




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case            = logging.get_logger(__name__)







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[int]    =		torch.load(snake_case_,map_location="""cpu"""  )
      if "model" in sd.keys():
            _A							:  Dict    =		torch.load(snake_case_,map_location="""cpu"""  )["""model"""]

      # pop unnecessary weights
      _A							:  int    =		[
          """decoder.version""",
          """decoder.output_projection.weight""",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(snake_case_  )

      _A							:  str    =		{
          """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
          """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
          """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
          """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  _A							:  Dict    =		sd.pop(snake_case_  )

      _A							:  List[str]    =		list(sd.keys()  )
      for key in keys:
            if ".qkv_proj." in key:
                  _A							:  Tuple    =		sd[key]
                  # We split QKV in separate Q,K,V

                  _A							:  Optional[Any]    =		key.replace(""".qkv_proj.""",""".q_proj."""  )
                  _A							:  Tuple    =		key.replace(""".qkv_proj.""",""".k_proj."""  )
                  _A							:  List[str]    =		key.replace(""".qkv_proj.""",""".v_proj."""  )

                  _A							:  Dict    =		value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  _A    ,     _A    ,     _A							:  List[str]    =		torch.split(snake_case_,depth // 3,dim=0  )

                  _A							:  str    =		q
                  _A							:  int    =		k
                  _A							:  List[str]    =		v
                  del sd[key]

      return sd







@torch.no_grad()
def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_=None  ):
      _A							:  Any    =		load_checkpoint(snake_case_  )

      if config is not None:
            _A							:  Union[str, Any]    =		OPTConfig.from_pretrained(snake_case_  )
      else:
            _A							:  str    =		OPTConfig()

      _A							:  Optional[Any]    =		OPTModel(snake_case_  ).half().eval()
      model.load_state_dict(snake_case_  )

      # Check results
      Path(snake_case_  ).mkdir(exist_ok=snake_case_  )
      model.save_pretrained(snake_case_  )


if __name__ == "__main__":
  _snake_case            = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fairseq_path",
      type=str,
      help=(
          "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
          " https://huggingface.co/models?other=opt_metasq"
      ),
  )
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
  _snake_case            = parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

