



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_rembert import RemBertTokenizer
else:
    UpperCAmelCase_									=     None

UpperCAmelCase_									=     logging.get_logger(__name__)
UpperCAmelCase_									=     {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_									=     {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase_									=     {
    "google/rembert": 2_5_6,
}

UpperCAmelCase_									=     "▁"





class 		UpperCAmelCase						(						__lowerCAmelCase				):
   SCREAMING_SNAKE_CASE__							=     VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE__							=     PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE__							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE__							=     RemBertTokenizer





   def __init__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=False       ,     _lowerCAmelCase="[CLS]"       ,     _lowerCAmelCase="[SEP]"       ,     _lowerCAmelCase="<unk>"       ,     _lowerCAmelCase="[SEP]"       ,     _lowerCAmelCase="<pad>"       ,     _lowerCAmelCase="[CLS]"       ,     _lowerCAmelCase="[MASK]"       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  AddedToken(_UpperCamelCase       ,     lstrip=_UpperCamelCase       ,     rstrip=_UpperCamelCase			) if isinstance(_UpperCamelCase       ,     _UpperCamelCase			) else mask_token

         super().__init__(
             _UpperCamelCase       ,     tokenizer_file=_UpperCamelCase       ,     do_lower_case=_UpperCamelCase       ,     remove_space=_UpperCamelCase       ,     keep_accents=_UpperCamelCase       ,     bos_token=_UpperCamelCase       ,     eos_token=_UpperCamelCase       ,     unk_token=_UpperCamelCase       ,     sep_token=_UpperCamelCase       ,     pad_token=_UpperCamelCase       ,     cls_token=_UpperCamelCase       ,     mask_token=_UpperCamelCase       ,     **_UpperCamelCase       ,     )

         _lowerCAmelCase 			=  do_lower_case
         _lowerCAmelCase 			=  remove_space
         _lowerCAmelCase 			=  keep_accents
         _lowerCAmelCase 			=  vocab_file
         _lowerCAmelCase 			=  False if not self.vocab_file else True





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):
         _lowerCAmelCase 			=  [self.sep_token_id]
         _lowerCAmelCase 			=  [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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = False			):


         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 not None:
               return [1] + ([0] * len(_UpperCamelCase			)) + [1] + ([0] * len(_UpperCamelCase			)) + [1]
         return [1] + ([0] * len(_UpperCamelCase			)) + [1]





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):
         _lowerCAmelCase 			=  [self.sep_token_id]
         _lowerCAmelCase 			=  [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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):


         if not os.path.isdir(_UpperCamelCase			):
               logger.error('''Vocabulary path ({}) should be a directory'''.format(_UpperCamelCase			)			)
               return
         _lowerCAmelCase 			=  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			):
               copyfile(self.vocab_file       ,     _UpperCamelCase			)

         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_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor





class 		UpperCAmelCase						(						unittest.TestCase				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=7       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=10       ,     _lowerCAmelCase=18       ,     _lowerCAmelCase=30       ,     _lowerCAmelCase=400       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=None       ,     ):
         _lowerCAmelCase 			=  size if size is not None else {'''shortest_edge''': 18}
         _lowerCAmelCase 			=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  num_channels
         _lowerCAmelCase 			=  num_frames
         _lowerCAmelCase 			=  image_size
         _lowerCAmelCase 			=  min_resolution
         _lowerCAmelCase 			=  max_resolution
         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  image_mean
         _lowerCAmelCase 			=  image_std
         _lowerCAmelCase 			=  crop_size





   def 	__lowerCAmelCase							(	self			):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_size": self.crop_size,
         }






@require_torch
@require_vision
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     VivitImageProcessor if is_vision_available() else None





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  VivitImageProcessingTester(self			)





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.image_processor_tester.prepare_image_processor_dict()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''image_mean'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''image_std'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_normalize'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_resize'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_center_crop'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''size'''			)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 18}			)
         self.assertEqual(image_processor.crop_size       ,     {'''height''': 18, '''width''': 18}			)

         _lowerCAmelCase 			=  self.image_processing_class.from_dict(self.image_processor_dict       ,     size=42       ,     crop_size=84			)
         self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 42}			)
         self.assertEqual(image_processor.crop_size       ,     {'''height''': 84, '''width''': 84}			)





   def 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random PIL videos
         _lowerCAmelCase 			=  prepare_video_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCAmelCase			)
         for video in video_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.assertIsInstance(video[0]       ,     Image.Image			)

         # Test not batched input
         _lowerCAmelCase 			=  image_processing(video_inputs[0]       ,     return_tensors='''pt'''			).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,     (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,     )

         # Test batched
         _lowerCAmelCase 			=  image_processing(_lowerCAmelCase       ,     return_tensors='''pt'''			).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,     )





   def 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random numpy tensors
         _lowerCAmelCase 			=  prepare_video_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCAmelCase       ,     numpify=_lowerCAmelCase			)
         for video in video_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.assertIsInstance(video[0]       ,     np.ndarray			)

         # Test not batched input
         _lowerCAmelCase 			=  image_processing(video_inputs[0]       ,     return_tensors='''pt'''			).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,     (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,     )

         # Test batched
         _lowerCAmelCase 			=  image_processing(_lowerCAmelCase       ,     return_tensors='''pt'''			).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,     )





   def 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random PyTorch tensors
         _lowerCAmelCase 			=  prepare_video_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCAmelCase       ,     torchify=_lowerCAmelCase			)
         for video in video_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.assertIsInstance(video[0]       ,     torch.Tensor			)

         # Test not batched input
         _lowerCAmelCase 			=  image_processing(video_inputs[0]       ,     return_tensors='''pt'''			).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,     (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,     )

         # Test batched
         _lowerCAmelCase 			=  image_processing(_lowerCAmelCase       ,     return_tensors='''pt'''			).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,     )