

'''simple docstring'''
import unittest

from parameterized import parameterized

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

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


if is_torch_available():
    import torch

    from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class        a__  :


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase=13      ,    _UpperCamelCase=7      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=False      ,    _UpperCamelCase=True      ,    _UpperCamelCase=99      ,    _UpperCamelCase=32      ,    _UpperCamelCase=5      ,    _UpperCamelCase=4      ,    _UpperCamelCase=37      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=16      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=3      ,    _UpperCamelCase=4      ,    _UpperCamelCase=None      ,    ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size  )

      _lowercase      :				Tuple      					=       None
      if self.use_input_mask:
         _lowercase      :				Dict      					=       random_attention_mask([self.batch_size, self.seq_length]  )

      _lowercase      :				Optional[int]      					=       None
      if self.use_token_type_ids:
         _lowercase      :				Any      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size  )

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

      _lowercase      :				Tuple      					=       self.get_config()

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


   def 						_lowerCamelCase       (			self  ):





      """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=_UpperCamelCase      ,    initializer_range=self.initializer_range      ,    )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       LlamaModel(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Union[str, Any]      					=       model(_UpperCamelCase      ,    attention_mask=_UpperCamelCase  )
      _lowercase      :				int      					=       model(_UpperCamelCase  )
      self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       True
      _lowercase      :				str      					=       LlamaModel(_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Union[str, Any]      					=       model(
          _UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    encoder_hidden_states=_UpperCamelCase      ,    encoder_attention_mask=_UpperCamelCase      ,    )
      _lowercase      :				Union[str, Any]      					=       model(
          _UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    encoder_hidden_states=_UpperCamelCase      ,    )
      _lowercase      :				Union[str, Any]      					=       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      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Dict      					=       LlamaForCausalLM(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Union[str, Any]      					=       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      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Any      					=       True
      _lowercase      :				Tuple      					=       True
      _lowercase      :				str      					=       LlamaForCausalLM(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()

      # first forward pass
      _lowercase      :				Tuple      					=       model(
          _UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    encoder_hidden_states=_UpperCamelCase      ,    encoder_attention_mask=_UpperCamelCase      ,    use_cache=_UpperCamelCase      ,    )
      _lowercase      :				List[str]      					=       outputs.past_key_values

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

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

      _lowercase      :				str      					=       model(
          _UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    encoder_hidden_states=_UpperCamelCase      ,    encoder_attention_mask=_UpperCamelCase      ,    output_hidden_states=_UpperCamelCase      ,    )["hidden_states"][0]
      _lowercase      :				List[str]      					=       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
      _lowercase      :				List[Any]      					=       ids_tensor((1,)      ,    output_from_past.shape[-1]  ).item()
      _lowercase      :				Tuple      					=       output_from_no_past[:, -3:, random_slice_idx].detach()
      _lowercase      :				Optional[Any]      					=       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  ):





      """simple docstring"""





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




@require_torch
class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							(LlamaForCausalLM,) if is_torch_available() else ()
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							(
       {
           'feature-extraction': LlamaModel,
           'text-classification': LlamaForSequenceClassification,
           'text-generation': LlamaForCausalLM,
           'zero-shot': LlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							False
   _SCREAMING_SNAKE_CASE   :					int     	=							False


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowercase      :				Dict      					=       type
         self.model_tester.create_and_check_model(*_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase					,				_lowercase      :				Optional[Any]      					=       self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase      :				Optional[int]      					=       3
      _lowercase      :				Optional[int]      					=       input_dict["input_ids"]
      _lowercase      :				Any      					=       input_ids.ne(1  ).to(_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       ids_tensor([self.model_tester.batch_size]      ,    self.model_tester.type_sequence_label_size  )
      _lowercase      :				List[Any]      					=       LlamaForSequenceClassification(_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Any      					=       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  ):





      """simple docstring"""





      _lowercase					,				_lowercase      :				List[Any]      					=       self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase      :				Tuple      					=       3
      _lowercase      :				Union[str, Any]      					=       "single_label_classification"
      _lowercase      :				Tuple      					=       input_dict["input_ids"]
      _lowercase      :				Tuple      					=       input_ids.ne(1  ).to(_UpperCamelCase  )
      _lowercase      :				Optional[Any]      					=       ids_tensor([self.model_tester.batch_size]      ,    self.model_tester.type_sequence_label_size  )
      _lowercase      :				Dict      					=       LlamaForSequenceClassification(_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Optional[int]      					=       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  ):





      """simple docstring"""





      _lowercase					,				_lowercase      :				str      					=       self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase      :				List[str]      					=       3
      _lowercase      :				List[Any]      					=       "multi_label_classification"
      _lowercase      :				List[str]      					=       input_dict["input_ids"]
      _lowercase      :				str      					=       input_ids.ne(1  ).to(_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       ids_tensor(
          [self.model_tester.batch_size, config.num_labels]      ,    self.model_tester.type_sequence_label_size  ).to(torch.float  )
      _lowercase      :				Any      					=       LlamaForSequenceClassification(_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				List[str]      					=       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  ):





      """simple docstring"""





      pass






   @parameterized.expand([("linear",), ("dynamic",)]  )
   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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

      set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
      _lowercase      :				Any      					=       {"type": scaling_type, "factor": 1_0.0}
      _lowercase      :				Union[str, Any]      					=       LlamaModel(_UpperCamelCase  )
      scaled_model.to(_UpperCamelCase  )
      scaled_model.eval()
      _lowercase      :				int      					=       scaled_model(_UpperCamelCase  ).last_hidden_state
      _lowercase      :				Optional[Any]      					=       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        a__  (							unittest.TestCase  ):


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





      """simple docstring"""





      _lowercase      :				List[Any]      					=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
      _lowercase      :				Optional[int]      					=       LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"      ,    device_map="auto"  )
      _lowercase      :				Optional[Any]      					=       model(torch.tensor([input_ids]  )  )
      # Expected mean on dim = -1
      _lowercase      :				Any      					=       torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]  )
      torch.testing.assert_close(out.mean(-1  )      ,    _UpperCamelCase      ,    atol=1E-2      ,    rtol=1E-2  )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowercase      :				List[str]      					=       torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]  )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :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  ):





      """simple docstring"""





      _lowercase      :				Any      					=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
      _lowercase      :				List[Any]      					=       LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"      ,    device_map="auto"  )
      _lowercase      :				Tuple      					=       model(torch.tensor(_UpperCamelCase  )  )
      # Expected mean on dim = -1
      _lowercase      :				List[str]      					=       torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]  )
      torch.testing.assert_close(out.mean(-1  )      ,    _UpperCamelCase      ,    atol=1E-2      ,    rtol=1E-2  )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowercase      :				Any      					=       torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]  )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :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  ):





      """simple docstring"""





      _lowercase      :				str      					=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
      _lowercase      :				Optional[Any]      					=       LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"      ,    device_map="auto"  )
      _lowercase      :				Optional[Any]      					=       model(torch.tensor(_UpperCamelCase  )  )
      # Expected mean on dim = -1
      _lowercase      :				Optional[int]      					=       torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]  )
      torch.testing.assert_close(out.mean(-1  )      ,    _UpperCamelCase      ,    atol=1E-2      ,    rtol=1E-2  )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowercase      :				Optional[Any]      					=       torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]  )
      # fmt: on
      torch.testing.assert_close(out.mean(-1  )      ,    _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  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
      _lowercase      :				List[str]      					=       LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"      ,    device_map="auto"  )
      _lowercase      :				int      					=       model(torch.tensor(_UpperCamelCase  )  )

      _lowercase      :				Union[str, Any]      					=       torch.tensor(
          [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]      ,    dtype=torch.floataa  )
      torch.testing.assert_close(out.mean(-1  )      ,    _UpperCamelCase      ,    atol=1E-2      ,    rtol=1E-2  )
      # fmt: off
      _lowercase      :				Dict      					=       torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]  )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]      ,    _UpperCamelCase      ,    atol=1E-5      ,    rtol=1E-5  )






   @unittest.skip("Model is curently gated"  )
   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[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"
      _lowercase      :				int      					=       "Simply put, the theory of relativity states that "
      _lowercase      :				Optional[int]      					=       LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"  )
      _lowercase      :				int      					=       tokenizer.encode(_UpperCamelCase      ,    return_tensors="pt"  )
      _lowercase      :				Dict      					=       LlamaForCausalLM.from_pretrained(
          "meta-llama/Llama-2-13b-chat-hf"      ,    device_map="sequential"      ,    use_safetensors=_UpperCamelCase  )

      # greedy generation outputs
      _lowercase      :				Union[str, Any]      					=       model.generate(_UpperCamelCase      ,    max_new_tokens=64      ,    top_p=_UpperCamelCase      ,    temperature=1      ,    do_sample=_UpperCamelCase  )
      _lowercase      :				Dict      					=       tokenizer.decode(generated_ids[0]      ,    skip_special_tokens=_UpperCamelCase  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )









'''simple docstring'''
import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							IFImgaImgSuperResolutionPipeline
   _SCREAMING_SNAKE_CASE   :					int     	=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							PipelineTesterMixin.required_optional_params - {'latents'}


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self._get_superresolution_dummy_components()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=0  ):





      """simple docstring"""





      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase      :				Tuple      					=       torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase      :				int      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      _lowercase      :				List[Any]      					=       floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       floats_tensor((1, 3, 16, 16)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

      _lowercase      :				List[str]      					=       {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "original_image": original_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs


   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()      ,    reason="XFormers attention is only available with CUDA and `xformers` installed"      ,    )
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != "cuda"      ,    reason="float16 requires CUDA"  )
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      super().test_save_load_floataa(expected_max_diff=1E-1  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_save_load_local()






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,    )






