






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


A_						:						Optional[int]						      =					logging.get_logger(__name__)


A_						:						List[str]						      =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
A_						:						List[str]						      =					{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

A_						:						List[str]						      =					{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}

@lru_cache()
def     snake_case   ()			->							List[Any]:
  UpperCamelCase_:   Tuple        =							(
      list(range(ord('!'    )      ,       ord('~'    ) + 1    )    ) + list(range(ord('¡'    )      ,       ord('¬'    ) + 1    )    ) + list(range(ord('®'    )      ,       ord('ÿ'    ) + 1    )    )
  )
  UpperCamelCase_:   int        =							bs[:]
  UpperCamelCase_:   Any        =							0
  for b in range(2**8    ):
    if b not in bs:
      bs.append(UpperCAmelCase__    )
      cs.append(2**8 + n    )
      n += 1
  UpperCamelCase_:   Tuple        =							[chr(UpperCAmelCase__    ) for n in cs]
  return dict(zip(UpperCAmelCase__      ,       UpperCAmelCase__    )    )

def     snake_case   (UpperCAmelCase__    )			->							Tuple:
  UpperCamelCase_:   List[Any]        =							set()
  UpperCamelCase_:   Optional[Any]        =							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    UpperCamelCase_:   Tuple        =							char
  return pairs




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







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

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





       @property
       def      _a       (   self				):
         return len(self.encoder				)





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





       def      _a       (   self    ,     _lowerCamelCase				):
         if token in self.cache:
           return self.cache[token]
         UpperCamelCase_:   Union[str, Any]        =							tuple(_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							get_pairs(_lowerCamelCase				)

         if not pairs:
           return token

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





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





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





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





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         if not os.path.isdir(_lowerCamelCase				):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
           return
         UpperCamelCase_:   Union[str, Any]        =							os.path.join(
             _lowerCamelCase    ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
         UpperCamelCase_:   Optional[int]        =							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_:   str        =							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_:   str        =							token_index
             writer.write(' '.join(_lowerCamelCase				) + '\n'				)
             index += 1

         return vocab_file, merge_file





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_:   Optional[int]        =							[self.cls_token_id]
         UpperCamelCase_:   Union[str, Any]        =							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





       def      _a       (   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      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         UpperCamelCase_:   List[str]        =							[self.sep_token_id]
         UpperCamelCase_:   List[Any]        =							[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      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=False    ,     **_lowerCamelCase				):
         UpperCamelCase_:   str        =							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_:   Optional[Any]        =							' ' + 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_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor



class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							Any       , __a :							Optional[Any]=7       , __a :							Tuple=3       , __a :							int=18       , __a :							Optional[Any]=30       , __a :							Optional[Any]=400       , __a :							List[str]=True       , __a :							Any=32       , __a :							Tuple=True       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							parent
        _UpperCamelCase   :						int    =							batch_size
        _UpperCamelCase   :						List[str]    =							num_channels
        _UpperCamelCase   :						Union[str, Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							min_resolution
        _UpperCamelCase   :						Union[str, Any]    =							max_resolution
        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						str    =							size_divisor
        _UpperCamelCase   :						Optional[Any]    =							do_rescale


  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       GLPNImageProcessor if is_vision_available() else None

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[Any]    =							GLPNImageProcessingTester(self						)

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

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__a       , "do_resize"						)						)
        self.assertTrue(hasattr(__a       , "size_divisor"						)						)
        self.assertTrue(hasattr(__a       , "resample"						)						)
        self.assertTrue(hasattr(__a       , "do_rescale"						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[Any]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , Image.Image						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , numpify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , np.ndarray						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Dict    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _UpperCamelCase   :						Dict    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , torchify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , torch.Tensor						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)
