




"""simple docstring"""

import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a         =      get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')





@require_sentencepiece
@require_tokenizers
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[int]				  =     PegasusTokenizer
 UpperCAmelCase   :					List[str]				  =     PegasusTokenizerFast
 UpperCAmelCase   :					Optional[int]				  =     True
 UpperCAmelCase   :					List[str]				  =     True

 def       lowerCAmelCase_							(			self	:	List[str]		):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _A        =       PegasusTokenizer(_UpperCAmelCase		)
       tokenizer.save_pretrained(self.tmpdirname		)

 @cached_property
 def       lowerCAmelCase_							(			self	:	Any		):
       return PegasusTokenizer.from_pretrained('google/pegasus-large'		)

 def       lowerCAmelCase_							(			self	:	str   ,							**_UpperCAmelCase	:	int		):
       return PegasusTokenizer.from_pretrained(self.tmpdirname   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Tuple		):
       return ("This is a test", "This is a test")

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       '</s>'
       _A        =       1

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

 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       list(self.get_tokenizer().get_vocab().keys()		)

       self.assertEqual(vocab_keys[0]   ,							'<pad>'		)
       self.assertEqual(vocab_keys[1]   ,							'</s>'		)
       self.assertEqual(vocab_keys[-1]   ,							'v'		)
       self.assertEqual(len(_UpperCAmelCase		)   ,							1_103		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_103		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       self.tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       (
           'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
           ' </s> <pad> <pad> <pad>'
       )
       _A        =       rust_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       _A        =       py_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       _A        =       '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
       _A        =       [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
       _A        =       tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 96_103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1_024
       _A        =       'To ensure a smooth flow of bank resolutions.'
       _A        =       [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
       _A        =       tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

 @require_torch
 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       ['This is going to be way too long.' * 150, 'short example']
       _A        =       ['not super long but more than 5 tokens', 'tiny']
       _A        =       self._large_tokenizer(_UpperCAmelCase   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)
       _A        =       self._large_tokenizer(
           text_target=_UpperCAmelCase   ,							max_length=5   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)

       assert batch.input_ids.shape == (2, 1_024)
       assert batch.attention_mask.shape == (2, 1_024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase		) == 2  # input_ids, attention_mask.



 @slow
 def       lowerCAmelCase_							(			self	:	Dict		):
       # fmt: off
       _A        =       {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_UpperCAmelCase   ,							model_name='google/bigbird-pegasus-large-arxiv'   ,							revision='ba85d0851d708441f91440d509690f1ab6353415'   ,							)





@require_sentencepiece
@require_tokenizers
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     PegasusTokenizer
 UpperCAmelCase   :					Tuple				  =     PegasusTokenizerFast
 UpperCAmelCase   :					Union[str, Any]				  =     True
 UpperCAmelCase   :					Dict				  =     True

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _A        =       PegasusTokenizer(_UpperCAmelCase   ,							offset=0   ,							mask_token_sent=_UpperCAmelCase   ,							mask_token='[MASK]'		)
       tokenizer.save_pretrained(self.tmpdirname		)

 @cached_property
 def       lowerCAmelCase_							(			self	:	int		):
       return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							**_UpperCAmelCase	:	Any		):
       return PegasusTokenizer.from_pretrained(self.tmpdirname   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Optional[int]		):
       return ("This is a test", "This is a test")

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       self.tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       (
           'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
           ' <pad> <pad> <pad>'
       )
       _A        =       rust_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       _A        =       py_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       ['This is going to be way too long.' * 1_000, 'short example']
       _A        =       ['not super long but more than 5 tokens', 'tiny']
       _A        =       self._large_tokenizer(_UpperCAmelCase   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)
       _A        =       self._large_tokenizer(
           text_target=_UpperCAmelCase   ,							max_length=5   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)

       assert batch.input_ids.shape == (2, 4_096)
       assert batch.attention_mask.shape == (2, 4_096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase		) == 2  # input_ids, attention_mask.



 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       (
           'This is an example string that is used to test the original TF implementation against the HF'
           ' implementation'
       )

       _A        =       self._large_tokenizer(_UpperCAmelCase		).input_ids

       self.assertListEqual(
           _UpperCAmelCase   ,							[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]   ,							)







"""simple docstring"""

import argparse
from collections import defaultdict

import yaml


a         =      '''docs/source/en/_toctree.yml'''






def 						_snake_case					( _snake_case							: List[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       defaultdict(_snake_case							)
      _A        =       []
      _A        =       []
      for doc in doc_list:
            if "local" in doc:
                  counts[doc["local"]] += 1

            if doc["title"].lower() == "overview":
                  overview_doc.append({'local': doc['local'], 'title': doc['title']}							)
            else:
                  new_doc_list.append(_snake_case							)

      _A        =       new_doc_list
      _A        =       [key for key, value in counts.items() if value > 1]

      _A        =       []
      for duplicate_key in duplicates:
            _A        =       list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}							)
            if len(_snake_case							) > 1:
                  raise ValueError(
                      F'''{duplicate_key} is present several times in the documentation table of content at '''
                      '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                      'others.'							)
            # Only add this once
            new_doc.append({'local': duplicate_key, 'title': titles[0]}							)

      # Add none duplicate-keys
      new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]							)
      _A        =       sorted(_snake_case    ,  key=lambda _snake_case							: s["title"].lower()							)

      # "overview" gets special treatment and is always first
      if len(_snake_case							) > 1:
            raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'							)

      overview_doc.extend(_snake_case							)

      # Sort
      return overview_doc






def 						_snake_case					( _snake_case							: Tuple=False							)   -> List[Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[scheduler_idx]["title"] != "Schedulers":
            scheduler_idx += 1

      _A        =       api_doc[scheduler_idx]['sections']
      _A        =       clean_doc_toc(_snake_case							)

      _A        =       False
      if new_scheduler_doc != scheduler_doc:
            _A        =       True
            if overwrite:
                  _A        =       new_scheduler_doc

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)






def 						_snake_case					( _snake_case							: str=False							)   -> Union[str, Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[pipeline_idx]["title"] != "Pipelines":
            pipeline_idx += 1

      _A        =       False
      _A        =       api_doc[pipeline_idx]['sections']
      _A        =       []

      # sort sub pipeline docs
      for pipeline_doc in pipeline_docs:
            if "section" in pipeline_doc:
                  _A        =       pipeline_doc['section']
                  _A        =       clean_doc_toc(_snake_case							)
                  if overwrite:
                        _A        =       new_sub_pipeline_doc
            new_pipeline_docs.append(_snake_case							)

      # sort overall pipeline doc
      _A        =       clean_doc_toc(_snake_case							)

      if new_pipeline_docs != pipeline_docs:
            _A        =       True
            if overwrite:
                  _A        =       new_pipeline_docs

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       a         =      parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

