
"""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      _UpperCAmelCase							:




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=False   ,       lowercase_=True   ,       lowercase_=9_9   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=5_1_2   ,       lowercase_=1_6   ,       lowercase_=2   ,       lowercase_=0.0_2   ,       lowercase_=3   ,       lowercase_=4   ,       lowercase_=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     a_      (							self							)						->					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     a_      (							self							)						->					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     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					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     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					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     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					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     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					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     a_      (							self							)						->					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      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	(LlamaForCausalLM,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE   :							Dict							      =	(
          {
              "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   :							str							      =	False




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					LlamaModelTester(self							)
          UpperCAmelCase									=					ConfigTester(self   ,       config_class=UpperCAmelCase__   ,       hidden_size=3_7							)




      def     a_      (							self							)						->					Tuple:
          self.config_tester.run_common_tests()




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase__							)




      def     a_      (							self							)						->					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     a_      (							self							)						->					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     a_      (							self							)						->					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     a_      (							self							)						->					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     a_      (							self							)						->					Optional[int]:
          pass




      @parameterized.expand([('linear',), ('dynamic',)]							)
      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase									=					ids_tensor([1, 1_0]   ,       config.vocab_size							)
          UpperCAmelCase									=					ids_tensor([1, int(config.max_position_embeddings * 1.5							)]   ,       config.vocab_size							)

          set_seed(4_2							)  # 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(4_2							)  # Fixed seed at init time so the two models get the same random weights
          UpperCAmelCase									=					{'type': scaling_type, 'factor': 1_0.0}
          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      _UpperCAmelCase							(		unittest.TestCase							):




      @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'							)
      @slow
      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
          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([[-6.6_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
          UpperCAmelCase									=					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, :3_0]   ,       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     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
          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([[-2.0_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
          UpperCAmelCase									=					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, :3_0]   ,       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     a_      (							self							)						->					int:
          UpperCAmelCase									=					[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
          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([[-0.8_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
          UpperCAmelCase									=					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     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
          UpperCAmelCase									=					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'   ,       device_map='auto'							)
          UpperCAmelCase									=					model(torch.tensor(UpperCAmelCase__							)							)

          UpperCAmelCase									=					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
          UpperCAmelCase									=					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, :3_0]   ,       UpperCAmelCase__   ,       atol=1E-5   ,       rtol=1E-5							)




      @unittest.skip('Model is curently gated'							)
      @slow
      def     a_      (							self							)						->					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=6_4   ,       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 json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							str							      =	CTRLTokenizer
      __SCREAMING_SNAKE_CASE   :							int							      =	False
      __SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	False




      def     a_      (							self							)						->					Dict:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCAmelCase									=					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
          UpperCAmelCase									=					dict(zip(lowercase_   ,       range(len(lowercase_							)							)							)							)
          UpperCAmelCase									=					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
          UpperCAmelCase									=					{'unk_token': '<unk>'}

          UpperCAmelCase									=					os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES['vocab_file']							)
          UpperCAmelCase									=					os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES['merges_file']							)
          with open(self.vocab_file   ,       'w'   ,       encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)
          with open(self.merges_file   ,       'w'   ,       encoding='utf-8'							) as fp:
              fp.write('\n'.join(lowercase_							)							)




      def     a_      (							self   ,       **lowercase_							)						->					Tuple:
          kwargs.update(self.special_tokens_map							)
          return CTRLTokenizer.from_pretrained(self.tmpdirname   ,       **lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          UpperCAmelCase									=					'adapt react readapt apt'
          UpperCAmelCase									=					'adapt react readapt apt'
          return input_text, output_text




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					CTRLTokenizer(self.vocab_file   ,       self.merges_file   ,       **self.special_tokens_map							)
          UpperCAmelCase									=					'adapt react readapt apt'
          UpperCAmelCase									=					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
          UpperCAmelCase									=					tokenizer.tokenize(lowercase_							)
          self.assertListEqual(lowercase_   ,       lowercase_							)

          UpperCAmelCase									=					tokens + [tokenizer.unk_token]

          UpperCAmelCase									=					[0, 1, 2, 4, 5, 1, 0, 3, 6]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)   ,       lowercase_							)



