
'''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


UpperCamelCase__      :		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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   for pegasus_name, hf_name in PATTERNS:
      _SCREAMING_SNAKE_CASE      =				k.replace(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   return k
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     PegasusForConditionalGeneration:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				DEFAULTS.copy()
   cfg_kwargs.update(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				PegasusConfig(**SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				torch_model.model.state_dict()
   _SCREAMING_SNAKE_CASE      =				{}
   for k, v in tf_weights.items():
      _SCREAMING_SNAKE_CASE      =				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:
         _SCREAMING_SNAKE_CASE      =				v.T
      _SCREAMING_SNAKE_CASE      =				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
   _SCREAMING_SNAKE_CASE      =				torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]						)
   _SCREAMING_SNAKE_CASE      =				mapping["""shared.weight"""]
   _SCREAMING_SNAKE_CASE      =				mapping["""shared.weight"""]
   _SCREAMING_SNAKE_CASE      =				{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_						)
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				[
       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_	(				SCREAMING_SNAKE_CASE_="./ckpt/aeslc/model.ckpt-32000"						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tf.train.list_variables(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				{}
   _SCREAMING_SNAKE_CASE      =				["""Adafactor""", """global_step"""]
   for name, shape in tqdm(SCREAMING_SNAKE_CASE_    ,      desc="""converting tf checkpoint to dict"""						):
      _SCREAMING_SNAKE_CASE      =				any(pat in name for pat in ignore_name						)
      if skip_key:
         continue
      _SCREAMING_SNAKE_CASE      =				tf.train.load_variable(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				array
   return tf_weights
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   # save tokenizer first
   _SCREAMING_SNAKE_CASE      =				Path(SCREAMING_SNAKE_CASE_						).parent.name
   _SCREAMING_SNAKE_CASE      =				task_specific_params[F"summarization_{dataset}"]["""max_position_embeddings"""]
   _SCREAMING_SNAKE_CASE      =				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
   _SCREAMING_SNAKE_CASE      =				get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				task_specific_params[F"summarization_{dataset}"]
   if dataset == "large":
      _SCREAMING_SNAKE_CASE      =				task_specific_params
   _SCREAMING_SNAKE_CASE      =				convert_pegasus(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   torch_model.save_pretrained(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				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__":
     UpperCamelCase__      :		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.")
     UpperCamelCase__      :		int       							=  parser.parse_args()
     if args.save_dir is None:
          UpperCamelCase__      :		Tuple       							=  Path(args.tf_ckpt_path).parent.name
          UpperCamelCase__      :		int       							=  os.path.join("pegasus", dataset)
     convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








'''simple docstring'''



from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ''
       SCREAMING_SNAKE_CASE      =  'hf-legacy'  # "hf://"" is reserved for hffs






       def __init__(							self       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			Optional[int]:
          super().__init__(self       ,							**A__      )
          _SCREAMING_SNAKE_CASE      =				repo_info
          _SCREAMING_SNAKE_CASE      =				token
          _SCREAMING_SNAKE_CASE      =				None






       def        UpperCamelCase				(							self      )    ->			Tuple:
          if self.dir_cache is None:
             _SCREAMING_SNAKE_CASE      =				{}
             for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _SCREAMING_SNAKE_CASE      =				{
                    """name""": hf_file.rfilename,
                    """size""": None,
                    """type""": """file""",
                }
                self.dir_cache.update(
                    {
                        str(A__      ): {"""name""": str(A__      ), """size""": None, """type""": """directory"""}
                        for d in list(PurePosixPath(hf_file.rfilename      ).parents      )[:-1]
                    }      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = "rb"       ,							**A__       ,							)    ->			Optional[int]:
          if not isinstance(self.repo_info       ,							A__      ):
             raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"      )
          _SCREAMING_SNAKE_CASE      =				hf_hub_url(self.repo_info.id       ,							A__       ,							revision=self.repo_info.sha      )
          return fsspec.open(
              A__       ,							mode=A__       ,							headers=get_authentication_headers_for_url(A__       ,							use_auth_token=self.token      )       ,							client_kwargs={"""trust_env""": True}       ,							).open()






       def        UpperCamelCase				(							self       ,							A__       ,							**A__      )    ->			str:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				self._strip_protocol(A__      )
          if path in self.dir_cache:
             return self.dir_cache[path]
          else:
             raise FileNotFoundError(A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False       ,							**A__      )    ->			List[Any]:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				PurePosixPath(path.strip("""/"""      )      )
          _SCREAMING_SNAKE_CASE      =				{}
          for p, f in self.dir_cache.items():
             _SCREAMING_SNAKE_CASE      =				PurePosixPath(p.strip("""/"""      )      )
             _SCREAMING_SNAKE_CASE      =				p.parent
             if root == path:
                _SCREAMING_SNAKE_CASE      =				f
          _SCREAMING_SNAKE_CASE      =				list(paths.values()      )
          if detail:
             return out
          else:
             return sorted(f["""name"""] for f in out      )






