'''simple docstring'''

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def __a			(						__lowerCamelCase      :    Any					)						->				List[Any]:
      '''simple docstring'''


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

      return orig_key


def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    List[str]					)						->				Union[str, Any]:
      '''simple docstring'''


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

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

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

      return orig_state_dict


def __a			(						__lowerCamelCase      :    Any			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    Union[str, Any]					)						->				Any:
      '''simple docstring'''


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

      lowercase_     			=   convert_checkpoint_helper(config.max_position_embeddings			,		__SCREAMING_SNAKE_CASE					)

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

      print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'					)


if __name__ == "__main__":
       lowerCAmelCase_			:			Tuple    	=   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."
       )
       lowerCAmelCase_			:			Tuple    	=   parser.parse_args()
       convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    json.load(open(__SCREAMING_SNAKE_CASE,		"r"							)							)
    snake_case_			:     Optional[int]        =    GPTNeoConfig(
        hidden_size=config_json["n_embd"],		num_layers=config_json["n_layer"],		num_heads=config_json["n_head"],		attention_types=config_json["attention_types"],		max_position_embeddings=config_json["n_positions"],		resid_dropout=config_json["res_dropout"],		embed_dropout=config_json["embed_dropout"],		attention_dropout=config_json["attn_dropout"],		)
    print(f'Building PyTorch model from configuration: {config}'							)
    snake_case_			:     Optional[Any]        =    GPTNeoForCausalLM(__SCREAMING_SNAKE_CASE							)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'							)
    model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
  a_           =    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(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained mesh-tf 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_           =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




