
def 						UpperCAmelCase			(	a_		,   a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= word.split()

				def justify(a_		,   a_		,   a_						) -> str:
								__A   			= max_width - width
								__A   			= len(a_						)
								if len(a_						) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__A   			= words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__A   			= spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__A   			= (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(a_						):
																num_spaces_between_words_list[i] += 1
												__A   			= []
												for i in range(a_						):
																# add the word
																aligned_words_list.append(line[i]						)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * " "						)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]						)
												# join the aligned words list to form a justified line
												return "".join(a_						)

				__A   			= []
				__A   			= []
				__A   			= 0
				for word in words:
								if width + len(a_						) + len(a_						) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(a_						)
												width += len(a_						)
								else:
												# justify the line and add it to result
												answer.append(justify(a_		,   a_		,   a_						)						)
												# reset new line and new width
												__A   			= [word], len(a_						)
				__A   			= max_width - width - len(a_						)
				answer.append(" ".join(a_						) + (remaining_spaces + 1) * " "						)
				return answer


if __name__ == "__main__":
					from doctest import testmod

					testmod()







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


UpperCAmelCase	:	int		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Optional[Any]		     =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase	:	List[Any]		     =       [
    """small""",
    """small-base""",
    """medium""",
    """medium-base""",
    """intermediate""",
    """intermediate-base""",
    """large""",
    """large-base""",
    """xlarge""",
    """xlarge-base""",
]

UpperCAmelCase	:	Optional[int]		     =       {
    """vocab_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt""",
        """funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt""",
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt""",
        """funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json""",
        """funnel-transformer/small-base""": (
            """https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json""",
        """funnel-transformer/large-base""": (
            """https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"""
        ),
    },
}
UpperCAmelCase	:	Optional[int]		     =       {F"""funnel-transformer/{name}""": 512 for name in _model_names}
UpperCAmelCase	:	Optional[int]		     =       {F"""funnel-transformer/{name}""": {"""do_lower_case""": True} for name in _model_names}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					str						=			VOCAB_FILES_NAMES
      _lowercase				:					List[Any]						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					Dict						=			PRETRAINED_INIT_CONFIGURATION
      _lowercase				:					Union[str, Any]						=			FunnelTokenizer
      _lowercase				:					Any						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					int						=			2







      def __init__(    self		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__="<unk>"		,      lowerCAmelCase__="<sep>"		,      lowerCAmelCase__="<pad>"		,      lowerCAmelCase__="<cls>"		,      lowerCAmelCase__="<mask>"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__=True		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__="##"		,      **lowerCAmelCase__		,      )   ->						Optional[int]:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,      tokenizer_file=lowerCAmelCase__		,      do_lower_case=lowerCAmelCase__		,      unk_token=lowerCAmelCase__		,      sep_token=lowerCAmelCase__		,      pad_token=lowerCAmelCase__		,      cls_token=lowerCAmelCase__		,      mask_token=lowerCAmelCase__		,      bos_token=lowerCAmelCase__		,      eos_token=lowerCAmelCase__		,      clean_text=lowerCAmelCase__		,      tokenize_chinese_chars=lowerCAmelCase__		,      strip_accents=lowerCAmelCase__		,      wordpieces_prefix=lowerCAmelCase__		,      **lowerCAmelCase__		,      )

        a__	:					Optional[Any]										=json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
        if (
            normalizer_state.get("lowercase"		,      lowerCAmelCase__							) != do_lower_case
            or normalizer_state.get("strip_accents"		,      lowerCAmelCase__							) != strip_accents
            or normalizer_state.get("handle_chinese_chars"		,      lowerCAmelCase__							) != tokenize_chinese_chars
        ):
          a__	:					List[str]										=getattr(lowerCAmelCase__		,      normalizer_state.pop("type"							)							)
          a__	:					Union[str, Any]										=do_lower_case
          a__	:					Any										=strip_accents
          a__	:					Optional[Any]										=tokenize_chinese_chars
          a__	:					Dict										=normalizer_class(**lowerCAmelCase__							)

        a__	:					Any										=do_lower_case







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=None							)   ->						str:



        '''simple docstring'''




        a__	:					Dict										=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

        return output







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						List[int]:



        '''simple docstring'''




        a__	:					Optional[int]										=[self.sep_token_id]
        a__	:					Union[str, Any]										=[self.cls_token_id]
        if token_ids_a is None:
          return len(cls							) * [self.cls_token_type_id] + len(token_ids_a + sep							) * [0]
        return len(cls							) * [self.cls_token_type_id] + len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						Tuple[str]:



        '''simple docstring'''




        a__	:					Tuple										=self._tokenizer.model.save(lowerCAmelCase__		,      name=lowerCAmelCase__							)
        return tuple(lowerCAmelCase__							)






