






'''simple docstring'''

import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
     if is_torch_available():
          import torch
     if is_tf_available():
          import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE__     =       {
    'Salesforce/codegen-350M-mono': 2_0_4_8,
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]
  lowercase							=				CodeGenTokenizer

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,		_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,		_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,		_SCREAMING_SNAKE_CASE=False      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Union[str, Any]:



        """simple docstring"""






        super().__init__(
            _SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		tokenizer_file=_SCREAMING_SNAKE_CASE      ,		unk_token=_SCREAMING_SNAKE_CASE      ,		bos_token=_SCREAMING_SNAKE_CASE      ,		eos_token=_SCREAMING_SNAKE_CASE      ,		add_prefix_space=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

        if kwargs.pop("""add_bos_token"""      ,		_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = kwargs.pop("""name_or_path"""      ,		""""""				)
              raise ValueError(
                  """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
                  """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
                  F"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
                  F"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
                  """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
                  """ so that the fast tokenizer works correctly."""				)

        UpperCamelCase      = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
        if pre_tok_state.get("""add_prefix_space"""      ,		_SCREAMING_SNAKE_CASE				) != add_prefix_space:
              UpperCamelCase      = getattr(_SCREAMING_SNAKE_CASE      ,		pre_tok_state.pop("""type"""				)				)
              UpperCamelCase      = add_prefix_space
              UpperCamelCase      = pre_tok_class(**_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = add_prefix_space

  def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				BatchEncoding:



        """simple docstring"""






        UpperCamelCase      = kwargs.get("""is_split_into_words"""      ,		_SCREAMING_SNAKE_CASE				)
        assert self.add_prefix_space or not is_split_into_words, (
            F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				BatchEncoding:



        """simple docstring"""






        UpperCamelCase      = kwargs.get("""is_split_into_words"""      ,		_SCREAMING_SNAKE_CASE				)

        assert self.add_prefix_space or not is_split_into_words, (
            F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				Tuple[str]:



        """simple docstring"""






        UpperCamelCase      = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE      ,		name=_SCREAMING_SNAKE_CASE				)
        return tuple(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				str:



        """simple docstring"""






        UpperCamelCase      = super().decode(
            token_ids=_SCREAMING_SNAKE_CASE      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE      ,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

        if truncate_before_pattern is not None and len(_SCREAMING_SNAKE_CASE				) > 0:
              UpperCamelCase      = self.truncate(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

        return decoded_text


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        def find_re(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = pattern.search(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
              return m.start() if m else -1

        UpperCamelCase      = [re.compile(_SCREAMING_SNAKE_CASE      ,		re.MULTILINE				) for pattern in truncate_before_pattern]

        UpperCamelCase      = list(re.finditer("""^print"""      ,		_SCREAMING_SNAKE_CASE      ,		re.MULTILINE				)				)

        if len(_SCREAMING_SNAKE_CASE				) > 1:
              UpperCamelCase      = completion[: prints[1].start()]

        UpperCamelCase      = list(re.finditer("""^def"""      ,		_SCREAMING_SNAKE_CASE      ,		re.MULTILINE				)				)

        if len(_SCREAMING_SNAKE_CASE				) > 1:
              UpperCamelCase      = completion[: defs[1].start()]

        UpperCamelCase      = 0

        UpperCamelCase      = [
            pos for pos in [find_re(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for terminal in terminals] if pos != -1
        ]

        if len(_SCREAMING_SNAKE_CASE				) > 0:
              return completion[: min(_SCREAMING_SNAKE_CASE				)]
        else:
              return completion








'''simple docstring'''

def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			list:
      UpperCamelCase      = word.split()

      def justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   ) -> str:
            UpperCamelCase      = max_width - width
            UpperCamelCase      = len(__UpperCamelCase   )
            if len(__UpperCamelCase   ) == 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:
                  UpperCamelCase      = 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]
                  UpperCamelCase      = spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  UpperCamelCase      = (
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(__UpperCamelCase   ):
                        num_spaces_between_words_list[i] += 1
                  UpperCamelCase      = []
                  for i in range(__UpperCamelCase   ):
                        # 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(__UpperCamelCase   )

      UpperCamelCase      = []
      UpperCamelCase      = []
      UpperCamelCase      = 0
      for word in words:
            if width + len(__UpperCamelCase   ) + len(__UpperCamelCase   ) <= 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(__UpperCamelCase   )
                  width += len(__UpperCamelCase   )
            else:
                  # justify the line and add it to result
                  answer.append(justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )   )
                  # reset new line and new width
                  UpperCamelCase  ,UpperCamelCase      = [word], len(__UpperCamelCase   )
      UpperCamelCase      = max_width - width - len(__UpperCamelCase   )
      answer.append(""" """.join(__UpperCamelCase   ) + (remaining_spaces + 1) * """ """   )
      return answer


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

     testmod()
