
'''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	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str]=13,    SCREAMING_SNAKE_CASE_	:				List[str]=7,    SCREAMING_SNAKE_CASE_	:				int=True,    SCREAMING_SNAKE_CASE_	:				str=True,    SCREAMING_SNAKE_CASE_	:				Optional[int]=False,    SCREAMING_SNAKE_CASE_	:				Optional[int]=True,    SCREAMING_SNAKE_CASE_	:				List[str]=99,    SCREAMING_SNAKE_CASE_	:				Optional[int]=32,    SCREAMING_SNAKE_CASE_	:				Optional[int]=5,    SCREAMING_SNAKE_CASE_	:				str=4,    SCREAMING_SNAKE_CASE_	:				Any=37,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="gelu",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=0.1,    SCREAMING_SNAKE_CASE_	:				List[Any]=0.1,    SCREAMING_SNAKE_CASE_	:				str=5_12,    SCREAMING_SNAKE_CASE_	:				Tuple=16,    SCREAMING_SNAKE_CASE_	:				List[str]=2,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=0.02,    SCREAMING_SNAKE_CASE_	:				str=3,    SCREAMING_SNAKE_CASE_	:				str=4,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None,    ):
          snake_case       :     Tuple =    parent
          snake_case       :     List[Any] =    batch_size
          snake_case       :     Dict =    seq_length
          snake_case       :     Optional[Any] =    is_training
          snake_case       :     List[Any] =    use_input_mask
          snake_case       :     Any =    use_token_type_ids
          snake_case       :     List[str] =    use_labels
          snake_case       :     int =    vocab_size
          snake_case       :     int =    hidden_size
          snake_case       :     Any =    num_hidden_layers
          snake_case       :     Optional[Any] =    num_attention_heads
          snake_case       :     int =    intermediate_size
          snake_case       :     str =    hidden_act
          snake_case       :     Any =    hidden_dropout_prob
          snake_case       :     Optional[int] =    attention_probs_dropout_prob
          snake_case       :     Any =    max_position_embeddings
          snake_case       :     Optional[int] =    type_vocab_size
          snake_case       :     Optional[int] =    type_sequence_label_size
          snake_case       :     Dict =    initializer_range
          snake_case       :     Optional[int] =    num_labels
          snake_case       :     int =    num_choices
          snake_case       :     str =    scope




    def 	__snake_case							(							self	:				Optional[int]      ):
          snake_case       :     List[str] =    ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

          snake_case       :     Any =    None
          if self.use_input_mask:
                snake_case       :     Optional[int] =    random_attention_mask([self.batch_size, self.seq_length]      )

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

          snake_case       :     Optional[int] =    None
          snake_case       :     int =    None
          snake_case       :     int =    None
          if self.use_labels:
                snake_case       :     str =    ids_tensor([self.batch_size],    self.type_sequence_label_size      )
                snake_case       :     str =    ids_tensor([self.batch_size, self.seq_length],    self.num_labels      )
                snake_case       :     int =    ids_tensor([self.batch_size],    self.num_choices      )

          snake_case       :     Optional[int] =    self.get_config()

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




    def 	__snake_case							(							self	:				Tuple      ):
          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=SCREAMING_SNAKE_CASE_,    initializer_range=self.initializer_range,    )




    def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
          snake_case       :     Optional[int] =    LlamaModel(config=SCREAMING_SNAKE_CASE_      )
          model.to(SCREAMING_SNAKE_CASE_      )
          model.eval()
          snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_      )
          snake_case       :     List[Any] =    model(SCREAMING_SNAKE_CASE_      )
          self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )




    def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[int],    ):
          snake_case       :     int =    True
          snake_case       :     Optional[int] =    LlamaModel(SCREAMING_SNAKE_CASE_      )
          model.to(SCREAMING_SNAKE_CASE_      )
          model.eval()
          snake_case       :     int =    model(
              SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    encoder_attention_mask=SCREAMING_SNAKE_CASE_,    )
          snake_case       :     Optional[Any] =    model(
              SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    )
          snake_case       :     Any =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_      )
          self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )




    def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    ):
          snake_case       :     Optional[Any] =    LlamaForCausalLM(config=SCREAMING_SNAKE_CASE_      )
          model.to(SCREAMING_SNAKE_CASE_      )
          model.eval()
          snake_case       :     Union[str, Any] =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    labels=SCREAMING_SNAKE_CASE_      )
          self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.vocab_size)      )




    def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    ):
          snake_case       :     List[Any] =    True
          snake_case       :     Optional[Any] =    True
          snake_case       :     int =    LlamaForCausalLM(config=SCREAMING_SNAKE_CASE_      )
          model.to(SCREAMING_SNAKE_CASE_      )
          model.eval()

          # first forward pass
          snake_case       :     str =    model(
              SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    encoder_attention_mask=SCREAMING_SNAKE_CASE_,    use_cache=SCREAMING_SNAKE_CASE_,    )
          snake_case       :     int =    outputs.past_key_values

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

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

          snake_case       :     List[str] =    model(
              SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    encoder_attention_mask=SCREAMING_SNAKE_CASE_,    output_hidden_states=SCREAMING_SNAKE_CASE_,    )['''hidden_states'''][0]
          snake_case       :     List[str] =    model(
              SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    encoder_attention_mask=SCREAMING_SNAKE_CASE_,    past_key_values=SCREAMING_SNAKE_CASE_,    output_hidden_states=SCREAMING_SNAKE_CASE_,    )['''hidden_states'''][0]

          # select random slice
          snake_case       :     Union[str, Any] =    ids_tensor((1,),    output_from_past.shape[-1]      ).item()
          snake_case       :     int =    output_from_no_past[:, -3:, random_slice_idx].detach()
          snake_case       :     Union[str, 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(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=1e-3      )      )




    def 	__snake_case							(							self	:				Any      ):
          snake_case       :     Union[str, Any] =    self.prepare_config_and_inputs()
          (
              snake_case					
          )       :     Optional[int] =    config_and_inputs
          snake_case       :     Optional[Any] =    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




@require_torch
class 						a   (				__magic_name__				,__magic_name__				,__magic_name__				,unittest.TestCase    ):
    _snake_case		    = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    _snake_case		    = (LlamaForCausalLM,) if is_torch_available() else ()
    _snake_case		    = (
        {
            '''feature-extraction''': LlamaModel,
            '''text-classification''': LlamaForSequenceClassification,
            '''text-generation''': LlamaForCausalLM,
            '''zero-shot''': LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _snake_case		    = False
    _snake_case		    = False




    def 	__snake_case							(							self	:				Any      ):
          snake_case       :     Optional[Any] =    LlamaModelTester(self      )
          snake_case       :     Optional[int] =    ConfigTester(self,    config_class=SCREAMING_SNAKE_CASE_,    hidden_size=37      )




    def 	__snake_case							(							self	:				Optional[int]      ):
          self.config_tester.run_common_tests()




    def 	__snake_case							(							self	:				int      ):
          snake_case       :     Union[str, Any] =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )




    def 	__snake_case							(							self	:				List[Any]      ):
          snake_case       :     List[Any] =    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                snake_case       :     Optional[int] =    type
                self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )




    def 	__snake_case							(							self	:				Dict      ):
          snake_case       :     Optional[Any] =    self.model_tester.prepare_config_and_inputs_for_common()
          snake_case       :     Optional[Any] =    3
          snake_case       :     Dict =    input_dict['''input_ids''']
          snake_case       :     str =    input_ids.ne(1      ).to(SCREAMING_SNAKE_CASE_      )
          snake_case       :     Tuple =    ids_tensor([self.model_tester.batch_size],    self.model_tester.type_sequence_label_size      )
          snake_case       :     Optional[int] =    LlamaForSequenceClassification(SCREAMING_SNAKE_CASE_      )
          model.to(SCREAMING_SNAKE_CASE_      )
          model.eval()
          snake_case       :     str =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    labels=SCREAMING_SNAKE_CASE_      )
          self.assertEqual(result.logits.shape,    (self.model_tester.batch_size, self.model_tester.num_labels)      )




    def 	__snake_case							(							self	:				Optional[Any]      ):
          snake_case       :     Union[str, Any] =    self.model_tester.prepare_config_and_inputs_for_common()
          snake_case       :     List[Any] =    3
          snake_case       :     List[Any] =    '''single_label_classification'''
          snake_case       :     int =    input_dict['''input_ids''']
          snake_case       :     List[Any] =    input_ids.ne(1      ).to(SCREAMING_SNAKE_CASE_      )
          snake_case       :     List[str] =    ids_tensor([self.model_tester.batch_size],    self.model_tester.type_sequence_label_size      )
          snake_case       :     Optional[Any] =    LlamaForSequenceClassification(SCREAMING_SNAKE_CASE_      )
          model.to(SCREAMING_SNAKE_CASE_      )
          model.eval()
          snake_case       :     Dict =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    labels=SCREAMING_SNAKE_CASE_      )
          self.assertEqual(result.logits.shape,    (self.model_tester.batch_size, self.model_tester.num_labels)      )




    def 	__snake_case							(							self	:				str      ):
          snake_case       :     int =    self.model_tester.prepare_config_and_inputs_for_common()
          snake_case       :     List[str] =    3
          snake_case       :     Dict =    '''multi_label_classification'''
          snake_case       :     Optional[int] =    input_dict['''input_ids''']
          snake_case       :     Union[str, Any] =    input_ids.ne(1      ).to(SCREAMING_SNAKE_CASE_      )
          snake_case       :     str =    ids_tensor(
              [self.model_tester.batch_size, config.num_labels],    self.model_tester.type_sequence_label_size      ).to(torch.float      )
          snake_case       :     Dict =    LlamaForSequenceClassification(SCREAMING_SNAKE_CASE_      )
          model.to(SCREAMING_SNAKE_CASE_      )
          model.eval()
          snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    labels=SCREAMING_SNAKE_CASE_      )
          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 	__snake_case							(							self	:				Union[str, Any]      ):
          pass




    @parameterized.expand([('''linear''',), ('''dynamic''',)]      )
    def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
          snake_case       :     Optional[Any] =    self.model_tester.prepare_config_and_inputs_for_common()
          snake_case       :     Union[str, Any] =    ids_tensor([1, 10],    config.vocab_size      )
          snake_case       :     List[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
          snake_case       :     List[Any] =    LlamaModel(SCREAMING_SNAKE_CASE_      )
          original_model.to(SCREAMING_SNAKE_CASE_      )
          original_model.eval()
          snake_case       :     Optional[int] =    original_model(SCREAMING_SNAKE_CASE_      ).last_hidden_state
          snake_case       :     Dict =    original_model(SCREAMING_SNAKE_CASE_      ).last_hidden_state

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

          # The output should be different for long inputs
          self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    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 	__snake_case							(							self	:				Any      ):
          snake_case       :     List[str] =    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          snake_case       :     str =    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''',    device_map='''auto'''      )
          snake_case       :     List[Any] =    model(torch.tensor([input_ids]      )      )
          # Expected mean on dim = -1
          snake_case       :     Optional[Any] =    torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]      )
          torch.testing.assert_close(out.mean(-1      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
          # slicing logits[0, 0, 0:30]
          # fmt: off
          snake_case       :     Optional[Any] =    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],    SCREAMING_SNAKE_CASE_,    atol=1e-5,    rtol=1e-5      )




    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''      )
    @slow
    def 	__snake_case							(							self	:				List[str]      ):
          snake_case       :     Optional[Any] =    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          snake_case       :     Dict =    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''',    device_map='''auto'''      )
          snake_case       :     int =    model(torch.tensor(SCREAMING_SNAKE_CASE_      )      )
          # Expected mean on dim = -1
          snake_case       :     Dict =    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      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
          # slicing logits[0, 0, 0:30]
          # fmt: off
          snake_case       :     Any =    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],    SCREAMING_SNAKE_CASE_,    atol=1e-5,    rtol=1e-5      )




    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''      )
    @slow
    def 	__snake_case							(							self	:				Any      ):
          snake_case       :     List[str] =    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          snake_case       :     Optional[Any] =    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''',    device_map='''auto'''      )
          snake_case       :     List[Any] =    model(torch.tensor(SCREAMING_SNAKE_CASE_      )      )
          # Expected mean on dim = -1
          snake_case       :     Optional[Any] =    torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]      )
          torch.testing.assert_close(out.mean(-1      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
          # slicing logits[0, 0, 0:30]
          # fmt: off
          snake_case       :     Any =    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      ),    SCREAMING_SNAKE_CASE_,    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 	__snake_case							(							self	:				Optional[Any]      ):
          snake_case       :     Optional[Any] =    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          snake_case       :     List[Any] =    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''',    device_map='''auto'''      )
          snake_case       :     Dict =    model(torch.tensor(SCREAMING_SNAKE_CASE_      )      )

          snake_case       :     int =    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      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
          # fmt: off
          snake_case       :     List[Any] =    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],    SCREAMING_SNAKE_CASE_,    atol=1e-5,    rtol=1e-5      )




    @unittest.skip('''Model is curently gated'''      )
    @slow
    def 	__snake_case							(							self	:				Tuple      ):
          snake_case       :     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'''
          snake_case       :     Union[str, Any] =    '''Simply put, the theory of relativity states that '''
          snake_case       :     Optional[Any] =    LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''      )
          snake_case       :     List[Any] =    tokenizer.encode(SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      )
          snake_case       :     List[Any] =    LlamaForCausalLM.from_pretrained(
              '''meta-llama/Llama-2-13b-chat-hf''',    device_map='''sequential''',    use_safetensors=SCREAMING_SNAKE_CASE_      )

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



'''simple docstring'''





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase    					=   {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = GPTaTokenizer




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Dict="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=False,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Dict =    add_prefix_space
																snake_case       :     List[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space




				def 	__snake_case							(							self	:				Optional[Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     List[Any] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Conversation"      ):
										snake_case       :     int =    []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

										if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
																snake_case       :     str =    input_ids[-self.model_max_length :]
										return input_ids

