






'''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 							__SCREAMING_SNAKE_CASE       :




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=13 ,   lowerCamelCase=7 ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=False ,   lowerCamelCase=True ,   lowerCamelCase=99 ,   lowerCamelCase=32 ,   lowerCamelCase=5 ,   lowerCamelCase=4 ,   lowerCamelCase=37 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.1 ,   lowerCamelCase=512 ,   lowerCamelCase=16 ,   lowerCamelCase=2 ,   lowerCamelCase=0.02 ,   lowerCamelCase=3 ,   lowerCamelCase=4 ,   lowerCamelCase=None ,   )							->Any:



         '''simple docstring'''
         __a				     =    parent
         __a				     =    batch_size
         __a				     =    seq_length
         __a				     =    is_training
         __a				     =    use_input_mask
         __a				     =    use_token_type_ids
         __a				     =    use_labels
         __a				     =    vocab_size
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    max_position_embeddings
         __a				     =    type_vocab_size
         __a				     =    type_sequence_label_size
         __a				     =    initializer_range
         __a				     =    num_labels
         __a				     =    num_choices
         __a				     =    scope




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size      )

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

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

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

         __a				     =    self.get_config()

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




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''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=lowerCamelCase ,   initializer_range=self.initializer_range ,   )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         __a				     =    LlamaModel(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase      )
         __a				     =    model(lowerCamelCase      )
         self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Any:



         '''simple docstring'''
         __a				     =    True
         __a				     =    LlamaModel(lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   encoder_hidden_states=lowerCamelCase ,   encoder_attention_mask=lowerCamelCase ,   )
         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   encoder_hidden_states=lowerCamelCase ,   )
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase      )
         self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    LlamaForCausalLM(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase ,   labels=lowerCamelCase      )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    True
         __a				     =    True
         __a				     =    LlamaForCausalLM(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()

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

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

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

         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   encoder_hidden_states=lowerCamelCase ,   encoder_attention_mask=lowerCamelCase ,   output_hidden_states=lowerCamelCase ,   )['hidden_states'][0]
         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   encoder_hidden_states=lowerCamelCase ,   encoder_attention_mask=lowerCamelCase ,   past_key_values=lowerCamelCase ,   output_hidden_states=lowerCamelCase ,   )['hidden_states'][0]

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






      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.prepare_config_and_inputs()
         (
             (
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			
         )				     =    config_and_inputs
         __a				     =    {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
      __a  					=(LlamaForCausalLM,) if is_torch_available() else ()
      __a  					=(
          {
              "feature-extraction": LlamaModel,
              "text-classification": LlamaForSequenceClassification,
              "text-generation": LlamaForCausalLM,
              "zero-shot": LlamaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __a  					=False
      __a  					=False




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    LlamaModelTester(self      )
         __a				     =    ConfigTester(self ,   config_class=lowerCamelCase ,   hidden_size=37      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         self.config_tester.run_common_tests()




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            __a				     =    type
            self.model_tester.create_and_check_model(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a     ,			__a				     =    self.model_tester.prepare_config_and_inputs_for_common()
         __a				     =    3
         __a				     =    input_dict['input_ids']
         __a				     =    input_ids.ne(1      ).to(lowerCamelCase      )
         __a				     =    ids_tensor([self.model_tester.batch_size] ,   self.model_tester.type_sequence_label_size      )
         __a				     =    LlamaForSequenceClassification(lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase ,   labels=lowerCamelCase      )
         self.assertEqual(result.logits.shape ,   (self.model_tester.batch_size, self.model_tester.num_labels)      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a     ,			__a				     =    self.model_tester.prepare_config_and_inputs_for_common()
         __a				     =    3
         __a				     =    'single_label_classification'
         __a				     =    input_dict['input_ids']
         __a				     =    input_ids.ne(1      ).to(lowerCamelCase      )
         __a				     =    ids_tensor([self.model_tester.batch_size] ,   self.model_tester.type_sequence_label_size      )
         __a				     =    LlamaForSequenceClassification(lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase ,   labels=lowerCamelCase      )
         self.assertEqual(result.logits.shape ,   (self.model_tester.batch_size, self.model_tester.num_labels)      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a     ,			__a				     =    self.model_tester.prepare_config_and_inputs_for_common()
         __a				     =    3
         __a				     =    'multi_label_classification'
         __a				     =    input_dict['input_ids']
         __a				     =    input_ids.ne(1      ).to(lowerCamelCase      )
         __a				     =    ids_tensor(
             [self.model_tester.batch_size, config.num_labels] ,   self.model_tester.type_sequence_label_size      ).to(torch.float      )
         __a				     =    LlamaForSequenceClassification(lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase ,   labels=lowerCamelCase      )
         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  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         pass






      @parameterized.expand([('linear',), ('dynamic',)]      )
      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         __a     ,			__a				     =    self.model_tester.prepare_config_and_inputs_for_common()
         __a				     =    ids_tensor([1, 10] ,   config.vocab_size      )
         __a				     =    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
         __a				     =    LlamaModel(lowerCamelCase      )
         original_model.to(lowerCamelCase      )
         original_model.eval()
         __a				     =    original_model(lowerCamelCase      ).last_hidden_state
         __a				     =    original_model(lowerCamelCase      ).last_hidden_state

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

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







@require_torch
class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
      @slow
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         __a				     =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' ,   device_map='auto'      )
         __a				     =    model(torch.tensor([input_ids]      )      )
         # Expected mean on dim = -1
         __a				     =    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      ) ,   lowerCamelCase ,   atol=1e-2 ,   rtol=1e-2      )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __a				     =    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] ,   lowerCamelCase ,   atol=1e-5 ,   rtol=1e-5      )




      @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
      @slow
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         __a				     =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' ,   device_map='auto'      )
         __a				     =    model(torch.tensor(lowerCamelCase      )      )
         # Expected mean on dim = -1
         __a				     =    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      ) ,   lowerCamelCase ,   atol=1e-2 ,   rtol=1e-2      )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __a				     =    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] ,   lowerCamelCase ,   atol=1e-5 ,   rtol=1e-5      )




      @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
      @slow
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         __a				     =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' ,   device_map='auto'      )
         __a				     =    model(torch.tensor(lowerCamelCase      )      )
         # Expected mean on dim = -1
         __a				     =    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      ) ,   lowerCamelCase ,   atol=1e-2 ,   rtol=1e-2      )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __a				     =    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      ) ,   lowerCamelCase ,   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  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         __a				     =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' ,   device_map='auto'      )
         __a				     =    model(torch.tensor(lowerCamelCase      )      )

         __a				     =    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      ) ,   lowerCamelCase ,   atol=1e-2 ,   rtol=1e-2      )
         # fmt: off
         __a				     =    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] ,   lowerCamelCase ,   atol=1e-5 ,   rtol=1e-5      )






      @unittest.skip('Model is curently gated'      )
      @slow
      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



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

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







'''simple docstring'''



__UpperCamelCase      :       Optional[Any]      			=      [
    """DownloadConfig""",
    """DownloadManager""",
    """DownloadMode""",
    """StreamingDownloadManager""",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager