
import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__		:     Tuple     			=      get_tests_dir('fixtures/spiece.model')






@require_sentencepiece
@require_tokenizers
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    str 				=    AlbertTokenizer
   __UpperCamelCase		:    Optional[Any] 				=    AlbertTokenizerFast
   __UpperCamelCase		:    List[Any] 				=    True
   __UpperCamelCase		:    str 				=    True
   __UpperCamelCase		:    Dict 				=    True


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      super().setUp()

      # We have a SentencePiece fixture for testing
      _A:  List[str]					=       AlbertTokenizer(lowerCAmelCase_     )
      tokenizer.save_pretrained(self.tmpdirname     )


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Tuple					=       '''this is a test'''
      _A:  Any					=       '''this is a test'''
      return input_text, output_text


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  Optional[int]					=       '''<pad>'''
      _A:  List[str]					=       0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_     )			,			lowerCAmelCase_     )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_     )			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  List[str]					=       list(self.get_tokenizer().get_vocab().keys()     )

      self.assertEqual(vocab_keys[0]			,			'''<pad>'''     )
      self.assertEqual(vocab_keys[1]			,			'''<unk>'''     )
      self.assertEqual(vocab_keys[-1]			,			'''▁eloquent'''     )
      self.assertEqual(len(lowerCAmelCase_     )			,			3_0_0_0_0     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      self.assertEqual(self.get_tokenizer().vocab_size			,			3_0_0_0_0     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      if not self.test_rust_tokenizer:
         return

      _A:  Tuple					=       self.get_tokenizer()
      _A:  str					=       self.get_rust_tokenizer()

      _A:  Tuple					=       '''I was born in 92000, and this is falsé.'''

      _A:  Dict					=       tokenizer.tokenize(lowerCAmelCase_     )
      _A:  str					=       rust_tokenizer.tokenize(lowerCAmelCase_     )
      self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_     )

      _A:  Union[str, Any]					=       tokenizer.encode(lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_     )
      _A:  Union[str, Any]					=       rust_tokenizer.encode(lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_     )
      self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_     )

      _A:  Dict					=       self.get_rust_tokenizer()
      _A:  Optional[int]					=       tokenizer.encode(lowerCAmelCase_     )
      _A:  str					=       rust_tokenizer.encode(lowerCAmelCase_     )
      self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       AlbertTokenizer(lowerCAmelCase_			,			keep_accents=lowerCAmelCase_     )

      _A:  Tuple					=       tokenizer.tokenize('''This is a test'''     )
      self.assertListEqual(lowerCAmelCase_			,			['''▁this''', '''▁is''', '''▁a''', '''▁test''']     )

      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )			,			[4_8, 2_5, 2_1, 1_2_8_9]     )

      _A:  List[str]					=       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
      self.assertListEqual(
          lowerCAmelCase_			,			['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']     )
      _A:  int					=       tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )
      self.assertListEqual(lowerCAmelCase_			,			[3_1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]     )

      _A:  List[Any]					=       tokenizer.convert_ids_to_tokens(lowerCAmelCase_     )
      self.assertListEqual(
          lowerCAmelCase_			,			['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']			,			)


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  int					=       AlbertTokenizer(lowerCAmelCase_     )

      _A:  str					=       tokenizer.encode('''sequence builders'''     )
      _A:  str					=       tokenizer.encode('''multi-sequence build'''     )

      _A:  Optional[int]					=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_     )
      _A:  Optional[Any]					=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_			,			lowerCAmelCase_     )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]


   @slow
   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      # fmt: off
      _A:  Optional[Any]					=       {'''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''input_ids''': [[2, 2_1_9_7_0, 1_3, 5, 6_0_9_2, 1_6_7, 2_8, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 7_0_2_8, 1_2_0_5_1, 1_8, 1_7, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 3_5_1_5, 1_8_6_8_4, 8, 4_4_6_1, 6, 1_9_2_7, 2_9_7, 8, 1_2_0_6_0, 2_6_0_7, 1_8, 1_3, 5, 4_4_6_1, 1_5, 1_0_5_3_8, 3_8, 8, 1_3_5, 1_5, 8_2_2, 5_8, 1_5, 9_9_3, 1_0_3_6_3, 1_5, 1_4_6_0, 8_0_0_5, 4_4_6_1, 1_5, 9_9_3, 2_5_5, 2_3_2_8, 9, 9, 9, 6, 2_6, 1_1_1_2, 8_1_6, 3_2_6_0, 1_3, 5, 1_0_3, 2_3_7_7, 6, 1_7, 1_1_1_2, 8_1_6, 2_7_8_2, 1_3, 5, 1_0_3, 1_0_6_4_1, 6, 2_9, 8_4, 2_5_1_2, 2_4_3_0, 7_8_2, 1_8_6_8_4, 2_7_6_1, 1_9, 8_0_8, 2_4_3_0, 2_5_5_6, 1_7, 8_5_5, 1_4_8_0, 9_4_7_7, 4_0_9_1, 1_2_8, 1_1_7_1_2, 1_5, 7_1_0_3, 2_1_5_3, 6_7_3, 1_7, 2_4_8_8_3, 9_9_9_0, 9, 3], [2, 1_1_5_0_2, 2_5, 1_0_0_6, 2_0, 7_8_2, 8, 1_1_8_0_9, 8_5_5, 1_7_3_2, 1_9_3_9_3, 1_8_6_6_7, 3_7, 3_6_7, 2_1_0_1_8, 6_9, 1_8_5_4, 3_4, 1_1_8_6_0, 1_9_1_2_4, 2_7, 1_5_6, 2_2_5, 1_7, 1_9_3, 4_1_4_1, 1_9, 6_5, 9_1_2_4, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 1_4, 2_2_3_1, 8_8_6, 2_3_8_5, 1_7_6_5_9, 8_4, 1_4, 1_6_7_9_2, 1_9_5_2, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCAmelCase_			,			model_name='''albert-base-v2'''			,			revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'''			,			)





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   lowerCamelCase__							(					a      )      ->   int:
   _A:  int					=       filter(lambda a      : p.requires_grad		,		model.parameters()      )
   _A:  Dict					=       sum([np.prod(p.size()      ) for p in model_parameters]      )
   return params


UpperCAmelCase__		:     str     			=      logging.getLogger(__name__)






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if metric == "rouge2":
      _A:  Tuple					=       '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      _A:  List[str]					=       '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      _A:  str					=       '''{val_avg_em:.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.'''      )

   _A:  List[str]					=       ModelCheckpoint(
       dirpath=a		,		filename=a		,		monitor=f"""val_{metric}"""		,		mode='''max'''		,		save_top_k=3		,		every_n_epochs=1		,		)
   return checkpoint_callback






def   lowerCamelCase__							(					a		,		a      )      ->   Optional[Any]:
   return EarlyStopping(
       monitor=f"""val_{metric}"""		,		mode='''min''' if '''loss''' in metric else '''max'''		,		patience=a		,		verbose=a		,		)






class UpperCAmelCase   (						pl.Callback   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       {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  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Any=True     ):

      """simple docstring"""
      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
      _A:  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
      _A:  Tuple					=       Path(pl_module.hparams.output_dir     )
      if type_path == "test":
         _A:  List[str]					=       od / '''test_results.txt'''
         _A:  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.
         _A:  Any					=       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
         _A:  Dict					=       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
            _A:  Optional[int]					=       metrics[key]
            if isinstance(lowerCAmelCase_			,			torch.Tensor     ):
               _A:  List[str]					=       val.item()
            _A:  List[Any]					=       F"""{key}: {val:.6f}\n"""
            writer.write(lowerCAmelCase_     )

      if not save_generations:
         return

      if "preds" in metrics:
         _A:  Optional[int]					=       '''\n'''.join(metrics['''preds''']     )
         generations_file.open('''w+'''     ).write(lowerCAmelCase_     )


   @rank_zero_only
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      try:
         _A:  Union[str, Any]					=       pl_module.model.model.num_parameters()
      except AttributeError:
         _A:  Any					=       pl_module.model.num_parameters()

      _A:  Optional[int]					=       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  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule     ):

      """simple docstring"""
      save_json(pl_module.metrics			,			pl_module.metrics_save_path     )
      return self._write_logs(lowerCAmelCase_			,			lowerCAmelCase_			,			'''test'''     )


   @rank_zero_only
   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      save_json(pl_module.metrics			,			pl_module.metrics_save_path     )
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")



