



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


lowerCamelCase__	=       logging.get_logger(__name__)

lowerCamelCase__	=       """▁"""

lowerCamelCase__	=       {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

lowerCamelCase__	=       {
    """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""",
    },
}

lowerCamelCase__	=       {"""vinai/bartpho-syllable""": 1024}






class       SCREAMING_SNAKE_CASE	(							_a    ):
							__lowerCamelCase		:		Any			=VOCAB_FILES_NAMES
							__lowerCamelCase		:		Optional[Any]			=PRETRAINED_VOCAB_FILES_MAP
							__lowerCamelCase		:		Tuple			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCamelCase		:		Any			=['''input_ids''', '''attention_mask''']





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

											'''simple docstring'''
											__a        =						AddedToken(__lowercase					, lstrip=__lowercase					, rstrip=__lowercase		) if isinstance(__lowercase					, __lowercase		) else mask_token

											__a        =						{} if sp_model_kwargs is None else sp_model_kwargs

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

											__a        =						vocab_file
											__a        =						monolingual_vocab_file
											__a        =						spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(str(__lowercase		)		)

											# Load the reduced vocab

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

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





							def __getstate__(      self						:						Optional[int]		):

											'''simple docstring'''
											__a        =						self.__dict__.copy()
											__a        =						None
											__a        =						self.sp_model.serialized_model_proto()
											return state





							def __setstate__(      self						:						Any					, __lowercase						:						List[str]		):

											'''simple docstring'''
											__a        =						d

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

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





							def   UpperCamelCase_    (      self						:						Union[str, Any]					, __lowercase						:						Dict					, __lowercase						:						List[str] = None		):

											'''simple docstring'''



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





							def   UpperCamelCase_    (      self						:						Dict					, __lowercase						:						Dict					, __lowercase						:						str = None					, __lowercase						:						str = False		):

											'''simple docstring'''



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

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





							def   UpperCamelCase_    (      self						:						int					, __lowercase						:						int					, __lowercase						:						int = None		):

											'''simple docstring'''
											__a        =						[self.sep_token_id]
											__a        =						[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   UpperCamelCase_    (      self						:						int		):

											'''simple docstring'''
											return len(self.fairseq_ids_to_tokens		)





							def   UpperCamelCase_    (      self						:						Dict		):

											'''simple docstring'''
											__a        =						{self.convert_ids_to_tokens(__lowercase		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab





							def   UpperCamelCase_    (      self						:						Optional[Any]					, __lowercase						:						Optional[Any]		):

											'''simple docstring'''
											return self.sp_model.encode(__lowercase					, out_type=__lowercase		)





							def   UpperCamelCase_    (      self						:						Tuple					, __lowercase						:						Dict		):

											'''simple docstring'''



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





							def   UpperCamelCase_    (      self						:						List[str]					, __lowercase						:						Any		):

											'''simple docstring'''
											return self.fairseq_ids_to_tokens[index]





							def   UpperCamelCase_    (      self						:						Optional[int]					, __lowercase						:						int		):

											'''simple docstring'''
											__a        =						''''''.join(__lowercase		).replace(__lowercase					, """ """		).strip()
											return out_string







							def   UpperCamelCase_    (      self						:						List[str]					, __lowercase						:						Dict					, __lowercase						:						Union[str, Any] = None		):

											'''simple docstring'''



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

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

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

											return out_vocab_file, out_monolingual_vocab_file







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 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






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


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






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


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






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


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    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 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  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 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    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    )
