

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if "model" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''model.'''   ,	''''''       )
     if "norm1" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''norm1'''   ,	'''attention.output.LayerNorm'''       )
     if "norm2" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''norm2'''   ,	'''output.LayerNorm'''       )
     if "norm" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''norm'''   ,	'''LayerNorm'''       )
     if "transformer" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.split('''.'''       )[0].split('''_'''       )[-1]
          SCREAMING_SNAKE_CASE__					=					orig_key.replace(F'''transformer_{layer_num}'''   ,	F'''encoder.layer.{layer_num}'''       )
     if "mha.attn" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''mha.attn'''   ,	'''attention.self'''       )
     if "mha" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''mha'''   ,	'''attention'''       )
     if "W_q" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''W_q'''   ,	'''self.query'''       )
     if "W_k" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''W_k'''   ,	'''self.key'''       )
     if "W_v" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''W_v'''   ,	'''self.value'''       )
     if "ff1" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''ff1'''   ,	'''intermediate.dense'''       )
     if "ff2" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''ff2'''   ,	'''output.dense'''       )
     if "ff" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''ff'''   ,	'''output.dense'''       )
     if "mlm_class" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''mlm.mlm_class'''   ,	'''cls.predictions.decoder'''       )
     if "mlm" in orig_key:
          SCREAMING_SNAKE_CASE__					=					orig_key.replace('''mlm'''   ,	'''cls.predictions.transform'''       )
     if "cls" not in orig_key:
          SCREAMING_SNAKE_CASE__					=					'''yoso.''' + orig_key

     return orig_key
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__					=					orig_state_dict.pop(_A       )

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

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

     return orig_state_dict
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					torch.load(_A   ,	map_location='''cpu'''       )['''model_state_dict''']
     SCREAMING_SNAKE_CASE__					=					YosoConfig.from_json_file(_A       )
     SCREAMING_SNAKE_CASE__					=					YosoForMaskedLM(_A       )

     SCREAMING_SNAKE_CASE__					=					convert_checkpoint_helper(config.max_position_embeddings   ,	_A       )

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The json file for YOSO model config.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    parser.parse_args()
       convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be trained."} )
  a     =      field(
      default="./"		,  metadata={"help": "Save dir where model repo is cloned and models updates are saved to."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path of training dataset."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for training."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for evaluation."} )
  a     =      field(default=0.1		,  metadata={"help": "Value of weight decay."} )
  a     =      field(
      default=1_00_00		,  metadata={"help": "Size of buffer used to shuffle streaming dataset."} )
  a     =      field(default=2e-4		,  metadata={"help": "Learning rate fo training."} )
  a     =      field(default="cosine"		,  metadata={"help": "Learning rate."} )
  a     =      field(
      default=7_50		,  metadata={"help": "Number of warmup steps in the learning rate schedule."} )
  a     =      field(
      default=16		,  metadata={"help": "Number of gradient accumulation steps."} )
  a     =      field(
      default=A__		,  metadata={"help": "Use gradient checkpointing to reduce memory footprint."} )
  a     =      field(default=5_00_00		,  metadata={"help": "Maximum number of training steps."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Sequence lengths used for training."} )
  a     =      field(default=1		,  metadata={"help": "Training seed."} )
  a     =      field(
      default=10_24		,  metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "States path if the training should continue from a checkpoint folder."} )
  a     =      field(default=A__		,  metadata={"help": "If True the data is pretokenized."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size used for evaluation."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Length of sequences to be evaluated."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )
  a     =      field(
      default=A__		,  metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Sample from the language model's output distribution."} )
  a     =      field(default=0.2		,  metadata={"help": "Sampling temperature used for generation."} )
  a     =      field(default=2_56		,  metadata={"help": "Maximum number of newly generated tokens."} )
  a     =      field(default=0		,  metadata={"help": "Top-k parameter used for generation."} )
  a     =      field(default=0.9_5		,  metadata={"help": "Top-p parameter used for nucleus sampling."} )
  a     =      field(default=10		,  metadata={"help": "Number of generations to run in parallel."} )
  a     =      field(
      default=2_00		,  metadata={"help": "Number of completions to generate for each sample."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="eval_results.json"		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="0"		,  metadata={"help": "Allow `code_eval` to execute Python code on machine"} )
  a     =      field(
      default=-1		,  metadata={
          "help": (
              "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
              " number corresponds to which GPU device id to run on."
          )
      }		,  )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={
          "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
      }		,  )
  a     =      field(
      default="transformersbook/codeparrot"		,  metadata={"help": "Folder or name of dataset to process."} )
  a     =      field(
      default="codeparrot-clean"		,  metadata={"help": "Folder to save processed processed dataset."} )
  a     =      field(
      default=10_00_00		,  metadata={"help": "Number of files to save per JSON output file."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(
      default=10_00		,  metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
  a     =      field(
      default=1_00		,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
  a     =      field(
      default=0.2_5		,  metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."} )
  a     =      field(
      default=1.5		,  metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."} )
  a     =      field(
      default=0.7		,  metadata={"help": "Probability for filtering config, test and uncommon files."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "If True, near-duplicate samples are removed."} )
  a     =      field(
      default=0.8_5		,  metadata={"help": "Jaccard threshold for near-duplicate samples."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2"		,  metadata={"help": "Base tokenizer to build new tokenizer from."} )
  a     =      field(
      default="transformersbook/codeparrot-train"		,  metadata={"help": "Dataset to train tokenizer on."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(default=20_00_00		,  metadata={"help": "Number of examples to train tokenizer on."} )
  a     =      field(
      default=3_27_68		,  metadata={"help": "Number of examples to train the tokenizer on."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of new tokenizer."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path to the dataset to pretokenize."} )
  a     =      field(
      default="tokenized-codeparrot-train"		,  metadata={"help": "Repo name of the pretokenized data."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2-large"		,  metadata={"help": "Configuration to use for model initialization."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Tokenizer attached to model."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of the created model."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )


