'''simple docstring'''


import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:List[str]		       =						RealmTokenizer
  lowerCamelCase_						:Union[str, Any]		       =						RealmTokenizerFast
  lowerCamelCase_						:Dict		       =						True
  lowerCamelCase_						:Optional[Any]		       =						True
  lowerCamelCase_						:List[str]		       =						filter_non_english


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().setUp()

       UpperCAmelCase_				:  Optional[Any]  = [
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       UpperCAmelCase_				:  Tuple  = os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['vocab_file']		)
       with open(self.vocab_file ,						'w' ,						encoding='utf-8'		) as vocab_writer:
            vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = 'UNwant\u00E9d,running'
       UpperCAmelCase_				:  str  = 'unwanted, running'
       return input_text, output_text


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = self.tokenizer_class(self.vocab_file		)

       UpperCAmelCase_				:  List[Any]  = tokenizer.tokenize('UNwant\u00E9d,running'		)
       self.assertListEqual(snake_case_ ,						['un', '##want', '##ed', ',', 'runn', '##ing']		)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_		) ,						[9, 6, 7, 1_2, 1_0, 1_1]		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if not self.test_rust_tokenizer:
            return

       UpperCAmelCase_				:  str  = self.get_tokenizer()
       UpperCAmelCase_				:  Optional[int]  = self.get_rust_tokenizer()

       UpperCAmelCase_				:  Tuple  = 'UNwant\u00E9d,running'

       UpperCAmelCase_				:  int  = tokenizer.tokenize(snake_case_		)
       UpperCAmelCase_				:  Dict  = rust_tokenizer.tokenize(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  Dict  = tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       UpperCAmelCase_				:  Dict  = rust_tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  str  = self.get_rust_tokenizer()
       UpperCAmelCase_				:  Union[str, Any]  = tokenizer.encode(snake_case_		)
       UpperCAmelCase_				:  List[str]  = rust_tokenizer.encode(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       # With lower casing
       UpperCAmelCase_				:  Tuple  = self.get_tokenizer(do_lower_case=snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = self.get_rust_tokenizer(do_lower_case=snake_case_		)

       UpperCAmelCase_				:  Tuple  = 'UNwant\u00E9d,running'

       UpperCAmelCase_				:  List[str]  = tokenizer.tokenize(snake_case_		)
       UpperCAmelCase_				:  List[str]  = rust_tokenizer.tokenize(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       UpperCAmelCase_				:  Dict  = rust_tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  Dict  = self.get_rust_tokenizer()
       UpperCAmelCase_				:  Tuple  = tokenizer.encode(snake_case_		)
       UpperCAmelCase_				:  Dict  = rust_tokenizer.encode(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'		) ,						['ah', '\u535A', '\u63A8', 'zz']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = BasicTokenizer(do_lower_case=snake_case_		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '		) ,						['hello', '!', 'how', 'are', 'you', '?']		)
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		) ,						['hello']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = BasicTokenizer(do_lower_case=snake_case_ ,						strip_accents=snake_case_		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		) ,						['hällo', '!', 'how', 'are', 'you', '?']		)
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		) ,						['h\u00E9llo']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = BasicTokenizer(do_lower_case=snake_case_ ,						strip_accents=snake_case_		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		) ,						['hallo', '!', 'how', 'are', 'you', '?']		)
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		) ,						['hello']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = BasicTokenizer(do_lower_case=snake_case_		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		) ,						['hallo', '!', 'how', 'are', 'you', '?']		)
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		) ,						['hello']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = BasicTokenizer(do_lower_case=snake_case_		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '		) ,						['HeLLo', '!', 'how', 'Are', 'yoU', '?']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = BasicTokenizer(do_lower_case=snake_case_ ,						strip_accents=snake_case_		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		) ,						['HäLLo', '!', 'how', 'Are', 'yoU', '?']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = BasicTokenizer(do_lower_case=snake_case_ ,						strip_accents=snake_case_		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		) ,						['HaLLo', '!', 'how', 'Are', 'yoU', '?']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = BasicTokenizer(do_lower_case=snake_case_ ,						never_split=['[UNK]']		)

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'		) ,						['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

       UpperCAmelCase_				:  Optional[Any]  = {}
       for i, token in enumerate(snake_case_		):
            UpperCAmelCase_				:  List[str]  = i
       UpperCAmelCase_				:  Union[str, Any]  = WordpieceTokenizer(vocab=snake_case_ ,						unk_token='[UNK]'		)

       self.assertListEqual(tokenizer.tokenize(''		) ,						[]		)

       self.assertListEqual(tokenizer.tokenize('unwanted running'		) ,						['un', '##want', '##ed', 'runn', '##ing']		)

       self.assertListEqual(tokenizer.tokenize('unwantedX running'		) ,						['[UNK]', 'runn', '##ing']		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.assertTrue(_is_whitespace(' '		)		)
       self.assertTrue(_is_whitespace('\t'		)		)
       self.assertTrue(_is_whitespace('\r'		)		)
       self.assertTrue(_is_whitespace('\n'		)		)
       self.assertTrue(_is_whitespace('\u00A0'		)		)

       self.assertFalse(_is_whitespace('A'		)		)
       self.assertFalse(_is_whitespace('-'		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.assertTrue(_is_control('\u0005'		)		)

       self.assertFalse(_is_control('A'		)		)
       self.assertFalse(_is_control(' '		)		)
       self.assertFalse(_is_control('\t'		)		)
       self.assertFalse(_is_control('\r'		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.assertTrue(_is_punctuation('-'		)		)
       self.assertTrue(_is_punctuation('$'		)		)
       self.assertTrue(_is_punctuation('`'		)		)
       self.assertTrue(_is_punctuation('.'		)		)

       self.assertFalse(_is_punctuation('A'		)		)
       self.assertFalse(_is_punctuation(' '		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_tokenizer()

       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(snake_case_		) for t in ['Test', '\xad', 'test']] ,						[['[UNK]'], [], ['[UNK]']]		)

       if self.test_rust_tokenizer:
            UpperCAmelCase_				:  Union[str, Any]  = self.get_rust_tokenizer()
            self.assertListEqual(
                [rust_tokenizer.tokenize(snake_case_		) for t in ['Test', '\xad', 'test']] ,						[['[UNK]'], [], ['[UNK]']]		)


  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.tokenizer_class.from_pretrained('bert-base-uncased'		)

       UpperCAmelCase_				:  Optional[int]  = tokenizer.encode('sequence builders' ,						add_special_tokens=snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = tokenizer.encode('multi-sequence build' ,						add_special_tokens=snake_case_		)

       UpperCAmelCase_				:  Any  = tokenizer.build_inputs_with_special_tokens(snake_case_		)
       UpperCAmelCase_				:  Tuple  = tokenizer.build_inputs_with_special_tokens(snake_case_ ,						snake_case_		)

       assert encoded_sentence == [1_0_1] + text + [1_0_2]
       assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                 UpperCAmelCase_				:  List[str]  = self.rust_tokenizer_class.from_pretrained(snake_case_ ,						**snake_case_		)

                 UpperCAmelCase_				:  Optional[int]  = F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
                 UpperCAmelCase_				:  int  = tokenizer_r.encode_plus(
                     snake_case_ ,						return_attention_mask=snake_case_ ,						return_token_type_ids=snake_case_ ,						return_offsets_mapping=snake_case_ ,						add_special_tokens=snake_case_ ,						)

                 UpperCAmelCase_				:  Tuple  = tokenizer_r.do_lower_case if hasattr(snake_case_ ,						'do_lower_case'		) else False
                 UpperCAmelCase_				:  Optional[int]  = (
                     [
                         ((0, 0), tokenizer_r.cls_token),
                         ((0, 1), 'A'),
                         ((1, 2), ','),
                         ((3, 5), 'na'),
                         ((5, 6), '##ï'),
                         ((6, 8), '##ve'),
                         ((9, 1_5), tokenizer_r.mask_token),
                         ((1_6, 2_1), 'Allen'),
                         ((2_1, 2_3), '##NL'),
                         ((2_3, 2_4), '##P'),
                         ((2_5, 3_3), 'sentence'),
                         ((3_3, 3_4), '.'),
                         ((0, 0), tokenizer_r.sep_token),
                     ]
                     if not do_lower_case
                     else [
                         ((0, 0), tokenizer_r.cls_token),
                         ((0, 1), 'a'),
                         ((1, 2), ','),
                         ((3, 8), 'naive'),
                         ((9, 1_5), tokenizer_r.mask_token),
                         ((1_6, 2_1), 'allen'),
                         ((2_1, 2_3), '##nl'),
                         ((2_3, 2_4), '##p'),
                         ((2_5, 3_3), 'sentence'),
                         ((3_3, 3_4), '.'),
                         ((0, 0), tokenizer_r.sep_token),
                     ]
                 )

                 self.assertEqual(
                     [e[1] for e in expected_results] ,						tokenizer_r.convert_ids_to_tokens(tokens['input_ids']		)		)
                 self.assertEqual([e[0] for e in expected_results] ,						tokens['offset_mapping']		)






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                 UpperCAmelCase_				:  Optional[int]  = self.rust_tokenizer_class.from_pretrained(snake_case_ ,						**snake_case_		)
                 UpperCAmelCase_				:  List[str]  = self.tokenizer_class.from_pretrained(snake_case_ ,						**snake_case_		)
                 UpperCAmelCase_				:  Optional[int]  = [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

                 UpperCAmelCase_				:  str  = tokenizer_r.batch_encode_candidates(snake_case_ ,						max_length=1_0 ,						return_tensors='np'		)
                 UpperCAmelCase_				:  Tuple  = tokenizer_p.batch_encode_candidates(snake_case_ ,						max_length=1_0 ,						return_tensors='np'		)

                 UpperCAmelCase_				:  Optional[int]  = (2, 2, 1_0)

                 self.assertEqual(encoded_sentence_r['input_ids'].shape ,						snake_case_		)
                 self.assertEqual(encoded_sentence_r['attention_mask'].shape ,						snake_case_		)
                 self.assertEqual(encoded_sentence_r['token_type_ids'].shape ,						snake_case_		)

                 self.assertEqual(encoded_sentence_p['input_ids'].shape ,						snake_case_		)
                 self.assertEqual(encoded_sentence_p['attention_mask'].shape ,						snake_case_		)
                 self.assertEqual(encoded_sentence_p['token_type_ids'].shape ,						snake_case_		)












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 import save_json



def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


_UpperCAmelCase						:    Optional[Any]      =  logging.getLogger(__name__)




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :Tuple   						=    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
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :Optional[int]   						=    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.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
         with open(_lowerCAmelCase							,       "a+"					) as writer:
           for key in sorted(_lowerCAmelCase					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    count_trainable_parameters(_lowerCAmelCase					)
         # 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        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=1,      lowerCamelCase=False ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{val_avg_loss:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
        " this function." )

  lowercase     :int   						=    ModelCheckpoint(
      dirpath=lowerCamelCase,      filename=lowerCamelCase,      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      save_top_k=lowerCamelCase,      )
  return checkpoint_callback







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



