






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


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{"""vocab_file""": """spiece.model"""}

snake_case    				=		{
    """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"""
        ),
    }
}

snake_case    				=		{
    """google/bigbird-roberta-base""": 4_096,
    """google/bigbird-roberta-large""": 4_096,
    """google/bigbird-base-trivia-itc""": 4_096,
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[str]			=	VOCAB_FILES_NAMES
     UpperCamelCase_							:		List[Any]			=	PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_							:		Dict			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_							:		int			=	['''input_ids''', '''attention_mask''']
     UpperCamelCase_							:		List[int]			=	[]


     def __init__(				self       :	Optional[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Any="<unk>"					, UpperCAmelCase_       :	Tuple="<s>"					, UpperCAmelCase_       :	List[Any]="</s>"					, UpperCAmelCase_       :	List[str]="<pad>"					, UpperCAmelCase_       :	Tuple="[SEP]"					, UpperCAmelCase_       :	List[Any]="[MASK]"					, UpperCAmelCase_       :	Dict="[CLS]"					, UpperCAmelCase_       :	Optional[Dict[str, Any]] = None					, **UpperCAmelCase_       :	Optional[Any]					, ):
       SCREAMING_SNAKE_CASE       :      str							=    AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else bos_token
       SCREAMING_SNAKE_CASE       :      Any							=    AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else eos_token
       SCREAMING_SNAKE_CASE       :      List[str]							=    AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else unk_token
       SCREAMING_SNAKE_CASE       :      List[Any]							=    AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else pad_token
       SCREAMING_SNAKE_CASE       :      List[Any]							=    AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else cls_token
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else sep_token

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

       SCREAMING_SNAKE_CASE       :      Tuple							=    {} if sp_model_kwargs is None else sp_model_kwargs

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

       SCREAMING_SNAKE_CASE       :      List[Any]							=    vocab_file

       SCREAMING_SNAKE_CASE       :      Any							=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(UpperCAmelCase_      )


     @property
     def 				_A       (				self       :	Union[str, Any]      ):
       return self.sp_model.get_piece_size()


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {self.convert_ids_to_tokens(UpperCAmelCase_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab


     def __getstate__(				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.__dict__.copy()
       SCREAMING_SNAKE_CASE       :      List[str]							=    None
       return state


     def __setstate__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    d

       # for backward compatibility
       if not hasattr(self					, "sp_model_kwargs"      ):
         SCREAMING_SNAKE_CASE       :      List[Any]							=    {}

       SCREAMING_SNAKE_CASE       :      Tuple							=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	str      ):
       return self.sp_model.encode(UpperCAmelCase_					, out_type=UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	List[Any]      ):
       return self.sp_model.piece_to_id(UpperCAmelCase_      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.sp_model.IdToPiece(UpperCAmelCase_      )
       return token


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    []
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    ""
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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(UpperCAmelCase_      ) + token
           SCREAMING_SNAKE_CASE       :      Dict							=    True
           SCREAMING_SNAKE_CASE       :      Dict							=    []
         else:
           current_sub_tokens.append(UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      int							=    False
       out_string += self.sp_model.decode(UpperCAmelCase_      )
       return out_string.strip()


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	bool = None					, UpperCAmelCase_       :	bool = True					, **UpperCAmelCase_       :	Tuple					, ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    kwargs.pop("use_source_tokenizer"					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.convert_ids_to_tokens(UpperCAmelCase_					, skip_special_tokens=UpperCAmelCase_      )

       # 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
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
       SCREAMING_SNAKE_CASE       :      Tuple							=    []
       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(UpperCAmelCase_      )      )
             SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
           sub_texts.append(UpperCAmelCase_      )
         else:
           current_sub_text.append(UpperCAmelCase_      )
       if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase_      )      )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
         SCREAMING_SNAKE_CASE       :      List[str]							=    re.sub(r" (\[(MASK|SEP)\])"					, r"\1"					, " ".join(UpperCAmelCase_      )      )
       else:
         SCREAMING_SNAKE_CASE       :      Dict							=    "".join(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.clean_up_tokenization(UpperCAmelCase_      )
         return clean_text
       else:
         return text


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[str] = None      ):
       if not os.path.isdir(UpperCAmelCase_      ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
         return
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    os.path.join(
           UpperCAmelCase_					, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

       return (out_vocab_file,)


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       SCREAMING_SNAKE_CASE       :      Dict							=    [self.cls_token_id]
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None					, UpperCAmelCase_       :	bool = False      ):
       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=UpperCAmelCase_					, token_ids_a=UpperCAmelCase_					, already_has_special_tokens=UpperCAmelCase_      )

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






     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    [self.sep_token_id]
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    [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]














import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DonutImageProcessor


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple=7					, UpperCAmelCase_       :	Dict=3					, UpperCAmelCase_       :	Any=18					, UpperCAmelCase_       :	Optional[int]=30					, UpperCAmelCase_       :	Any=400					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase_       :	Dict=[0.5, 0.5, 0.5]					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    batch_size
       SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    image_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min_resolution
       SCREAMING_SNAKE_CASE       :      Any							=    max_resolution
       SCREAMING_SNAKE_CASE       :      int							=    do_resize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    size if size is not None else {"height": 18, "width": 20}
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_thumbnail
       SCREAMING_SNAKE_CASE       :      Tuple							=    do_align_axis
       SCREAMING_SNAKE_CASE       :      List[str]							=    do_pad
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_normalize
       SCREAMING_SNAKE_CASE       :      Any							=    image_mean
       SCREAMING_SNAKE_CASE       :      Any							=    image_std






     def 				_A       (				self       :	str      ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }





@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	DonutImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    DonutImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Tuple      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "size"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_thumbnail"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_align_long_axis"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_pad"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"      )      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					, {"height": 18, "width": 20}      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=42      )
       self.assertEqual(image_processor.size					, {"height": 42, "width": 42}      )

       # Previous config had dimensions in (width, height) order
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=(42, 84)      )
       self.assertEqual(image_processor.size					, {"height": 84, "width": 42}      )


     def 				_A       (				self       :	Dict      ):
       pass


     @is_flaky()
     def 				_A       (				self       :	Optional[Any]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, Image.Image      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      List[str]							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Dict							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )


     @is_flaky()
     def 				_A       (				self       :	List[str]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      List[Any]							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, numpify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, np.ndarray      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      Any							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






     @is_flaky()
     def 				_A       (				self       :	Dict      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, torchify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, torch.Tensor      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      int							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






