






"""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


_UpperCamelCase						:    Optional[Any]		=						logging.get_logger(__name__)

_UpperCamelCase						:    Optional[Any]		=						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_UpperCamelCase						:    List[Any]		=						{
    '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'
        ),
    },
}

_UpperCamelCase						:    Optional[int]		=						{
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}
@lru_cache()
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





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





  '''simple docstring'''





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



class 			a	(  a_      ):
    UpperCAmelCase_					:	List[str]      	=VOCAB_FILES_NAMES
    UpperCAmelCase_					:	Any      	=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_					:	Tuple      	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_					:	Dict      	=["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				,						):
      lowercase				       =			AddedToken(_lowerCamelCase				,						lstrip=_lowerCamelCase				,						rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else bos_token
      lowercase				       =			AddedToken(_lowerCamelCase				,						lstrip=_lowerCamelCase				,						rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else eos_token
      lowercase				       =			AddedToken(_lowerCamelCase				,						lstrip=_lowerCamelCase				,						rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else sep_token
      lowercase				       =			AddedToken(_lowerCamelCase				,						lstrip=_lowerCamelCase				,						rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else cls_token
      lowercase				       =			AddedToken(_lowerCamelCase				,						lstrip=_lowerCamelCase				,						rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else unk_token
      lowercase				       =			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
      lowercase				       =			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:
        lowercase				       =			json.load(_lowerCamelCase			)
      lowercase				       =			{v: k for k, v in self.encoder.items()}
      lowercase				       =			errors  # how to handle errors in decoding
      lowercase				       =			bytes_to_unicode()
      lowercase				       =			{v: k for k, v in self.byte_encoder.items()}
      with open(_lowerCamelCase				,						encoding='utf-8'			) as merges_handle:
        lowercase				       =			merges_handle.read().split('\n'			)[1:-1]
      lowercase				       =			[tuple(merge.split()			) for merge in bpe_merges]
      lowercase				       =			dict(zip(_lowerCamelCase				,						range(len(_lowerCamelCase			)			)			)			)
      lowercase				       =			{}
      lowercase				       =			add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      lowercase				       =			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			):
      return len(self.encoder			)







    def      UpperCamelCase_ (     self			):
      return dict(self.encoder				,						**self.added_tokens_encoder			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if token in self.cache:
        return self.cache[token]
      lowercase				       =			tuple(_lowerCamelCase			)
      lowercase				       =			get_pairs(_lowerCamelCase			)

      if not pairs:
        return token

      while True:
        lowercase				       =			min(_lowerCamelCase				,						key=lambda _lowerCamelCase			: self.bpe_ranks.get(_lowerCamelCase				,						float('inf'			)			)			)
        if bigram not in self.bpe_ranks:
          break
        lowercase       ,	lowercase				       =			bigram
        lowercase				       =			[]
        lowercase				       =			0
        while i < len(_lowerCamelCase			):
          try:
            lowercase				       =			word.index(_lowerCamelCase				,						_lowerCamelCase			)
          except ValueError:
            new_word.extend(word[i:]			)
            break
          else:
            new_word.extend(word[i:j]			)
            lowercase				       =			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
        lowercase				       =			tuple(_lowerCamelCase			)
        lowercase				       =			new_word
        if len(_lowerCamelCase			) == 1:
          break
        else:
          lowercase				       =			get_pairs(_lowerCamelCase			)
      lowercase				       =			' '.join(_lowerCamelCase			)
      lowercase				       =			word
      return word







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			[]
      for token in re.findall(self.pat				,						_lowerCamelCase			):
        lowercase				       =			''.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			):
      return self.encoder.get(_lowerCamelCase				,						self.encoder.get(self.unk_token			)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      return self.decoder.get(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			''.join(_lowerCamelCase			)
      lowercase				       =			bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'				,						errors=self.errors			)
      return text







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):
      if not os.path.isdir(_lowerCamelCase			):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
        return
      lowercase				       =			os.path.join(
          _lowerCamelCase				,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
      lowercase				       =			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'			)

      lowercase				       =			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!'			)
            lowercase				       =			token_index
          writer.write(' '.join(_lowerCamelCase			) + '\n'			)
          index += 1

      return vocab_file, merge_file







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowercase				       =			[self.cls_token_id]
      lowercase				       =			[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None				,						_lowerCamelCase = False			):
      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			):
      lowercase				       =			[self.sep_token_id]
      lowercase				       =			[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			):
      lowercase				       =			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()):
        lowercase				       =			' ' + text
      return (text, kwargs)










"""simple docstring"""



import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import FlavaImageProcessor, FlavaProcessor
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )

@require_vision
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			tempfile.mkdtemp()

      # fmt: off
      lowercase				       =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)

      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(''.join([x + '\n' for x in vocab_tokens]			)			)

      lowercase				       =			{
          'image_mean': FLAVA_IMAGE_MEAN,
          'image_std': FLAVA_IMAGE_STD,
          'do_normalize': True,
          'do_resize': True,
          'size': 2_2_4,
          'do_center_crop': True,
          'crop_size': 2_2_4,
          'input_size_patches': 1_4,
          'total_mask_patches': 7_5,
          'mask_group_max_patches': None,
          'mask_group_min_patches': 1_6,
          'mask_group_min_aspect_ratio': 0.3,
          'mask_group_max_aspect_ratio': None,
          'codebook_do_resize': True,
          'codebook_size': 1_1_2,
          'codebook_do_center_crop': True,
          'codebook_crop_size': 1_1_2,
          'codebook_do_map_pixels': True,
          'codebook_do_normalize': True,
          'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
          'codebook_image_std': FLAVA_CODEBOOK_STD,
      }

      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.image_processor_file				,						'w'				,						encoding='utf-8'			) as fp:
        json.dump(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizerFast.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return FlavaImageProcessor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta			)]

      lowercase				       =			[Image.fromarray(np.moveaxis(_lowerCamelCase				,						0				,						-1			)			) for x in image_inputs]

      return image_inputs







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_rust_tokenizer()
      lowercase				       =			self.get_image_processor()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_slow.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname				,						use_fast=_lowerCamelCase			)

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_fast.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor_slow.tokenizer.get_vocab()				,						tokenizer_slow.get_vocab()			)
      self.assertEqual(processor_fast.tokenizer.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertEqual(tokenizer_slow.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertIsInstance(processor_slow.tokenizer				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor_slow.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertEqual(processor_fast.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertIsInstance(processor_slow.image_processor				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			FlavaProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_image_processor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			FlavaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.image_processor.to_json_string()				,						image_processor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			image_processor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)

      # With rest of the args
      random.seed(1_2_3_4			)
      lowercase				       =			image_processor(
          _lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)
      random.seed(1_2_3_4			)
      lowercase				       =			processor(
          images=_lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']			)

      # add extra args
      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_image_mask=_lowerCamelCase			)

      self.assertListEqual(
          list(inputs.keys()			)				,						[
              'input_ids',
              'token_type_ids',
              'attention_mask',
              'pixel_values',
              'codebook_pixel_values',
              'bool_masked_pos',
          ]				,						)

      # test if it raises when no input is passed
      with pytest.raises(_lowerCamelCase			):
        processor()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						processor.model_input_names			)


