
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		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		"token-classification"





     def __init__(self				:   List[str]    ,   _A				:   List[str]       )						->						Optional[int]:
         if type(_A       ) == dict:
             snake_case								= Namespace(**_A       )
         snake_case								= import_module("tasks"       )
         try:
             snake_case								= getattr(_A    ,   hparams.task_type       )
             snake_case								= 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								= self.token_classification_task.get_labels(hparams.labels       )
         snake_case								= CrossEntropyLoss().ignore_index
         super().__init__(_A    ,   len(self.labels       )    ,   self.mode       )





     def 		UpperCAmelCase(self				:   Dict    ,   **_A				:   Optional[Any]       )						->						int:
         return self.model(**_A       )





     def 		UpperCAmelCase(self				:   int    ,   _A				:   Dict    ,   _A				:   int       )						->						Dict:
         snake_case								= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
             snake_case								= (
                 batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
             )  # XLM and RoBERTa don"t use token_type_ids

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





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





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





     def 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   List[str]       )						->						Optional[int]:
         """Compute validation""" ""
         snake_case								= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
             snake_case								= (
                 batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
             )  # XLM and RoBERTa don"t use token_type_ids
         snake_case								= self(**_A       )
         snake_case								= outputs[:2]
         snake_case								= logits.detach().cpu().numpy()
         snake_case								= inputs['''labels'''].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





     def 		UpperCAmelCase(self				:   str    ,   _A				:   List[str]       )						->						Dict:
         snake_case								= torch.stack([x["val_loss"] for x in outputs]       ).mean()
         snake_case								= np.concatenate([x["pred"] for x in outputs]    ,   axis=0       )
         snake_case								= np.argmax(_A    ,   axis=2       )
         snake_case								= np.concatenate([x["target"] for x in outputs]    ,   axis=0       )

         snake_case								= dict(enumerate(self.labels       )       )
         snake_case								= [[] for _ in range(out_label_ids.shape[0]       )]
         snake_case								= [[] 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								= {
             '''val_loss''': val_loss_mean,
             '''accuracy_score''': accuracy_score(_A    ,   _A       ),
             '''precision''': precision_score(_A    ,   _A       ),
             '''recall''': recall_score(_A    ,   _A       ),
             '''f1''': fa_score(_A    ,   _A       ),
         }

         snake_case								= dict(results.items()       )
         snake_case								= results
         return ret, preds_list, out_label_list





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





     def 		UpperCAmelCase(self				:   Optional[int]    ,   _A				:   Union[str, Any]       )						->						List[Any]:
         # updating to test_epoch_end instead of deprecated test_end
         snake_case								= self._eval_end(_A       )

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         snake_case								= 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(_A				:   Tuple    ,   _A				:   List[Any]       )						->						Union[str, Any]:
         # Add NER specific options
         BaseTransformer.add_model_specific_args(_A    ,   _A       )
         parser.add_argument(
             "--task_type"    ,   default="NER"    ,   type=_A    ,   help="Task type to fine tune in training (e.g. NER, POS, etc)"       )
         parser.add_argument(
             "--max_seq_length"    ,   default=1_2_8    ,   type=_A    ,   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=_A    ,   help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."    ,   )
         parser.add_argument(
             "--gpus"    ,   default=0    ,   type=_A    ,   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)



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Dict									=		["image_processor", "tokenizer"]
     UpperCAmelCase__    :							Dict									=		"LayoutLMv2ImageProcessor"
     UpperCAmelCase__    :							Optional[Any]									=		("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")





     def __init__(self				:   str    ,   _A				:   Any=None    ,   _A				:   Tuple=None    ,   **_A				:   Optional[Any]       )						->						Optional[int]:
         if "feature_extractor" in kwargs:
             warnings.warn(
                 "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                 " instead."    ,   _A    ,   )
             snake_case								= kwargs.pop("feature_extractor"       )

         snake_case								= image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError("You need to specify an `image_processor`."       )
         if tokenizer is None:
             raise ValueError("You need to specify a `tokenizer`."       )

         super().__init__(_A    ,   _A       )





     def __call__(self				:   int    ,   _A				:   List[str]    ,   _A				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,   _A				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,   _A				:   Union[List[List[int]], List[List[List[int]]]] = None    ,   _A				:   Optional[Union[List[int], List[List[int]]]] = None    ,   _A				:   bool = True    ,   _A				:   Union[bool, str, PaddingStrategy] = False    ,   _A				:   Union[bool, str, TruncationStrategy] = None    ,   _A				:   Optional[int] = None    ,   _A				:   int = 0    ,   _A				:   Optional[int] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = True    ,   _A				:   Optional[Union[str, TensorType]] = None    ,   **_A				:   Dict    ,   )						->						BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 "You cannot provide bounding boxes "
                 "if you initialized the image processor with apply_ocr set to True."       )

         if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."       )

         # first, apply the image processor
         snake_case								= self.image_processor(images=_A    ,   return_tensors=_A       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(_A    ,   _A       ):
                 snake_case								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
             snake_case								= features["words"]

         snake_case								= self.tokenizer(
             text=text if text is not None else features["words"]    ,   text_pair=text_pair if text_pair is not None else None    ,   boxes=boxes if boxes is not None else features["boxes"]    ,   word_labels=_A    ,   add_special_tokens=_A    ,   padding=_A    ,   truncation=_A    ,   max_length=_A    ,   stride=_A    ,   pad_to_multiple_of=_A    ,   return_token_type_ids=_A    ,   return_attention_mask=_A    ,   return_overflowing_tokens=_A    ,   return_special_tokens_mask=_A    ,   return_offsets_mapping=_A    ,   return_length=_A    ,   verbose=_A    ,   return_tensors=_A    ,   **_A    ,   )

         # add pixel values
         snake_case								= features.pop("pixel_values"       )
         if return_overflowing_tokens is True:
             snake_case								= self.get_overflowing_images(_A    ,   encoded_inputs["overflow_to_sample_mapping"]       )
         snake_case								= images

         return encoded_inputs





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Dict    ,   _A				:   List[str]       )						->						Optional[int]:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         snake_case								= []
         for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]       )

         if len(_A       ) != len(_A       ):
             raise ValueError(
                 "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                 f' {len(_A       )} and {len(_A       )}'       )

         return images_with_overflow





     def 		UpperCAmelCase(self				:   Tuple    ,   *_A				:   int    ,   **_A				:   Dict       )						->						str:
         return self.tokenizer.batch_decode(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   str    ,   *_A				:   List[Any]    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         return self.tokenizer.decode(*_A    ,   **_A       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Optional[int]:
         return ["input_ids", "bbox", "attention_mask", "image"]





     @property
     def 		UpperCAmelCase(self				:   List[Any]       )						->						int:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,   _A    ,   )
         return self.image_processor_class





     @property
     def 		UpperCAmelCase(self				:   Dict       )						->						Union[str, Any]:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,   _A    ,   )
         return self.image_processor

