






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__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			List[str]=13       ,				lowerCamelCase		:			Optional[int]=7       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			Dict=99       ,				lowerCamelCase		:			Optional[int]=32       ,				lowerCamelCase		:			Optional[Any]=5       ,				lowerCamelCase		:			Optional[int]=4       ,				lowerCamelCase		:			List[Any]=37       ,				lowerCamelCase		:			Dict="gelu"       ,				lowerCamelCase		:			Union[str, Any]=0.1       ,				lowerCamelCase		:			Optional[int]=0.1       ,				lowerCamelCase		:			List[Any]=512       ,				lowerCamelCase		:			List[Any]=16       ,				lowerCamelCase		:			Optional[int]=2       ,				lowerCamelCase		:			Tuple=0.02       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			int=4       ,				lowerCamelCase		:			List[str]=None       ,				)						->	List[str]:


         """simple docstring"""
         _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  lowerCamelCase       (      self		:			Tuple							)						->	int:


         """simple docstring"""
         _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  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """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  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Any							)						->	str:


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


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			int       ,				)						->	int:


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


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			int       ,				)						->	List[Any]:


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


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[Any]       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    LlamaForCausalLM(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         # first forward pass
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				encoder_attention_mask=lowerCamelCase       ,				use_cache=lowerCamelCase       ,				)
         _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(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				encoder_attention_mask=lowerCamelCase       ,				output_hidden_states=lowerCamelCase       ,				)["""hidden_states"""][0]
         _UpperCAmelCase							=    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
         _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(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-3							)							)





  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _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		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    LlamaModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


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


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


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _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(*lowerCamelCase							)


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


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


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


         """simple docstring"""
         _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(lowerCamelCase							)
         _UpperCAmelCase							=    ids_tensor([self.model_tester.batch_size]       ,				self.model_tester.type_sequence_label_size							)
         _UpperCAmelCase							=    LlamaForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)


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


         """simple docstring"""
         _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(lowerCamelCase							)
         _UpperCAmelCase							=    ids_tensor(
             [self.model_tester.batch_size, config.num_labels]       ,				self.model_tester.type_sequence_label_size							).to(torch.float							)
         _UpperCAmelCase							=    LlamaForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         pass





  @parameterized.expand([("""linear""",), ("""dynamic""",)]							)
  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Optional[Any]							)						->	Optional[Any]:


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

         set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
         _UpperCAmelCase							=    {"""type""": scaling_type, """factor""": 10.0}
         _UpperCAmelCase							=    LlamaModel(lowerCamelCase							)
         scaled_model.to(lowerCamelCase							)
         scaled_model.eval()
         _UpperCAmelCase							=    scaled_model(lowerCamelCase							).last_hidden_state
         _UpperCAmelCase							=    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     ):

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         _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([[-6.6550, -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
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""       ,				device_map="""auto"""							)
         _UpperCAmelCase							=    model(torch.tensor(lowerCamelCase							)							)
         # Expected mean on dim = -1
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""       ,				device_map="""auto"""							)
         _UpperCAmelCase							=    model(torch.tensor(lowerCamelCase							)							)
         # Expected mean on dim = -1
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


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

         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _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(lowerCamelCase       ,				return_tensors="""pt"""							)
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained(
             """meta-llama/Llama-2-13b-chat-hf"""       ,				device_map="""sequential"""       ,				use_safetensors=lowerCamelCase							)

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







import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__a:     Tuple   					=					datasets.utils.logging.get_logger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE__	(  datasets.BuilderConfig     ):

  '''simple docstring'''







  _lowerCamelCase    =  None
  _lowerCamelCase    =  "utf-8"
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  True  # deprecated
  _lowerCamelCase    =  None  # deprecated
  _lowerCamelCase    =  10 << 20  # 10MB
  _lowerCamelCase    =  None



class 		SCREAMING_SNAKE_CASE__	(  datasets.ArrowBasedBuilder     ):

  '''simple docstring'''







  _lowerCamelCase    =  JsonConfig


  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         if self.config.block_size is not None:
                logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""							)
                _UpperCAmelCase							=    self.config.block_size
         if self.config.use_threads is not True:
                logger.warning(
                    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""							)
         if self.config.newlines_in_values is not None:
                raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""							)
         return datasets.DatasetInfo(features=self.config.features							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         if not self.config.data_files:
                raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
         _UpperCAmelCase							=    dl_manager.download_and_extract(self.config.data_files							)
         if isinstance(lowerCamelCase       ,				(str, list, tuple)							):
                _UpperCAmelCase							=    data_files
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,				gen_kwargs={"""files""": files}							)]
         _UpperCAmelCase							=    []
         for split_name, files in data_files.items():
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                splits.append(datasets.SplitGenerator(name=lowerCamelCase       ,				gen_kwargs={"""files""": files}							)							)
         return splits


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			pa.Table							)						->	pa.Table:


         """simple docstring"""
         if self.config.features is not None:
                # adding missing columns
                for column_name in set(self.config.features							) - set(pa_table.column_names							):
                       _UpperCAmelCase							=    self.config.features.arrow_schema.field(lowerCamelCase							).type
                       _UpperCAmelCase							=    pa_table.append_column(lowerCamelCase       ,				pa.array([None] * len(lowerCamelCase							)       ,				type=lowerCamelCase							)							)
                # more expensive cast to support nested structures with keys in a different order
                # allows str <-> int/float or str to Audio for example
                _UpperCAmelCase							=    table_cast(lowerCamelCase       ,				self.config.features.arrow_schema							)
         return pa_table





  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]							)						->	str:


         """simple docstring"""
         for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase							)							):
                # If the file is one json object and if we need to look at the list of items in one specific field
                if self.config.field is not None:
                       with open(lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                              _UpperCAmelCase							=    json.load(lowerCamelCase							)

                       # We keep only the field we are interested in
                       _UpperCAmelCase							=    dataset[self.config.field]

                       # We accept two format: a list of dicts or a dict of lists
                       if isinstance(lowerCamelCase       ,				(list, tuple)							):
                              _UpperCAmelCase							=    set().union(*[row.keys() for row in dataset]							)
                              _UpperCAmelCase							=    {col: [row.get(lowerCamelCase							) for row in dataset] for col in keys}
                       else:
                              _UpperCAmelCase							=    dataset
                       _UpperCAmelCase							=    pa.Table.from_pydict(lowerCamelCase							)
                       yield file_idx, self._cast_table(lowerCamelCase							)

                # If the file has one json object per line
                else:
                       with open(lowerCamelCase       ,				"""rb"""							) as f:
                              _UpperCAmelCase							=    0
                              # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                              # Set a default minimum value of 16kB if the chunk size is really small
                              _UpperCAmelCase							=    max(self.config.chunksize // 32       ,				16 << 10							)
                              _UpperCAmelCase							=    (
                                  self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                              )
                              while True:
                                     _UpperCAmelCase							=    f.read(self.config.chunksize							)
                                     if not batch:
                                            break
                                     # Finish current line
                                     try:
                                            batch += f.readline()
                                     except (AttributeError, io.UnsupportedOperation):
                                            batch += readline(lowerCamelCase							)
                                     # PyArrow only accepts utf-8 encoded bytes
                                     if self.config.encoding != "utf-8":
                                            _UpperCAmelCase							=    batch.decode(self.config.encoding       ,				errors=lowerCamelCase							).encode("""utf-8"""							)
                                     try:
                                            while True:
                                                   try:
                                                          _UpperCAmelCase							=    paj.read_json(
                                                              io.BytesIO(lowerCamelCase							)       ,				read_options=paj.ReadOptions(block_size=lowerCamelCase							)							)
                                                          break
                                                   except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                          if (
                                                              isinstance(lowerCamelCase       ,				pa.ArrowInvalid							)
                                                              and "straddling" not in str(lowerCamelCase							)
                                                              or block_size > len(lowerCamelCase							)
                                                          ):
                                                                 raise
                                                          else:
                                                                 # Increase the block size in case it was too small.
                                                                 # The block size will be reset for the next file.
                                                                 logger.debug(
                                                                     f"""Batch of {len(lowerCamelCase							)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""							)
                                                                 block_size *= 2
                                     except pa.ArrowInvalid as e:
                                            try:
                                                   with open(
                                                       lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                                                          _UpperCAmelCase							=    json.load(lowerCamelCase							)
                                            except json.JSONDecodeError:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                   raise e
                                            # If possible, parse the file as a list of json objects and exit the loop
                                            if isinstance(lowerCamelCase       ,				lowerCamelCase							):  # list is the only sequence type supported in JSON
                                                   try:
                                                          _UpperCAmelCase							=    set().union(*[row.keys() for row in dataset]							)
                                                          _UpperCAmelCase							=    {col: [row.get(lowerCamelCase							) for row in dataset] for col in keys}
                                                          _UpperCAmelCase							=    pa.Table.from_pydict(lowerCamelCase							)
                                                   except (pa.ArrowInvalid, AttributeError) as e:
                                                          logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                          raise ValueError(f"""Not able to read records in the JSON file at {file}."""							) from None
                                                   yield file_idx, self._cast_table(lowerCamelCase							)
                                                   break
                                            else:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                   raise ValueError(
                                                       f"""Not able to read records in the JSON file at {file}. """
                                                       f"""You should probably indicate the field of the JSON file containing your records. """
                                                       f"""This JSON file contain the following fields: {str(list(dataset.keys()							)							)}. """
                                                       f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """							) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                     yield (file_idx, batch_idx), self._cast_table(lowerCamelCase							)
                                     batch_idx += 1