
'''simple docstring'''
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__   :		Any 		=[
    # 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        A_     (						__SCREAMING_SNAKE_CASE						:  Any )				->       Any:





  """simple docstring"""

  for pegasus_name, hf_name in PATTERNS:
    __A		:     Any												=		k.replace(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return k







def        A_     (						__SCREAMING_SNAKE_CASE						:  dict			,     __SCREAMING_SNAKE_CASE						:  dict )				->       PegasusForConditionalGeneration:





  """simple docstring"""

  __A		:     Dict												=		DEFAULTS.copy()
  cfg_kwargs.update(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		PegasusConfig(**__SCREAMING_SNAKE_CASE )
  __A		:     str												=		PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		torch_model.model.state_dict()
  __A		:     str												=		{}
  for k, v in tf_weights.items():
    __A		:     List[str]												=		rename_state_dict_key(__SCREAMING_SNAKE_CASE )
    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		:     List[Any]												=		v.T
    __A		:     Tuple												=		torch.tensor(__SCREAMING_SNAKE_CASE			,     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		:     Dict												=		torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
  __A		:     Union[str, Any]												=		mapping["""shared.weight"""]
  __A		:     List[str]												=		mapping["""shared.weight"""]
  __A		:     Tuple												=		{k: torch.zeros_like(__SCREAMING_SNAKE_CASE ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
  mapping.update(**__SCREAMING_SNAKE_CASE )
  __A							,					__A		:     List[str]												=		torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE			,     strict=__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		[
      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        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]="./ckpt/aeslc/model.ckpt-32000" )				->       Dict:





  """simple docstring"""

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  str )				->       Optional[int]:





  """simple docstring"""

  __A		:     Optional[int]												=		Path(__SCREAMING_SNAKE_CASE ).parent.name
  __A		:     Optional[Any]												=		task_specific_params[F"summarization_{dataset}"]["""max_position_embeddings"""]
  __A		:     Optional[int]												=		PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""			,     model_max_length=__SCREAMING_SNAKE_CASE )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(__SCREAMING_SNAKE_CASE )

  # convert model
  __A		:     str												=		get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE )
  __A		:     Optional[int]												=		task_specific_params[F"summarization_{dataset}"]
  if dataset == "large":
    __A		:     Any												=		task_specific_params
  __A		:     List[Any]												=		convert_pegasus(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  torch_model.save_pretrained(__SCREAMING_SNAKE_CASE )
  __A		:     List[str]												=		torch_model.state_dict()
  sd.pop("""model.decoder.embed_positions.weight""" )
  sd.pop("""model.encoder.embed_positions.weight""" )
  torch.save(__SCREAMING_SNAKE_CASE			,     Path(__SCREAMING_SNAKE_CASE ) / """pytorch_model.bin""" )


if __name__ == "__main__":
     A__   :		Tuple 		=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__   :		List[str] 		=parser.parse_args()
     if args.save_dir is None:
          A__   :		str 		=Path(args.tf_ckpt_path).parent.name
          A__   :		int 		=os.path.join('pegasus', dataset)
     convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





'''simple docstring'''
import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[int]=7 )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		None
  if token is not None:
    __A		:     str												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  # The id of a workflow (not of a workflow run)
  __A		:     Union[str, Any]												=		"""636036"""

  __A		:     Optional[int]												=		F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
  # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
  url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

  __A		:     int												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()

  return result["workflow_runs"]







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Tuple:





  """simple docstring"""

  __A		:     str												=		get_daily_ci_runs(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      __A		:     Dict												=		workflow_run["""id"""]
      break

  return workflow_run_id







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  Tuple )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		get_last_daily_ci_runs(__SCREAMING_SNAKE_CASE )
  if workflow_run_id is not None:
    __A		:     str												=		get_artifacts_links(worflow_run_id=__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        __A		:     List[Any]												=		artifacts_links[artifact_name]
        download_artifact(
            artifact_name=__SCREAMING_SNAKE_CASE			,     artifact_url=__SCREAMING_SNAKE_CASE			,     output_dir=__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE )







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Union[str, Any]:





  """simple docstring"""

  get_last_daily_ci_artifacts(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  __A		:     List[Any]												=		{}
  for artifact_name in artifact_names:
    __A		:     Any												=		os.path.join(__SCREAMING_SNAKE_CASE			,     F"{artifact_name}.zip" )
    if os.path.isfile(__SCREAMING_SNAKE_CASE ):
      __A		:     str												=		{}
      with zipfile.ZipFile(__SCREAMING_SNAKE_CASE ) as z:
        for filename in z.namelist():
          if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
            # read the file
            with z.open(__SCREAMING_SNAKE_CASE ) as f:
              __A		:     Tuple												=		f.read().decode("""UTF-8""" )

  return results



