'''simple docstring'''






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

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			'▁'

SCREAMING_SNAKE_CASE_	   =			{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

SCREAMING_SNAKE_CASE_	   =			{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

SCREAMING_SNAKE_CASE_	   =			{'vinai/bartpho-syllable': 10_24}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					VOCAB_FILES_NAMES
  __lowerCAmelCase				      =					PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase				      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase				      =					["""input_ids""", """attention_mask"""]







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_="<s>"   ,    snake_case_="</s>"   ,    snake_case_="</s>"   ,    snake_case_="<s>"   ,    snake_case_="<unk>"   ,    snake_case_="<pad>"   ,    snake_case_="<mask>"   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= AddedToken(snake_case_   ,    lstrip=snake_case_   ,    rstrip=snake_case_					) if isinstance(snake_case_   ,    snake_case_					) else mask_token

    __UpperCAmelCase:					Union[str, Any]		= {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=snake_case_   ,    eos_token=snake_case_   ,    unk_token=snake_case_   ,    sep_token=snake_case_   ,    cls_token=snake_case_   ,    pad_token=snake_case_   ,    mask_token=snake_case_   ,    sp_model_kwargs=self.sp_model_kwargs   ,    **snake_case_   ,    )

    __UpperCAmelCase:					List[Any]		= vocab_file
    __UpperCAmelCase:					int		= monolingual_vocab_file
    __UpperCAmelCase:					int		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(str(snake_case_					)					)

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    __UpperCAmelCase:					List[str]		= {}
    __UpperCAmelCase:					Any		= 0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
      if str(snake_case_					) not in self.fairseq_tokens_to_ids:
        __UpperCAmelCase:					int		= cnt
        cnt += 1
    with open(snake_case_   ,    """r"""   ,    encoding="""utf-8"""					) as f:
      for line in f.readlines():
        __UpperCAmelCase:					Optional[int]		= line.strip().split()[0]
        __UpperCAmelCase:					Union[str, Any]		= len(self.fairseq_tokens_to_ids					)
    if str(snake_case_					) not in self.fairseq_tokens_to_ids:
      __UpperCAmelCase:					Dict		= len(self.fairseq_tokens_to_ids					)

    __UpperCAmelCase:					Any		= {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def __getstate__(							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.__dict__.copy()
    __UpperCAmelCase:					int		= None
    __UpperCAmelCase:					List[str]		= self.sp_model.serialized_model_proto()
    return state







  def __setstate__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= d

    # for backward compatibility
    if not hasattr(self   ,    """sp_model_kwargs"""					):
      __UpperCAmelCase:					int		= {}

    __UpperCAmelCase:					Optional[Any]		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None					):

    '''simple docstring'''







    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __UpperCAmelCase:					List[str]		= [self.cls_token_id]
    __UpperCAmelCase:					Tuple		= [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = False					):

    '''simple docstring'''







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

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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None					):

    '''simple docstring'''







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







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return len(self.fairseq_ids_to_tokens					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= {self.convert_ids_to_tokens(snake_case_					): i for i in range(self.vocab_size					)}
    vocab.update(self.added_tokens_encoder					)
    return vocab







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.encode(snake_case_   ,    out_type=snake_case_					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    else:
      return self.unk_token_id







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.fairseq_ids_to_tokens[index]







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= """""".join(snake_case_					).replace(snake_case_   ,    """ """					).strip()
    return out_string







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None					):

    '''simple docstring'''







    if not os.path.isdir(snake_case_					):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
      return
    __UpperCAmelCase:					Optional[int]		= os.path.join(
        snake_case_   ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
    __UpperCAmelCase:					List[str]		= os.path.join(
        snake_case_   ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]   ,    )

    if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case_					) and os.path.isfile(self.vocab_file					):
      copyfile(self.vocab_file   ,    snake_case_					)
    elif not os.path.isfile(self.vocab_file					):
      with open(snake_case_   ,    """wb"""					) as fi:
        __UpperCAmelCase:					Dict		= self.sp_model.serialized_model_proto()
        fi.write(snake_case_					)

    if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
        snake_case_					) and os.path.isfile(self.monolingual_vocab_file					):
      copyfile(self.monolingual_vocab_file   ,    snake_case_					)
    elif not os.path.isfile(self.monolingual_vocab_file					):
      with open(snake_case_   ,    """w"""   ,    encoding="""utf-8"""					) as fp:
        for token in self.fairseq_tokens_to_ids:
          if token not in self.all_special_tokens:
            fp.write(F'''{str(snake_case_					)} \n'''					)

    return out_vocab_file, out_monolingual_vocab_file
'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    ):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]					),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					ImageGPTImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= ImageGPTImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """clusters"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""height""": 18, """width""": 18}					)

    __UpperCAmelCase:					int		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42					)
    self.assertEqual(image_processor.size   ,    {"""height""": 42, """width""": 42}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.image_processing_class(**self.image_processor_dict					)
    __UpperCAmelCase:					str		= json.loads(image_processor.to_json_string()					)
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    obj[key]					)					)
      else:
        self.assertEqual(obj[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    """image_processor.json"""					)
      image_processor_first.to_json_file(snake_case_					)
      __UpperCAmelCase:					List[str]		= self.image_processing_class.from_json_file(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_					)
      __UpperCAmelCase:					Any		= self.image_processing_class.from_pretrained(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  @unittest.skip("""ImageGPT requires clusters at initialization"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	List[str]:
  __UpperCAmelCase:					Any		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,    split="""test"""     )

  __UpperCAmelCase:					List[Any]		= Image.open(dataset[4]["""file"""]     )
  __UpperCAmelCase:					Dict		= Image.open(dataset[5]["""file"""]     )

  __UpperCAmelCase:					Dict		= [imagea, imagea]

  return images







@require_vision
@require_torch
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

    __UpperCAmelCase:					Dict		= prepare_images()

    # test non-batched
    __UpperCAmelCase:					Tuple		= image_processing(images[0]   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)					)

    __UpperCAmelCase:					int		= [306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case_					)

    # test batched
    __UpperCAmelCase:					Union[str, Any]		= image_processing(snake_case_   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)					)

    __UpperCAmelCase:					Tuple		= [303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case_					)