


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_A			=						logging.getLogger(__name__)






@dataclass
class    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		str
    UpperCAmelCase__						:		List[str]
    UpperCAmelCase__						:		Optional[List[str]]






@dataclass
class    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		List[int]
    UpperCAmelCase__						:		List[int]
    UpperCAmelCase__						:		Optional[List[int]]              =  None
    UpperCAmelCase__						:		Optional[List[int]]              =  None






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[str, Any]              =  "train"
    UpperCAmelCase__						:		Tuple              =  "dev"
    UpperCAmelCase__						:		Optional[Any]              =  "test"






class    UpperCAmelCase__	:



    """simple docstring"""






    @staticmethod
    def _a (      A_ ,       A_   )  ->	List[InputExample]:
         raise NotImplementedError






    @staticmethod
    def _a (      A_   )  ->	List[str]:
         raise NotImplementedError






    @staticmethod
    def _a (      A_ ,       A_ ,       A_ ,       A_ ,       A_=False ,       A_="[CLS]" ,       A_=1 ,       A_="[SEP]" ,       A_=False ,       A_=False ,       A_=0 ,       A_=0 ,       A_=-100 ,       A_=0 ,       A_=True ,       )  ->	List[InputFeatures]:
         __UpperCamelCase   							={label: i for i, label in enumerate(A_   )}

         __UpperCamelCase   							=[]
         for ex_index, example in enumerate(A_   ):
              if ex_index % 10000 == 0:
                   logger.info('Writing example %d of %d' ,       A_ ,       len(A_   )   )

              __UpperCamelCase   							=[]
              __UpperCamelCase   							=[]
              for word, label in zip(example.words ,       example.labels   ):
                   __UpperCamelCase   							=tokenizer.tokenize(A_   )

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(A_   ) > 0:
                        tokens.extend(A_   )
                        # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                        label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A_   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              __UpperCamelCase   							=tokenizer.num_special_tokens_to_add()
              if len(A_   ) > max_seq_length - special_tokens_count:
                   __UpperCamelCase   							=tokens[: (max_seq_length - special_tokens_count)]
                   __UpperCamelCase   							=label_ids[: (max_seq_length - special_tokens_count)]

              # The convention in BERT is:
              # (a) For sequence pairs:
              #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
              #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
              # (b) For single sequences:
              #  tokens:   [CLS] the dog is hairy . [SEP]
              #  type_ids:   0   0   0   0  0     0   0
              #
              # Where "type_ids" are used to indicate whether this is the first
              # sequence or the second sequence. The embedding vectors for `type=0` and
              # `type=1` were learned during pre-training and are added to the wordpiece
              # embedding vector (and position vector). This is not *strictly* necessary
              # since the [SEP] token unambiguously separates the sequences, but it makes
              # it easier for the model to learn the concept of sequences.
              #
              # For classification tasks, the first vector (corresponding to [CLS]) is
              # used as the "sentence vector". Note that this only makes sense because
              # the entire model is fine-tuned.
              tokens += [sep_token]
              label_ids += [pad_token_label_id]
              if sep_token_extra:
                   # roberta uses an extra separator b/w pairs of sentences
                   tokens += [sep_token]
                   label_ids += [pad_token_label_id]
              __UpperCamelCase   							=[sequence_a_segment_id] * len(A_   )

              if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
              else:
                   __UpperCamelCase   							=[cls_token] + tokens
                   __UpperCamelCase   							=[pad_token_label_id] + label_ids
                   __UpperCamelCase   							=[cls_token_segment_id] + segment_ids

              __UpperCamelCase   							=tokenizer.convert_tokens_to_ids(A_   )

              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              __UpperCamelCase   							=[1 if mask_padding_with_zero else 0] * len(A_   )

              # Zero-pad up to the sequence length.
              __UpperCamelCase   							=max_seq_length - len(A_   )
              if pad_on_left:
                   __UpperCamelCase   							=([pad_token] * padding_length) + input_ids
                   __UpperCamelCase   							=([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   __UpperCamelCase   							=([pad_token_segment_id] * padding_length) + segment_ids
                   __UpperCamelCase   							=([pad_token_label_id] * padding_length) + label_ids
              else:
                   input_ids += [pad_token] * padding_length
                   input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                   segment_ids += [pad_token_segment_id] * padding_length
                   label_ids += [pad_token_label_id] * padding_length

              assert len(A_   ) == max_seq_length
              assert len(A_   ) == max_seq_length
              assert len(A_   ) == max_seq_length
              assert len(A_   ) == max_seq_length

              if ex_index < 5:
                   logger.info('*** Example ***'   )
                   logger.info('guid: %s' ,       example.guid   )
                   logger.info('tokens: %s' ,       ' '.join([str(A_   ) for x in tokens]   )   )
                   logger.info('input_ids: %s' ,       ' '.join([str(A_   ) for x in input_ids]   )   )
                   logger.info('input_mask: %s' ,       ' '.join([str(A_   ) for x in input_mask]   )   )
                   logger.info('segment_ids: %s' ,       ' '.join([str(A_   ) for x in segment_ids]   )   )
                   logger.info('label_ids: %s' ,       ' '.join([str(A_   ) for x in label_ids]   )   )

              if "token_type_ids" not in tokenizer.model_input_names:
                   __UpperCamelCase   							=None

              features.append(
                  InputFeatures(
                      input_ids=A_ ,       attention_mask=A_ ,       token_type_ids=A_ ,       label_ids=A_   )   )
         return features


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset






   class    UpperCAmelCase__	( A_		):



       """simple docstring"""






       UpperCAmelCase__						:		List[InputFeatures]
       UpperCAmelCase__						:		int              =  nn.CrossEntropyLoss().ignore_index






       def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_=False ,       A_ = Split.train ,       )  ->	List[str]:
            # Load data features from cache or dataset file
            __UpperCamelCase   							=os.path.join(
                A_ ,       'cached_{}_{}_{}'.format(mode.value ,       tokenizer.__class__.__name__ ,       str(A_   )   ) ,       )

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            __UpperCamelCase   							=cached_features_file + '.lock'
            with FileLock(A_   ):
                 if os.path.exists(A_   ) and not overwrite_cache:
                      logger.info(f'Loading features from cached file {cached_features_file}'   )
                      __UpperCamelCase   							=torch.load(A_   )
                 else:
                      logger.info(f'Creating features from dataset file at {data_dir}'   )
                      __UpperCamelCase   							=token_classification_task.read_examples_from_file(A_ ,       A_   )
                      # TODO clean up all this to leverage built-in features of tokenizers
                      __UpperCamelCase   							=token_classification_task.convert_examples_to_features(
                          A_ ,       A_ ,       A_ ,       A_ ,       cls_token_at_end=bool(model_type in ['xlnet']   ) ,       cls_token=tokenizer.cls_token ,       cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,       sep_token=tokenizer.sep_token ,       sep_token_extra=A_ ,       pad_on_left=bool(tokenizer.padding_side == 'left'   ) ,       pad_token=tokenizer.pad_token_id ,       pad_token_segment_id=tokenizer.pad_token_type_id ,       pad_token_label_id=self.pad_token_label_id ,       )
                      logger.info(f'Saving features into cached file {cached_features_file}'   )
                      torch.save(self.features ,       A_   )






       def __len__(      self   )  ->	List[Any]:
            return len(self.features   )






       def __getitem__(      self ,       A_   )  ->	InputFeatures:
            return self.features[i]


if is_tf_available():
   import tensorflow as tf






   class    UpperCAmelCase__	:



       """simple docstring"""






       UpperCAmelCase__						:		List[InputFeatures]
       UpperCAmelCase__						:		int              =  -1_0_0






       def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_=False ,       A_ = Split.train ,       )  ->	List[Any]:
            __UpperCamelCase   							=token_classification_task.read_examples_from_file(A_ ,       A_   )
            # TODO clean up all this to leverage built-in features of tokenizers
            __UpperCamelCase   							=token_classification_task.convert_examples_to_features(
                A_ ,       A_ ,       A_ ,       A_ ,       cls_token_at_end=bool(model_type in ['xlnet']   ) ,       cls_token=tokenizer.cls_token ,       cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,       sep_token=tokenizer.sep_token ,       sep_token_extra=A_ ,       pad_on_left=bool(tokenizer.padding_side == 'left'   ) ,       pad_token=tokenizer.pad_token_id ,       pad_token_segment_id=tokenizer.pad_token_type_id ,       pad_token_label_id=self.pad_token_label_id ,       )

            def gen():
                 for ex in self.features:
                      if ex.token_type_ids is None:
                           yield (
                               {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                               ex.label_ids,
                           )
                      else:
                           yield (
                               {
                                   "input_ids": ex.input_ids,
                                   "attention_mask": ex.attention_mask,
                                   "token_type_ids": ex.token_type_ids,
                               },
                               ex.label_ids,
                           )

            if "token_type_ids" not in tokenizer.model_input_names:
                 __UpperCamelCase   							=tf.data.Dataset.from_generator(
                     A_ ,       ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa) ,       (
                         {'input_ids': tf.TensorShape([None]   ), 'attention_mask': tf.TensorShape([None]   )},
                         tf.TensorShape([None]   ),
                     ) ,       )
            else:
                 __UpperCamelCase   							=tf.data.Dataset.from_generator(
                     A_ ,       ({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa) ,       (
                         {
                             'input_ids': tf.TensorShape([None]   ),
                             'attention_mask': tf.TensorShape([None]   ),
                             'token_type_ids': tf.TensorShape([None]   ),
                         },
                         tf.TensorShape([None]   ),
                     ) ,       )






       def _a (      self   )  ->	Optional[Any]:
            __UpperCamelCase   							=self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

            return self.dataset






       def __len__(      self   )  ->	Optional[int]:
            return len(self.features   )






       def __getitem__(      self ,       A_   )  ->	InputFeatures:
            return self.features[i]









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=filter(lambda SCREAMING_SNAKE_CASE__    : p.requires_grad      ,					model.parameters()    )
     __UpperCamelCase   							=sum([np.prod(p.size()    ) for p in model_parameters]    )
     return params


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     if metric == "rouge2":
          __UpperCamelCase   							='{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __UpperCamelCase   							='{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __UpperCamelCase   							='{val_avg_em:.4f}-{step_count}'
     elif metric == "loss":
          __UpperCamelCase   							='{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
              ' function.'    )

     __UpperCamelCase   							=ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     return EarlyStopping(
         monitor=F'val_{metric}'      ,					mode='min' if 'loss' in metric else 'max'      ,					patience=SCREAMING_SNAKE_CASE__      ,					verbose=SCREAMING_SNAKE_CASE__      ,					)






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							={f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
         pl_module.logger.log_metrics(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_=True   )  ->	None:
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'   )
         __UpperCamelCase   							=trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}   )
         # Log results
         __UpperCamelCase   							=Path(pl_module.hparams.output_dir   )
         if type_path == "test":
              __UpperCamelCase   							=od / 'test_results.txt'
              __UpperCamelCase   							=od / 'test_generations.txt'
         else:
              # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
              # If people want this it will be easy enough to add back.
              __UpperCamelCase   							=od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              __UpperCamelCase   							=od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
              results_file.parent.mkdir(exist_ok=A_   )
              generations_file.parent.mkdir(exist_ok=A_   )
         with open(A_ ,       'a+'   ) as writer:
              for key in sorted(A_   ):
                   if key in ["log", "progress_bar", "preds"]:
                        continue
                   __UpperCamelCase   							=metrics[key]
                   if isinstance(A_ ,       torch.Tensor   ):
                        __UpperCamelCase   							=val.item()
                   __UpperCamelCase   							=f'{key}: {val:.6f}\n'
                   writer.write(A_   )

         if not save_generations:
              return

         if "preds" in metrics:
              __UpperCamelCase   							='\n'.join(metrics['preds']   )
              generations_file.open('w+'   ).write(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         try:
              __UpperCamelCase   							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase   							=pl_module.model.num_parameters()

         __UpperCamelCase   							=count_trainable_parameters(A_   )
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





