





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


UpperCAmelCase	:	int		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Dict		     =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase	:	Optional[Any]		     =       {
    """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"""
        ),
    },
}

UpperCAmelCase	:	Any		     =       {
    """Salesforce/codegen-350M-mono""": 2048,
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					List[Any]						=			VOCAB_FILES_NAMES
      _lowercase				:					Dict						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					List[str]						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					Optional[int]						=			["""input_ids""", """attention_mask"""]
      _lowercase				:					int						=			CodeGenTokenizer







      def __init__(    self		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__="<|endoftext|>"		,      lowerCAmelCase__="<|endoftext|>"		,      lowerCAmelCase__="<|endoftext|>"		,      lowerCAmelCase__=False		,      **lowerCAmelCase__		,      )   ->						Tuple:



        '''simple docstring'''




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

        if kwargs.pop("add_bos_token"		,      lowerCAmelCase__							):
          a__	:					List[str]										=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."							)

        a__	:					Optional[Any]										=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"		,      lowerCAmelCase__							) != add_prefix_space:
          a__	:					Dict										=getattr(lowerCAmelCase__		,      pre_tok_state.pop("type"							)							)
          a__	:					Tuple										=add_prefix_space
          a__	:					int										=pre_tok_class(**lowerCAmelCase__							)

        a__	:					List[str]										=add_prefix_space







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						BatchEncoding:



        '''simple docstring'''




        a__	:					Dict										=kwargs.get("is_split_into_words"		,      lowerCAmelCase__							)
        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(*lowerCAmelCase__		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						BatchEncoding:



        '''simple docstring'''




        a__	:					Union[str, Any]										=kwargs.get("is_split_into_words"		,      lowerCAmelCase__							)

        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(*lowerCAmelCase__		,      **lowerCAmelCase__							)







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



        '''simple docstring'''




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







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



        '''simple docstring'''




        a__	:					Tuple										=super().decode(
            token_ids=lowerCAmelCase__		,      skip_special_tokens=lowerCAmelCase__		,      clean_up_tokenization_spaces=lowerCAmelCase__		,      **lowerCAmelCase__		,      )

        if truncate_before_pattern is not None and len(lowerCAmelCase__							) > 0:
          a__	:					Any										=self.truncate(lowerCAmelCase__		,      lowerCAmelCase__							)

        return decoded_text







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        def find_re(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							):
          a__	:					Union[str, Any]										=pattern.search(lowerCAmelCase__		,      lowerCAmelCase__							)
          return m.start() if m else -1

        a__	:					List[str]										=[re.compile(lowerCAmelCase__		,      re.MULTILINE							) for pattern in truncate_before_pattern]

        a__	:					Optional[Any]										=list(re.finditer("^print"		,      lowerCAmelCase__		,      re.MULTILINE							)							)

        if len(lowerCAmelCase__							) > 1:
          a__	:					Tuple										=completion[: prints[1].start()]

        a__	:					Any										=list(re.finditer("^def"		,      lowerCAmelCase__		,      re.MULTILINE							)							)

        if len(lowerCAmelCase__							) > 1:
          a__	:					List[Any]										=completion[: defs[1].start()]

        a__	:					Dict										=0

        a__	:					Tuple										=[
            pos for pos in [find_re(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							) for terminal in terminals] if pos != -1
        ]

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







'''simple docstring'''

import doctest
from collections import deque

import numpy as np



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self )  ->  None:
        lowercase__				:    str  =					[2, 1, 2, -1]
        lowercase__				:    str  =					[1, 2, 3, 4]


       def _lowerCAmelCase(						self )  ->  list[float]:
        lowercase__				:    Optional[Any]  =					len(self.first_signal )
        lowercase__				:    Union[str, Any]  =					len(self.second_signal )

        lowercase__				:    int  =					max(__lowerCAmelCase	,   __lowerCAmelCase )

        # create a zero matrix of max_length x max_length
        lowercase__				:    List[str]  =					[[0] * max_length for i in range(__lowerCAmelCase )]

        # fills the smaller signal with zeros to make both signals of same length
        if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
        elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
        for i in range(__lowerCAmelCase ):
         lowercase__				:    int  =					deque(self.second_signal )
         rotated_signal.rotate(__lowerCAmelCase )
         for j, item in enumerate(__lowerCAmelCase ):
          matrix[i][j] += item

        # multiply the matrix with the first signal
        lowercase__				:    Optional[int]  =					np.matmul(np.transpose(__lowerCAmelCase )	,   np.transpose(self.first_signal ) )

        # rounding-off to two decimal places
        return [round(__lowerCAmelCase	,   2 ) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()

