



'''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 							lowercase       :


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







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						int			     = ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size							)

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

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

        _UpperCAmelCase  :						Any			     = self.get_config()

        return config, input_ids, input_mask, token_labels







 def _snake_case (							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=a_  ,initializer_range=self.initializer_range  ,pad_token_id=self.pad_token_id  ,)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = self.prepare_config_and_inputs()

        _UpperCAmelCase  :						Any			     = True

        return config, input_ids, input_mask, token_labels







 def _snake_case (							self  ,a_  ,a_  ,a_							)		->   str:
        _UpperCAmelCase  :						Any			     = GPTNeoXModel(config=a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						Tuple			     = model(a_  ,attention_mask=a_							)
        _UpperCAmelCase  :						Optional[int]			     = 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:
        _UpperCAmelCase  :						Dict			     = True
        _UpperCAmelCase  :						str			     = GPTNeoXModel(a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						Any			     = 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_							)		->   Tuple:
        _UpperCAmelCase  :						Union[str, Any]			     = GPTNeoXForCausalLM(config=a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						Dict			     = 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_							)		->   List[Any]:
        _UpperCAmelCase  :						List[str]			     = self.num_labels
        _UpperCAmelCase  :						int			     = GPTNeoXForQuestionAnswering(a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						str			     = 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_							)		->   List[Any]:
        _UpperCAmelCase  :						Tuple			     = self.num_labels
        _UpperCAmelCase  :						List[str]			     = GPTNeoXForSequenceClassification(a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						int			     = ids_tensor([self.batch_size]  ,self.type_sequence_label_size							)
        _UpperCAmelCase  :						int			     = 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_							)		->   Union[str, Any]:
        _UpperCAmelCase  :						int			     = self.num_labels
        _UpperCAmelCase  :						str			     = GPTNeoXForTokenClassification(a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						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_							)		->   List[Any]:
        _UpperCAmelCase  :						Optional[Any]			     = True
        _UpperCAmelCase  :						int			     = GPTNeoXForCausalLM(config=a_							)
        model.to(a_							)
        model.eval()

        # first forward pass
        _UpperCAmelCase  :						List[str]			     = model(a_  ,attention_mask=a_  ,use_cache=a_							)
        _UpperCAmelCase  :						List[Any]			     = outputs.past_key_values

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

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

        _UpperCAmelCase  :						Dict			     = model(a_  ,attention_mask=a_  ,output_hidden_states=a_							)
        _UpperCAmelCase  :						List[str]			     = output_from_no_past["""hidden_states"""][0]
        _UpperCAmelCase  :						Optional[Any]			     = model(
            a_  ,attention_mask=a_  ,past_key_values=a_  ,output_hidden_states=a_  ,)["""hidden_states"""][0]

        # select random slice
        _UpperCAmelCase  :						Union[str, Any]			     = ids_tensor((1,)  ,output_from_past.shape[-1]							).item()
        _UpperCAmelCase  :						List[str]			     = output_from_no_past[:, -3:, random_slice_idx].detach()
        _UpperCAmelCase  :						int			     = 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							)		->   List[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = self.prepare_config_and_inputs()
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = config_and_inputs
        _UpperCAmelCase  :						Any			     = {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 							lowercase       (  _lowerCamelCase					,					_lowerCamelCase					,					_lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  (
     (
         GPTNeoXModel,
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase							=  (GPTNeoXForCausalLM,) if is_torch_available() else ()
 UpperCAmelCase							=  (
     {
         """feature-extraction""": GPTNeoXModel,
         """question-answering""": GPTNeoXForQuestionAnswering,
         """text-classification""": GPTNeoXForSequenceClassification,
         """text-generation""": GPTNeoXForCausalLM,
         """token-classification""": GPTNeoXForTokenClassification,
         """zero-shot""": GPTNeoXForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase							=  False
 UpperCAmelCase							=  False
 UpperCAmelCase							=  False
 UpperCAmelCase							=  False







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = GPTNeoXModelTester(self							)
        _UpperCAmelCase  :						Tuple			     = ConfigTester(self  ,config_class=a_  ,hidden_size=64  ,num_attention_heads=8							)







 def _snake_case (							self							)		->   int:
        self.config_tester.run_common_tests()







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(a_  ,a_  ,a_							)







 def _snake_case (							self							)		->   int:
        _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:
        # This regression test was failing with PyTorch < 1.3
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Optional[Any]			     = self.model_tester.prepare_config_and_inputs_for_decoder()

        _UpperCAmelCase  :						List[Any]			     = None

        self.model_tester.create_and_check_model_as_decoder(a_  ,a_  ,a_							)







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = 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							)		->   Any:
        _UpperCAmelCase  :						Optional[Any]			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*a_							)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						Union[str, Any]			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*a_							)







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						List[Any]			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*a_							)







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Optional[int]			     = 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							)		->   str:
        pass







 @parameterized.expand([("""linear""",), ("""dynamic""",)]							)
 def _snake_case (							self  ,a_							)		->   List[str]:
        _UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase  :						Optional[Any]			     = ids_tensor([1, 10]  ,config.vocab_size							)
        _UpperCAmelCase  :						Tuple			     = 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  :						str			     = GPTNeoXModel(a_							)
        original_model.to(a_							)
        original_model.eval()
        _UpperCAmelCase  :						str			     = original_model(a_							).last_hidden_state
        _UpperCAmelCase  :						List[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  :						List[str]			     = {"""type""": scaling_type, """factor""": 10.0}
        _UpperCAmelCase  :						Optional[int]			     = GPTNeoXModel(a_							)
        scaled_model.to(a_							)
        scaled_model.eval()
        _UpperCAmelCase  :						Union[str, Any]			     = scaled_model(a_							).last_hidden_state
        _UpperCAmelCase  :						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 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)
        for checkpointing in [True, False]:
               _UpperCAmelCase  :						Optional[int]			     = 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  :						Dict			     = """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

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

               self.assertEqual(a_  ,a_							)








'''simple docstring'''






import argparse

import torch

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


logging.set_verbosity_info()




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = RemBertConfig.from_json_file(lowerCAmelCase_		)
       print("""Building PyTorch model from configuration: {}""".format(str(lowerCAmelCase_		)		)		)
       _UpperCAmelCase  :						Any			     = RemBertModel(lowerCAmelCase_		)

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

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


if __name__ == "__main__":
   A_							: Optional[Any]						 =	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."""
   )
   A_							: Any						 =	parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



