import unittest

from parameterized import parameterized

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

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


if is_torch_available():
      import torch

      from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class lowerCamelCase							:




  '''simple docstring'''



  def __init__(      self			:       Tuple  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Dict=13  ,							UpperCAmelCase__			:       Union[str, Any]=7  ,							UpperCAmelCase__			:       Optional[Any]=True  ,							UpperCAmelCase__			:       Dict=True  ,							UpperCAmelCase__			:       Dict=False  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							UpperCAmelCase__			:       str=99  ,							UpperCAmelCase__			:       List[Any]=32  ,							UpperCAmelCase__			:       int=5  ,							UpperCAmelCase__			:       Dict=4  ,							UpperCAmelCase__			:       Optional[int]=37  ,							UpperCAmelCase__			:       Tuple="gelu"  ,							UpperCAmelCase__			:       Dict=0.1  ,							UpperCAmelCase__			:       Any=0.1  ,							UpperCAmelCase__			:       List[str]=512  ,							UpperCAmelCase__			:       Union[str, Any]=16  ,							UpperCAmelCase__			:       Tuple=2  ,							UpperCAmelCase__			:       Optional[int]=0.02  ,							UpperCAmelCase__			:       str=3  ,							UpperCAmelCase__			:       Optional[int]=4  ,							UpperCAmelCase__			:       Tuple=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  lowerCAmelCase__ (      self			:       List[Any]  )				->int:
        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  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Union[str, 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=UpperCAmelCase__  ,							initializer_range=self.initializer_range  ,							)






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Optional[Any]:
        UpperCAmelCase_            =  LlamaModel(config=UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  )
        UpperCAmelCase_            =  model(UpperCAmelCase__  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)  )






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       List[Any]  ,							)				->List[Any]:
        UpperCAmelCase_            =  True
        UpperCAmelCase_            =  LlamaModel(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							encoder_attention_mask=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)  )






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






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Dict  ,							)				->Tuple:
        UpperCAmelCase_            =  True
        UpperCAmelCase_            =  True
        UpperCAmelCase_            =  LlamaForCausalLM(config=UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()

        # first forward pass
        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							encoder_attention_mask=UpperCAmelCase__  ,							use_cache=UpperCAmelCase__  ,							)
        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(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							encoder_attention_mask=UpperCAmelCase__  ,							output_hidden_states=UpperCAmelCase__  ,							)['''hidden_states'''][0]
        UpperCAmelCase_            =  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
        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(UpperCAmelCase__  ,							UpperCAmelCase__  ,							atol=1e-3  )  )






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->int:
        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 lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase     ,		lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



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






  def  lowerCAmelCase__ (      self			:       int  )				->Optional[int]:
        UpperCAmelCase_            =  LlamaModelTester(self  )
        UpperCAmelCase_            =  ConfigTester(self  ,							config_class=UpperCAmelCase__  ,							hidden_size=37  )






  def  lowerCAmelCase__ (      self			:       Any  )				->Tuple:
        self.config_tester.run_common_tests()






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[int]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->str:
        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(*UpperCAmelCase__  )






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






  def  lowerCAmelCase__ (      self			:       List[str]  )				->List[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(UpperCAmelCase__  )
        UpperCAmelCase_            =  ids_tensor([self.model_tester.batch_size]  ,							self.model_tester.type_sequence_label_size  )
        UpperCAmelCase_            =  LlamaForSequenceClassification(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							labels=UpperCAmelCase__  )
        self.assertEqual(result.logits.shape  ,							(self.model_tester.batch_size, self.model_tester.num_labels)  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Dict:
        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(UpperCAmelCase__  )
        UpperCAmelCase_            =  ids_tensor(
            [self.model_tester.batch_size, config.num_labels]  ,							self.model_tester.type_sequence_label_size  ).to(torch.float  )
        UpperCAmelCase_            =  LlamaForSequenceClassification(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							labels=UpperCAmelCase__  )
        self.assertEqual(result.logits.shape  ,							(self.model_tester.batch_size, self.model_tester.num_labels)  )






  @unittest.skip('''LLaMA buffers include complex numbers, which breaks this test'''  )
  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Optional[int]:
        pass






  @parameterized.expand([('''linear''',), ('''dynamic''',)]  )
  def  lowerCAmelCase__ (      self			:       Tuple  ,							UpperCAmelCase__			:       str  )				->Tuple:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase_            =  ids_tensor([1, 10]  ,							config.vocab_size  )
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  LlamaModel(UpperCAmelCase__  )
        original_model.to(UpperCAmelCase__  )
        original_model.eval()
        UpperCAmelCase_            =  original_model(UpperCAmelCase__  ).last_hidden_state
        UpperCAmelCase_            =  original_model(UpperCAmelCase__  ).last_hidden_state

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







@require_torch
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        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([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]  )
        torch.testing.assert_close(out.mean(-1  )  ,							UpperCAmelCase__  ,							atol=1e-2  ,							rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        UpperCAmelCase_            =  torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,							UpperCAmelCase__  ,							atol=1e-5  ,							rtol=1e-5  )






  @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''  ,							device_map='''auto'''  )
        UpperCAmelCase_            =  model(torch.tensor(UpperCAmelCase__  )  )
        # Expected mean on dim = -1
        UpperCAmelCase_            =  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  )  ,							UpperCAmelCase__  ,							atol=1e-2  ,							rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        UpperCAmelCase_            =  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]  ,							UpperCAmelCase__  ,							atol=1e-5  ,							rtol=1e-5  )






  @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       int  )				->int:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''  ,							device_map='''auto'''  )
        UpperCAmelCase_            =  model(torch.tensor(UpperCAmelCase__  )  )
        # Expected mean on dim = -1
        UpperCAmelCase_            =  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  )  ,							UpperCAmelCase__  ,							atol=1e-2  ,							rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        UpperCAmelCase_            =  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  )  ,							UpperCAmelCase__  ,							atol=1e-2  ,							rtol=1e-2  )






  @unittest.skip(
      '''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Dict:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''  ,							device_map='''auto'''  )
        UpperCAmelCase_            =  model(torch.tensor(UpperCAmelCase__  )  )

        UpperCAmelCase_            =  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  )  ,							UpperCAmelCase__  ,							atol=1e-2  ,							rtol=1e-2  )
        # fmt: off
        UpperCAmelCase_            =  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]  ,							UpperCAmelCase__  ,							atol=1e-5  ,							rtol=1e-5  )






  @unittest.skip('''Model is curently gated'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Any:
        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(UpperCAmelCase__  ,							return_tensors='''pt'''  )
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained(
            '''meta-llama/Llama-2-13b-chat-hf'''  ,							device_map='''sequential'''  ,							use_safetensors=UpperCAmelCase__  )

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






'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      lowercase__  :      Optional[Any]        =					"platform"

      import jax
      import jax.numpy as jnp

      from transformers import BlenderbotTokenizer
      from transformers.models.blenderbot.modeling_flax_blenderbot import (
          FlaxBlenderbotForConditionalGeneration,
          FlaxBlenderbotModel,
          shift_tokens_right,
      )



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[int]=None							,    _UpperCamelCase  :  Any=None							,    _UpperCamelCase  :  int=None							,    _UpperCamelCase  :  int=None							,    _UpperCamelCase  :  Union[str, Any]=None							,    _UpperCamelCase  :  Any=None							,    ):


      '''simple docstring'''


      if attention_mask is None:
            UpperCAmelCase_            =  np.where(input_ids != config.pad_token_id							,    1							,    0					)
      if decoder_attention_mask is None:
            UpperCAmelCase_            =  np.where(decoder_input_ids != config.pad_token_id							,    1							,    0					)
      if head_mask is None:
            UpperCAmelCase_            =  np.ones((config.encoder_layers, config.encoder_attention_heads)					)
      if decoder_head_mask is None:
            UpperCAmelCase_            =  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
      if cross_attn_head_mask is None:
            UpperCAmelCase_            =  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }







class lowerCamelCase							:




  '''simple docstring'''



  def __init__(      self			:       Any  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Tuple=13  ,							UpperCAmelCase__			:       Tuple=7  ,							UpperCAmelCase__			:       Any=True  ,							UpperCAmelCase__			:       Union[str, Any]=False  ,							UpperCAmelCase__			:       Optional[int]=99  ,							UpperCAmelCase__			:       Dict=16  ,							UpperCAmelCase__			:       Any=2  ,							UpperCAmelCase__			:       Dict=4  ,							UpperCAmelCase__			:       str=4  ,							UpperCAmelCase__			:       int="gelu"  ,							UpperCAmelCase__			:       Tuple=0.1  ,							UpperCAmelCase__			:       List[str]=0.1  ,							UpperCAmelCase__			:       List[Any]=32  ,							UpperCAmelCase__			:       Union[str, Any]=2  ,							UpperCAmelCase__			:       Dict=1  ,							UpperCAmelCase__			:       Optional[int]=0  ,							UpperCAmelCase__			:       Union[str, Any]=0.02  ,							)				->Optional[int]:
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  seq_length
        UpperCAmelCase_            =  is_training
        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_            =  eos_token_id
        UpperCAmelCase_            =  pad_token_id
        UpperCAmelCase_            =  bos_token_id
        UpperCAmelCase_            =  initializer_range






  def  lowerCAmelCase__ (      self			:       int  )				->Any:
        UpperCAmelCase_            =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,							self.vocab_size  )  ,							3  ,							self.vocab_size  )
        UpperCAmelCase_            =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,							dtype=np.intaa  ))  ,							-1  )

        UpperCAmelCase_            =  shift_tokens_right(UpperCAmelCase__  ,							1  ,							2  )

        UpperCAmelCase_            =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,							d_model=self.hidden_size  ,							encoder_layers=self.num_hidden_layers  ,							decoder_layers=self.num_hidden_layers  ,							encoder_attention_heads=self.num_attention_heads  ,							decoder_attention_heads=self.num_attention_heads  ,							encoder_ffn_dim=self.intermediate_size  ,							decoder_ffn_dim=self.intermediate_size  ,							dropout=self.hidden_dropout_prob  ,							attention_dropout=self.attention_probs_dropout_prob  ,							max_position_embeddings=self.max_position_embeddings  ,							eos_token_id=self.eos_token_id  ,							bos_token_id=self.bos_token_id  ,							pad_token_id=self.pad_token_id  ,							initializer_range=self.initializer_range  ,							use_cache=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  prepare_blenderbot_inputs_dict(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )
        return config, inputs_dict






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[str]:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.prepare_config_and_inputs()
        return config, inputs_dict






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Tuple  )				->Tuple:
        UpperCAmelCase_            =  20
        UpperCAmelCase_            =  model_class_name(UpperCAmelCase__  )

        UpperCAmelCase_            =  model.encode(inputs_dict['''input_ids''']  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        UpperCAmelCase_            =  model.init_cache(decoder_input_ids.shape[0]  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,							dtype='''i4'''  )

        UpperCAmelCase_            =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]  ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,							)
        UpperCAmelCase_            =  model.decode(
            decoder_input_ids[:, :-1]  ,							UpperCAmelCase__  ,							decoder_attention_mask=UpperCAmelCase__  ,							past_key_values=UpperCAmelCase__  ,							decoder_position_ids=UpperCAmelCase__  ,							)

        UpperCAmelCase_            =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,							dtype='''i4'''  )
        UpperCAmelCase_            =  model.decode(
            decoder_input_ids[:, -1:]  ,							UpperCAmelCase__  ,							decoder_attention_mask=UpperCAmelCase__  ,							past_key_values=outputs_cache.past_key_values  ,							decoder_position_ids=UpperCAmelCase__  ,							)

        UpperCAmelCase_            =  model.decode(UpperCAmelCase__  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
        self.parent.assertTrue(diff < 1e-3  ,							msg=f"""Max diff is {diff}"""  )






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Any  )				->Union[str, Any]:
        UpperCAmelCase_            =  20
        UpperCAmelCase_            =  model_class_name(UpperCAmelCase__  )

        UpperCAmelCase_            =  model.encode(inputs_dict['''input_ids''']  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        UpperCAmelCase_            =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
            ]  ,							axis=-1  ,							)

        UpperCAmelCase_            =  model.init_cache(decoder_input_ids.shape[0]  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]  ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,							)

        UpperCAmelCase_            =  model.decode(
            decoder_input_ids[:, :-1]  ,							UpperCAmelCase__  ,							decoder_attention_mask=UpperCAmelCase__  ,							past_key_values=UpperCAmelCase__  ,							decoder_position_ids=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,							dtype='''i4'''  )
        UpperCAmelCase_            =  model.decode(
            decoder_input_ids[:, -1:]  ,							UpperCAmelCase__  ,							past_key_values=outputs_cache.past_key_values  ,							decoder_attention_mask=UpperCAmelCase__  ,							decoder_position_ids=UpperCAmelCase__  ,							)

        UpperCAmelCase_            =  model.decode(UpperCAmelCase__  ,							UpperCAmelCase__  ,							decoder_attention_mask=UpperCAmelCase__  )

        UpperCAmelCase_            =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
        self.parent.assertTrue(diff < 1e-3  ,							msg=f"""Max diff is {diff}"""  )







@require_flax
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	99






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Optional[Any]:
        UpperCAmelCase_            =  np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]  ,							dtype=np.intaa  ,							)

        UpperCAmelCase_            =  input_ids.shape[0]
        UpperCAmelCase_            =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,							d_model=24  ,							encoder_layers=2  ,							decoder_layers=2  ,							encoder_attention_heads=2  ,							decoder_attention_heads=2  ,							encoder_ffn_dim=32  ,							decoder_ffn_dim=32  ,							max_position_embeddings=48  ,							eos_token_id=2  ,							pad_token_id=1  ,							bos_token_id=0  ,							)
        return config, input_ids, batch_size






  def  lowerCAmelCase__ (      self			:       Any  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  self._get_config_and_data()
        UpperCAmelCase_            =  FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__  )
        UpperCAmelCase_            =  lm_model(input_ids=UpperCAmelCase__  )
        UpperCAmelCase_            =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,							d_model=14  ,							encoder_layers=2  ,							decoder_layers=2  ,							encoder_attention_heads=2  ,							decoder_attention_heads=2  ,							encoder_ffn_dim=8  ,							decoder_ffn_dim=8  ,							max_position_embeddings=48  ,							)
        UpperCAmelCase_            =  FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__  )
        UpperCAmelCase_            =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,							dtype=np.intaa  )
        UpperCAmelCase_            =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,							dtype=np.intaa  )
        UpperCAmelCase_            =  lm_model(input_ids=UpperCAmelCase__  ,							decoder_input_ids=UpperCAmelCase__  )
        UpperCAmelCase_            =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[Any]:
        UpperCAmelCase_            =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,							dtype=np.intaa  )
        UpperCAmelCase_            =  shift_tokens_right(UpperCAmelCase__  ,							1  ,							2  )
        UpperCAmelCase_            =  np.equal(UpperCAmelCase__  ,							1  ).astype(np.floataa  ).sum()
        UpperCAmelCase_            =  np.equal(UpperCAmelCase__  ,							1  ).astype(np.floataa  ).sum()
        self.assertEqual(shifted.shape  ,							input_ids.shape  )
        self.assertEqual(UpperCAmelCase__  ,							n_pad_before - 1  )
        self.assertTrue(np.equal(shifted[:, 0]  ,							2  ).all()  )







@require_flax
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase     ,		lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  lowerCAmelCase__										=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[Any]:
        UpperCAmelCase_            =  FlaxBlenderbotModelTester(self  )






  def  lowerCAmelCase__ (      self			:       str  )				->Tuple:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Dict  )				->Tuple:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__  ):
                    UpperCAmelCase_            =  self._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  )
                    UpperCAmelCase_            =  model_class(UpperCAmelCase__  )

                    @jax.jit
                    def encode_jitted(UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							**UpperCAmelCase__			:       Union[str, Any]  ):
                          return model.encode(input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  )

                    with self.subTest('''JIT Enabled'''  ):
                          UpperCAmelCase_            =  encode_jitted(**UpperCAmelCase__  ).to_tuple()

                    with self.subTest('''JIT Disabled'''  ):
                          with jax.disable_jit():
                                UpperCAmelCase_            =  encode_jitted(**UpperCAmelCase__  ).to_tuple()

                    self.assertEqual(len(UpperCAmelCase__  )  ,							len(UpperCAmelCase__  )  )
                    for jitted_output, output in zip(UpperCAmelCase__  ,							UpperCAmelCase__  ):
                          self.assertEqual(jitted_output.shape  ,							output.shape  )






  def  lowerCAmelCase__ (      self			:       str  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__  ):
                    UpperCAmelCase_            =  model_class(UpperCAmelCase__  )
                    UpperCAmelCase_            =  model.encode(inputs_dict['''input_ids''']  ,							inputs_dict['''attention_mask''']  )

                    UpperCAmelCase_            =  {
                        '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                        '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                        '''encoder_outputs''': encoder_outputs,
                    }

                    @jax.jit
                    def decode_jitted(UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       int  ):
                          return model.decode(
                              decoder_input_ids=UpperCAmelCase__  ,							decoder_attention_mask=UpperCAmelCase__  ,							encoder_outputs=UpperCAmelCase__  ,							)

                    with self.subTest('''JIT Enabled'''  ):
                          UpperCAmelCase_            =  decode_jitted(**UpperCAmelCase__  ).to_tuple()

                    with self.subTest('''JIT Disabled'''  ):
                          with jax.disable_jit():
                                UpperCAmelCase_            =  decode_jitted(**UpperCAmelCase__  ).to_tuple()

                    self.assertEqual(len(UpperCAmelCase__  )  ,							len(UpperCAmelCase__  )  )
                    for jitted_output, output in zip(UpperCAmelCase__  ,							UpperCAmelCase__  ):
                          self.assertEqual(jitted_output.shape  ,							output.shape  )






  @slow
  def  lowerCAmelCase__ (      self			:       int  )				->int:
        for model_class_name in self.all_model_classes:
              UpperCAmelCase_            =  model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''  )
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              UpperCAmelCase_            =  np.ones((1, 1)  ) * model.config.eos_token_id
              UpperCAmelCase_            =  model(UpperCAmelCase__  )
              self.assertIsNotNone(UpperCAmelCase__  )






  @unittest.skipUnless(jax_device != '''cpu'''  ,							'''3B test too slow on CPU.'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[Any]:
        UpperCAmelCase_            =  {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
        UpperCAmelCase_            =  {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

        UpperCAmelCase_            =  FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''  ,							from_pt=UpperCAmelCase__  )
        UpperCAmelCase_            =  BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''  )

        UpperCAmelCase_            =  ['''Sam''']
        UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							return_tensors='''jax'''  )

        UpperCAmelCase_            =  model.generate(**UpperCAmelCase__  ,							**UpperCAmelCase__  )
        UpperCAmelCase_            =  '''Sam is a great name. It means "sun" in Gaelic.'''

        UpperCAmelCase_            =  tokenizer.batch_decode(UpperCAmelCase__  ,							**UpperCAmelCase__  )
        assert generated_txt[0].strip() == tgt_text





