





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

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


if is_sentencepiece_available():
    from .tokenization_big_bird import BigBirdTokenizer
else:
    __lowerCamelCase	:					str			  =					None

__lowerCamelCase	:					Tuple			  =					logging.get_logger(__name__)
__lowerCamelCase	:					int			  =					{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase	:					str			  =					{
    """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"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

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


__lowerCamelCase	:					List[Any]			  =					"""▁"""



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Any						   =       VOCAB_FILES_NAMES
      _UpperCAmelCase     :Optional[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase     :Union[str, Any]						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase     :Optional[int]						   =       BigBirdTokenizer
      _UpperCAmelCase     :Dict						   =       ['input_ids', 'attention_mask']
      _UpperCAmelCase     :List[int]						   =       []





      def __init__(			self	,							A_=None	,							A_=None	,							A_="<unk>"	,							A_="<s>"	,							A_="</s>"	,							A_="<pad>"	,							A_="[SEP]"	,							A_="[MASK]"	,							A_="[CLS]"	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else bos_token
       UpperCamelCase     : Tuple	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else eos_token
       UpperCamelCase     : Optional[int]	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else unk_token
       UpperCamelCase     : Optional[int]	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else pad_token
       UpperCamelCase     : int	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else cls_token
       UpperCamelCase     : int	     =       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
       UpperCamelCase     : List[str]	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else mask_token

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

       UpperCamelCase     : Optional[Any]	     =       vocab_file
       UpperCamelCase     : List[str]	     =       False if not self.vocab_file else True





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = False       ):
       '''simple docstring'''




       if already_has_special_tokens:
        if token_ids_a is not None:
         raise ValueError(
             "You should not supply a second sequence if the provided sequence of "
             "ids is already formatted with special tokens for the model."       )
        return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       [self.sep_token_id]
       UpperCamelCase     : List[str]	     =       [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]





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''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(A_       ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
        return
       UpperCamelCase     : Optional[int]	     =       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_       ):
        copyfile(self.vocab_file	,							A_       )

       return (out_vocab_file,)







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 ViTImageProcessor



class  A__       (	unittest.TestCase      ):





      def __init__(			self	,							A_	,							A_=13	,							A_=3	,							A_=224	,							A_=30	,							A_=400	,							A_=True	,							A_=None	,							A_=True	,							A_=[0.5, 0.5, 0.5]	,							A_=[0.5, 0.5, 0.5]	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase     : Tuple	     =       parent
       UpperCamelCase     : Tuple	     =       batch_size
       UpperCamelCase     : Tuple	     =       num_channels
       UpperCamelCase     : str	     =       image_size
       UpperCamelCase     : Optional[int]	     =       min_resolution
       UpperCamelCase     : List[Any]	     =       max_resolution
       UpperCamelCase     : Union[str, Any]	     =       do_resize
       UpperCamelCase     : str	     =       size
       UpperCamelCase     : List[str]	     =       do_normalize
       UpperCamelCase     : Any	     =       image_mean
       UpperCamelCase     : int	     =       image_std





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       ViTImageProcessor if is_vision_available() else None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       EfficientFormerImageProcessorTester(self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.image_proc_tester.prepare_image_processor_dict()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(A_	,							"image_mean"       )       )
       self.assertTrue(hasattr(A_	,							"image_std"       )       )
       self.assertTrue(hasattr(A_	,							"do_normalize"       )       )
       self.assertTrue(hasattr(A_	,							"do_resize"       )       )
       self.assertTrue(hasattr(A_	,							"size"       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       UpperCamelCase     : Union[str, Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							Image.Image       )

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

       # Test batched
       UpperCamelCase     : Tuple	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       UpperCamelCase     : List[Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							numpify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							np.ndarray       )

       # Test not batched input
       UpperCamelCase     : Dict	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Optional[Any]	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       UpperCamelCase     : Any	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							torchify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							torch.Tensor       )

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

       # Test batched
       UpperCamelCase     : Any	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)
