


"""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_   :   Optional[int]									=  [
    # 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						(						lowercase		):







    """simple docstring"""


    for pegasus_name, hf_name in PATTERNS:
        a 		=k.replace(_lowercase       ,    _lowercase		)
    return k
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=DEFAULTS.copy()
    cfg_kwargs.update(_lowercase		)
    a 		=PegasusConfig(**_lowercase		)
    a 		=PegasusForConditionalGeneration(_lowercase		)
    a 		=torch_model.model.state_dict()
    a 		={}
    for k, v in tf_weights.items():
        a 		=rename_state_dict_key(_lowercase		)
        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 		=v.T
        a 		=torch.tensor(_lowercase       ,    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 		=torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]		)
    a 		=mapping["""shared.weight"""]
    a 		=mapping["""shared.weight"""]
    a 		={k: torch.zeros_like(_lowercase		) for k, v in sd.items() if k.endswith('''bias'''		) and k not in mapping}
    mapping.update(**_lowercase		)
    a 		=torch_model.model.load_state_dict(_lowercase       ,    strict=_lowercase		)
    a 		=[
        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						(						lowercase="./ckpt/aeslc/model.ckpt-32000"		):







    """simple docstring"""
    a 		=tf.train.list_variables(_lowercase		)
    a 		={}
    a 		=["""Adafactor""", """global_step"""]
    for name, shape in tqdm(_lowercase       ,    desc='''converting tf checkpoint to dict'''		):
        a 		=any(pat in name for pat in ignore_name		)
        if skip_key:
            continue
        a 		=tf.train.load_variable(_lowercase       ,    _lowercase		)
        a 		=array
    return tf_weights
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=Path(_lowercase		).parent.name
    a 		=task_specific_params[f'''summarization_{dataset}''']["""max_position_embeddings"""]
    a 		=PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''       ,    model_max_length=_lowercase		)
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(_lowercase		)

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


if __name__ == "__main__":
       lowerCamelCase_   :   str									=  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_   :   Any									=  parser.parse_args()
       if args.save_dir is None:
              lowerCamelCase_   :   Tuple									=  Path(args.tf_ckpt_path).parent.name
              lowerCamelCase_   :   Any									=  os.path.join("""pegasus""", dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__a      =				logging.getLogger(__name__)
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     """token-classification"""






					def __init__(       self:							Any						,     snake_case:							Tuple						)   ->	List[Any]:
											if type(snake_case						) == dict:
																	snake_case_ :Optional[int]						=			Namespace(**snake_case						)
											snake_case_ :Optional[int]						=			import_module("""tasks"""						)
											try:
																	snake_case_ :Any						=			getattr(snake_case						,     hparams.task_type						)
																	snake_case_ :TokenClassificationTask						=			token_classification_task_clazz()
											except AttributeError:
																	raise ValueError(
																	    f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
																	    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""						)
											snake_case_ :Any						=			self.token_classification_task.get_labels(hparams.labels						)
											snake_case_ :str						=			CrossEntropyLoss().ignore_index
											super().__init__(snake_case						,     len(self.labels						)						,     self.mode						)






					def    lowerCAmelCase_				(       self:							Dict						,     **snake_case:							List[Any]						)   ->	Any:
											return self.model(**snake_case						)






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Tuple						,     snake_case:							List[Any]						)   ->	Optional[int]:
											snake_case_ :List[str]						=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
											if self.config.model_type != "distilbert":
																	snake_case_ :List[str]						=			(
																	    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
																	)  # XLM and RoBERTa don"t use token_type_ids

											snake_case_ :Optional[Any]						=			self(**snake_case						)
											snake_case_ :List[str]						=			outputs[0]
											# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
											return {"loss": loss}






					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											snake_case_ :List[Any]						=			self.hparams
											for mode in ["train", "dev", "test"]:
																	snake_case_ :Optional[int]						=			self._feature_file(snake_case						)
																	if os.path.exists(snake_case						) and not args.overwrite_cache:
																							logger.info("""Loading features from cached file %s"""						,     snake_case						)
																							snake_case_ :Optional[int]						=			torch.load(snake_case						)
																	else:
																							logger.info("""Creating features from dataset file at %s"""						,     args.data_dir						)
																							snake_case_ :Optional[int]						=			self.token_classification_task.read_examples_from_file(args.data_dir						,     snake_case						)
																							snake_case_ :Any						=			self.token_classification_task.convert_examples_to_features(
																							    snake_case						,     self.labels						,     args.max_seq_length						,     self.tokenizer						,     cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]						)						,     cls_token=self.tokenizer.cls_token						,     cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0						,     sep_token=self.tokenizer.sep_token						,     sep_token_extra=snake_case						,     pad_on_left=bool(self.config.model_type in ["""xlnet"""]						)						,     pad_token=self.tokenizer.pad_token_id						,     pad_token_segment_id=self.tokenizer.pad_token_type_id						,     pad_token_label_id=self.pad_token_label_id						,     )
																							logger.info("""Saving features into cached file %s"""						,     snake_case						)
																							torch.save(snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							int						,     snake_case:							int						,     snake_case:							bool = False						)   ->	DataLoader:
											snake_case_ :int						=			self._feature_file(snake_case						)
											logger.info("""Loading features from cached file %s"""						,     snake_case						)
											snake_case_ :str						=			torch.load(snake_case						)
											snake_case_ :Dict						=			torch.tensor([f.input_ids for f in features]						,     dtype=torch.long						)
											snake_case_ :str						=			torch.tensor([f.attention_mask for f in features]						,     dtype=torch.long						)
											if features[0].token_type_ids is not None:
																	snake_case_ :List[Any]						=			torch.tensor([f.token_type_ids for f in features]						,     dtype=torch.long						)
											else:
																	snake_case_ :List[str]						=			torch.tensor([0 for f in features]						,     dtype=torch.long						)
																	# HACK(we will not use this anymore soon)
											snake_case_ :Any						=			torch.tensor([f.label_ids for f in features]						,     dtype=torch.long						)
											return DataLoader(
											    TensorDataset(snake_case						,     snake_case						,     snake_case						,     snake_case						)						,     batch_size=snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Dict						,     snake_case:							Union[str, Any]						)   ->	List[str]:
											"""Compute validation""" ""
											snake_case_ :List[str]						=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
											if self.config.model_type != "distilbert":
																	snake_case_ :Dict						=			(
																	    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
																	)  # XLM and RoBERTa don"t use token_type_ids
											snake_case_ :Dict						=			self(**snake_case						)
											snake_case_,       snake_case_ :Dict						=			outputs[:2]
											snake_case_ :Union[str, Any]						=			logits.detach().cpu().numpy()
											snake_case_ :List[Any]						=			inputs["""labels"""].detach().cpu().numpy()
											return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							int						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			torch.stack([x["""val_loss"""] for x in outputs]						).mean()
											snake_case_ :Tuple						=			np.concatenate([x["""pred"""] for x in outputs]						,     axis=0						)
											snake_case_ :Tuple						=			np.argmax(snake_case						,     axis=2						)
											snake_case_ :List[str]						=			np.concatenate([x["""target"""] for x in outputs]						,     axis=0						)

											snake_case_ :Optional[Any]						=			dict(enumerate(self.labels						)						)
											snake_case_ :Dict						=			[[] for _ in range(out_label_ids.shape[0]						)]
											snake_case_ :Dict						=			[[] for _ in range(out_label_ids.shape[0]						)]

											for i in range(out_label_ids.shape[0]						):
																	for j in range(out_label_ids.shape[1]						):
																							if out_label_ids[i, j] != self.pad_token_label_id:
																													out_label_list[i].append(label_map[out_label_ids[i][j]]						)
																													preds_list[i].append(label_map[preds[i][j]]						)

											snake_case_ :str						=			{
											    """val_loss""": val_loss_mean,
											    """accuracy_score""": accuracy_score(snake_case						,     snake_case						),
											    """precision""": precision_score(snake_case						,     snake_case						),
											    """recall""": recall_score(snake_case						,     snake_case						),
											    """f1""": fa_score(snake_case						,     snake_case						),
											}

											snake_case_ :List[Any]						=			dict(results.items()						)
											snake_case_ :Union[str, Any]						=			results
											return ret, preds_list, out_label_list






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							Dict						)   ->	Optional[Any]:
											# when stable
											snake_case_,       snake_case_,       snake_case_ :Tuple						=			self._eval_end(snake_case						)
											snake_case_ :str						=			ret["""log"""]
											return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							Optional[int]						)   ->	Any:
											# updating to test_epoch_end instead of deprecated test_end
											snake_case_,       snake_case_,       snake_case_ :Any						=			self._eval_end(snake_case						)

											# Converting to the dict required by pl
											# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
											# pytorch_lightning/trainer/logging.py#L139
											snake_case_ :Optional[int]						=			ret["""log"""]
											# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
											return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






					@staticmethod
					def    lowerCAmelCase_				(       snake_case:							Any						,     snake_case:							int						)   ->	Dict:
											# Add NER specific options
											BaseTransformer.add_model_specific_args(snake_case						,     snake_case						)
											parser.add_argument(
											    """--task_type"""						,     default="""NER"""						,     type=snake_case						,     help="""Task type to fine tune in training (e.g. NER, POS, etc)"""						)
											parser.add_argument(
											    """--max_seq_length"""						,     default=128						,     type=snake_case						,     help=(
											        """The maximum total input sequence length after tokenization. Sequences longer """
											        """than this will be truncated, sequences shorter will be padded."""
											    )						,     )

											parser.add_argument(
											    """--labels"""						,     default=""""""						,     type=snake_case						,     help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""						,     )
											parser.add_argument(
											    """--gpus"""						,     default=0						,     type=snake_case						,     help="""The number of GPUs allocated for this, it is by default 0 meaning none"""						,     )

											parser.add_argument(
											    """--overwrite_cache"""						,     action="""store_true"""						,     help="""Overwrite the cached training and evaluation sets"""						)

											return parser


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						add_generic_args(parser, os.getcwd())
						__a      =				NERTransformer.add_model_specific_args(parser, os.getcwd())
						__a      =				parser.parse_args()
						__a      =				NERTransformer(args)
						__a      =				generic_train(model, args)

						if args.do_predict:
												# See https://github.com/huggingface/transformers/issues/3159
												# pl use this default format to create a checkpoint:
												# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
												# /pytorch_lightning/callbacks/model_checkpoint.py#L322
												__a      =				sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
												__a      =				model.load_from_checkpoint(checkpoints[-1])
												trainer.test(model)
