


'''simple docstring'''






import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, 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 (
     GPTNeoXForCausalLM,
     GPTNeoXForQuestionAnswering,
     GPTNeoXForSequenceClassification,
     GPTNeoXForTokenClassification,
     GPTNeoXModel,
 )




class 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=7 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=99 ,			__magic_name__=64 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=5_12 ,			__magic_name__=16 ,			__magic_name__=2 ,			__magic_name__=0.0_2 ,			__magic_name__=3 ,			__magic_name__=4 ,			__magic_name__=None ,			)   ->							str:
      _a      =							parent
      _a      =							batch_size
      _a      =							seq_length
      _a      =							is_training
      _a      =							use_input_mask
      _a      =							use_token_type_ids
      _a      =							use_labels
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							hidden_act
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							type_vocab_size
      _a      =							type_sequence_label_size
      _a      =							initializer_range
      _a      =							num_labels
      _a      =							num_choices
      _a      =							scope
      _a      =							vocab_size - 1
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

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

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

      _a      =							self.get_config()

      return config, input_ids, input_mask, token_labels
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      return GPTNeoXConfig(
          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=__magic_name__ ,			initializer_range=self.initializer_range ,			pad_token_id=self.pad_token_id ,			)
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a ,				_a ,				_a ,				_a      =							self.prepare_config_and_inputs()

      _a      =							True

      return config, input_ids, input_mask, token_labels
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Any:
      _a      =							GPTNeoXModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__  )
      _a      =							model(__magic_name__  )
      self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[Any]:
      _a      =							True
      _a      =							GPTNeoXModel(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__  )
      self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							GPTNeoXForCausalLM(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Tuple:
      _a      =							self.num_labels
      _a      =							GPTNeoXForQuestionAnswering(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__  )
      self.parent.assertEqual(result.start_logits.shape ,			(self.batch_size, self.seq_length)  )
      self.parent.assertEqual(result.end_logits.shape ,			(self.batch_size, self.seq_length)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							str:
      _a      =							self.num_labels
      _a      =							GPTNeoXForSequenceClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							ids_tensor([self.batch_size] ,			self.type_sequence_label_size  )
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[str]:
      _a      =							self.num_labels
      _a      =							GPTNeoXForTokenClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Any:
      _a      =							True
      _a      =							GPTNeoXForCausalLM(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()

      # first forward pass
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			use_cache=__magic_name__  )
      _a      =							outputs.past_key_values

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

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

      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			output_hidden_states=__magic_name__  )
      _a      =							output_from_no_past['hidden_states'][0]
      _a      =							model(
          __magic_name__ ,			attention_mask=__magic_name__ ,			past_key_values=__magic_name__ ,			output_hidden_states=__magic_name__ ,			)['hidden_states'][0]

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

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__magic_name__ ,			__magic_name__ ,			atol=1e-3  )  )




 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.prepare_config_and_inputs()
      _a ,				_a ,				_a ,				_a      =							config_and_inputs
      _a      =							{'input_ids': input_ids, 'attention_mask': input_mask}
      return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (
         GPTNeoXModel,
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase	   =		(GPTNeoXForCausalLM,) if is_torch_available() else ()
 _lowerCAmelCase	   =		(
     {
         """feature-extraction""": GPTNeoXModel,
         """question-answering""": GPTNeoXForQuestionAnswering,
         """text-classification""": GPTNeoXForSequenceClassification,
         """text-generation""": GPTNeoXForCausalLM,
         """token-classification""": GPTNeoXForTokenClassification,
         """zero-shot""": GPTNeoXForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							GPTNeoXModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			hidden_size=64 ,			num_attention_heads=8  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a ,				_a ,				_a ,				_a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(__magic_name__ ,			__magic_name__ ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a ,				_a ,				_a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(__magic_name__ ,			__magic_name__ ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      # This regression test was failing with PyTorch < 1.3
      _a ,				_a ,				_a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_decoder()

      _a      =							None

      self.model_tester.create_and_check_model_as_decoder(__magic_name__ ,			__magic_name__ ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a ,				_a ,				_a ,				_a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(__magic_name__ ,			__magic_name__ ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__magic_name__  )
 @unittest.skip(reason='Feed forward chunking is not implemented'  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      pass




 @parameterized.expand([('linear',), ('dynamic',)]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[Any]:
      _a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a      =							ids_tensor([1, 10] ,			config.vocab_size  )
      _a      =							ids_tensor([1, int(config.max_position_embeddings * 1.5  )] ,			config.vocab_size  )

      set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
      _a      =							GPTNeoXModel(__magic_name__  )
      original_model.to(__magic_name__  )
      original_model.eval()
      _a      =							original_model(__magic_name__  ).last_hidden_state
      _a      =							original_model(__magic_name__  ).last_hidden_state

      set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
      _a      =							{'type': scaling_type, 'factor': 1_0.0}
      _a      =							GPTNeoXModel(__magic_name__  )
      scaled_model.to(__magic_name__  )
      scaled_model.eval()
      _a      =							scaled_model(__magic_name__  ).last_hidden_state
      _a      =							scaled_model(__magic_name__  ).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(__magic_name__ ,			__magic_name__ ,			atol=1e-5  )  )
      else:
           self.assertFalse(torch.allclose(__magic_name__ ,			__magic_name__ ,			atol=1e-5  )  )

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





@require_torch
class 	a       (       unittest.TestCase    ):
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'  )
      for checkpointing in [True, False]:
           _a      =							GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'  )

           if checkpointing:
                model.gradient_checkpointing_enable()
           else:
                model.gradient_checkpointing_disable()
           model.to(__magic_name__  )

           _a      =							tokenizer('My favorite food is' ,			return_tensors='pt'  ).to(__magic_name__  )
           # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
           # See: https://github.com/huggingface/transformers/pull/24193
           _a      =							'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

           _a      =							model.generate(**__magic_name__ ,			do_sample=__magic_name__ ,			max_new_tokens=20  )
           _a      =							tokenizer.batch_decode(__magic_name__  )[0]

           self.assertEqual(__magic_name__ ,			__magic_name__  )





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

 a_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

