



'''simple docstring'''





import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_									=    get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			GPTSwaTokenizer
 __lowerCamelCase       :			Optional[Any]		=			False
 __lowerCamelCase       :			List[Any]		=			True
 __lowerCamelCase       :			Union[str, Any]		=			False



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      super().setUp()

      # We have a SentencePiece fixture for testing
      _a           =    GPTSwaTokenizer(A					,  eos_token='''<unk>'''					,  bos_token='''<unk>'''					,  pad_token='''<unk>'''			)

      tokenizer.save_pretrained(self.tmpdirname			)



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    '''This is a test'''
      _a           =    '''This is a test'''
      return input_text, output_text



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    '''<s>'''
      _a           =    1

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



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]					,  '''<unk>'''			)
      self.assertEqual(vocab_keys[1]					,  '''<s>'''			)
      self.assertEqual(vocab_keys[-1]					,  '''j'''			)
      self.assertEqual(len(A			)					,  2_000			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      self.assertEqual(self.get_tokenizer().vocab_size					,  2_000			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    GPTSwaTokenizer(A			)

      _a           =    tokenizer.tokenize('''This is a test'''			)
      self.assertListEqual(A					,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  [465, 287, 265, 631, 842]			)

      _a           =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
      # fmt: off
      self.assertListEqual(
          A					,  ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']					,  )
      # fmt: on

      _a           =    tokenizer.convert_tokens_to_ids(A			)
      self.assertListEqual(
          A					,  [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,  )

      _a           =    tokenizer.convert_ids_to_tokens(A			)
      # fmt: off
      self.assertListEqual(
          A					,  ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']			)
      # fmt: on



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


      """simple docstring"""



      _a           =    GPTSwaTokenizer(A			)
      _a           =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
      _a           =    [
          [465, 287, 265, 631, 842],
          [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
      ]

      # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
      for text, expected_ids in zip(A					,  A			):
           self.assertListEqual(tokenizer.encode_fast(A			)					,  A			)

      # Test that decode_fast returns the input text
      for text, token_ids in zip(A					,  A			):
           self.assertEqual(tokenizer.decode_fast(A			)					,  A			)



 @slow
 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    [
          '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
          '''Hey there, how are you doing this fine day?''',
          '''This is a text with a trailing spaces followed by a dot     .''',
          '''Häj sväjs lillebrör! =)''',
          '''Det är inget fel på Mr. Cool''',
      ]

      # fmt: off
      _a           =    {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 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]], '''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, 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, 1, 1, 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], [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]]}
      # fmt: on
      self.tokenizer_integration_test_util(
          expected_encoding=A					,  model_name='''AI-Sweden/gpt-sw3-126m'''					,  sequences=A					,  )




import argparse
from collections import defaultdict

import yaml


_lowercase:	List[Any]       			=		'''docs/source/en/_toctree.yml'''






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							defaultdict(snake_case  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							[]
							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  )

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

							_lowerCAmelCase							=							[]
							for duplicate_key in duplicates:
														_lowerCAmelCase							=							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]  )
							_lowerCAmelCase							=							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 _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

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

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

							_lowerCAmelCase							=							api_doc[scheduler_idx]['sections']
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

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

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							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 _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

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

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

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

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

							# sort overall pipeline doc
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

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

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							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__":
				_lowercase:	Tuple       			=		argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				_lowercase:	Optional[Any]       			=		parser.parse_args()

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






