





import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A       :   Optional[int]					= [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["""memory_attention""", """encoder_attn"""],
    ["""attention""", """attn"""],
    ["""/""", """."""],
    [""".LayerNorm.gamma""", """_layer_norm.weight"""],
    [""".LayerNorm.beta""", """_layer_norm.bias"""],
    ["""r.layer_""", """r.layers."""],
    ["""output_proj""", """out_proj"""],
    ["""ffn.dense_1.""", """fc2."""],
    ["""ffn.dense.""", """fc1."""],
    ["""ffn_layer_norm""", """final_layer_norm"""],
    ["""kernel""", """weight"""],
    ["""encoder_layer_norm.""", """encoder.layer_norm."""],
    ["""decoder_layer_norm.""", """decoder.layer_norm."""],
    ["""embeddings.weights""", """shared.weight"""],
]







def   __lowerCamelCase   (				__a  :Any       )						->							Union[str, Any]:






		"""simple docstring"""

		for pegasus_name, hf_name in PATTERNS:
				A__     =  k.replace(__lowerCAmelCase       ,	__lowerCAmelCase       )
		return k







def   __lowerCamelCase   (				__a  :Optional[Any]       ,	__a  :str       )						->							int:






		"""simple docstring"""

		A__     =  DEFAULTS.copy()
		cfg_kwargs.update(__lowerCAmelCase       )
		A__     =  PegasusConfig(**__lowerCAmelCase       )
		A__     =  PegasusForConditionalGeneration(__lowerCAmelCase       )
		A__     =  torch_model.model.state_dict()
		A__     =  {}
		for k, v in tf_weights.items():
				A__     =  rename_state_dict_key(__lowerCAmelCase       )
				if new_k not in sd:
						raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'       )

				if "dense" in k or "proj" in new_k:
						A__     =  v.T
				A__     =  torch.tensor(__lowerCAmelCase       ,	dtype=sd[new_k].dtype       )
				assert v.shape == sd[new_k].shape, F'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
		# make sure embedding.padding_idx is respected
		A__     =  torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
		A__     =  mapping["""shared.weight"""]
		A__     =  mapping["""shared.weight"""]
		A__     =  {k: torch.zeros_like(__lowerCAmelCase       ) for k, v in sd.items() if k.endswith("""bias"""       ) and k not in mapping}
		mapping.update(**__lowerCAmelCase       )
		A__ ,     A__     =  torch_model.model.load_state_dict(__lowerCAmelCase       ,	strict=__lowerCAmelCase       )
		A__     =  [
		    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
		]
		assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
		assert extra == [], F'no matches found for the following tf keys {extra}'
		return torch_model







def   __lowerCamelCase   (				__a  :int="./ckpt/aeslc/model.ckpt-32000"       )						->							Tuple:






		"""simple docstring"""

		A__     =  tf.train.list_variables(__lowerCAmelCase       )
		A__     =  {}
		A__     =  ["""Adafactor""", """global_step"""]
		for name, shape in tqdm(__lowerCAmelCase       ,	desc="""converting tf checkpoint to dict"""       ):
				A__     =  any(pat in name for pat in ignore_name       )
				if skip_key:
						continue
				A__     =  tf.train.load_variable(__lowerCAmelCase       ,	__lowerCAmelCase       )
				A__     =  array
		return tf_weights







def   __lowerCamelCase   (				__a  :str       ,	__a  :Any       )						->							Optional[Any]:






		"""simple docstring"""

		A__     =  Path(__lowerCAmelCase       ).parent.name
		A__     =  task_specific_params[F'summarization_{dataset}']["""max_position_embeddings"""]
		A__     =  PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""       ,	model_max_length=__lowerCAmelCase       )
		assert tok.model_max_length == desired_max_model_length
		tok.save_pretrained(__lowerCAmelCase       )

		# convert model
		A__     =  get_tf_weights_as_numpy(__lowerCAmelCase       )
		A__     =  task_specific_params[F'summarization_{dataset}']
		if dataset == "large":
				A__     =  task_specific_params
		A__     =  convert_pegasus(__lowerCAmelCase       ,	__lowerCAmelCase       )
		torch_model.save_pretrained(__lowerCAmelCase       )
		A__     =  torch_model.state_dict()
		sd.pop("""model.decoder.embed_positions.weight"""       )
		sd.pop("""model.encoder.embed_positions.weight"""       )
		torch.save(__lowerCAmelCase       ,	Path(__lowerCAmelCase       ) / """pytorch_model.bin"""       )


if __name__ == "__main__":
					A       :   Optional[int]					= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
					parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
					A       :   Union[str, Any]					= parser.parse_args()
					if args.save_dir is None:
										A       :   str					= Path(args.tf_ckpt_path).parent.name
										A       :   List[str]					= os.path.join('''pegasus''', dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









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


__magic_name__  :  List[Any]					=       logging.get_logger(__name__)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()
  lowerCAmelCase__										=      []

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

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

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

  return selected_warnings


if __name__ == "__main__":
      def 		a_							( __lowerCAmelCase				):
        return values.split(''','''				)

      __magic_name__  :  Optional[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.""",
      )

      __magic_name__  :  Optional[Any]					=       parser.parse_args()

      __magic_name__  :  Optional[int]					=       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
            __magic_name__  :  Optional[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
      __magic_name__  :  Optional[Any]					=       extract_warnings(args.output_dir, args.targets)
      __magic_name__  :  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)

