






"""simple docstring"""
import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

A_     :					int							=     [[12, 10], [3, 9]]
A_     :					int							=     [[3, 4], [7, 4]]
A_     :					List[str]							=     [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
A_     :					Optional[Any]							=     [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
A_     :					Optional[int]							=     [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
A_     :					Optional[int]							=     [1]
A_     :					List[str]							=     [2]

A_     :					Dict							=     logging.getLogger()
A_     :					List[str]							=     logging.DEBUG
A_     :					Dict							=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('mat1', 'mat2')     ,  [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if (np.array(UpperCAmelCase__							)).shape < (2, 2) or (np.array(UpperCAmelCase__							)).shape < (2, 2):
  with pytest.raises(UpperCAmelCase__							):
   logger.info(f'''\n\t{test_addition.__name__} returned integer'''							)
   matop.add(UpperCAmelCase__     ,  UpperCAmelCase__							)
 elif (np.array(UpperCAmelCase__							)).shape == (np.array(UpperCAmelCase__							)).shape:
  logger.info(f'''\n\t{test_addition.__name__} with same matrix dims'''							)
  lowerCamelCase__	:     Dict								=		(np.array(UpperCAmelCase__							) + np.array(UpperCAmelCase__							)).tolist()
  lowerCamelCase__	:     Optional[int]								=		matop.add(UpperCAmelCase__     ,  UpperCAmelCase__							)
  assert theo == act
 else:
  with pytest.raises(UpperCAmelCase__							):
   logger.info(f'''\n\t{test_addition.__name__} with different matrix dims'''							)
   matop.add(UpperCAmelCase__     ,  UpperCAmelCase__							)






@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('mat1', 'mat2')     ,  [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if (np.array(UpperCAmelCase__							)).shape < (2, 2) or (np.array(UpperCAmelCase__							)).shape < (2, 2):
  with pytest.raises(UpperCAmelCase__							):
   logger.info(f'''\n\t{test_subtraction.__name__} returned integer'''							)
   matop.subtract(UpperCAmelCase__     ,  UpperCAmelCase__							)
 elif (np.array(UpperCAmelCase__							)).shape == (np.array(UpperCAmelCase__							)).shape:
  logger.info(f'''\n\t{test_subtraction.__name__} with same matrix dims'''							)
  lowerCamelCase__	:     Union[str, Any]								=		(np.array(UpperCAmelCase__							) - np.array(UpperCAmelCase__							)).tolist()
  lowerCamelCase__	:     List[Any]								=		matop.subtract(UpperCAmelCase__     ,  UpperCAmelCase__							)
  assert theo == act
 else:
  with pytest.raises(UpperCAmelCase__							):
   logger.info(f'''\n\t{test_subtraction.__name__} with different matrix dims'''							)
   assert matop.subtract(UpperCAmelCase__     ,  UpperCAmelCase__							)






@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('mat1', 'mat2')     ,  [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if (np.array(UpperCAmelCase__							)).shape < (2, 2) or (np.array(UpperCAmelCase__							)).shape < (2, 2):
  logger.info(f'''\n\t{test_multiplication.__name__} returned integer'''							)
  with pytest.raises(UpperCAmelCase__							):
   matop.add(UpperCAmelCase__     ,  UpperCAmelCase__							)
 elif (np.array(UpperCAmelCase__							)).shape == (np.array(UpperCAmelCase__							)).shape:
  logger.info(f'''\n\t{test_multiplication.__name__} meets dim requirements'''							)
  lowerCamelCase__	:     int								=		(np.matmul(UpperCAmelCase__     ,  UpperCAmelCase__							)).tolist()
  lowerCamelCase__	:     Union[str, Any]								=		matop.multiply(UpperCAmelCase__     ,  UpperCAmelCase__							)
  assert theo == act
 else:
  with pytest.raises(UpperCAmelCase__							):
   logger.info(
       f'''\n\t{test_multiplication.__name__} does not meet dim requirements'''							)
   assert matop.subtract(UpperCAmelCase__     ,  UpperCAmelCase__							)






@pytest.mark.mat_ops
def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Optional[Any]								=		(3.5 * np.array(UpperCAmelCase__							)).tolist()
 lowerCamelCase__	:     List[Any]								=		matop.scalar_multiply(UpperCAmelCase__     ,  3.5							)
 assert theo == act






@pytest.mark.mat_ops
def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     List[Any]								=		(np.identity(5							)).tolist()
 lowerCamelCase__	:     int								=		matop.identity(5							)
 assert theo == act






@pytest.mark.mat_ops
@pytest.mark.parametrize('mat'     ,  [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]							)
def 			lowerCamelCase_    (		_lowerCamelCase							):
 if (np.array(UpperCAmelCase__							)).shape < (2, 2):
  with pytest.raises(UpperCAmelCase__							):
   logger.info(f'''\n\t{test_transpose.__name__} returned integer'''							)
   matop.transpose(UpperCAmelCase__							)
 else:
  lowerCamelCase__	:     str								=		(np.transpose(UpperCAmelCase__							)).tolist()
  lowerCamelCase__	:     Dict								=		matop.transpose(UpperCAmelCase__     ,  return_map=UpperCAmelCase__							)
  assert theo == act



'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xglm import XGLMTokenizer
else:
	__SCREAMING_SNAKE_CASE						=      None


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						=      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE						=      {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE						=      {
    "facebook/xglm-564M": 2048,
}




class     __snake_case			(    _a	):
		a_          =					VOCAB_FILES_NAMES
		a_          =					PRETRAINED_VOCAB_FILES_MAP
		a_          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_          =					["input_ids", "attention_mask"]
		a_          =					XGLMTokenizer




		def __init__(  self					:str			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:List[str]=None			,    _SCREAMING_SNAKE_CASE					:Dict="<s>"			,    _SCREAMING_SNAKE_CASE					:Dict="</s>"			,    _SCREAMING_SNAKE_CASE					:List[Any]="</s>"			,    _SCREAMING_SNAKE_CASE					:Tuple="<s>"			,    _SCREAMING_SNAKE_CASE					:int="<unk>"			,    _SCREAMING_SNAKE_CASE					:List[str]="<pad>"			,    **_SCREAMING_SNAKE_CASE					:Optional[int]			,    )					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     7
								UpperCamelCase_     :	int					     =     [F"""<madeupword{i}>""" for i in range(self.num_madeup_words)]

								UpperCamelCase_     :	Optional[Any]					     =     kwargs.get("""additional_special_tokens"""			,    [])
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    _SCREAMING_SNAKE_CASE			,    tokenizer_file=_SCREAMING_SNAKE_CASE			,    bos_token=_SCREAMING_SNAKE_CASE			,    eos_token=_SCREAMING_SNAKE_CASE			,    sep_token=_SCREAMING_SNAKE_CASE			,    cls_token=_SCREAMING_SNAKE_CASE			,    unk_token=_SCREAMING_SNAKE_CASE			,    pad_token=_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE			,    )

								UpperCamelCase_     :	int					     =     vocab_file
								UpperCamelCase_     :	Any					     =     False if not self.vocab_file else True




		def 	_snake_case	(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:List[int]			,    _SCREAMING_SNAKE_CASE					:Optional[List[int]] = None)					->			List[int]:






								'''simple docstring'''






								if token_ids_a is None:
														return [self.sep_token_id] + token_ids_a
								UpperCamelCase_     :	str					     =     [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




		def 	_snake_case	(  self					:int			,    _SCREAMING_SNAKE_CASE					:List[int]			,    _SCREAMING_SNAKE_CASE					:Optional[List[int]] = None)					->			List[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     [self.sep_token_id]

								if token_ids_a is None:
														return len(sep + token_ids_a) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]





		def 	_snake_case	(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:Optional[str] = None)					->			Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
														raise ValueError(
														    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
														    """tokenizer.""")

								if not os.path.isdir(_SCREAMING_SNAKE_CASE):
														logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""")
														return
								UpperCamelCase_     :	Dict					     =     os.path.join(
								    _SCREAMING_SNAKE_CASE			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

								if os.path.abspath(self.vocab_file) != os.path.abspath(_SCREAMING_SNAKE_CASE):
														copyfile(self.vocab_file			,    _SCREAMING_SNAKE_CASE)

								return (out_vocab_file,)




