


import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
UpperCAmelCase    =					logging.getLogger(__name__)


UpperCAmelCase    =					"""Hello world! cécé herlolip"""


UpperCAmelCase    =					namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   BertAbsConfig(
          temp_dir='''.'''      ,      finetune_bert=SCREAMING_SNAKE_CASE      ,      large=SCREAMING_SNAKE_CASE      ,      share_emb=SCREAMING_SNAKE_CASE      ,      use_bert_emb=SCREAMING_SNAKE_CASE      ,      encoder='''bert'''      ,      max_pos=512      ,      enc_layers=6      ,      enc_hidden_size=512      ,      enc_heads=8      ,      enc_ff_size=512      ,      enc_dropout=0.2      ,      dec_layers=6      ,      dec_hidden_size=768      ,      dec_heads=8      ,      dec_ff_size=2048      ,      dec_dropout=0.2      ,      )
      snake_case_ =   torch.load(SCREAMING_SNAKE_CASE      ,      lambda SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      : storage      )
      snake_case_ =   AbsSummarizer(SCREAMING_SNAKE_CASE      ,      torch.device('''cpu'''      )      ,      SCREAMING_SNAKE_CASE      )
      original.eval()

      snake_case_ =   BertAbsSummarizer(SCREAMING_SNAKE_CASE      ,      torch.device('''cpu'''      )      )
      new_model.eval()

      # -------------------
      # Convert the weights
      # -------------------

      logging.info('''convert the model'''      )
      new_model.bert.load_state_dict(original.bert.state_dict()      )
      new_model.decoder.load_state_dict(original.decoder.state_dict()      )
      new_model.generator.load_state_dict(original.generator.state_dict()      )

      # ----------------------------------
      # Make sure the outpus are identical
      # ----------------------------------

      logging.info('''Make sure that the models\' outputs are identical'''      )
      snake_case_ =   BertTokenizer.from_pretrained('''bert-base-uncased'''      )

      # prepare the model inputs
      snake_case_ =   tokenizer.encode('''This is sample éàalj\'-.'''      )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(SCREAMING_SNAKE_CASE      ))      )
      snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      ).unsqueeze(0      )
      snake_case_ =   tokenizer.encode('''This is sample 3 éàalj\'-.'''      )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(SCREAMING_SNAKE_CASE      ))      )
      snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      ).unsqueeze(0      )

      # failsafe to make sure the weights reset does not affect the
      # loaded weights.
      assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight      )      ) == 0

      # forward pass
      snake_case_ =   encoder_input_ids
      snake_case_ =   decoder_input_ids
      snake_case_ =   snake_case_ =   None
      snake_case_ =   None
      snake_case_ =   snake_case_ =   None
      snake_case_ =   snake_case_ =   None
      snake_case_ =   None

      # The original model does not apply the geneator layer immediatly but rather in
      # the beam search (where it combines softmax + linear layer). Since we already
      # apply the softmax in our generation process we only apply the linear layer here.
      # We make sure that the outputs of the full stack are identical
      snake_case_ =   original(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )[0]
      snake_case_ =   original.generator(SCREAMING_SNAKE_CASE      )

      snake_case_ =   new_model(
          SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )[0]
      snake_case_ =   new_model.generator(SCREAMING_SNAKE_CASE      )

      snake_case_ =   torch.max(torch.abs(output_converted_model - output_original_model      )      ).item()
      print('''Maximum absolute difference beween weights: {:.2f}'''.format(SCREAMING_SNAKE_CASE      )      )
      snake_case_ =   torch.max(torch.abs(output_converted_generator - output_original_generator      )      ).item()
      print('''Maximum absolute difference beween weights: {:.2f}'''.format(SCREAMING_SNAKE_CASE      )      )

      snake_case_ =   torch.allclose(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      atol=1E-3      )
      if are_identical:
            logging.info('''all weights are equal up to 1e-3'''      )
      else:
            raise ValueError('''the weights are different. The new model is likely different from the original one.'''      )

      # The model has been saved with torch.save(model) and this is bound to the exact
      # directory structure. We save the state_dict instead.
      logging.info('''saving the model\'s state dictionary'''      )
      torch.save(
          new_model.state_dict()      ,      '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'''      )


if __name__ == "__main__":
   UpperCAmelCase    =					argparse.ArgumentParser()
   parser.add_argument(
       """--bertabs_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help="""Path the official PyTorch dump.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the output PyTorch model.""",
   )
   UpperCAmelCase    =					parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )



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


UpperCAmelCase    =					logging.get_logger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						List[str]:




      """simple docstring"""

      snake_case_ =   set()
      snake_case_ =   []

      def parse_line(SCREAMING_SNAKE_CASE      ):
            for line in fp:
                  if isinstance(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ):
                        snake_case_ =   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(SCREAMING_SNAKE_CASE      ) > 0:
                              snake_case_ =   '''\n'''.join(SCREAMING_SNAKE_CASE      )
                              # Only keep the warnings specified in `targets`
                              if any(f''': {x}: ''' in warning for x in targets      ):
                                    selected_warnings.add(SCREAMING_SNAKE_CASE      )
                              buffer.clear()
                        continue
                  else:
                        snake_case_ =   line.strip()
                        buffer.append(SCREAMING_SNAKE_CASE      )

      if from_gh:
            for filename in os.listdir(SCREAMING_SNAKE_CASE      ):
                  snake_case_ =   os.path.join(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
                  if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
                        # read the file
                        if filename != "warnings.txt":
                              continue
                        with open(SCREAMING_SNAKE_CASE      ) as fp:
                              parse_line(SCREAMING_SNAKE_CASE      )
      else:
            try:
                  with zipfile.ZipFile(SCREAMING_SNAKE_CASE      ) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
                                    # read the file
                                    if filename != "warnings.txt":
                                          continue
                                    with z.open(SCREAMING_SNAKE_CASE      ) as fp:
                                          parse_line(SCREAMING_SNAKE_CASE      )
            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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   set()

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

      return selected_warnings


if __name__ == "__main__":






   def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[str]:




         """simple docstring"""

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

   UpperCAmelCase    =					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.""",
   )

   UpperCAmelCase    =					parser.parse_args()

   UpperCAmelCase    =					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
      UpperCAmelCase    =					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
   UpperCAmelCase    =					extract_warnings(args.output_dir, args.targets)
   UpperCAmelCase    =					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)