




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








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


_UpperCAmelCase    			=					logging.get_logger(__name__)

_UpperCAmelCase    			=					{
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = "gpt2"
  _UpperCamelCase						 = ["past_key_values"]
  _UpperCamelCase						 = {
      "hidden_size": "n_embd",
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }


  def __init__(					self	,		a__=5_02_57	,		a__=10_24	,		a__=7_68	,		a__=12	,		a__=12	,		a__=None	,		a__="gelu_new"	,		a__=0.1	,		a__=0.1	,		a__=0.1	,		a__=1E-5	,		a__=0.02	,		a__="cls_index"	,		a__=True	,		a__=None	,		a__=True	,		a__=0.1	,		a__=True	,		a__=True	,		a__=5_02_56	,		a__=5_02_56	,		a__=False	,		a__=False	,		**a__	,		):
    _lowerCamelCase      =						vocab_size
    _lowerCamelCase      =						n_positions
    _lowerCamelCase      =						n_embd
    _lowerCamelCase      =						n_layer
    _lowerCamelCase      =						n_head
    _lowerCamelCase      =						n_inner
    _lowerCamelCase      =						activation_function
    _lowerCamelCase      =						resid_pdrop
    _lowerCamelCase      =						embd_pdrop
    _lowerCamelCase      =						attn_pdrop
    _lowerCamelCase      =						layer_norm_epsilon
    _lowerCamelCase      =						initializer_range
    _lowerCamelCase      =						summary_type
    _lowerCamelCase      =						summary_use_proj
    _lowerCamelCase      =						summary_activation
    _lowerCamelCase      =						summary_first_dropout
    _lowerCamelCase      =						summary_proj_to_labels
    _lowerCamelCase      =						scale_attn_weights
    _lowerCamelCase      =						use_cache
    _lowerCamelCase      =						scale_attn_by_inverse_layer_idx
    _lowerCamelCase      =						reorder_and_upcast_attn

    _lowerCamelCase      =						bos_token_id
    _lowerCamelCase      =						eos_token_id

    super().__init__(bos_token_id=a__	,		eos_token_id=a__	,		**a__		)







class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		a__	,		a__ = "default"	,		a__ = None	,		a__ = False	,		):
    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?
      _lowerCamelCase      =						0


  @property
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
    if self.use_past:
      self.fill_with_past_key_values_(a__	,		direction='''inputs'''		)
      _lowerCamelCase      =						{0: '''batch''', 1: '''past_sequence + sequence'''}
    else:
      _lowerCamelCase      =						{0: '''batch''', 1: '''sequence'''}

    return common_inputs


  @property
  def     _UpperCAmelCase  (					self		):
    return self._config.n_layer


  @property
  def     _UpperCAmelCase  (					self		):
    return self._config.n_head


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = -1	,		a__ = -1	,		a__ = False	,		a__ = None	,		):
    _lowerCamelCase      =						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()
    _lowerCamelCase      =						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

        _lowerCamelCase    ,    _lowerCamelCase      =						common_inputs['''input_ids'''].shape
        # Not using the same length for past_key_values
        _lowerCamelCase      =						seqlen + 2
        _lowerCamelCase      =						(
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        _lowerCamelCase      =						[
            (torch.zeros(a__		), torch.zeros(a__		)) for _ in range(self.num_layers		)
        ]

    _lowerCamelCase      =						common_inputs['''attention_mask''']
    if self.use_past:
      _lowerCamelCase      =						ordered_inputs['''attention_mask'''].dtype
      _lowerCamelCase      =						torch.cat(
          [ordered_inputs['''attention_mask'''], torch.ones(a__	,		a__	,		dtype=a__		)]	,		dim=1		)

    return ordered_inputs


  @property
  def     _UpperCAmelCase  (					self		):
    return 13




