

import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Optional[int]       ,      **_SCREAMING_SNAKE_CASE    :						int				):





    """simple docstring"""


    return RunBeamCommand(
        args.dataset       ,      args.name       ,      args.cache_dir       ,      args.beam_pipeline_options       ,      args.data_dir       ,      args.all_configs       ,      args.save_info or args.save_infos       ,      args.ignore_verifications       ,      args.force_redownload       ,      **_SCREAMING_SNAKE_CASE       ,      )





class 					__snake_case     (			lowerCAmelCase__				):

  @staticmethod
  def   lowerCAmelCase__   (    _A):
      SCREAMING_SNAKE_CASE_													=  parser.add_parser('run_beam'  ,						help='Run a Beam dataset processing pipeline')
      run_beam_parser.add_argument('dataset'  ,						type=_A  ,						help='Name of the dataset to download')
      run_beam_parser.add_argument('--name'  ,						type=_A  ,						default=_A  ,						help='Dataset config name')
      run_beam_parser.add_argument(
          '--cache_dir'  ,						type=_A  ,						default=_A  ,						help='Cache directory where the datasets are stored'  ,						)
      run_beam_parser.add_argument(
          '--beam_pipeline_options'  ,						type=_A  ,						default=''  ,						help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'  ,						)
      run_beam_parser.add_argument(
          '--data_dir'  ,						type=_A  ,						default=_A  ,						help='Can be used to specify a manual directory to get the files from'  ,						)
      run_beam_parser.add_argument('--all_configs'  ,						action='store_true'  ,						help='Test all dataset configurations')
      run_beam_parser.add_argument('--save_info'  ,						action='store_true'  ,						help='Save the dataset infos file')
      run_beam_parser.add_argument(
          '--ignore_verifications'  ,						action='store_true'  ,						help='Run the test without checksums and splits checks')
      run_beam_parser.add_argument('--force_redownload'  ,						action='store_true'  ,						help='Force dataset redownload')
      # aliases
      run_beam_parser.add_argument('--save_infos'  ,						action='store_true'  ,						help='alias for save_info')
      run_beam_parser.set_defaults(func=_A)

  def __init__(    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  dataset
      SCREAMING_SNAKE_CASE_													=  name
      SCREAMING_SNAKE_CASE_													=  cache_dir
      SCREAMING_SNAKE_CASE_													=  beam_pipeline_options
      SCREAMING_SNAKE_CASE_													=  data_dir
      SCREAMING_SNAKE_CASE_													=  all_configs
      SCREAMING_SNAKE_CASE_													=  save_infos
      SCREAMING_SNAKE_CASE_													=  ignore_verifications
      SCREAMING_SNAKE_CASE_													=  force_redownload
      SCREAMING_SNAKE_CASE_													=  config_kwargs


  def   lowerCAmelCase__   (    self):
      import apache_beam as beam

      if self._name is not None and self._all_configs:
          print('Both parameters `name` and `all_configs` can\'t be used at once.')
          exit(1)
      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self._dataset, self._name
      SCREAMING_SNAKE_CASE_													=  dataset_module_factory(_A)
      SCREAMING_SNAKE_CASE_													=  import_main_class(dataset_module.module_path)
      SCREAMING_SNAKE_CASE_													=  []
      if self._beam_pipeline_options:
          SCREAMING_SNAKE_CASE_													=  beam.options.pipeline_options.PipelineOptions(
              flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(',') if opt])
      else:
          SCREAMING_SNAKE_CASE_													=  None
      if self._all_configs and len(builder_cls.BUILDER_CONFIGS) > 0:
          for builder_config in builder_cls.BUILDER_CONFIGS:
              builders.append(
                  builder_cls(
                      config_name=builder_config.name  ,						data_dir=self._data_dir  ,						hash=dataset_module.hash  ,						beam_options=_A  ,						cache_dir=self._cache_dir  ,						base_path=dataset_module.builder_kwargs.get('base_path')  ,						))
      else:
          builders.append(
              builder_cls(
                  config_name=_A  ,						data_dir=self._data_dir  ,						beam_options=_A  ,						cache_dir=self._cache_dir  ,						base_path=dataset_module.builder_kwargs.get('base_path')  ,						**self._config_kwargs  ,						))

      for builder in builders:
          builder.download_and_prepare(
              download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
              if not self._force_redownload
              else DownloadMode.FORCE_REDOWNLOAD  ,						download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH)  ,						verification_mode=VerificationMode.NO_CHECKS
              if self._ignore_verifications
              else VerificationMode.ALL_CHECKS  ,						try_from_hf_gcs=_A  ,						)
          if self._save_infos:
              builder._save_infos()

      print('Apache beam run successful.')

      # If save_infos=True, the dataset infos file is created next to the loaded module file.
      # Let's move it to the original directory of the dataset script, to allow the user to
      # upload them on S3 at the same time afterwards.
      if self._save_infos:
          SCREAMING_SNAKE_CASE_													=  os.path.join(builder_cls.get_imported_module_dir()  ,						config.DATASETDICT_INFOS_FILENAME)

          SCREAMING_SNAKE_CASE_													=  Path(_A).name + '.py'

          SCREAMING_SNAKE_CASE_													=  os.path.join(_A  ,						_A)
          if os.path.isfile(_A):
              SCREAMING_SNAKE_CASE_													=  os.path.dirname(_A)
          elif os.path.isfile(_A):
              SCREAMING_SNAKE_CASE_													=  path
          else:  # in case of a remote dataset
              print(f"""Dataset Infos file saved at {dataset_infos_path}""")
              exit(1)

          # Move datasetinfo back to the user
          SCREAMING_SNAKE_CASE_													=  os.path.join(_A  ,						config.DATASETDICT_INFOS_FILENAME)
          copyfile(_A  ,						_A)
          print(f"""Dataset Infos file saved at {user_dataset_infos_path}""")





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    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  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}"""				)
    SCREAMING_SNAKE_CASE_													=  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__":
       UpperCamelCase__     :					Tuple								=			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."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


