



"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_lowerCAmelCase    : str							=							logging.getLogger(__name__)






class      UpperCAmelCase_  (    _UpperCamelCase						):



      def __init__(					self					:     Dict					,     A					:     Optional[int]=-1						):
           # in NER datasets, the last column is usually reserved for NER label
           _UpperCAmelCase      :     List[str]        =   label_idx



      def 						snake_case_ (					self					:     Dict					,     A					:     Optional[Any]					,     A					:     Union[Split, str]						):
           if isinstance(A					,     A						):
                _UpperCAmelCase      :     Dict        =   mode.value
           _UpperCAmelCase      :     Optional[int]        =   os.path.join(A					,     f'{mode}.txt'						)
           _UpperCAmelCase      :     List[Any]        =   1
           _UpperCAmelCase      :     Optional[int]        =   []
           with open(A					,     encoding="utf-8"						) as f:
                _UpperCAmelCase      :     Union[str, Any]        =   []
                _UpperCAmelCase      :     Dict        =   []
                for line in f:
                     if line.startswith("-DOCSTART-"						) or line == "" or line == "\n":
                          if words:
                               examples.append(InputExample(guid=f'{mode}-{guid_index}'					,     words=A					,     labels=A						)						)
                               guid_index += 1
                               _UpperCAmelCase      :     Any        =   []
                               _UpperCAmelCase      :     str        =   []
                     else:
                          _UpperCAmelCase      :     Optional[Any]        =   line.split(" "						)
                          words.append(splits[0]						)
                          if len(A						) > 1:
                               labels.append(splits[self.label_idx].replace("\n"					,     ""						)						)
                          else:
                               # Examples could have no label for mode = "test"
                               labels.append("O"						)
                if words:
                     examples.append(InputExample(guid=f'{mode}-{guid_index}'					,     words=A					,     labels=A						)						)
           return examples



      def 						snake_case_ (					self					:     int					,     A					:     TextIO					,     A					:     TextIO					,     A					:     List						):
           _UpperCAmelCase      :     Optional[int]        =   0
           for line in test_input_reader:
                if line.startswith("-DOCSTART-"						) or line == "" or line == "\n":
                     writer.write(A						)
                     if not preds_list[example_id]:
                          example_id += 1
                elif preds_list[example_id]:
                     _UpperCAmelCase      :     Optional[Any]        =   line.split()[0] + " " + preds_list[example_id].pop(0						) + "\n"
                     writer.write(A						)
                else:
                     logger.warning("Maximum sequence length exceeded: No prediction for '%s'."					,     line.split()[0]						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     str						):
           if path:
                with open(A					,     "r"						) as f:
                     _UpperCAmelCase      :     Union[str, Any]        =   f.read().splitlines()
                if "O" not in labels:
                     _UpperCAmelCase      :     Tuple        =   ["O"] + labels
                return labels
           else:
                return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class      UpperCAmelCase_  (    _UpperCamelCase						):



      def __init__(					self					:     Tuple						):
           # in CONLL2003 dataset chunk column is second-to-last
           super().__init__(label_idx=-2						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     str						):
           if path:
                with open(A					,     "r"						) as f:
                     _UpperCAmelCase      :     Any        =   f.read().splitlines()
                if "O" not in labels:
                     _UpperCAmelCase      :     str        =   ["O"] + labels
                return labels
           else:
                return [
                    "O",
                    "B-ADVP",
                    "B-INTJ",
                    "B-LST",
                    "B-PRT",
                    "B-NP",
                    "B-SBAR",
                    "B-VP",
                    "B-ADJP",
                    "B-CONJP",
                    "B-PP",
                    "I-ADVP",
                    "I-INTJ",
                    "I-LST",
                    "I-PRT",
                    "I-NP",
                    "I-SBAR",
                    "I-VP",
                    "I-ADJP",
                    "I-CONJP",
                    "I-PP",
                ]







class      UpperCAmelCase_  (    _UpperCamelCase						):



      def 						snake_case_ (					self					:     Tuple					,     A					:     Optional[int]					,     A					:     Union[Split, str]						):
           if isinstance(A					,     A						):
                _UpperCAmelCase      :     Optional[int]        =   mode.value
           _UpperCAmelCase      :     Union[str, Any]        =   os.path.join(A					,     f'{mode}.txt'						)
           _UpperCAmelCase      :     Optional[Any]        =   1
           _UpperCAmelCase      :     int        =   []

           with open(A					,     encoding="utf-8"						) as f:
                for sentence in parse_incr(A						):
                     _UpperCAmelCase      :     Any        =   []
                     _UpperCAmelCase      :     Union[str, Any]        =   []
                     for token in sentence:
                          words.append(token["form"]						)
                          labels.append(token["upos"]						)
                     assert len(A						) == len(A						)
                     if words:
                          examples.append(InputExample(guid=f'{mode}-{guid_index}'					,     words=A					,     labels=A						)						)
                          guid_index += 1
           return examples



      def 						snake_case_ (					self					:     str					,     A					:     TextIO					,     A					:     TextIO					,     A					:     List						):
           _UpperCAmelCase      :     List[Any]        =   0
           for sentence in parse_incr(A						):
                _UpperCAmelCase      :     Dict        =   preds_list[example_id]
                _UpperCAmelCase      :     int        =   ""
                for token in sentence:
                     out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0						)}) '
                out += "\n"
                writer.write(A						)
                example_id += 1



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     str						):
           if path:
                with open(A					,     "r"						) as f:
                     return f.read().splitlines()
           else:
                return [
                    "ADJ",
                    "ADP",
                    "ADV",
                    "AUX",
                    "CCONJ",
                    "DET",
                    "INTJ",
                    "NOUN",
                    "NUM",
                    "PART",
                    "PRON",
                    "PROPN",
                    "PUNCT",
                    "SCONJ",
                    "SYM",
                    "VERB",
                    "X",
                ]





"""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


_lowerCAmelCase    : List[str]							=							[
    # 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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     for pegasus_name, hf_name in PATTERNS:
          _UpperCAmelCase      :     str        =   k.replace(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return k

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    )  ->       PegasusForConditionalGeneration:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusConfig(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   torch_model.model.state_dict()
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     for k, v in tf_weights.items():
          _UpperCAmelCase      :     Union[str, Any]        =   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:
               _UpperCAmelCase      :     Any        =   v.T
          _UpperCAmelCase      :     str        =   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
     _UpperCAmelCase      :     Tuple        =   torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]    )
     _UpperCAmelCase      :     Any        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   {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__    )
     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__						,  strict=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   [
         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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict="./ckpt/aeslc/model.ckpt-32000"    )  ->       Dict:





     '''simple docstring'''

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   Path(SCREAMING_SNAKE_CASE__    ).parent.name
     _UpperCAmelCase      :     Tuple        =   task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
     _UpperCAmelCase      :     Dict        =   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
     _UpperCAmelCase      :     Union[str, Any]        =   get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          _UpperCAmelCase      :     Optional[int]        =   task_specific_params
     _UpperCAmelCase      :     str        =   convert_pegasus(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     torch_model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   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__":
 _lowerCAmelCase    : 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.")
 _lowerCAmelCase    : Union[str, Any]							=							parser.parse_args()
 if args.save_dir is None:
  _lowerCAmelCase    : Tuple							=							Path(args.tf_ckpt_path).parent.name
  _lowerCAmelCase    : Dict							=							os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
