





import argparse
import os
import re


a_			=      "src/transformers/models/auto"


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







def 		__lowercase					(       snake_case_  :  Any    ,snake_case_  :  bool = False						)       ->List[Any]:



    '''simple docstring'''







    with open(snake_case_    ,'''r'''    ,encoding='''utf-8'''						) as f:
        __A   :						List[Any]									=					f.read()

    __A   :						Optional[Any]									=					content.split('''\n'''						)
    __A   :						Tuple									=					[]
    __A   :						Optional[Any]									=					0
    while line_idx < len(snake_case_						):
        if _re_intro_mapping.search(lines[line_idx]						) is not None:
            __A   :						int									=					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   :						List[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(snake_case_    ,key=lambda snake_case_						: _re_identifier.search(snake_case_						).groups()[0]						)
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx]						)
            line_idx += 1

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







def 		__lowercase					(       snake_case_  :  bool = False						)       ->List[Any]:



    '''simple docstring'''







    __A   :						int									=					[os.path.join(snake_case_    ,snake_case_						) for f in os.listdir(snake_case_						) if f.endswith('''.py'''						)]
    __A   :						str									=					[sort_auto_mapping(snake_case_    ,overwrite=snake_case_						) for fname in fnames]

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


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

       sort_all_auto_mappings(not args.check_only)











"""simple docstring"""



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 (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					XLMTokenizer
 _lowerCamelCase				=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __A   :						Tuple									=					[
         '''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									=					dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase  )  )  )  )
     __A   :						Union[str, Any]									=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']  )
     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''merges_file''']  )
     with open(self.vocab_file ,						'''w'''  ) as fp:
         fp.write(json.dumps(__lowerCamelCase  )  )
     with open(self.merges_file ,						'''w'''  ) as fp:
         fp.write('''\n'''.join(__lowerCamelCase  )  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					'''lower newer'''
     __A   :						int									=					'''lower newer'''
     return input_text, output_text

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					XLMTokenizer(self.vocab_file ,						self.merges_file  )

     __A   :						Optional[Any]									=					'''lower'''
     __A   :						Any									=					['''low''', '''er</w>''']
     __A   :						Tuple									=					tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokens + ['''<unk>''']
     __A   :						List[str]									=					[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase  ) ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''  )

     __A   :						Union[str, Any]									=					tokenizer.encode('''sequence builders''' ,						add_special_tokens=__lowerCamelCase  )
     __A   :						int									=					tokenizer.encode('''multi-sequence build''' ,						add_special_tokens=__lowerCamelCase  )

     __A   :						int									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase  )
     __A   :						List[Any]									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,						__lowerCamelCase  )

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




