'''simple docstring'''

import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__     :	Dict				=			'▁'

UpperCamelCase__     :	List[str]				=			get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
class 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      str      							=   BertGenerationTokenizer
      UpperCAmelCase_      :      Tuple      							=   False
      UpperCAmelCase_      :      Any      							=   True




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         super().setUp()

         UpperCAmelCase__							:				Optional[int]   				=  BertGenerationTokenizer(lowerCamelCase_					,keep_accents=lowerCamelCase_							)
         tokenizer.save_pretrained(self.tmpdirname							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  '''<s>'''
         UpperCAmelCase__							:				Dict   				=  1

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




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  list(self.get_tokenizer().get_vocab().keys()							)

         self.assertEqual(vocab_keys[0]					,'''<unk>'''							)
         self.assertEqual(vocab_keys[1]					,'''<s>'''							)
         self.assertEqual(vocab_keys[-1]					,'''<pad>'''							)
         self.assertEqual(len(lowerCamelCase_							)					,1002							)




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         self.assertEqual(self.get_tokenizer().vocab_size					,1000							)




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  BertGenerationTokenizer(lowerCamelCase_					,keep_accents=lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  tokenizer.tokenize('''This is a test'''							)
         self.assertListEqual(lowerCamelCase_					,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCamelCase_							)					,[285, 46, 10, 170, 382]					,)

         UpperCAmelCase__							:				List[Any]   				=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
         self.assertListEqual(
             lowerCamelCase_					,[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''9''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''é''',
                 '''.''',
             ]					,)
         UpperCAmelCase__							:				Any   				=  tokenizer.convert_tokens_to_ids(lowerCamelCase_							)
         self.assertListEqual(
             lowerCamelCase_					,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]					,)

         UpperCAmelCase__							:				List[Any]   				=  tokenizer.convert_ids_to_tokens(lowerCamelCase_							)
         self.assertListEqual(
             lowerCamelCase_					,[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''<unk>''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''<unk>''',
                 '''.''',
             ]					,)




      @cached_property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''							)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  '''Hello World!'''
         UpperCAmelCase__							:				Optional[int]   				=  [18536, 2260, 101]

         self.assertListEqual(lowerCamelCase_					,self.big_tokenizer.encode(lowerCamelCase_							)							)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  (
             '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
             ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
         )
         UpperCAmelCase__							:				List[Any]   				=  [
             871,
             419,
             358,
             946,
             991,
             2521,
             452,
             358,
             1357,
             387,
             7751,
             3536,
             112,
             985,
             456,
             126,
             865,
             938,
             5400,
             5734,
             458,
             1368,
             467,
             786,
             2462,
             5246,
             1159,
             633,
             865,
             4519,
             457,
             582,
             852,
             2557,
             427,
             916,
             508,
             405,
             34324,
             497,
             391,
             408,
             11342,
             1244,
             385,
             100,
             938,
             985,
             456,
             574,
             362,
             12597,
             3200,
             3129,
             1172,
         ]

         self.assertListEqual(lowerCamelCase_					,self.big_tokenizer.encode(lowerCamelCase_							)							)




      @require_torch
      @slow
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         import torch

         from transformers import BertGenerationConfig, BertGenerationEncoder

         # Build sequence
         UpperCAmelCase__							:				List[str]   				=  list(self.big_tokenizer.get_vocab().keys()							)[:10]
         UpperCAmelCase__							:				List[str]   				=  ''' '''.join(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  self.big_tokenizer.encode_plus(lowerCamelCase_					,return_tensors='''pt'''					,return_token_type_ids=lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  self.big_tokenizer.batch_encode_plus(
             [sequence + ''' ''' + sequence]					,return_tensors='''pt'''					,return_token_type_ids=lowerCamelCase_							)

         UpperCAmelCase__							:				Dict   				=  BertGenerationConfig()
         UpperCAmelCase__							:				List[str]   				=  BertGenerationEncoder(lowerCamelCase_							)

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
            model(**lowerCamelCase_							)
            model(**lowerCamelCase_							)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  {'''input_ids''': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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='''google/bert_for_seq_generation_L-24_bbc_encoder'''					,revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''					,)





'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Union[str, Any]  ,			_A					:				Optional[int]  ,			_A					:				Any  ,			_A					:				Optional[int]=True  ,			_A					:				List[Any]="pt"    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  {'''add_prefix_space''': True} if isinstance(_A  ,			_A    ) and not line.startswith(''' '''    ) else {}
   UpperCAmelCase__							:				str   				=  padding_side
   return tokenizer(
       [line]  ,			max_length=_A  ,			padding='''max_length''' if pad_to_max_length else None  ,			truncation=_A  ,			return_tensors=_A  ,			add_special_tokens=_A  ,			**_A  ,			)







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Union[str, Any]  ,			_A					:				str=None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  input_ids.ne(_A    ).any(dim=0    )
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_="train"					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=""					,)			->			Any:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				List[str]   				=  Path(lowerCamelCase_							).joinpath(type_path + '''.source'''							)
         UpperCAmelCase__							:				Optional[int]   				=  Path(lowerCamelCase_							).joinpath(type_path + '''.target'''							)
         UpperCAmelCase__							:				Tuple   				=  self.get_char_lens(self.src_file							)
         UpperCAmelCase__							:				Union[str, Any]   				=  max_source_length
         UpperCAmelCase__							:				Optional[int]   				=  max_target_length
         assert min(self.src_lens							) > 0, f'''found empty line in {self.src_file}'''
         UpperCAmelCase__							:				str   				=  tokenizer
         UpperCAmelCase__							:				Optional[int]   				=  prefix
         if n_obs is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.src_lens[:n_obs]
         UpperCAmelCase__							:				int   				=  src_lang
         UpperCAmelCase__							:				Dict   				=  tgt_lang




      def __len__(				self							)			->			Optional[int]:



         '''simple docstring'''



         return len(self.src_lens							)




      def __getitem__(				self					,lowerCamelCase_							)			->			Dict[str, torch.Tensor]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  index + 1  # linecache starts at 1
         UpperCAmelCase__							:				Tuple   				=  self.prefix + linecache.getline(str(self.src_file							)					,lowerCamelCase_							).rstrip('''\n'''							)
         UpperCAmelCase__							:				List[str]   				=  linecache.getline(str(self.tgt_file							)					,lowerCamelCase_							).rstrip('''\n'''							)
         assert source_line, f'''empty source line for index {index}'''
         assert tgt_line, f'''empty tgt line for index {index}'''

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer					,lowerCamelCase_							):
            source_line += self.tokenizer.eos_token
            tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         UpperCAmelCase__							:				int   				=  (
             self.tokenizer.question_encoder if isinstance(self.tokenizer					,lowerCamelCase_							) else self.tokenizer
         )
         UpperCAmelCase__							:				Any   				=  self.tokenizer.generator if isinstance(self.tokenizer					,lowerCamelCase_							) else self.tokenizer

         UpperCAmelCase__							:				List[Any]   				=  encode_line(lowerCamelCase_					,lowerCamelCase_					,self.max_source_length					,'''right'''							)
         UpperCAmelCase__							:				Dict   				=  encode_line(lowerCamelCase_					,lowerCamelCase_					,self.max_target_length					,'''right'''							)

         UpperCAmelCase__							:				Any   				=  source_inputs['''input_ids'''].squeeze()
         UpperCAmelCase__							:				Any   				=  target_inputs['''input_ids'''].squeeze()
         UpperCAmelCase__							:				int   				=  source_inputs['''attention_mask'''].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }




      @staticmethod
      def 		lowerCAmelCase__					(				lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         return [len(lowerCamelCase_							) for x in Path(lowerCamelCase_							).open().readlines()]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Dict[str, torch.Tensor]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  torch.stack([x['''input_ids'''] for x in batch]							)
         UpperCAmelCase__							:				int   				=  torch.stack([x['''attention_mask'''] for x in batch]							)
         UpperCAmelCase__							:				List[Any]   				=  torch.stack([x['''decoder_input_ids'''] for x in batch]							)
         UpperCAmelCase__							:				Optional[Any]   				=  (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer					,lowerCamelCase_							)
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase__							:				Optional[Any]   				=  (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer					,lowerCamelCase_							)
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase__							:				Dict   				=  trim_batch(lowerCamelCase_					,lowerCamelCase_							)
         UpperCAmelCase__				,      UpperCAmelCase__							:				List[str]   				=  trim_batch(lowerCamelCase_					,lowerCamelCase_					,attention_mask=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  {
             '''input_ids''': source_ids,
             '''attention_mask''': source_mask,
             '''decoder_input_ids''': y,
         }
         return batch




UpperCamelCase__     :	int				=			getLogger(__name__)







def __UpperCamelCase(   _A					:				List[List]    ):
   '''simple docstring'''
   return list(itertools.chain.from_iterable(_A    )    )







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  get_git_info()
   save_json(_A  ,			os.path.join(_A  ,			'''git_log.json'''    )    )







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Optional[Any]  ,			_A					:				List[Any]=4  ,			**_A					:				Optional[Any]    ):
   '''simple docstring'''
   with open(_A  ,			'''w'''    ) as f:
      json.dump(_A  ,			_A  ,			indent=_A  ,			**_A    )







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   with open(_A    ) as f:
      return json.load(_A    )







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  git.Repo(search_parent_directories=_A    )
   UpperCAmelCase__							:				int   				=  {
       '''repo_id''': str(_A    ),
       '''repo_sha''': str(repo.head.object.hexsha    ),
       '''repo_branch''': str(repo.active_branch    ),
       '''hostname''': str(socket.gethostname()    ),
   }
   return repo_infos







def __UpperCamelCase(   _A					:				Callable  ,			_A					:				Iterable    ):
   '''simple docstring'''
   return list(map(_A  ,			_A    )    )







def __UpperCamelCase(   _A					:				str  ,			_A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''wb'''    ) as f:
      return pickle.dump(_A  ,			_A    )







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''

   def remove_articles(_A					:				int    ):
      return re.sub(R'''\b(a|an|the)\b'''  ,			''' '''  ,			_A    )

   def white_space_fix(_A					:				List[Any]    ):
      return " ".join(text.split()    )

   def remove_punc(_A					:				Tuple    ):
      UpperCAmelCase__							:				Optional[int]   				=  set(string.punctuation    )
      return "".join(ch for ch in text if ch not in exclude    )

   def lower(_A					:				Any    ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Any    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  normalize_answer(_A    ).split()
   UpperCAmelCase__							:				str   				=  normalize_answer(_A    ).split()
   UpperCAmelCase__							:				str   				=  Counter(_A    ) & Counter(_A    )
   UpperCAmelCase__							:				Optional[Any]   				=  sum(common.values()    )
   if num_same == 0:
      return 0
   UpperCAmelCase__							:				Union[str, Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				List[Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Dict   				=  (2 * precision * recall) / (precision + recall)
   return fa







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Tuple    ):
   '''simple docstring'''
   return normalize_answer(_A    ) == normalize_answer(_A    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   assert len(_A    ) == len(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  0
   for hypo, pred in zip(_A  ,			_A    ):
      em += exact_match_score(_A  ,			_A    )
   if len(_A    ) > 0:
      em /= len(_A    )
   return {"em": em}







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''
   return model_prefix.startswith('''rag'''    )







def __UpperCamelCase(   _A					:				str  ,			_A					:				Any  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   UpperCAmelCase__							:				Optional[int]   				=  '''dropout_rate'''
   for p in extra_params:
      if getattr(_A  ,			_A  ,			_A    ):
         if not hasattr(_A  ,			_A    ) and not hasattr(_A  ,			equivalent_param[p]    ):
            logger.info('''config doesn\'t have a `{}` attribute'''.format(_A    )    )
            delattr(_A  ,			_A    )
            continue
         UpperCAmelCase__							:				int   				=  p if hasattr(_A  ,			_A    ) else equivalent_param[p]
         setattr(_A  ,			_A  ,			getattr(_A  ,			_A    )    )
         delattr(_A  ,			_A    )
   return hparams, config




