
"""simple docstring"""

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)
logging.set_verbosity_info()




def      lowercase__    (       lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Dict			)			->   int:






    """simple docstring"""




    if "xprophetnet" in prophetnet_checkpoint_path:
        UpperCAmelCase          =       XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase_			)
        UpperCAmelCase      ,				UpperCAmelCase          =       XLMProphetNetForConditionalGeneration.from_pretrained(
            lowercase_			,					output_loading_info=lowercase_			)
    else:
        UpperCAmelCase          =       ProphetNetForConditionalGenerationOld.from_pretrained(lowercase_			)
        UpperCAmelCase      ,				UpperCAmelCase          =       ProphetNetForConditionalGeneration.from_pretrained(
            lowercase_			,					output_loading_info=lowercase_			)

    UpperCAmelCase          =       ['key_proj', 'value_proj', 'query_proj']

    UpperCAmelCase          =       {
        'self_attn': 'ngram_self_attn',
        'cross_attn': 'encoder_attn',
        'cross_attn_layer_norm': 'encoder_attn_layer_norm',
        'feed_forward_layer_norm': 'final_layer_norm',
        'feed_forward': '',
        'intermediate': 'fc1',
        'output': 'fc2',
        'key_proj': 'k_proj',
        'query_proj': 'q_proj',
        'value_proj': 'v_proj',
        'word_embeddings': 'embed_tokens',
        'embeddings_layer_norm': 'emb_layer_norm',
        'relative_pos_embeddings': 'relative_linear',
        'ngram_embeddings': 'ngram_input_embed',
        'position_embeddings': 'embed_positions',
    }

    for key in loading_info["missing_keys"]:
        UpperCAmelCase          =       key.split('.'			)

        if attributes[0] == "lm_head":
            UpperCAmelCase          =       prophet
            UpperCAmelCase          =       prophet_old
        else:
            UpperCAmelCase          =       prophet.prophetnet
            UpperCAmelCase          =       prophet_old.model

        UpperCAmelCase          =       False
        for attribute in attributes:
            if attribute in mapping:
                UpperCAmelCase          =       mapping[attribute]
                if not hasattr(lowercase_			,					lowercase_			) and len(lowercase_			) > 0:
                    UpperCAmelCase          =       attribute
            elif hasattr(lowercase_			,					lowercase_			):
                UpperCAmelCase          =       attribute

            if attribute == "weight":
                assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                UpperCAmelCase          =       old_model.weight
                logger.info(F"{attribute} is initialized."			)
                UpperCAmelCase          =       True
                break
            elif attribute == "bias":
                assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                UpperCAmelCase          =       old_model.bias
                logger.info(F"{attribute} is initialized"			)
                UpperCAmelCase          =       True
                break
            elif attribute in special_keys and hasattr(lowercase_			,					'in_proj_weight'			):
                UpperCAmelCase          =       old_model.in_proj_weight.shape[0] // 3
                UpperCAmelCase          =       getattr(lowercase_			,					lowercase_			)
                param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                if attribute == "query_proj":
                    UpperCAmelCase          =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]			)
                    UpperCAmelCase          =       nn.Parameter(old_model.in_proj_bias[:embed_dim]			)

                elif attribute == "key_proj":
                    UpperCAmelCase          =       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]			)
                    UpperCAmelCase          =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]			)
                elif attribute == "value_proj":
                    UpperCAmelCase          =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]			)
                    UpperCAmelCase          =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]			)
                UpperCAmelCase          =       True
                break
            elif attribute == "position_embeddings":
                assert (
                    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                ), "Hidden size has to match"
                assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                UpperCAmelCase          =       nn.Parameter(old_model.embed_positions.weight[:512, :]			)
                UpperCAmelCase          =       True
                break

            if attribute.isdigit():
                UpperCAmelCase          =       model[int(lowercase_			)]
                UpperCAmelCase          =       old_model[int(lowercase_			)]
            else:
                UpperCAmelCase          =       getattr(lowercase_			,					lowercase_			)

                if old_attribute == "":
                    UpperCAmelCase          =       old_model
                else:
                    if not hasattr(lowercase_			,					lowercase_			):
                        raise ValueError(F"{old_model} does not have {old_attribute}"			)
                    UpperCAmelCase          =       getattr(lowercase_			,					lowercase_			)

        if not is_key_init:
            raise ValueError(F"{key} was not correctly initialized!"			)

    print(F"Saving model to {pytorch_dump_folder_path}"			)
    prophet.save_pretrained(lowercase_			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--prophetnet_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.'''
   )
   SCREAMING_SNAKE_CASE_            =    parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_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


_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)


