


"""simple docstring"""







import argparse
import os
import re


__lowercase    					=      """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowercase    					=      re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__lowercase    					=      re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")


def      lowercase			(				A_  ,   A_ = False       )->      Dict:
  '''simple docstring'''







  with open(A_  ,   "r"  ,   encoding="utf-8"       ) as f:
    a						:		List[str]      							=			f.read()

  a						:		Union[str, Any]      							=			content.split("\n"       )
  a						:		Any      							=			[]
  a						:		Tuple      							=			0
  while line_idx < len(A_       ):
    if _re_intro_mapping.search(lines[line_idx]       ) is not None:
      a						:		str      							=			len(re.search(R"^(\s*)\S"  ,   lines[line_idx]       ).groups()[0]       ) + 8
      # Start of a new mapping!
      while not lines[line_idx].startswith(" " * indent + "("       ):
        new_lines.append(lines[line_idx]       )
        line_idx += 1

      a						:		int      							=			[]
      while lines[line_idx].strip() != "]":
        # Blocks either fit in one line or not
        if lines[line_idx].strip() == "(":
          a						:		Any      							=			line_idx
          while not lines[line_idx].startswith(" " * indent + ")"       ):
            line_idx += 1
          blocks.append("\n".join(lines[start_idx : line_idx + 1]       )       )
        else:
          blocks.append(lines[line_idx]       )
        line_idx += 1

      # Sort blocks by their identifiers
      a						:		Tuple      							=			sorted(A_  ,   key=lambda A_       : _re_identifier.search(A_       ).groups()[0]       )
      new_lines += blocks
    else:
      new_lines.append(lines[line_idx]       )
      line_idx += 1

  if overwrite:
    with open(A_  ,   "w"  ,   encoding="utf-8"       ) as f:
      f.write("\n".join(A_       )       )
  elif "\n".join(A_       ) != content:
    return True


def      lowercase			(				A_ = False       )->      List[str]:
  '''simple docstring'''







  a						:		Dict      							=			[os.path.join(A_  ,   A_       ) for f in os.listdir(A_       ) if f.endswith(".py"       )]
  a						:		int      							=			[sort_auto_mapping(A_  ,   overwrite=A_       ) for fname in fnames]

  if not overwrite and any(A_       ):
    a						:		Any      							=			[f for f, d in zip(A_  ,   A_       ) if d]
    raise ValueError(
        F'''The following files have auto mappings that need sorting: {", ".join(A_       )}. Run `make style` to fix'''
        " this."       )


if __name__ == "__main__":
 __lowercase    					=      argparse.ArgumentParser()
 parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
 __lowercase    					=      parser.parse_args()

 sort_all_auto_mappings(not args.check_only)










import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			XLMTokenizer
					UpperCAmelCase__					:    List[str]          =			False
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												a__             =     [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    'w</w>',
												    'r</w>',
												    't</w>',
												    'lo',
												    'low',
												    'er</w>',
												    'low</w>',
												    'lowest</w>',
												    'newer</w>',
												    'wider</w>',
												    '<unk>',
												]
												a__             =     dict(zip(__snake_case			,range(len(__snake_case      )      )      )      )
												a__             =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']      )
												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['merges_file']      )
												with open(self.vocab_file			,'w'      ) as fp:
																			fp.write(json.dumps(__snake_case      )      )
												with open(self.merges_file			,'w'      ) as fp:
																			fp.write('\n'.join(__snake_case      )      )
					def  lowerCamelCase__(      self   :Any			,__snake_case   :int      )					->    Optional[int]:
												a__             =     'lower newer'
												a__             =     'lower newer'
												return input_text, output_text
					def  lowerCamelCase__(      self   :Tuple      )					->    Tuple:
												a__             =     XLMTokenizer(self.vocab_file			,self.merges_file      )

												a__             =     'lower'
												a__             =     ['low', 'er</w>']
												a__             =     tokenizer.tokenize(__snake_case      )
												self.assertListEqual(__snake_case			,__snake_case      )

												a__             =     tokens + ['<unk>']
												a__             =     [14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case      )			,__snake_case      )



					@slow
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[Any]:
												a__             =     XLMTokenizer.from_pretrained('xlm-mlm-en-2048'      )

												a__             =     tokenizer.encode('sequence builders'			,add_special_tokens=__snake_case      )
												a__             =     tokenizer.encode('multi-sequence build'			,add_special_tokens=__snake_case      )

												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case      )
												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case			,__snake_case      )

												assert encoded_sentence == [0] + text + [1]
												assert encoded_pair == [0] + text + [1] + text_a + [1]




