





'''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


__SCREAMING_SNAKE_CASE	:List[Any]	    =							logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:List[Any]	    =							{'vocab_file': 'spiece.model'}

__SCREAMING_SNAKE_CASE	:Optional[int]	    =							{
    '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'
        ),
    }
}

__SCREAMING_SNAKE_CASE	:Union[str, Any]	    =							{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}







class     A_      (    _a     ):
 _lowerCamelCase    :					Tuple       			=				VOCAB_FILES_NAMES
 _lowerCamelCase    :					Tuple       			=				PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase    :					Optional[int]       			=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase    :					Optional[int]       			=				["""input_ids""", """attention_mask"""]
 _lowerCamelCase    :					List[int]       			=				[]




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

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

      _UpperCAmelCase					=  {} if sp_model_kwargs is None else sp_model_kwargs

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

      _UpperCAmelCase					=  vocab_file

      _UpperCAmelCase					=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(snake_case_       )




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




 def 	lowercase      (   self			:       Any       ):
      _UpperCAmelCase					=  {self.convert_ids_to_tokens(snake_case_       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab




 def __getstate__(   self			:       Any       ):
      _UpperCAmelCase					=  self.__dict__.copy()
      _UpperCAmelCase					=  None
      return state




 def __setstate__(   self			:       Any      ,     snake_case_			:       Tuple       ):
      _UpperCAmelCase					=  d

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

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




 def 	lowercase      (   self			:       List[str]      ,     snake_case_			:       str       ):
      return self.sp_model.encode(snake_case_      ,     out_type=snake_case_       )




 def 	lowercase      (   self			:       List[str]      ,     snake_case_			:       Any       ):
      return self.sp_model.piece_to_id(snake_case_       )




 def 	lowercase      (   self			:       str      ,     snake_case_			:       List[Any]       ):
      _UpperCAmelCase					=  self.sp_model.IdToPiece(snake_case_       )
      return token




 def 	lowercase      (   self			:       Dict      ,     snake_case_			:       List[Any]       ):
      _UpperCAmelCase					=  []
      _UpperCAmelCase					=  ""
      _UpperCAmelCase					=  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(snake_case_       ) + token
                _UpperCAmelCase					=  True
                _UpperCAmelCase					=  []
           else:
                current_sub_tokens.append(snake_case_       )
                _UpperCAmelCase					=  False
      out_string += self.sp_model.decode(snake_case_       )
      return out_string.strip()




 def 	lowercase      (   self			:       Any      ,     snake_case_			:       List[int]      ,     snake_case_			:       bool = False      ,     snake_case_			:       bool = None      ,     snake_case_			:       bool = True      ,     **snake_case_			:       List[str]      ,     ):
      _UpperCAmelCase					=  kwargs.pop("use_source_tokenizer"      ,     snake_case_       )

      _UpperCAmelCase					=  self.convert_ids_to_tokens(snake_case_      ,     skip_special_tokens=snake_case_       )

      # 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
      _UpperCAmelCase					=  []
      _UpperCAmelCase					=  []
      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(snake_case_       )       )
                     _UpperCAmelCase					=  []
                sub_texts.append(snake_case_       )
           else:
                current_sub_text.append(snake_case_       )
      if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(snake_case_       )       )

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

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




 def 	lowercase      (   self			:       Dict      ,     snake_case_			:       str      ,     snake_case_			:       Optional[str] = None       ):

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

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

      return (out_vocab_file,)




 def 	lowercase      (   self			:       Any      ,     snake_case_			:       List[int]      ,     snake_case_			:       Optional[List[int]] = None       ):

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




 def 	lowercase      (   self			:       Union[str, Any]      ,     snake_case_			:       List[int]      ,     snake_case_			:       Optional[List[int]] = None      ,     snake_case_			:       bool = False       ):

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

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




 def 	lowercase      (   self			:       str      ,     snake_case_			:       List[int]      ,     snake_case_			:       Optional[List[int]] = None       ):
      _UpperCAmelCase					=  [self.sep_token_id]
      _UpperCAmelCase					=  [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 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 PoolFormerImageProcessor






class      UpperCamelCase(	unittest.TestCase ):


					def __init__(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=7  ,     SCREAMING_SNAKE_CASE						:		Dict=3  ,     SCREAMING_SNAKE_CASE						:		Any=3_0  ,     SCREAMING_SNAKE_CASE						:		List[str]=4_0_0  ,     SCREAMING_SNAKE_CASE						:		Any=True  ,     SCREAMING_SNAKE_CASE						:		List[Any]=None  ,     SCREAMING_SNAKE_CASE						:		int=0.9  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     SCREAMING_SNAKE_CASE						:		int=True  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=[0.5, 0.5, 0.5]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=[0.5, 0.5, 0.5]  ,     )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		size if size is not None else {"shortest_edge": 3_0}
											__snake_case				=		crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		num_channels
											__snake_case				=		min_resolution
											__snake_case				=		max_resolution
											__snake_case				=		do_resize_and_center_crop
											__snake_case				=		size
											__snake_case				=		crop_pct
											__snake_case				=		crop_size
											__snake_case				=		do_normalize
											__snake_case				=		image_mean
											__snake_case				=		image_std






					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Any:




											'''simple docstring'''

											return {
											    "size": self.size,
											    "do_resize_and_center_crop": self.do_resize_and_center_crop,
											    "crop_pct": self.crop_pct,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}






@require_torch
@require_vision
class      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					List[str]    					=						PoolFormerImageProcessor if is_vision_available() else None


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						str:




											'''simple docstring'''

											__snake_case				=		PoolFormerImageProcessingTester(self    )


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Dict:




											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "do_resize_and_center_crop"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "size"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "crop_pct"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "do_normalize"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "image_mean"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "image_std"    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class.from_dict(self.image_processor_dict    )
											self.assertEqual(image_processor.size  ,     {"shortest_edge": 3_0}    )
											self.assertEqual(image_processor.crop_size  ,     {"height": 3_0, "width": 3_0}    )

											__snake_case				=		self.image_processing_class.from_dict(self.image_processor_dict  ,     size=4_2  ,     crop_size=8_4    )
											self.assertEqual(image_processor.size  ,     {"shortest_edge": 4_2}    )
											self.assertEqual(image_processor.crop_size  ,     {"height": 8_4, "width": 8_4}    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						List[Any]:




											'''simple docstring'''

											pass


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     Image.Image    )

											# Test not batched input
											__snake_case				=		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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )

											# Test batched
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE  ,     numpify=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     np.ndarray    )

											# Test not batched input
											__snake_case				=		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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )

											# Test batched
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE  ,     torchify=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     torch.Tensor    )

											# Test not batched input
											__snake_case				=		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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )

											# Test batched
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )





