



'''simple docstring'''






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


A_							: str						 =	logging.get_logger(__name__)

A_							: Union[str, Any]						 =	{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """gptj"""
 UpperCAmelCase							=  {
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }







 def __init__(							self  ,a_=50_400  ,a_=2_048  ,a_=4_096  ,a_=28  ,a_=16  ,a_=64  ,a_=None  ,a_="gelu_new"  ,a_=0.0  ,a_=0.0  ,a_=0.0  ,a_=1E-5  ,a_=0.02  ,a_=True  ,a_=50_256  ,a_=50_256  ,a_=False  ,**a_  ,)		->   str:
        _UpperCAmelCase  :						Optional[int]			     = vocab_size
        _UpperCAmelCase  :						int			     = n_positions
        _UpperCAmelCase  :						Optional[Any]			     = n_embd
        _UpperCAmelCase  :						List[Any]			     = n_layer
        _UpperCAmelCase  :						Dict			     = n_head
        _UpperCAmelCase  :						Dict			     = n_inner
        _UpperCAmelCase  :						str			     = rotary_dim
        _UpperCAmelCase  :						Any			     = activation_function
        _UpperCAmelCase  :						Optional[Any]			     = resid_pdrop
        _UpperCAmelCase  :						List[Any]			     = embd_pdrop
        _UpperCAmelCase  :						Tuple			     = attn_pdrop
        _UpperCAmelCase  :						Optional[Any]			     = layer_norm_epsilon
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						Optional[Any]			     = use_cache

        _UpperCAmelCase  :						Union[str, Any]			     = bos_token_id
        _UpperCAmelCase  :						Optional[int]			     = eos_token_id

        super().__init__(
            bos_token_id=a_  ,eos_token_id=a_  ,tie_word_embeddings=a_  ,**a_							)







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_ = "default"  ,a_ = None  ,a_ = False  ,)		->   Optional[int]:
        super().__init__(a_  ,task=a_  ,patching_specs=a_  ,use_past=a_							)
        if not getattr(self._config  ,"""pad_token_id"""  ,a_							):
               # TODO: how to do that better?
               _UpperCAmelCase  :						List[str]			     = 0







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        _UpperCAmelCase  :						Any			     = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
        if self.use_past:
               self.fill_with_past_key_values_(a_  ,direction="""inputs"""							)
               _UpperCAmelCase  :						str			     = {0: """batch""", 1: """past_sequence + sequence"""}
        else:
               _UpperCAmelCase  :						Any			     = {0: """batch""", 1: """sequence"""}

        return common_inputs







 @property
 def _snake_case (							self							)		->   int:
        return self._config.n_layer







 @property
 def _snake_case (							self							)		->   int:
        return self._config.n_head







 def _snake_case (							self  ,a_  ,a_ = -1  ,a_ = -1  ,a_ = False  ,a_ = None  ,)		->   Mapping[str, Any]:
        _UpperCAmelCase  :						Dict			     = super(a_  ,self							).generate_dummy_inputs(
            a_  ,batch_size=a_  ,seq_length=a_  ,is_pair=a_  ,framework=a_							)

        # We need to order the input in the way they appears in the forward()
        _UpperCAmelCase  :						int			     = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
               else:
                      import torch

                      _UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = common_inputs["""input_ids"""].shape
                      # Not using the same length for past_key_values
                      _UpperCAmelCase  :						Optional[int]			     = seqlen + 2
                      _UpperCAmelCase  :						List[Any]			     = (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      _UpperCAmelCase  :						Optional[int]			     = [
                          (torch.zeros(a_							), torch.zeros(a_							)) for _ in range(self.num_layers							)
                      ]

        _UpperCAmelCase  :						int			     = common_inputs["""attention_mask"""]
        if self.use_past:
               _UpperCAmelCase  :						int			     = ordered_inputs["""attention_mask"""].dtype
               _UpperCAmelCase  :						List[Any]			     = torch.cat(
                   [ordered_inputs["""attention_mask"""], torch.ones(a_  ,a_  ,dtype=a_							)]  ,dim=1							)

        return ordered_inputs







 @property
 def _snake_case (							self							)		->   int:
        return 13








'''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)



