
"""simple docstring"""

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			)			->   Dict:






    """simple docstring"""




    return ConvertCommand(
        args.model_type			,					args.tf_checkpoint			,					args.pytorch_dump_output			,					args.config			,					args.finetuning_task_name			)


SCREAMING_SNAKE_CASE_            =    """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""







class   _UpperCAmelCase				(		UpperCAmelCase__				):




      @staticmethod
      def      a_   (				lowercase_				)    ->       Tuple:
          UpperCAmelCase          =       parser.add_parser(
              'convert' ,							help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.' ,							)
          train_parser.add_argument('--model_type' ,							type=UpperCAmelCase__ ,							required=UpperCAmelCase__ ,							help='Model\'s type.'				)
          train_parser.add_argument(
              '--tf_checkpoint' ,							type=UpperCAmelCase__ ,							required=UpperCAmelCase__ ,							help='TensorFlow checkpoint path or folder.'				)
          train_parser.add_argument(
              '--pytorch_dump_output' ,							type=UpperCAmelCase__ ,							required=UpperCAmelCase__ ,							help='Path to the PyTorch saved model output.'				)
          train_parser.add_argument('--config' ,							type=UpperCAmelCase__ ,							default='' ,							help='Configuration file path or folder.'				)
          train_parser.add_argument(
              '--finetuning_task_name' ,							type=UpperCAmelCase__ ,							default=UpperCAmelCase__ ,							help='Optional fine-tuning task name if the TF model was a finetuned model.' ,							)
          train_parser.set_defaults(func=UpperCAmelCase__				)




      def __init__(				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							*lowercase_ ,							)    ->       Optional[int]:
          UpperCAmelCase          =       logging.get_logger('transformers-cli/converting'				)

          self._logger.info(F"Loading model {model_type}"				)
          UpperCAmelCase          =       model_type
          UpperCAmelCase          =       tf_checkpoint
          UpperCAmelCase          =       pytorch_dump_output
          UpperCAmelCase          =       config
          UpperCAmelCase          =       finetuning_task_name




      def      a_   (				self				)    ->       Any:

          if self._model_type == "albert":
              try:
                  from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                      convert_tf_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(UpperCAmelCase__				)

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output				)
          elif self._model_type == "bert":
              try:
                  from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                      convert_tf_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(UpperCAmelCase__				)

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output				)
          elif self._model_type == "funnel":
              try:
                  from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                      convert_tf_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(UpperCAmelCase__				)

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output				)
          elif self._model_type == "t5":
              try:
                  from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
              except ImportError:
                  raise ImportError(UpperCAmelCase__				)

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output				)
          elif self._model_type == "gpt":
              from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                  convert_openai_checkpoint_to_pytorch,
              )

              convert_openai_checkpoint_to_pytorch(self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output				)
          elif self._model_type == "transfo_xl":
              try:
                  from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                      convert_transfo_xl_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(UpperCAmelCase__				)

              if "ckpt" in self._tf_checkpoint.lower():
                  UpperCAmelCase          =       self._tf_checkpoint
                  UpperCAmelCase          =       ''
              else:
                  UpperCAmelCase          =       self._tf_checkpoint
                  UpperCAmelCase          =       ''
              convert_transfo_xl_checkpoint_to_pytorch(
                  UpperCAmelCase__ ,							self._config ,							self._pytorch_dump_output ,							UpperCAmelCase__				)
          elif self._model_type == "gpt2":
              try:
                  from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                      convert_gpta_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(UpperCAmelCase__				)

              convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output				)
          elif self._model_type == "xlnet":
              try:
                  from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                      convert_xlnet_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(UpperCAmelCase__				)

              convert_xlnet_checkpoint_to_pytorch(
                  self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output ,							self._finetuning_task_name				)
          elif self._model_type == "xlm":
              from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                  convert_xlm_checkpoint_to_pytorch,
              )

              convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint ,							self._pytorch_dump_output				)
          elif self._model_type == "lxmert":
              from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                  convert_lxmert_checkpoint_to_pytorch,
              )

              convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint ,							self._pytorch_dump_output				)
          elif self._model_type == "rembert":
              from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                  convert_rembert_tf_checkpoint_to_pytorch,
              )

              convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,							self._config ,							self._pytorch_dump_output				)
          else:
              raise ValueError(
                  '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'				)









import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_lowerCamelCase      :						int							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Dict:



       """simple docstring"""

       A__ 					=       set()
       A__ 					=       []

       def parse_line(lowercase_		):
              for line in fp:
                     if isinstance(lowercase_					,							lowercase_		):
                            A__ 					=       line.decode('''UTF-8'''		)
                     if "warnings summary (final)" in line:
                            continue
                     # This means we are outside the body of a warning
                     elif not line.startswith(''' '''		):
                            # process a single warning and move it to `selected_warnings`.
                            if len(lowercase_		) > 0:
                                   A__ 					=       '''\n'''.join(lowercase_		)
                                   # Only keep the warnings specified in `targets`
                                   if any(f""": {x}: """ in warning for x in targets		):
                                          selected_warnings.add(lowercase_		)
                                   buffer.clear()
                            continue
                     else:
                            A__ 					=       line.strip()
                            buffer.append(lowercase_		)

       if from_gh:
              for filename in os.listdir(lowercase_		):
                     A__ 					=       os.path.join(lowercase_					,							lowercase_		)
                     if not os.path.isdir(lowercase_		):
                            # read the file
                            if filename != "warnings.txt":
                                   continue
                            with open(lowercase_		) as fp:
                                   parse_line(lowercase_		)
       else:
              try:
                     with zipfile.ZipFile(lowercase_		) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(lowercase_		):
                                          # read the file
                                          if filename != "warnings.txt":
                                                 continue
                                          with z.open(lowercase_		) as fp:
                                                 parse_line(lowercase_		)
              except Exception:
                     logger.warning(
                         f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""		)

       return selected_warnings







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       set()

       A__ 					=       [os.path.join(lowercase_					,							lowercase_		) for p in os.listdir(lowercase_		) if (p.endswith('''.zip'''		) or from_gh)]
       for p in paths:
              selected_warnings.update(extract_warnings_from_single_artifact(lowercase_					,							lowercase_		)		)

       return selected_warnings


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



         """simple docstring"""

         return values.split(''','''		)

  _lowerCamelCase      :						int							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
  parser.add_argument(
      """--output_dir""",
      type=str,
      required=True,
      help="""Where to store the downloaded artifacts and other result files.""",
  )
  parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
  # optional parameters
  parser.add_argument(
      """--targets""",
      default="""DeprecationWarning,UserWarning,FutureWarning""",
      type=list_str,
      help="""Comma-separated list of target warning(s) which we want to extract.""",
  )
  parser.add_argument(
      """--from_gh""",
      action="""store_true""",
      help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
  )

  _lowerCamelCase      :						List[Any]							=  parser.parse_args()

  _lowerCamelCase      :						List[str]							=  args.from_gh
  if from_gh:
    # The artifacts have to be downloaded using `actions/download-artifact@v3`
    pass
  else:
    os.makedirs(args.output_dir, exist_ok=True)

    # get download links
    _lowerCamelCase      :						Any							=  get_artifacts_links(args.workflow_run_id, token=args.token)
    with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
      json.dump(artifacts, fp, ensure_ascii=False, indent=4)

    # download artifacts
    for idx, (name, url) in enumerate(artifacts.items()):
      print(name)
      print(url)
      print("""=""" * 80)
      download_artifact(name, url, args.output_dir, args.token)
      # Be gentle to GitHub
      time.sleep(1)

    # extract warnings from artifacts
  _lowerCamelCase      :						Any							=  extract_warnings(args.output_dir, args.targets)
  _lowerCamelCase      :						Optional[Any]							=  sorted(selected_warnings)
  with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
    json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)


