





'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class __a			:





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

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size     )

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

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

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

      __SCREAMING_SNAKE_CASE   				=							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					:      Optional[Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							LlamaModel(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							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					:      Tuple					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							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					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[Any]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

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

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

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

      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,output_hidden_states=lowerCamelCase					,)["""hidden_states"""][0]
      __SCREAMING_SNAKE_CASE   				=							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
      __SCREAMING_SNAKE_CASE   				=							ids_tensor((1,)					,output_from_past.shape[-1]     ).item()
      __SCREAMING_SNAKE_CASE   				=							output_from_no_past[:, -3:, random_slice_idx].detach()
      __SCREAMING_SNAKE_CASE   				=							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					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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
      )				,  
      )   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_torch
class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			Optional[Any]						   =						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
   __UpperCamelCase :			int						   =						(LlamaForCausalLM,) if is_torch_available() else ()
   __UpperCamelCase :			Union[str, Any]						   =						(
       {
           'feature-extraction': LlamaModel,
           'text-classification': LlamaForSequenceClassification,
           'text-generation': LlamaForCausalLM,
           'zero-shot': LlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCamelCase :			List[str]						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,hidden_size=37     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.model_tester.batch_size]					,self.model_tester.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							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					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							"""single_label_classification"""
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.model_tester.batch_size]					,self.model_tester.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							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					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							"""multi_label_classification"""
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					,self.model_tester.type_sequence_label_size     ).to(torch.float     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							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[str]     ):

      '''simple docstring'''

      pass





   @parameterized.expand([("""linear""",), ("""dynamic""",)]     )
   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

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

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





   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor([input_ids]     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]     )
      # 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					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]     )
      # 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					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]     )
      # 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					:      Union[str, Any]     ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]					,dtype=torch.floataa     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]     )
      # 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					:      List[str]     ):

      '''simple docstring'''

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

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











'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    PreTrainedTokenizer					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] = None					,		):





							'''simple docstring'''


							_a      	=							{}

							if train_file is not None:
														_a      	=							[train_file]
							if eval_file is not None:
														_a      	=							[eval_file]
							if test_file is not None:
														_a      	=							[test_file]

							_a      	=							datasets.load_dataset('''csv'''					,		data_files=UpperCamelCase )
							_a      	=							list(ds[list(files.keys() )[0]].features.keys() )
							_a      	=							features_name.pop(UpperCamelCase )
							_a      	=							list(set(ds[list(files.keys() )[0]][label_name] ) )
							_a      	=							{label: i for i, label in enumerate(UpperCamelCase )}
							_a      	=							tokenizer.model_input_names
							_a      	=							{}

							if len(UpperCamelCase ) == 1:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        example[features_name[0]]					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length''' )					,		batched=UpperCamelCase					,		)
							elif len(UpperCamelCase ) == 2:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length'''					,		)					,		batched=UpperCamelCase					,		)

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														_a      	=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														_a      	=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														_a      	=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

							return train_ds, val_ds, test_ds, labelaid


_snake_case : str  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							field(metadata={'help': 'Which column contains the label'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the training file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the development file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the test file'}       )
							lowercase_							     =							field(
							    default=128	,metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Overwrite the cached training and evaluation sets'}       )



@dataclass
class      A       :
							lowercase_							     =							field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'Set this flag to use fast tokenization.'}       )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,)



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
							_a	,				_a	,				_a      	=							parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir )
							    and os.listdir(training_args.output_dir )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
														    ''' --overwrite_output_dir to overcome.''' )

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,		datefmt='''%m/%d/%Y %H:%M:%S'''					,		level=logging.INFO					,		)
							logger.info(
							    f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '
							    f'16-bits training: {training_args.fpaa}' )
							logger.info(f'Training/evaluation parameters {training_args}' )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							_a      	=							AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,		cache_dir=model_args.cache_dir					,		)

							_a	,				_a	,				_a	,				_a      	=							get_tfds(
							    train_file=data_args.train_file					,		eval_file=data_args.dev_file					,		test_file=data_args.test_file					,		tokenizer=UpperCamelCase					,		label_column_id=data_args.label_column_id					,		max_seq_length=data_args.max_seq_length					,		)

							_a      	=							AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,		num_labels=len(UpperCamelCase )					,		labelaid=UpperCamelCase					,		idalabel={id: label for label, id in labelaid.items()}					,		finetuning_task='''text-classification'''					,		cache_dir=model_args.cache_dir					,		)

							with training_args.strategy.scope():
														_a      	=							TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path					,		from_pt=bool('''.bin''' in model_args.model_name_or_path )					,		config=UpperCamelCase					,		cache_dir=model_args.cache_dir					,		)

							def compute_metrics(UpperCamelCase			:    EvalPrediction ) -> Dict:
														_a      	=							np.argmax(p.predictions					,		axis=1 )

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							_a      	=							TFTrainer(
							    model=UpperCamelCase					,		args=UpperCamelCase					,		train_dataset=UpperCamelCase					,		eval_dataset=UpperCamelCase					,		compute_metrics=UpperCamelCase					,		)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir )

							# Evaluation
							_a      	=							{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***''' )
														_a      	=							trainer.evaluate()
														_a      	=							os.path.join(training_args.output_dir					,		'''eval_results.txt''' )

														with open(UpperCamelCase					,		'''w''' ) as writer:
																					logger.info('''***** Eval results *****''' )

																					for key, value in result.items():
																												logger.info(f'  {key} = {value}' )
																												writer.write(f'{key} = {value}\n' )

																					results.update(UpperCamelCase )

							return results


if __name__ == "__main__":
				main()

