



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


__snake_case    :		int  =logging.get_logger(__name__)

__snake_case    :		Optional[Any]  ='▁'

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

__snake_case    :		List[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',
    },
}

__snake_case    :		Tuple  ={'vinai/bartpho-syllable': 1_0_2_4}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =VOCAB_FILES_NAMES
		snake_case_     =PRETRAINED_VOCAB_FILES_MAP
		snake_case_     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_     =["""input_ids""", """attention_mask"""]



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

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					AddedToken(__lowerCamelCase		,lstrip=__lowerCamelCase		,rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase		,__lowerCamelCase			) else mask_token

						lowerCAmelCase__					:    str												=					{} if sp_model_kwargs is None else sp_model_kwargs

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

						lowerCAmelCase__					:    Dict												=					vocab_file
						lowerCAmelCase__					:    Dict												=					monolingual_vocab_file
						lowerCAmelCase__					:    Union[str, Any]												=					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.Load(str(__lowerCamelCase			)			)

						# Load the reduced vocab

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

						lowerCAmelCase__					:    Optional[int]												=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}



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

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.__dict__.copy()
						lowerCAmelCase__					:    List[Any]												=					None
						lowerCAmelCase__					:    List[Any]												=					self.sp_model.serialized_model_proto()
						return state



		def __setstate__(self		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					d

						# for backward compatibility
						if not hasattr(self		,'''sp_model_kwargs'''			):
										lowerCAmelCase__					:    Union[str, Any]												=					{}

						lowerCAmelCase__					:    Union[str, Any]												=					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      List[int]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None		,__lowerCamelCase = False			)					->      List[int]:

						"""simple docstring"""






						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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      List[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					[self.sep_token_id]
						lowerCAmelCase__					:    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 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						return len(self.fairseq_ids_to_tokens			)



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					{self.convert_ids_to_tokens(__lowerCamelCase			): i for i in range(self.vocab_size			)}
						vocab.update(self.added_tokens_encoder			)
						return vocab



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""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		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						return self.fairseq_ids_to_tokens[index]



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					''''''.join(__lowerCamelCase			).replace(__lowerCamelCase		,''' '''			).strip()
						return out_string



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      Tuple[str]:

						"""simple docstring"""






						if not os.path.isdir(__lowerCamelCase			):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
										return
						lowerCAmelCase__					:    Union[str, Any]												=					os.path.join(
						    __lowerCamelCase		,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
						lowerCAmelCase__					:    Tuple												=					os.path.join(
						    __lowerCamelCase		,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']		,)

						if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCamelCase			) and os.path.isfile(self.vocab_file			):
										copyfile(self.vocab_file		,__lowerCamelCase			)
						elif not os.path.isfile(self.vocab_file			):
										with open(__lowerCamelCase		,'''wb'''			) as fi:
														lowerCAmelCase__					:    str												=					self.sp_model.serialized_model_proto()
														fi.write(__lowerCamelCase			)

						if os.path.abspath(self.monolingual_vocab_file			) != os.path.abspath(
						    __lowerCamelCase			) and os.path.isfile(self.monolingual_vocab_file			):
										copyfile(self.monolingual_vocab_file		,__lowerCamelCase			)
						elif not os.path.isfile(self.monolingual_vocab_file			):
										with open(__lowerCamelCase		,'''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(__lowerCamelCase			)} \n"""			)

						return out_vocab_file, out_monolingual_vocab_file






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=7		,__lowerCamelCase=3		,__lowerCamelCase=18		,__lowerCamelCase=30		,__lowerCamelCase=4_00		,__lowerCamelCase=True		,__lowerCamelCase=None		,__lowerCamelCase=True		,__lowerCamelCase=False		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=[0.5, 0.5, 0.5]		,__lowerCamelCase=[0.5, 0.5, 0.5]		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					parent
						lowerCAmelCase__					:    Optional[Any]												=					batch_size
						lowerCAmelCase__					:    str												=					num_channels
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    List[Any]												=					min_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					max_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					do_resize
						lowerCAmelCase__					:    str												=					size if size is not None else {'''height''': 18, '''width''': 20}
						lowerCAmelCase__					:    List[str]												=					do_thumbnail
						lowerCAmelCase__					:    str												=					do_align_axis
						lowerCAmelCase__					:    Optional[Any]												=					do_pad
						lowerCAmelCase__					:    Tuple												=					do_normalize
						lowerCAmelCase__					:    List[Any]												=					image_mean
						lowerCAmelCase__					:    List[str]												=					image_std



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_thumbnail": self.do_thumbnail,
						    "do_align_long_axis": self.do_align_axis,
						    "do_pad": self.do_pad,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}



@require_torch
@require_vision
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =DonutImageProcessor if is_vision_available() else None



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

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					DonutImageProcessingTester(self			)



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_resize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''size'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_thumbnail'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_align_long_axis'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_pad'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_normalize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_mean'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_std'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size		,{'''height''': 18, '''width''': 20}			)

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

						# Previous config had dimensions in (width, height) order
						lowerCAmelCase__					:    Union[str, Any]												=					self.image_processing_class.from_dict(self.image_processor_dict		,size=(42, 84)			)
						self.assertEqual(image_processor.size		,{'''height''': 84, '''width''': 42}			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						pass



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						lowerCAmelCase__					:    Dict												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,Image.Image			)

						# Test not batched input
						lowerCAmelCase__					:    Optional[Any]												=					image_processing(image_inputs[0]		,return_tensors='''pt'''			).pixel_values
						self.assertEqual(
						    encoded_images.shape		,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Optional[int]												=					image_processing(__lowerCamelCase		,return_tensors='''pt'''			).pixel_values
						self.assertEqual(
						    encoded_images.shape		,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						lowerCAmelCase__					:    Any												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,numpify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,np.ndarray			)

						# Test not batched input
						lowerCAmelCase__					:    Union[str, Any]												=					image_processing(image_inputs[0]		,return_tensors='''pt'''			).pixel_values
						self.assertEqual(
						    encoded_images.shape		,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    int												=					image_processing(__lowerCamelCase		,return_tensors='''pt'''			).pixel_values
						self.assertEqual(
						    encoded_images.shape		,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						lowerCAmelCase__					:    int												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,torchify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,torch.Tensor			)

						# Test not batched input
						lowerCAmelCase__					:    Any												=					image_processing(image_inputs[0]		,return_tensors='''pt'''			).pixel_values
						self.assertEqual(
						    encoded_images.shape		,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Dict												=					image_processing(__lowerCamelCase		,return_tensors='''pt'''			).pixel_values
						self.assertEqual(
						    encoded_images.shape		,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

