






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_    :






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A=13							,  _A=7							,  _A=True							,  _A=True							,  _A=True							,  _A=True							,  _A=99							,  _A=64							,  _A=5							,  _A=4							,  _A=37							,  _A="gelu"							,  _A=0.1							,  _A=0.1							,  _A=512							,  _A=16							,  _A=2							,  _A=0.02							,  _A=3							,  _A=4							,  _A=None							,  ) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				parent
     _UpperCAmelCase				:      Optional[Any]			  =				batch_size
     _UpperCAmelCase				:      Union[str, Any]			  =				seq_length
     _UpperCAmelCase				:      List[Any]			  =				is_training
     _UpperCAmelCase				:      str			  =				use_input_mask
     _UpperCAmelCase				:      Union[str, Any]			  =				use_token_type_ids
     _UpperCAmelCase				:      int			  =				use_labels
     _UpperCAmelCase				:      Optional[Any]			  =				vocab_size
     _UpperCAmelCase				:      Dict			  =				hidden_size
     _UpperCAmelCase				:      List[Any]			  =				num_hidden_layers
     _UpperCAmelCase				:      Union[str, Any]			  =				num_attention_heads
     _UpperCAmelCase				:      Optional[int]			  =				intermediate_size
     _UpperCAmelCase				:      str			  =				hidden_act
     _UpperCAmelCase				:      Optional[Any]			  =				hidden_dropout_prob
     _UpperCAmelCase				:      Optional[Any]			  =				attention_probs_dropout_prob
     _UpperCAmelCase				:      Any			  =				max_position_embeddings
     _UpperCAmelCase				:      List[str]			  =				type_vocab_size
     _UpperCAmelCase				:      Optional[Any]			  =				type_sequence_label_size
     _UpperCAmelCase				:      Tuple			  =				initializer_range
     _UpperCAmelCase				:      List[str]			  =				num_labels
     _UpperCAmelCase				:      Any			  =				num_choices
     _UpperCAmelCase				:      Optional[Any]			  =				scope
     _UpperCAmelCase				:      int			  =				vocab_size - 1


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size)

     _UpperCAmelCase				:      int			  =				None
     if self.use_input_mask:
         _UpperCAmelCase				:      List[Any]			  =				random_attention_mask([self.batch_size, self.seq_length])

     _UpperCAmelCase				:      Optional[int]			  =				None
     if self.use_labels:
         _UpperCAmelCase				:      List[str]			  =				ids_tensor([self.batch_size, self.seq_length]							,  self.num_labels)

     _UpperCAmelCase				:      List[Any]			  =				self.get_config()

     return config, input_ids, input_mask, token_labels


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     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=_A							,  initializer_range=self.initializer_range							,  pad_token_id=self.pad_token_id							,  )


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

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				self.prepare_config_and_inputs()

     _UpperCAmelCase				:      Dict			  =				True

     return config, input_ids, input_mask, token_labels


 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				GPTNeoXModel(config=_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      Any			  =				model(_A							,  attention_mask=_A)
     _UpperCAmelCase				:      List[str]			  =				model(_A)
     self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size))


 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				True
     _UpperCAmelCase				:      Any			  =				GPTNeoXModel(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      List[str]			  =				model(_A							,  attention_mask=_A)
     self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size))


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				GPTNeoXForCausalLM(config=_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      Optional[Any]			  =				model(_A							,  attention_mask=_A							,  labels=_A)
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.vocab_size))


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				self.num_labels
     _UpperCAmelCase				:      Union[str, Any]			  =				GPTNeoXForQuestionAnswering(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      List[Any]			  =				model(_A							,  attention_mask=_A)
     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  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				self.num_labels
     _UpperCAmelCase				:      List[Any]			  =				GPTNeoXForSequenceClassification(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      int			  =				ids_tensor([self.batch_size]							,  self.type_sequence_label_size)
     _UpperCAmelCase				:      Tuple			  =				model(_A							,  attention_mask=_A							,  labels=_A)
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels))


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				self.num_labels
     _UpperCAmelCase				:      str			  =				GPTNeoXForTokenClassification(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      List[Any]			  =				model(_A							,  attention_mask=_A							,  labels=_A)
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels))


 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				True
     _UpperCAmelCase				:      Dict			  =				GPTNeoXForCausalLM(config=_A)
     model.to(_A)
     model.eval()

     # first forward pass
     _UpperCAmelCase				:      Dict			  =				model(_A							,  attention_mask=_A							,  use_cache=_A)
     _UpperCAmelCase				:      Tuple			  =				outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     _UpperCAmelCase				:      List[Any]			  =				ids_tensor((self.batch_size, 3)							,  config.vocab_size)
     _UpperCAmelCase				:      int			  =				ids_tensor((self.batch_size, 3)							,  vocab_size=2)

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

     _UpperCAmelCase				:      Dict			  =				model(_A							,  attention_mask=_A							,  output_hidden_states=_A)
     _UpperCAmelCase				:      List[Any]			  =				output_from_no_past['''hidden_states'''][0]
     _UpperCAmelCase				:      Optional[int]			  =				model(
         _A							,  attention_mask=_A							,  past_key_values=_A							,  output_hidden_states=_A							,  )['''hidden_states'''][0]

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


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				self.prepare_config_and_inputs()
     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Optional[int]			  =				config_and_inputs
     _UpperCAmelCase				:      Any			  =				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     return config, inputs_dict



@require_torch
class 							A_    (						lowercase__ ,   lowercase__ ,   lowercase__ ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[Any] =					(
     (
         GPTNeoXModel,
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _SCREAMING_SNAKE_CASE							:			str =					(GPTNeoXForCausalLM,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					(
     {
         'feature-extraction': GPTNeoXModel,
         'question-answering': GPTNeoXForQuestionAnswering,
         'text-classification': GPTNeoXForSequenceClassification,
         'text-generation': GPTNeoXForCausalLM,
         'token-classification': GPTNeoXForTokenClassification,
         'zero-shot': GPTNeoXForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					False
 _SCREAMING_SNAKE_CASE							:			int =					False
 _SCREAMING_SNAKE_CASE							:			Dict =					False
 _SCREAMING_SNAKE_CASE							:			Any =					False


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				GPTNeoXModelTester(self)
     _UpperCAmelCase				:      int			  =				ConfigTester(self							,  config_class=_A							,  hidden_size=64							,  num_attention_heads=8)


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     self.config_tester.run_common_tests()


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[Any]			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(_A							,  _A							,  _A)


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(_A							,  _A							,  _A)


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Tuple			  =				self.model_tester.prepare_config_and_inputs_for_decoder()

     _UpperCAmelCase				:      Tuple			  =				None

     self.model_tester.create_and_check_model_as_decoder(_A							,  _A							,  _A)


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(_A							,  _A							,  _A)


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_causal_lm(*_A)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*_A)


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

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*_A)


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*_A)


 @unittest.skip(reason='''Feed forward chunking is not implemented''')
 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     pass


 @parameterized.expand([('''linear''',), ('''dynamic''',)])
 def  snake_case__				(	self							,  _A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				self.model_tester.prepare_config_and_inputs_for_common()
     _UpperCAmelCase				:      Optional[int]			  =				ids_tensor([1, 10]							,  config.vocab_size)
     _UpperCAmelCase				:      int			  =				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				:      List[Any]			  =				GPTNeoXModel(_A)
     original_model.to(_A)
     original_model.eval()
     _UpperCAmelCase				:      Optional[Any]			  =				original_model(_A).last_hidden_state
     _UpperCAmelCase				:      Any			  =				original_model(_A).last_hidden_state

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

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



@require_torch
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 @slow
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped''')
     for checkpointing in [True, False]:
         _UpperCAmelCase				:      List[str]			  =				GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped''')

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

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

         _UpperCAmelCase				:      List[Any]			  =				model.generate(**_A							,  do_sample=_A							,  max_new_tokens=20)
         _UpperCAmelCase				:      Dict			  =				tokenizer.batch_decode(_A)[0]

         self.assertEqual(_A							,  _A)










import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




