


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

__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    '▁'

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

__SCREAMING_SNAKE_CASE							:    Any		=    {
    '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							:    Any		=    {'vinai/bartpho-syllable': 1_024}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Dict							      =    VOCAB_FILES_NAMES
							__lowercase:		str							      =    PRETRAINED_VOCAB_FILES_MAP
							__lowercase:		Optional[int]							      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase:		Tuple							      =    ["""input_ids""", """attention_mask"""]



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      str="<s>"		,       UpperCAmelCase_							:      str="</s>"		,       UpperCAmelCase_							:      Dict="</s>"		,       UpperCAmelCase_							:      int="<s>"		,       UpperCAmelCase_							:      Tuple="<unk>"		,       UpperCAmelCase_							:      Union[str, Any]="<pad>"		,       UpperCAmelCase_							:      Optional[int]="<mask>"		,       UpperCAmelCase_							:      Optional[Dict[str, Any]] = None		,       **UpperCAmelCase_							:      List[Any]		,       )					->None:



														"""simple docstring"""



														snake_case_           =		AddedToken(UpperCAmelCase_		,       lstrip=UpperCAmelCase_		,       rstrip=UpperCAmelCase_       ) if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) else mask_token

														snake_case_           =		{} if sp_model_kwargs is None else sp_model_kwargs

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

														snake_case_           =		vocab_file
														snake_case_           =		monolingual_vocab_file
														snake_case_           =		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
														self.sp_model.Load(str(UpperCAmelCase_       )       )

														# Load the reduced vocab

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

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



							def __getstate__(				self							:      List[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.__dict__.copy()
														snake_case_           =		None
														snake_case_           =		self.sp_model.serialized_model_proto()
														return state



							def __setstate__(				self							:      int		,       UpperCAmelCase_							:      List[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		d

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

														snake_case_           =		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      List[int]		,       UpperCAmelCase_							:      Optional[List[int]] = None       )					->List[int]:



														"""simple docstring"""



														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														snake_case_           =		[self.cls_token_id]
														snake_case_           =		[self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      List[int]		,       UpperCAmelCase_							:      Optional[List[int]] = None		,       UpperCAmelCase_							:      bool = False       )					->List[int]:



														"""simple docstring"""



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

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



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      List[int]		,       UpperCAmelCase_							:      Optional[List[int]] = None       )					->List[int]:



														"""simple docstring"""



														snake_case_           =		[self.sep_token_id]
														snake_case_           =		[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 		lowerCAmelCase       (				self							:      Dict       )					->Tuple:



														"""simple docstring"""



														return len(self.fairseq_ids_to_tokens       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		{self.convert_ids_to_tokens(UpperCAmelCase_       ): i for i in range(self.vocab_size       )}
														vocab.update(self.added_tokens_encoder       )
														return vocab



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      str       )					->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      Optional[Any]       )					->Any:



														"""simple docstring"""



														return self.fairseq_ids_to_tokens[index]



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      List[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		"""""".join(UpperCAmelCase_       ).replace(UpperCAmelCase_		,       """ """       ).strip()
														return out_string



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Optional[str] = None       )					->Tuple[str]:



														"""simple docstring"""



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

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

														if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
														    UpperCAmelCase_       ) and os.path.isfile(self.monolingual_vocab_file       ):
																					copyfile(self.monolingual_vocab_file		,       UpperCAmelCase_       )
														elif not os.path.isfile(self.monolingual_vocab_file       ):
																					with open(UpperCAmelCase_		,       """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(UpperCAmelCase_       )} \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							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Tuple=7		,       UpperCAmelCase_							:      Dict=3		,       UpperCAmelCase_							:      Tuple=18		,       UpperCAmelCase_							:      Optional[Any]=30		,       UpperCAmelCase_							:      str=400		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Optional[Any]=True		,       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""height""": 18, """width""": 18}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		image_size
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		do_normalize



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
														            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
														        ]       ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		List[Any]							      =    ImageGPTImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""height""": 18, """width""": 18}       )

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



							def 		lowerCAmelCase       (				self							:      Any       )					->List[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_           =		os.path.join(UpperCAmelCase_		,       """image_processor.json"""       )
																					image_processor_first.to_json_file(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_json_file(UpperCAmelCase_       ).to_dict()

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



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

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

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



							@unittest.skip("""ImageGPT requires clusters at initialization"""       )
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														pass







def 						_a				(      )    ->				str:
							snake_case_           =		load_dataset("""hf-internal-testing/fixtures_image_utils"""				, split="""test"""				)

							snake_case_           =		Image.open(dataset[4]["""file"""]				)
							snake_case_           =		Image.open(dataset[5]["""file"""]				)

							snake_case_           =		[imagea, imagea]

							return images





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





							'''simple docstring'''

							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""       )

														snake_case_           =		prepare_images()

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

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (1, 1_024)       )

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

														# test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (2, 1_024)       )

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



