

"""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        _lowercase					:







     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 ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      seq_length
      __magic_name__     =      is_training
      __magic_name__     =      use_input_mask
      __magic_name__     =      use_token_type_ids
      __magic_name__     =      use_labels
      __magic_name__     =      vocab_size
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      attention_probs_dropout_prob
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      type_vocab_size
      __magic_name__     =      type_sequence_label_size
      __magic_name__     =      initializer_range
      __magic_name__     =      num_labels
      __magic_name__     =      num_choices
      __magic_name__     =      scope







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

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

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

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

      __magic_name__     =      self.get_config()

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







     def 	lowerCAmelCase__    (      self       ):
      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_       ):
      __magic_name__     =      LlamaModel(config=UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_       )
      __magic_name__     =      model(UpperCamelCase_       )
      self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      True
      __magic_name__     =      LlamaModel(UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()
      __magic_name__     =      model(
          UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						encoder_hidden_states=UpperCamelCase_ ,						encoder_attention_mask=UpperCamelCase_ ,						)
      __magic_name__     =      model(
          UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						encoder_hidden_states=UpperCamelCase_ ,						)
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_       )
      self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      LlamaForCausalLM(config=UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						labels=UpperCamelCase_       )
      self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      True
      __magic_name__     =      True
      __magic_name__     =      LlamaForCausalLM(config=UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()

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

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

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

      __magic_name__     =      model(
          UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						encoder_hidden_states=UpperCamelCase_ ,						encoder_attention_mask=UpperCamelCase_ ,						output_hidden_states=UpperCamelCase_ ,						)['''hidden_states'''][0]
      __magic_name__     =      model(
          UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						encoder_hidden_states=UpperCamelCase_ ,						encoder_attention_mask=UpperCamelCase_ ,						past_key_values=UpperCamelCase_ ,						output_hidden_states=UpperCamelCase_ ,						)['''hidden_states'''][0]

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

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

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







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


@require_torch
class        _lowercase					(     lowercase_   , lowercase_   , lowercase_   , unittest.TestCase     ):
     _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       ):
      __magic_name__     =      LlamaModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=UpperCamelCase_ ,						hidden_size=37       )







     def 	lowerCAmelCase__    (      self       ):
      self.config_tester.run_common_tests()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       __magic_name__     =      type
       self.model_tester.create_and_check_model(*UpperCamelCase_       )







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







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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__     =      3
      __magic_name__     =      '''multi_label_classification'''
      __magic_name__     =      input_dict['''input_ids''']
      __magic_name__     =      input_ids.ne(1       ).to(UpperCamelCase_       )
      __magic_name__     =      ids_tensor(
          [self.model_tester.batch_size, config.num_labels] ,						self.model_tester.type_sequence_label_size       ).to(torch.float       )
      __magic_name__     =      LlamaForSequenceClassification(UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						labels=UpperCamelCase_       )
      self.assertEqual(result.logits.shape ,						(self.model_tester.batch_size, self.model_tester.num_labels)       )







     @unittest.skip('''LLaMA buffers include complex numbers, which breaks this test'''       )
     def 	lowerCAmelCase__    (      self       ):
      pass







     @parameterized.expand([('''linear''',), ('''dynamic''',)]       )
     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__     =      ids_tensor([1, 10] ,						config.vocab_size       )
      __magic_name__     =      ids_tensor([1, int(config.max_position_embeddings * 1.5       )] ,						config.vocab_size       )

      set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
      __magic_name__     =      LlamaModel(UpperCamelCase_       )
      original_model.to(UpperCamelCase_       )
      original_model.eval()
      __magic_name__     =      original_model(UpperCamelCase_       ).last_hidden_state
      __magic_name__     =      original_model(UpperCamelCase_       ).last_hidden_state

      set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
      __magic_name__     =      {'''type''': scaling_type, '''factor''': 1_0.0}
      __magic_name__     =      LlamaModel(UpperCamelCase_       )
      scaled_model.to(UpperCamelCase_       )
      scaled_model.eval()
      __magic_name__     =      scaled_model(UpperCamelCase_       ).last_hidden_state
      __magic_name__     =      scaled_model(UpperCamelCase_       ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
       self.assertTrue(torch.allclose(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-5       )       )
      else:
       self.assertFalse(torch.allclose(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-5       )       )

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


@require_torch
class        _lowercase					(     unittest.TestCase     ):







     @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''       )
     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __magic_name__     =      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''' ,						device_map='''auto'''       )
      __magic_name__     =      model(torch.tensor([input_ids]       )       )
      # Expected mean on dim = -1
      __magic_name__     =      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
      __magic_name__     =      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       ):
      __magic_name__     =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __magic_name__     =      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''' ,						device_map='''auto'''       )
      __magic_name__     =      model(torch.tensor(UpperCamelCase_       )       )
      # Expected mean on dim = -1
      __magic_name__     =      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
      __magic_name__     =      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       ):
      __magic_name__     =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __magic_name__     =      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' ,						device_map='''auto'''       )
      __magic_name__     =      model(torch.tensor(UpperCamelCase_       )       )
      # Expected mean on dim = -1
      __magic_name__     =      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
      __magic_name__     =      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       ):
      __magic_name__     =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __magic_name__     =      LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''' ,						device_map='''auto'''       )
      __magic_name__     =      model(torch.tensor(UpperCamelCase_       )       )

      __magic_name__     =      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
      __magic_name__     =      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       ):
      __magic_name__     =      '''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'''
      __magic_name__     =      '''Simply put, the theory of relativity states that '''
      __magic_name__     =      LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''       )
      __magic_name__     =      tokenizer.encode(UpperCamelCase_ ,						return_tensors='''pt'''       )
      __magic_name__     =      LlamaForCausalLM.from_pretrained(
          '''meta-llama/Llama-2-13b-chat-hf''' ,						device_map='''sequential''' ,						use_safetensors=UpperCamelCase_       )

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








"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		{
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       '''fnet'''







     def __init__(      self ,						UpperCamelCase_=3_2000 ,						UpperCamelCase_=768 ,						UpperCamelCase_=12 ,						UpperCamelCase_=3072 ,						UpperCamelCase_="gelu_new" ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=512 ,						UpperCamelCase_=4 ,						UpperCamelCase_=0.0_2 ,						UpperCamelCase_=1E-1_2 ,						UpperCamelCase_=False ,						UpperCamelCase_=512 ,						UpperCamelCase_=3 ,						UpperCamelCase_=1 ,						UpperCamelCase_=2 ,						**UpperCamelCase_ ,						):
      super().__init__(pad_token_id=UpperCamelCase_ ,						bos_token_id=UpperCamelCase_ ,						eos_token_id=UpperCamelCase_ ,						**UpperCamelCase_       )

      __magic_name__     =      vocab_size
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      initializer_range
      __magic_name__     =      type_vocab_size
      __magic_name__     =      layer_norm_eps
      __magic_name__     =      use_tpu_fourier_optimizations
      __magic_name__     =      tpu_short_seq_length

