






"""simple docstring"""




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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {'tokenizer_file': 'tokenizer.json'}

_snake_case          =   {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       Dict			=					VOCAB_FILES_NAMES
 UpperCamelCase					:       str			=					PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase					:       List[str]			=					['''input_ids''', '''attention_mask''']
 UpperCamelCase					:       str			=					None


 def __init__(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Tuple=None							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						Union[str, Any]=None							,						UpperCAmelCase__		:						int="<unk>"							,						UpperCAmelCase__		:						Dict="<s>"							,						UpperCAmelCase__		:						List[Any]="</s>"							,						UpperCAmelCase__		:						Any="<pad>"							,						UpperCAmelCase__		:						int=False							,						UpperCAmelCase__		:						Union[str, Any]=False							,						**UpperCAmelCase__		:						Any							,						)					->       Optional[Any]:
   super().__init__(
       UpperCAmelCase__							,						UpperCAmelCase__							,						tokenizer_file=UpperCAmelCase__							,						unk_token=UpperCAmelCase__							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						pad_token=UpperCAmelCase__							,						add_prefix_space=UpperCAmelCase__							,						clean_up_tokenization_spaces=UpperCAmelCase__							,						**UpperCAmelCase__							,						)
   _a  :       List[Any]             =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
   if pre_tok_state.get("""add_prefix_space"""							,						UpperCAmelCase__							) != add_prefix_space:
     _a  :       Optional[Any]             =	getattr(UpperCAmelCase__							,						pre_tok_state.pop("""type"""							)							)
     _a  :       Optional[Any]             =	add_prefix_space
     _a  :       Union[str, Any]             =	pre_tok_class(**UpperCAmelCase__							)

   _a  :       Optional[Any]             =	add_prefix_space


 def _lowercase	(     self		:						int							,						*UpperCAmelCase__		:						int							,						**UpperCAmelCase__		:						Tuple							)					->       BatchEncoding:
   _a  :       List[str]             =	kwargs.get("""is_split_into_words"""							,						UpperCAmelCase__							)
   if not (self.add_prefix_space or not is_split_into_words):
     raise Exception(
         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
         """ pretokenized inputs."""							)

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


 def _lowercase	(     self		:						List[str]							,						*UpperCAmelCase__		:						str							,						**UpperCAmelCase__		:						List[str]							)					->       BatchEncoding:
   _a  :       List[Any]             =	kwargs.get("""is_split_into_words"""							,						UpperCAmelCase__							)

   if not (self.add_prefix_space or not is_split_into_words):
     raise Exception(
         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
         """ pretokenized inputs."""							)

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


 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[str] = None							)					->       Tuple[str]:
   _a  :       Optional[int]             =	self._tokenizer.model.save(UpperCAmelCase__							,						name=UpperCAmelCase__							)
   return tuple(UpperCAmelCase__							)







 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						"Conversation"							)					->       List[int]:
   _a  :       List[str]             =	[]
   for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(UpperCAmelCase__							,						add_special_tokens=UpperCAmelCase__							) + [self.eos_token_id]							)

   if len(UpperCAmelCase__							) > self.model_max_length:
     _a  :       Union[str, Any]             =	input_ids[-self.model_max_length :]
   return input_ids









"""simple docstring"""




import unittest

import numpy as np

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = None     ,   ):



  '''simple docstring'''
  _a  :       List[Any]             =	np.shape(UpperCamelCase__				)
  _a  :       Any             =	np.shape(UpperCamelCase__				)
  _a  :       Union[str, Any]             =	np.shape(UpperCamelCase__				)

  if shape_a[0] != shape_b[0]:
    _a  :       int             =	(
        """Expected the same number of rows for A and B. """
        F"""Instead found A of size {shape_a} and B of size {shape_b}"""
    )
    raise ValueError(UpperCamelCase__				)

  if shape_b[1] != shape_c[1]:
    _a  :       Tuple             =	(
        """Expected the same number of columns for B and C. """
        F"""Instead found B of size {shape_b} and C of size {shape_c}"""
    )
    raise ValueError(UpperCamelCase__				)

  _a  :       int             =	pseudo_inv
  if a_inv is None:
    try:
      _a  :       Optional[int]             =	np.linalg.inv(UpperCamelCase__				)
    except np.linalg.LinAlgError:
      raise ValueError(
          """Input matrix A is not invertible. Cannot compute Schur complement."""				)

  return mat_c - mat_b.T @ a_inv @ mat_b






class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       str             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Tuple             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Optional[int]             =	np.array([[2, 1], [6, 3]]							)

   _a  :       Optional[Any]             =	schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	np.block([[a, b], [b.T, c]]							)

   _a  :       int             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       List[Any]             =	np.linalg.det(UpperCAmelCase__							)

   self.assertAlmostEqual(UpperCAmelCase__							,						det_a * det_s							)


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       Optional[int]             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Optional[int]             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Union[str, Any]             =	np.array([[2, 1], [6, 3]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						List[Any]							)					->       None:
   _a  :       Any             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Dict             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       List[Any]             =	np.array([[2, 1, 3], [6, 3, 5]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()

