


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


lowerCAmelCase__    :List[Any]       =				logging.getLogger(__name__)

class 						__a (							snake_case_     ):
		_a      :    List[str]           =      '''token-classification'''
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[Any]:




									"""simple docstring"""





									if type(UpperCamelCase__		) == dict:
																_UpperCAmelCase											=     Namespace(**UpperCamelCase__		)
									_UpperCAmelCase											=     import_module('tasks'		)
									try:
																_UpperCAmelCase											=     getattr(UpperCamelCase__     ,       hparams.task_type		)
																_UpperCAmelCase											=     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__()}'''		)
									_UpperCAmelCase											=     self.token_classification_task.get_labels(hparams.labels		)
									_UpperCAmelCase											=     CrossEntropyLoss().ignore_index
									super().__init__(UpperCamelCase__     ,       len(self.labels		)     ,       self.mode		)
		def       UpperCAmelCase__							(     self     ,       **_SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									return self.model(**UpperCamelCase__		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
									if self.config.model_type != "distilbert":
																_UpperCAmelCase											=     (
																    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
																)  # XLM and RoBERTa don"t use token_type_ids

									_UpperCAmelCase											=     self(**UpperCamelCase__		)
									_UpperCAmelCase											=     outputs[0]
									# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
									return {"loss": loss}
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.hparams
									for mode in ["train", "dev", "test"]:
																_UpperCAmelCase											=     self._feature_file(UpperCamelCase__		)
																if os.path.exists(UpperCamelCase__		) and not args.overwrite_cache:
																							logger.info('Loading features from cached file %s'     ,       UpperCamelCase__		)
																							_UpperCAmelCase											=     torch.load(UpperCamelCase__		)
																else:
																							logger.info('Creating features from dataset file at %s'     ,       args.data_dir		)
																							_UpperCAmelCase											=     self.token_classification_task.read_examples_from_file(args.data_dir     ,       UpperCamelCase__		)
																							_UpperCAmelCase											=     self.token_classification_task.convert_examples_to_features(
																							    UpperCamelCase__     ,       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=UpperCamelCase__     ,       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'     ,       UpperCamelCase__		)
																							torch.save(UpperCamelCase__     ,       UpperCamelCase__		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = False		)					->			DataLoader:




									"""simple docstring"""





									_UpperCAmelCase											=     self._feature_file(UpperCamelCase__		)
									logger.info('Loading features from cached file %s'     ,       UpperCamelCase__		)
									_UpperCAmelCase											=     torch.load(UpperCamelCase__		)
									_UpperCAmelCase											=     torch.tensor([f.input_ids for f in features]     ,       dtype=torch.long		)
									_UpperCAmelCase											=     torch.tensor([f.attention_mask for f in features]     ,       dtype=torch.long		)
									if features[0].token_type_ids is not None:
																_UpperCAmelCase											=     torch.tensor([f.token_type_ids for f in features]     ,       dtype=torch.long		)
									else:
																_UpperCAmelCase											=     torch.tensor([0 for f in features]     ,       dtype=torch.long		)
																# HACK(we will not use this anymore soon)
									_UpperCAmelCase											=     torch.tensor([f.label_ids for f in features]     ,       dtype=torch.long		)
									return DataLoader(
									    TensorDataset(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__		)     ,       batch_size=UpperCamelCase__		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





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




									"""simple docstring"""





									_UpperCAmelCase											=     torch.stack([x['val_loss'] for x in outputs]		).mean()
									_UpperCAmelCase											=     np.concatenate([x['pred'] for x in outputs]     ,       axis=0		)
									_UpperCAmelCase											=     np.argmax(UpperCamelCase__     ,       axis=2		)
									_UpperCAmelCase											=     np.concatenate([x['target'] for x in outputs]     ,       axis=0		)

									_UpperCAmelCase											=     dict(enumerate(self.labels		)		)
									_UpperCAmelCase											=     [[] for _ in range(out_label_ids.shape[0]		)]
									_UpperCAmelCase											=     [[] 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]]		)

									_UpperCAmelCase											=     {
									    "val_loss": val_loss_mean,
									    "accuracy_score": accuracy_score(UpperCamelCase__     ,       UpperCamelCase__		),
									    "precision": precision_score(UpperCamelCase__     ,       UpperCamelCase__		),
									    "recall": recall_score(UpperCamelCase__     ,       UpperCamelCase__		),
									    "f1": fa_score(UpperCamelCase__     ,       UpperCamelCase__		),
									}

									_UpperCAmelCase											=     dict(results.items()		)
									_UpperCAmelCase											=     results
									return ret, preds_list, out_label_list
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     self._eval_end(UpperCamelCase__		)
									_UpperCAmelCase											=     ret["log"]
									return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._eval_end(UpperCamelCase__		)

									# Converting to the dict required by pl
									# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
									# pytorch_lightning/trainer/logging.py#L139
									_UpperCAmelCase											=     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       UpperCAmelCase__							(     _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									BaseTransformer.add_model_specific_args(UpperCamelCase__     ,       UpperCamelCase__		)
									parser.add_argument(
									    '--task_type'     ,       default='NER'     ,       type=UpperCamelCase__     ,       help='Task type to fine tune in training (e.g. NER, POS, etc)'		)
									parser.add_argument(
									    '--max_seq_length'     ,       default=128     ,       type=UpperCamelCase__     ,       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=UpperCamelCase__     ,       help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'     ,       )
									parser.add_argument(
									    '--gpus'     ,       default=0     ,       type=UpperCamelCase__     ,       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__":
		lowerCAmelCase__    :Tuple       =				argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		lowerCAmelCase__    :Union[str, Any]       =				NERTransformer.add_model_specific_args(parser, os.getcwd())
		lowerCAmelCase__    :int       =				parser.parse_args()
		lowerCAmelCase__    :int       =				NERTransformer(args)
		lowerCAmelCase__    :int       =				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
				lowerCAmelCase__    :Tuple       =				sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
				lowerCAmelCase__    :Optional[Any]       =				model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)




"""simple docstring"""



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    '''umt5'''
   __UpperCAmelCase					:			str				    =    ['''past_key_values''']







   def __init__(      self		,      UpperCamelCase__=25_0112		,      UpperCamelCase__=512		,      UpperCamelCase__=64		,      UpperCamelCase__=1024		,      UpperCamelCase__=8		,      UpperCamelCase__=None		,      UpperCamelCase__=6		,      UpperCamelCase__=32		,      UpperCamelCase__=128		,      UpperCamelCase__=0.1		,      UpperCamelCase__=1e-6		,      UpperCamelCase__=1.0		,      UpperCamelCase__="gated-gelu"		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__="T5Tokenizer"		,      UpperCamelCase__=True		,      UpperCamelCase__=0		,      UpperCamelCase__=1		,      UpperCamelCase__=0		,      **UpperCamelCase__		,      )      ->						Dict:

       '''simple docstring'''




       super().__init__(
           is_encoder_decoder=UpperCamelCase__		,      tokenizer_class=UpperCamelCase__		,      tie_word_embeddings=UpperCamelCase__		,      pad_token_id=UpperCamelCase__		,      eos_token_id=UpperCamelCase__		,      decoder_start_token_id=UpperCamelCase__		,      **UpperCamelCase__		,      )
       snake_case       : Optional[int]    =       vocab_size
       snake_case       : Any    =       d_model
       snake_case       : Optional[Any]    =       d_kv
       snake_case       : Any    =       d_ff
       snake_case       : Optional[Any]    =       num_layers
       snake_case       : Optional[Any]    =       (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       snake_case       : Optional[Any]    =       num_heads
       snake_case       : Optional[Any]    =       relative_attention_num_buckets
       snake_case       : Dict    =       relative_attention_max_distance
       snake_case       : Tuple    =       dropout_rate
       snake_case       : List[Any]    =       layer_norm_epsilon
       snake_case       : Any    =       initializer_factor
       snake_case       : Optional[Any]    =       feed_forward_proj
       snake_case       : Tuple    =       use_cache

       snake_case       : Any    =       self.feed_forward_proj.split("-"		)
       snake_case       : Tuple    =       act_info[-1]
       snake_case       : Optional[int]    =       act_info[0] == "gated"

       if len(UpperCamelCase__		) > 1 and act_info[0] != "gated" or len(UpperCamelCase__		) > 2:
           raise ValueError(
               F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"		)

       if feed_forward_proj == "gated-gelu":
           snake_case       : List[str]    =       "gelu_new"







   @property
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       return self.d_model







   @property
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return self.num_heads







   @property
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.num_layers







class _lowerCAmelCase							( snake_case_      ):







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       {
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
           snake_case       : Tuple    =       "past_encoder_sequence + sequence"
           snake_case       : int    =       {0: "batch"}
           snake_case       : int    =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
           snake_case       : List[Any]    =       {0: "batch", 1: "decoder_sequence"}
           snake_case       : Any    =       {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
           self.fill_with_past_key_values_(UpperCamelCase__		,      direction="inputs"		)

       return common_inputs







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return 13







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 5e-4





