
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


a_											=						logging.get_logger(__name__)

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

a_											=						{
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

a_											=						{
    "gpt-neox-20b": 2048,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<|endoftext|>" ,       lowercase_="<|endoftext|>" ,       lowercase_="<|endoftext|>" ,       lowercase_=False ,       **lowercase_ ,       ):
     super().__init__(
         lowercase_ ,       lowercase_ ,       tokenizer_file=lowercase_ ,       unk_token=lowercase_ ,       bos_token=lowercase_ ,       eos_token=lowercase_ ,       add_prefix_space=lowercase_ ,       **lowercase_ ,       )

     snake_case_ :    Optional[Any]     			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
     if pre_tok_state.get("add_prefix_space" ,       lowercase_) != add_prefix_space:
         snake_case_ :    List[Any]     			=						getattr(lowercase_ ,       pre_tok_state.pop("type"))
         snake_case_ :    Optional[int]     			=						add_prefix_space
         snake_case_ :    Union[str, Any]     			=						pre_tok_class(**lowercase_)

     snake_case_ :    Optional[Any]     			=						add_prefix_space





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    Optional[Any]     			=						self._tokenizer.model.save(lowercase_ ,       name=lowercase_)
     return tuple(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[Any]     			=						[]
     for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowercase_ ,       add_special_tokens=lowercase_) + [self.eos_token_id])

     if len(lowercase_) > self.model_max_length:
         snake_case_ :    List[Any]     			=						input_ids[-self.model_max_length :]
     return input_ids






'''simple docstring'''
import doctest
from collections import deque

import numpy as np






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    List[Any]     			=						[2, 1, 2, -1]
     snake_case_ :    int     			=						[1, 2, 3, 4]





 def        snake_case__		(    self):
     snake_case_ :    str     			=						len(self.first_signal)
     snake_case_ :    Any     			=						len(self.second_signal)

     snake_case_ :    List[Any]     			=						max(lowercase_ ,       lowercase_)

     # create a zero matrix of max_length x max_length
     snake_case_ :    Dict     			=						[[0] * max_length for i in range(lowercase_)]

     # 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(lowercase_):
         snake_case_ :    List[str]     			=						deque(self.second_signal)
         rotated_signal.rotate(lowercase_)
         for j, item in enumerate(lowercase_):
             matrix[i][j] += item

        # multiply the matrix with the first signal
     snake_case_ :    Any     			=						np.matmul(np.transpose(lowercase_) ,       np.transpose(self.first_signal))

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


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




