





"""simple docstring"""







import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =					{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

SCREAMING_SNAKE_CASE_      =					{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

SCREAMING_SNAKE_CASE_      =					{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}





@lru_cache()
def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  (
        list(range(ord('''!'''						) ,ord('''~'''						) + 1						)						) + list(range(ord('''¡'''						) ,ord('''¬'''						) + 1						)						) + list(range(ord('''®'''						) ,ord('''ÿ'''						) + 1						)						)
    )
    UpperCamelCase										=  bs[:]
    UpperCamelCase										=  0
    for b in range(2**8						):
        if b not in bs:
            bs.append(_lowercase						)
            cs.append(2**8 + n						)
            n += 1
    UpperCamelCase										=  [chr(_lowercase						) for n in cs]
    return dict(zip(_lowercase ,_lowercase						)						)





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  set()
    UpperCamelCase										=  word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)						)
        UpperCamelCase										=  char
    return pairs


class 							snake_case_ (				lowerCAmelCase__ ):







   """simple docstring"""




   A_										=					VOCAB_FILES_NAMES
   A_										=					PRETRAINED_VOCAB_FILES_MAP
   A_										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_										=					['''input_ids''', '''attention_mask''']







   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_="replace"					,				lowerCamelCase_="<s>"					,				lowerCamelCase_="</s>"					,				lowerCamelCase_="</s>"					,				lowerCamelCase_="<s>"					,				lowerCamelCase_="<unk>"					,				lowerCamelCase_="<pad>"					,				lowerCamelCase_="<mask>"					,				lowerCamelCase_=False					,				**lowerCamelCase_					,				)							->      Optional[int]:
       UpperCamelCase										=  AddedToken(_lowerCamelCase					,				lstrip=_lowerCamelCase					,				rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase					,				_lowerCamelCase) else bos_token
       UpperCamelCase										=  AddedToken(_lowerCamelCase					,				lstrip=_lowerCamelCase					,				rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase					,				_lowerCamelCase) else eos_token
       UpperCamelCase										=  AddedToken(_lowerCamelCase					,				lstrip=_lowerCamelCase					,				rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase					,				_lowerCamelCase) else sep_token
       UpperCamelCase										=  AddedToken(_lowerCamelCase					,				lstrip=_lowerCamelCase					,				rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase					,				_lowerCamelCase) else cls_token
       UpperCamelCase										=  AddedToken(_lowerCamelCase					,				lstrip=_lowerCamelCase					,				rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase					,				_lowerCamelCase) else unk_token
       UpperCamelCase										=  AddedToken(_lowerCamelCase					,				lstrip=_lowerCamelCase					,				rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase					,				_lowerCamelCase) else pad_token

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

       super().__init__(
           errors=_lowerCamelCase					,				bos_token=_lowerCamelCase					,				eos_token=_lowerCamelCase					,				unk_token=_lowerCamelCase					,				sep_token=_lowerCamelCase					,				cls_token=_lowerCamelCase					,				pad_token=_lowerCamelCase					,				mask_token=_lowerCamelCase					,				add_prefix_space=_lowerCamelCase					,				**_lowerCamelCase					,				)

       with open(_lowerCamelCase					,				encoding='''utf-8''') as vocab_handle:
           UpperCamelCase										=  json.load(_lowerCamelCase)
       UpperCamelCase										=  {v: k for k, v in self.encoder.items()}
       UpperCamelCase										=  errors  # how to handle errors in decoding
       UpperCamelCase										=  bytes_to_unicode()
       UpperCamelCase										=  {v: k for k, v in self.byte_encoder.items()}
       with open(_lowerCamelCase					,				encoding='''utf-8''') as merges_handle:
           UpperCamelCase										=  merges_handle.read().split('''\n''')[1:-1]
       UpperCamelCase										=  [tuple(merge.split()) for merge in bpe_merges]
       UpperCamelCase										=  dict(zip(_lowerCamelCase					,				range(len(_lowerCamelCase))))
       UpperCamelCase										=  {}
       UpperCamelCase										=  add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCamelCase										=  re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''')







   @property
   def      UpperCAmelCase__ (		self)							->      Tuple:
       return len(self.encoder)







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return dict(self.encoder					,				**self.added_tokens_encoder)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:

       if token in self.cache:
           return self.cache[token]
       UpperCamelCase										=  tuple(_lowerCamelCase)
       UpperCamelCase										=  get_pairs(_lowerCamelCase)

       if not pairs:
           return token

       while True:
           UpperCamelCase										=  min(_lowerCamelCase					,				key=lambda lowerCamelCase_: self.bpe_ranks.get(_lowerCamelCase					,				float('''inf''')))
           if bigram not in self.bpe_ranks:
               break
           UpperCamelCase										=  bigram
           UpperCamelCase										=  []
           UpperCamelCase										=  0
           while i < len(_lowerCamelCase):
               try:
                   UpperCamelCase										=  word.index(_lowerCamelCase					,				_lowerCamelCase)
               except ValueError:
                   new_word.extend(word[i:])
                   break
               else:
                   new_word.extend(word[i:j])
                   UpperCamelCase										=  j

               if word[i] == first and i < len(_lowerCamelCase) - 1 and word[i + 1] == second:
                   new_word.append(first + second)
                   i += 2
               else:
                   new_word.append(word[i])
                   i += 1
           UpperCamelCase										=  tuple(_lowerCamelCase)
           UpperCamelCase										=  new_word
           if len(_lowerCamelCase) == 1:
               break
           else:
               UpperCamelCase										=  get_pairs(_lowerCamelCase)
       UpperCamelCase										=  """ """.join(_lowerCamelCase)
       UpperCamelCase										=  word
       return word







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      List[str]:
       UpperCamelCase										=  []
       for token in re.findall(self.pat					,				_lowerCamelCase):
           UpperCamelCase										=  """""".join(
               self.byte_encoder[b] for b in token.encode('''utf-8'''))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase).split(''' '''))
       return bpe_tokens







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      List[Any]:
       return self.encoder.get(_lowerCamelCase					,				self.encoder.get(self.unk_token))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Union[str, Any]:
       return self.decoder.get(_lowerCamelCase)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Union[str, Any]:
       UpperCamelCase										=  """""".join(_lowerCamelCase)
       UpperCamelCase										=  bytearray([self.byte_decoder[c] for c in text]).decode('''utf-8'''					,				errors=self.errors)
       return text







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      List[str]:

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

       with open(_lowerCamelCase					,				'''w'''					,				encoding='''utf-8''') as f:
           f.write(json.dumps(self.encoder					,				indent=2					,				sort_keys=_lowerCamelCase					,				ensure_ascii=_lowerCamelCase) + '''\n''')

       UpperCamelCase										=  0
       with open(_lowerCamelCase					,				'''w'''					,				encoding='''utf-8''') as writer:
           writer.write('''#version: 0.2\n''')
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,				key=lambda lowerCamelCase_: kv[1]):
               if index != token_index:
                   logger.warning(
                       F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                       ''' Please check that the tokenizer is not corrupted!''')
                   UpperCamelCase										=  token_index
               writer.write(''' '''.join(_lowerCamelCase) + '''\n''')
               index += 1

       return vocab_file, merge_file







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      Dict:

       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 + sep + token_ids_a + sep







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				lowerCamelCase_ = False)							->      Tuple:

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

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      Optional[Any]:
       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 + sep + token_ids_a + sep) * [0]







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_=False					,				**lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  kwargs.pop('''add_prefix_space'''					,				self.add_prefix_space)
       if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase) > 0 and not text[0].isspace()):
           UpperCamelCase										=  """ """ + text
       return (text, kwargs)





"""simple docstring"""







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 VideoMAEImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=7					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=1_8					,				lowerCamelCase_=3_0					,				lowerCamelCase_=4_0_0					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=None					,				)							->      Optional[Any]:
       UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 1_8}
       UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

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







   def      UpperCAmelCase__ (		self)							->      Tuple:
       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 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







   @property
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.image_processor_tester.prepare_image_processor_dict()







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  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      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 1_8})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 1_8, '''width''': 1_8})

       UpperCamelCase										=  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      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PIL videos
       UpperCamelCase										=  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
       UpperCamelCase										=  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
       UpperCamelCase										=  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      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase										=  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
       UpperCamelCase										=  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
       UpperCamelCase										=  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      UpperCAmelCase__ (		self)							->      Optional[int]:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase										=  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
       UpperCamelCase										=  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
       UpperCamelCase										=  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'''],
           )					,				)