
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


__lowerCAmelCase			:     Any           =   get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		GPTSwaTokenizer
  a__           =		False
  a__           =		True
  a__           =		False
  def 							_lowercase							( self			: Any    )			->	List[str]:







    """simple docstring"""






    super().setUp()

    # We have a SentencePiece fixture for testing
    __magic_name__				     =       GPTSwaTokenizer(UpperCamelCase__			,							eos_token="""<unk>"""			,							bos_token="""<unk>"""			,							pad_token="""<unk>"""    )

    tokenizer.save_pretrained(self.tmpdirname    )
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: List[str]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """This is a test"""
    __magic_name__				     =       """This is a test"""
    return input_text, output_text
  def 							_lowercase							( self			: Optional[int]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       """<s>"""
    __magic_name__				     =       1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__    )			,							UpperCamelCase__    )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__    )			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       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(UpperCamelCase__    )			,							2000    )
  def 							_lowercase							( self			: Dict    )			->	Any:







    """simple docstring"""






    self.assertEqual(self.get_tokenizer().vocab_size			,							2000    )
  def 							_lowercase							( self			: Any    )			->	str:







    """simple docstring"""






    __magic_name__				     =       GPTSwaTokenizer(UpperCamelCase__    )

    __magic_name__				     =       tokenizer.tokenize("""This is a test"""    )
    self.assertListEqual(UpperCamelCase__			,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

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

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

    __magic_name__				     =       tokenizer.convert_tokens_to_ids(UpperCamelCase__    )
    self.assertListEqual(
        UpperCamelCase__			,							[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]			,							)

    __magic_name__				     =       tokenizer.convert_ids_to_tokens(UpperCamelCase__    )
    # fmt: off
    self.assertListEqual(
        UpperCamelCase__			,							["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]    )
    # fmt: on
  def 							_lowercase							( self			: Optional[int]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       GPTSwaTokenizer(UpperCamelCase__    )
    __magic_name__				     =       ["""This is a test""", """I was born in 92000, and this is falsé."""]
    __magic_name__				     =       [
        [465, 287, 265, 631, 842],
        [262, 272, 1525, 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(UpperCamelCase__			,							UpperCamelCase__    ):
      self.assertListEqual(tokenizer.encode_fast(UpperCamelCase__    )			,							UpperCamelCase__    )

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





  @slow
  def 							_lowercase							( self			: Dict    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       [
        """<|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
    __magic_name__				     =       {"""input_ids""": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 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=UpperCamelCase__			,							model_name="""AI-Sweden/gpt-sw3-126m"""			,							sequences=UpperCamelCase__			,							)






import argparse
from collections import defaultdict

import yaml


_snake_case		=  "docs/source/en/_toctree.yml"






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    defaultdict(_lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    []
							_lowerCAmelCase     :							List[str]    =    []
							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(_lowerCamelCase  )

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

							_lowerCAmelCase     :							str    =    []
							for duplicate_key in duplicates:
														_lowerCAmelCase     :							List[str]    =    list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}  )
														if len(_lowerCamelCase  ) > 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     :							Optional[Any]    =    sorted(_lowerCamelCase				, key=lambda _lowerCamelCase  : s["title"].lower()  )

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

							overview_doc.extend(_lowerCamelCase  )

							# Sort
							return overview_doc






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							int    =    yaml.safe_load(f.read()  )

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

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

							_lowerCAmelCase     :							Optional[Any]    =    api_doc[scheduler_idx]["sections"]
							_lowerCAmelCase     :							Optional[Any]    =    clean_doc_toc(_lowerCamelCase  )

							_lowerCAmelCase     :							int    =    False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase     :							List[Any]    =    True
														if overwrite:
																					_lowerCAmelCase     :							Dict    =    new_scheduler_doc

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






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							Tuple    =    yaml.safe_load(f.read()  )

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

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

							_lowerCAmelCase     :							Dict    =    False
							_lowerCAmelCase     :							Optional[int]    =    api_doc[pipeline_idx]["sections"]
							_lowerCAmelCase     :							Tuple    =    []

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase     :							List[Any]    =    pipeline_doc["section"]
																					_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )
																					if overwrite:
																												_lowerCAmelCase     :							Optional[Any]    =    new_sub_pipeline_doc
														new_pipeline_docs.append(_lowerCamelCase  )

							# sort overall pipeline doc
							_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase     :							Dict    =    True
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    api_doc
																					with open(_lowerCamelCase				, "w"				, encoding="utf-8"  ) as f:
																												f.write(yaml.dump(_lowerCamelCase				, allow_unicode=_lowerCamelCase  )  )
														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__":
						_snake_case		=  argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						_snake_case		=  parser.parse_args()

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