






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




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				str									=ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size      )

       SCREAMING_SNAKE_CASE__     :				int									=None
       if self.use_input_mask:
             SCREAMING_SNAKE_CASE__     :				Tuple									=random_attention_mask([self.batch_size, self.seq_length]      )

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

       SCREAMING_SNAKE_CASE__     :				int									=None
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=None
       if self.use_labels:
             SCREAMING_SNAKE_CASE__     :				str									=ids_tensor([self.batch_size]					,      self.type_sequence_label_size      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels      )
             SCREAMING_SNAKE_CASE__     :				List[str]									=ids_tensor([self.batch_size]					,      self.num_choices      )

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

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




 def      __magic_name__	(      self	:							Tuple      )	->						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=__lowercase					,      initializer_range=self.initializer_range					,      )




 def      __magic_name__	(      self	:							Union[str, Any]					,      __lowercase	:							List[str]					,      __lowercase	:							str					,      __lowercase	:							int					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Tuple					,      __lowercase	:							List[str]					,      __lowercase	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=LlamaModel(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(__lowercase					,      attention_mask=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									=model(__lowercase      )
       self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Tuple					,      __lowercase	:							List[str]					,      __lowercase	:							List[str]					,      __lowercase	:							Optional[int]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any					,      __lowercase	:							int					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Union[str, Any]					,      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				int									=True
       SCREAMING_SNAKE_CASE__     :				int									=LlamaModel(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Dict									=model(
           __lowercase					,      attention_mask=__lowercase					,      encoder_hidden_states=__lowercase					,      encoder_attention_mask=__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(
           __lowercase					,      attention_mask=__lowercase					,      encoder_hidden_states=__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(__lowercase					,      attention_mask=__lowercase      )
       self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							List[str]					,      __lowercase	:							Dict					,      __lowercase	:							Any					,      __lowercase	:							str					,      __lowercase	:							Dict					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							int					,      __lowercase	:							List[str]					,      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=LlamaForCausalLM(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Tuple									=model(__lowercase					,      attention_mask=__lowercase					,      labels=__lowercase      )
       self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)      )




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Tuple					,      __lowercase	:							List[Any]					,      __lowercase	:							List[str]					,      __lowercase	:							Dict					,      __lowercase	:							Any					,      __lowercase	:							int					,      __lowercase	:							Optional[Any]					,      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=True
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
       SCREAMING_SNAKE_CASE__     :				Any									=LlamaForCausalLM(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()

       # first forward pass
       SCREAMING_SNAKE_CASE__     :				Any									=model(
           __lowercase					,      attention_mask=__lowercase					,      encoder_hidden_states=__lowercase					,      encoder_attention_mask=__lowercase					,      use_cache=__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=ids_tensor((self.batch_size, 3)					,      config.vocab_size      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=ids_tensor((self.batch_size, 3)					,      vocab_size=2      )

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

       SCREAMING_SNAKE_CASE__     :				List[str]									=model(
           __lowercase					,      attention_mask=__lowercase					,      encoder_hidden_states=__lowercase					,      encoder_attention_mask=__lowercase					,      output_hidden_states=__lowercase					,      )['''hidden_states'''][0]
       SCREAMING_SNAKE_CASE__     :				Any									=model(
           __lowercase					,      attention_mask=__lowercase					,      encoder_hidden_states=__lowercase					,      encoder_attention_mask=__lowercase					,      past_key_values=__lowercase					,      output_hidden_states=__lowercase					,      )['''hidden_states'''][0]

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




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Any									=self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       )		,	(
           SCREAMING_SNAKE_CASE__
       )		,	(
           SCREAMING_SNAKE_CASE__
       )		,	(
           SCREAMING_SNAKE_CASE__
       )		,	(
           SCREAMING_SNAKE_CASE__
       )		,	(
           SCREAMING_SNAKE_CASE__
       )		,	(
           SCREAMING_SNAKE_CASE__
       )		,	
       )     :				Union[str, Any]									=config_and_inputs
       SCREAMING_SNAKE_CASE__     :				Tuple									={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				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      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				str									=LlamaModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=ConfigTester(self					,      config_class=__lowercase					,      hidden_size=37      )




 def      __magic_name__	(      self	:							Dict      )	->						str:
       self.config_tester.run_common_tests()




 def      __magic_name__	(      self	:							List[str]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             SCREAMING_SNAKE_CASE__     :				str									=type
             self.model_tester.create_and_check_model(*__lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=3
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=input_dict['''input_ids''']
       SCREAMING_SNAKE_CASE__     :				Any									=input_ids.ne(1      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=ids_tensor([self.model_tester.batch_size]					,      self.model_tester.type_sequence_label_size      )
       SCREAMING_SNAKE_CASE__     :				Any									=LlamaForSequenceClassification(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				str									=model(__lowercase					,      attention_mask=__lowercase					,      labels=__lowercase      )
       self.assertEqual(result.logits.shape					,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Any:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				Dict									=3
       SCREAMING_SNAKE_CASE__     :				List[str]									='''single_label_classification'''
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=input_dict['''input_ids''']
       SCREAMING_SNAKE_CASE__     :				Any									=input_ids.ne(1      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=ids_tensor([self.model_tester.batch_size]					,      self.model_tester.type_sequence_label_size      )
       SCREAMING_SNAKE_CASE__     :				Any									=LlamaForSequenceClassification(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Dict									=model(__lowercase					,      attention_mask=__lowercase					,      labels=__lowercase      )
       self.assertEqual(result.logits.shape					,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=3
       SCREAMING_SNAKE_CASE__     :				Tuple									='''multi_label_classification'''
       SCREAMING_SNAKE_CASE__     :				Dict									=input_dict['''input_ids''']
       SCREAMING_SNAKE_CASE__     :				str									=input_ids.ne(1      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=ids_tensor(
           [self.model_tester.batch_size, config.num_labels]					,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=LlamaForSequenceClassification(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=model(__lowercase					,      attention_mask=__lowercase					,      labels=__lowercase      )
       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      __magic_name__	(      self	:							Any      )	->						str:
       pass




 @parameterized.expand([('''linear''',), ('''dynamic''',)]      )
 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Optional[int]      )	->						Any:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				int									=ids_tensor([1, 10]					,      config.vocab_size      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=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
       SCREAMING_SNAKE_CASE__     :				Tuple									=LlamaModel(__lowercase      )
       original_model.to(__lowercase      )
       original_model.eval()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=original_model(__lowercase      ).last_hidden_state
       SCREAMING_SNAKE_CASE__     :				Any									=original_model(__lowercase      ).last_hidden_state

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

       # The output should be different for long inputs
       self.assertFalse(torch.allclose(__lowercase					,      __lowercase					,      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      __magic_name__	(      self	:							List[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				str									=[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''					,      device_map='''auto'''      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(torch.tensor([input_ids]      )      )
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=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      )					,      __lowercase					,      atol=1e-2					,      rtol=1e-2      )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				List[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]					,      __lowercase					,      atol=1e-5					,      rtol=1e-5      )




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




 @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''      )
 @slow
 def      __magic_name__	(      self	:							str      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''					,      device_map='''auto'''      )
       SCREAMING_SNAKE_CASE__     :				str									=model(torch.tensor(__lowercase      )      )
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE__     :				List[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      )					,      __lowercase					,      atol=1e-2					,      rtol=1e-2      )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				str									=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      )					,      __lowercase					,      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      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
       SCREAMING_SNAKE_CASE__     :				List[Any]									=LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''					,      device_map='''auto'''      )
       SCREAMING_SNAKE_CASE__     :				int									=model(torch.tensor(__lowercase      )      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=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      )					,      __lowercase					,      atol=1e-2					,      rtol=1e-2      )
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				Dict									=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]					,      __lowercase					,      atol=1e-5					,      rtol=1e-5      )




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

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







'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
  raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a_					= False
a_					= False





def  _a(  UpperCamelCase__				:						Namespace							):




      '''simple docstring'''






      return TrainCommand(UpperCamelCase__							)







class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 @staticmethod
 def      __magic_name__	(      __lowercase	:							ArgumentParser      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=parser.add_parser('''train'''					,      help='''CLI tool to train a model on a task.'''      )

       train_parser.add_argument(
           '''--train_data'''					,      type=__lowercase					,      required=__lowercase					,      help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''					,      )
       train_parser.add_argument(
           '''--column_label'''					,      type=__lowercase					,      default=0					,      help='''Column of the dataset csv file with example labels.'''      )
       train_parser.add_argument(
           '''--column_text'''					,      type=__lowercase					,      default=1					,      help='''Column of the dataset csv file with example texts.'''      )
       train_parser.add_argument(
           '''--column_id'''					,      type=__lowercase					,      default=2					,      help='''Column of the dataset csv file with example ids.'''      )
       train_parser.add_argument(
           '''--skip_first_row'''					,      action='''store_true'''					,      help='''Skip the first row of the csv file (headers).'''      )

       train_parser.add_argument('''--validation_data'''					,      type=__lowercase					,      default=''''''					,      help='''path to validation dataset.'''      )
       train_parser.add_argument(
           '''--validation_split'''					,      type=__lowercase					,      default=0.1					,      help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.'''					,      )

       train_parser.add_argument('''--output'''					,      type=__lowercase					,      default='''./'''					,      help='''path to saved the trained model.'''      )

       train_parser.add_argument(
           '''--task'''					,      type=__lowercase					,      default='''text_classification'''					,      help='''Task to train the model on.'''      )
       train_parser.add_argument(
           '''--model'''					,      type=__lowercase					,      default='''bert-base-uncased'''					,      help='''Model\'s name or path to stored model.'''      )
       train_parser.add_argument('''--train_batch_size'''					,      type=__lowercase					,      default=32					,      help='''Batch size for training.'''      )
       train_parser.add_argument('''--valid_batch_size'''					,      type=__lowercase					,      default=64					,      help='''Batch size for validation.'''      )
       train_parser.add_argument('''--learning_rate'''					,      type=__lowercase					,      default=3e-5					,      help='''Learning rate.'''      )
       train_parser.add_argument('''--adam_epsilon'''					,      type=__lowercase					,      default=1e-08					,      help='''Epsilon for Adam optimizer.'''      )
       train_parser.set_defaults(func=__lowercase      )




 def __init__(      self	:							Tuple					,      __lowercase	:							Namespace      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=logging.get_logger('''transformers-cli/training'''      )

       SCREAMING_SNAKE_CASE__     :				int									='''tf''' if is_tf_available() else '''torch'''

       os.makedirs(args.output					,      exist_ok=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=args.output

       SCREAMING_SNAKE_CASE__     :				str									=args.column_label
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.column_text
       SCREAMING_SNAKE_CASE__     :				Tuple									=args.column_id

       self.logger.info(F"Loading {args.task} pipeline for {args.model}"      )
       if args.task == "text_classification":
             SCREAMING_SNAKE_CASE__     :				List[str]									=TextClassificationPipeline.from_pretrained(args.model      )
       elif args.task == "token_classification":
             raise NotImplementedError
       elif args.task == "question_answering":
             raise NotImplementedError

       self.logger.info(F"Loading dataset from {args.train_data}"      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Processor.create_from_csv(
           args.train_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None
       if args.validation_data:
             self.logger.info(F"Loading validation dataset from {args.validation_data}"      )
             SCREAMING_SNAKE_CASE__     :				List[Any]									=Processor.create_from_csv(
                 args.validation_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=args.validation_split
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.train_batch_size
       SCREAMING_SNAKE_CASE__     :				Any									=args.valid_batch_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=args.learning_rate
       SCREAMING_SNAKE_CASE__     :				int									=args.adam_epsilon




 def      __magic_name__	(      self	:							Any      )	->						str:
       if self.framework == "tf":
             return self.run_tf()
       return self.run_torch()




 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       raise NotImplementedError




 def      __magic_name__	(      self	:							Dict      )	->						List[Any]:
       self.pipeline.fit(
           self.train_dataset					,      validation_data=self.valid_dataset					,      validation_split=self.validation_split					,      learning_rate=self.learning_rate					,      adam_epsilon=self.adam_epsilon					,      train_batch_size=self.train_batch_size					,      valid_batch_size=self.valid_batch_size					,      )

       # Save trained pipeline
       self.pipeline.save_pretrained(self.output      )