Dict    =						model(torch.tensor([input_ids]   )   )
     # Expected mean on dim = -1
     __lowerCAmelCase  :      Union[str, Any]    =						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   )	,							_a	,							atol=1e-2	,							rtol=1e-2   )
     # slicing logits[0, 0, 0:30]
     # fmt: off
     __lowerCAmelCase  :      Optional[int]    =						torch.tensor([-12.82_81, -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, -12.82_81, -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]	,							_a	,							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			:   Optional[Any]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     __lowerCAmelCase  :      Optional[int]    =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""	,							device_map="""auto"""   )
     __lowerCAmelCase  :      Optional[Any]    =						model(torch.tensor(_a   )   )
     # Expected mean on dim = -1
     __lowerCAmelCase  :      List[Any]    =						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   )	,							_a	,							atol=1e-2	,							rtol=1e-2   )
     # slicing logits[0, 0, 0:30]
     # fmt: off
     __lowerCAmelCase  :      int    =						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]	,							_a	,							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[Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     __lowerCAmelCase  :      Union[str, Any]    =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""	,							device_map="""auto"""   )
     __lowerCAmelCase  :      int    =						model(torch.tensor(_a   )   )
     # Expected mean on dim = -1
     __lowerCAmelCase  :      Any    =						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   )	,							_a	,							atol=1e-2	,							rtol=1e-2   )
     # slicing logits[0, 0, 0:30]
     # fmt: off
     __lowerCAmelCase  :      Optional[int]    =						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   )	,							_a	,							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			:   Union[str, Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     __lowerCAmelCase  :      Optional[Any]    =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""	,							device_map="""auto"""   )
     __lowerCAmelCase  :      List[str]    =						model(torch.tensor(_a   )   )

     __lowerCAmelCase  :      str    =						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   )	,							_a	,							atol=1e-2	,							rtol=1e-2   )
     # fmt: off
     __lowerCAmelCase  :      Tuple    =						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]	,							_a	,							atol=1e-5	,							rtol=1e-5   )







  @unittest.skip("""Model is curently gated"""   )
  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Optional[Any]:






     """simple docstring"""







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

     # greedy generation outputs
     __lowerCAmelCase  :      Dict    =						model.generate(_a	,							max_new_tokens=64	,							top_p=_a	,							temperature=1	,							do_sample=_a   )
     __lowerCAmelCase  :      Optional[int]    =						tokenizer.decode(generated_ids[0]	,							skip_special_tokens=_a   )
     self.assertEqual(_a	,							_a   )





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   str=13	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=99	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Optional[Any]=37	,							lowerCAmelCase			:   str="gelu"	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   Any=5_12	,							lowerCAmelCase			:   Optional[Any]=16	,							lowerCAmelCase			:   Dict=2	,							lowerCAmelCase			:   Tuple=0.02	,							lowerCAmelCase			:   Optional[int]=4	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						parent
     __lowerCAmelCase  :      Dict    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      Union[str, Any]    =						is_training
     __lowerCAmelCase  :      List[Any]    =						use_attention_mask
     __lowerCAmelCase  :      List[Any]    =						use_token_type_ids
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      Any    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      Optional[int]    =						num_attention_heads
     __lowerCAmelCase  :      Dict    =						intermediate_size
     __lowerCAmelCase  :      Tuple    =						hidden_act
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      Any    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Union[str, Any]    =						max_position_embeddings
     __lowerCAmelCase  :      int    =						type_vocab_size
     __lowerCAmelCase  :      Tuple    =						type_sequence_label_size
     __lowerCAmelCase  :      int    =						initializer_range
     __lowerCAmelCase  :      Optional[int]    =						num_choices







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						None
     if self.use_attention_mask:
        __lowerCAmelCase  :      Optional[int]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      Union[str, Any]    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[str]    =						BertConfig(
         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	,							)

     return config, input_ids, token_type_ids, attention_mask







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						config_and_inputs
     __lowerCAmelCase  :      Any    =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						config_and_inputs

     __lowerCAmelCase  :      Any    =						True
     __lowerCAmelCase  :      str    =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
     __lowerCAmelCase  :      int    =						ids_tensor([self.batch_size, self.seq_length]	,							vocab_size=2   )

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      int										=True

  lowerCamelCase   :      Any										=(
      (
          FlaxBertModel,
          FlaxBertForPreTraining,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForQuestionAnswering,
          FlaxBertForNextSentencePrediction,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						FlaxBertModelTester(self   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						FlaxBertModel.from_pretrained("""bert-base-cased"""   )
     __lowerCAmelCase  :      str    =						model(np.ones((1, 1)   )   )
     self.assertIsNotNone(lowerCAmelCase   )


