
"""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   __magic_name__	(      __snake_case				:       Union[str, Any]  ) ->				Tuple:
       for pegasus_name, hf_name in PATTERNS:
              lowercase    :				Dict       		=							k.replace(__snake_case     , __snake_case  )
       return k


def   __magic_name__	(      __snake_case				:       dict     , __snake_case				:       dict  ) ->				PegasusForConditionalGeneration:
       lowercase    :				Tuple       		=							DEFAULTS.copy()
       cfg_kwargs.update(__snake_case  )
       lowercase    :				Any       		=							PegasusConfig(**__snake_case  )
       lowercase    :				Optional[int]       		=							PegasusForConditionalGeneration(__snake_case  )
       lowercase    :				Optional[Any]       		=							torch_model.model.state_dict()
       lowercase    :				int       		=							{}
       for k, v in tf_weights.items():
              lowercase    :				str       		=							rename_state_dict_key(__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:
                     lowercase    :				Dict       		=							v.T
              lowercase    :				Union[str, Any]       		=							torch.tensor(__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
       lowercase    :				List[Any]       		=							torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]  )
       lowercase    :				str       		=							mapping["shared.weight"]
       lowercase    :				Dict       		=							mapping["shared.weight"]
       lowercase    :				int       		=							{k: torch.zeros_like(__snake_case  ) for k, v in sd.items() if k.endswith("bias"  ) and k not in mapping}
       mapping.update(**__snake_case  )
       lowercase		,    lowercase    :				Tuple       		=							torch_model.model.load_state_dict(__snake_case     , strict=__snake_case  )
       lowercase    :				Optional[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   __magic_name__	(      __snake_case				:       List[str]="./ckpt/aeslc/model.ckpt-32000"  ) ->				Dict:
       lowercase    :				int       		=							tf.train.list_variables(__snake_case  )
       lowercase    :				Any       		=							{}
       lowercase    :				Optional[int]       		=							["Adafactor", "global_step"]
       for name, shape in tqdm(__snake_case     , desc="converting tf checkpoint to dict"  ):
              lowercase    :				Dict       		=							any(pat in name for pat in ignore_name  )
              if skip_key:
                     continue
              lowercase    :				Any       		=							tf.train.load_variable(__snake_case     , __snake_case  )
              lowercase    :				int       		=							array
       return tf_weights


def   __magic_name__	(      __snake_case				:       str     , __snake_case				:       str  ) ->				Union[str, Any]:
       # save tokenizer first
       lowercase    :				str       		=							Path(__snake_case  ).parent.name
       lowercase    :				List[str]       		=							task_specific_params[f"""summarization_{dataset}"""]["max_position_embeddings"]
       lowercase    :				Tuple       		=							PegasusTokenizer.from_pretrained("sshleifer/pegasus"     , model_max_length=__snake_case  )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__snake_case  )

       # convert model
       lowercase    :				Dict       		=							get_tf_weights_as_numpy(__snake_case  )
       lowercase    :				int       		=							task_specific_params[f"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase    :				Tuple       		=							task_specific_params
       lowercase    :				int       		=							convert_pegasus(__snake_case     , __snake_case  )
       torch_model.save_pretrained(__snake_case  )
       lowercase    :				str       		=							torch_model.state_dict()
       sd.pop("model.decoder.embed_positions.weight"  )
       sd.pop("model.encoder.embed_positions.weight"  )
       torch.save(__snake_case     , Path(__snake_case  ) / "pytorch_model.bin"  )


if __name__ == "__main__":
   _A			:       Union[str, Any]   		=       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			:       Any   		=       Path(args.tf_ckpt_path).parent.name
      _A			:       Dict   		=       os.path.join("""pegasus""", dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




"""simple docstring"""




def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       int  ) ->				float:
       lowercase    :				List[Any]       		=							(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total


def   __magic_name__	(      ) ->				int:
       print(sum_of_series(1     , 1     , 10  )  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


