

'''simple docstring'''







import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase		       =    logging.get_logger(__name__)

__lowerCamelCase		       =    {'''vocab_file''': '''spiece.model'''}

__lowerCamelCase		       =    {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

__lowerCamelCase		       =    {
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}


class 					A__      (      UpperCamelCase_			):
				lowercase						=		VOCAB_FILES_NAMES
				lowercase						=		PRETRAINED_VOCAB_FILES_MAP
				lowercase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase						=		["input_ids", "attention_mask"]
				lowercase						=		[]



				def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__="<unk>"		,				UpperCamelCase__="<s>"		,				UpperCamelCase__="</s>"		,				UpperCamelCase__="<pad>"		,				UpperCamelCase__="[SEP]"		,				UpperCamelCase__="[MASK]"		,				UpperCamelCase__="[CLS]"		,				UpperCamelCase__ = None		,				**UpperCamelCase__		,				)						->      None:





											'''simple docstring'''
											A_						=      AddedToken(__a		,				lstrip=__a		,				rstrip=__a       ) if isinstance(__a		,				__a       ) else bos_token
											A_						=      AddedToken(__a		,				lstrip=__a		,				rstrip=__a       ) if isinstance(__a		,				__a       ) else eos_token
											A_						=      AddedToken(__a		,				lstrip=__a		,				rstrip=__a       ) if isinstance(__a		,				__a       ) else unk_token
											A_						=      AddedToken(__a		,				lstrip=__a		,				rstrip=__a       ) if isinstance(__a		,				__a       ) else pad_token
											A_						=      AddedToken(__a		,				lstrip=__a		,				rstrip=__a       ) if isinstance(__a		,				__a       ) else cls_token
											A_						=      AddedToken(__a		,				lstrip=__a		,				rstrip=__a       ) if isinstance(__a		,				__a       ) else sep_token

											# Mask token behave like a normal word, i.e. include the space before it
											A_						=      AddedToken(__a		,				lstrip=__a		,				rstrip=__a       ) if isinstance(__a		,				__a       ) else mask_token

											A_						=      {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=__a		,				eos_token=__a		,				unk_token=__a		,				pad_token=__a		,				sep_token=__a		,				mask_token=__a		,				cls_token=__a		,				sp_model_kwargs=self.sp_model_kwargs		,				**__a		,				)

											A_						=      vocab_file

											A_						=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(__a       )



				@property
				def 					snake_case_			(					self       )						->      List[str]:





											'''simple docstring'''
											return self.sp_model.get_piece_size()



				def 					snake_case_			(					self       )						->      int:





											'''simple docstring'''
											A_						=      {self.convert_ids_to_tokens(__a       ): i for i in range(self.vocab_size       )}
											vocab.update(self.added_tokens_encoder       )
											return vocab



				def __getstate__(					self       )						->      Tuple:





											'''simple docstring'''
											A_						=      self.__dict__.copy()
											A_						=      None
											return state



				def __setstate__(					self		,				UpperCamelCase__       )						->      Tuple:





											'''simple docstring'''
											A_						=      d

											# for backward compatibility
											if not hasattr(self		,				"""sp_model_kwargs"""       ):
																		A_						=      {}

											A_						=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(self.vocab_file       )



				def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[str]:





											'''simple docstring'''
											return self.sp_model.encode(__a		,				out_type=__a       )



				def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[str]:





											'''simple docstring'''
											return self.sp_model.piece_to_id(__a       )



				def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[Any]:





											'''simple docstring'''
											A_						=      self.sp_model.IdToPiece(__a       )
											return token



				def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Optional[int]:





											'''simple docstring'''
											A_						=      []
											A_						=      ''
											A_						=      False
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									if not prev_is_special:
																																out_string += " "
																									out_string += self.sp_model.decode(__a       ) + token
																									A_						=      True
																									A_						=      []
																		else:
																									current_sub_tokens.append(__a       )
																									A_						=      False
											out_string += self.sp_model.decode(__a       )
											return out_string.strip()



				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = False		,				UpperCamelCase__ = None		,				UpperCamelCase__ = True		,				**UpperCamelCase__		,				)						->      str:





											'''simple docstring'''
											A_						=      kwargs.pop("""use_source_tokenizer"""		,				__a       )

											A_						=      self.convert_ids_to_tokens(__a		,				skip_special_tokens=__a       )

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											A_						=      []
											A_						=      []
											for token in filtered_tokens:
																		if skip_special_tokens and token in self.all_special_ids:
																									continue
																		if token in self.added_tokens_encoder:
																									if current_sub_text:
																																sub_texts.append(self.convert_tokens_to_string(__a       )       )
																																A_						=      []
																									sub_texts.append(__a       )
																		else:
																									current_sub_text.append(__a       )
											if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(__a       )       )

											# Mimic the behavior of the Rust tokenizer:
											# No space before [MASK] and [SEP]
											if spaces_between_special_tokens:
																		A_						=      re.sub(R""" (\[(MASK|SEP)\])"""		,				R"""\1"""		,				""" """.join(__a       )       )
											else:
																		A_						=      ''.join(__a       )

											A_						=      (
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
																		A_						=      self.clean_up_tokenization(__a       )
																		return clean_text
											else:
																		return text



				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      Tuple[str]:





											'''simple docstring'''






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

											if os.path.abspath(self.vocab_file       ) != os.path.abspath(__a       ) and os.path.isfile(self.vocab_file       ):
																		copyfile(self.vocab_file		,				__a       )
											elif not os.path.isfile(self.vocab_file       ):
																		with open(__a		,				"""wb"""       ) as fi:
																									A_						=      self.sp_model.serialized_model_proto()
																									fi.write(__a       )

											return (out_vocab_file,)



				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      List[int]:





											'''simple docstring'''






											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											A_						=      [self.cls_token_id]
											A_						=      [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep



				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = False       )						->      List[int]:





											'''simple docstring'''






											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=__a		,				token_ids_a=__a		,				already_has_special_tokens=__a       )

											if token_ids_a is None:
																		return [1] + ([0] * len(__a       )) + [1]
											return [1] + ([0] * len(__a       )) + [1] + ([0] * len(__a       )) + [1]







				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      List[int]:





											'''simple docstring'''
											A_						=      [self.sep_token_id]
											A_						=      [self.cls_token_id]
											if token_ids_a is None:
																		return len(cls + token_ids_a + sep       ) * [0]
											return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]



'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

       # Reading the image and converting it to grayscale.
       A_						=      imread(UpperCAmelCase__,       0				)

       # Test for get_neighbors_pixel function() return not None
       A_						=      0
       A_						=      0
       A_						=      image[x_coordinate][y_coordinate]

       A_						=      lbp.get_neighbors_pixel(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert neighbors_pixels is not None

       # Test for local_binary_pattern function()
       # Create a numpy array as the same height and width of read image
       A_						=      np.zeros((image.shape[0], image.shape[1])				)

       # Iterating through the image and calculating the local binary pattern value
       # for each pixel.
       for i in range(0,       image.shape[0]				):
              for j in range(0,       image.shape[1]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
