
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE :		int       			=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :		List[str]       			=							{
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

SCREAMING_SNAKE_CASE :		List[str]       			=							{
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

SCREAMING_SNAKE_CASE :		Union[str, Any]       			=							{
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}



def 							lowerCamelCase_	( __UpperCamelCase  ):
				A_													=	set()
				A_													=	word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)  )
								A_													=	char

				A_													=	set(SCREAMING_SNAKE_CASE__  )
				return pairs






class        __lowercase		(       _lowerCAmelCase							):
					__magic_name__    :				Optional[Any]  						=						VOCAB_FILES_NAMES
					__magic_name__    :				Optional[Any]  						=						PRETRAINED_VOCAB_FILES_MAP
					__magic_name__    :				Optional[int]  						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


					def __init__(						self							,							a__							,							a__							,							a__="<s>"							,							a__="</s>"							,							a__="</s>"							,							a__="<s>"							,							a__="<unk>"							,							a__="<pad>"							,							a__="<mask>"							,							**a__							,							)		->				str:





									'''simple docstring'''
									super().__init__(
									    bos_token=a__							,							eos_token=a__							,							unk_token=a__							,							sep_token=a__							,							cls_token=a__							,							pad_token=a__							,							mask_token=a__							,							**a__							,							)

									A_													=	vocab_file
									A_													=	merges_file

									A_													=	{}
									A_													=	0
									A_													=	1
									A_													=	2
									A_													=	3

									self.add_from_file(a__    )

									A_													=	{v: k for k, v in self.encoder.items()}

									with open(a__							,							encoding='''utf-8'''    ) as merges_handle:
													A_													=	merges_handle.read().split('''\n'''    )[:-1]
									A_													=	[tuple(merge.split()[:-1]    ) for merge in merges]
									A_													=	dict(zip(a__							,							range(len(a__    )    )    )    )
									A_													=	{}


					def 							lowerCAmelCase_     (						self							,							a__							,							a__ = None    )		->				List[int]:





									'''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 							lowerCAmelCase_     (						self							,							a__							,							a__ = None							,							a__ = False    )		->				List[int]:





									'''simple docstring'''

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

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


					def 							lowerCAmelCase_     (						self							,							a__							,							a__ = None    )		->				List[int]:





									'''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 							lowerCAmelCase_     (						self    )		->				Union[str, Any]:





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


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





									'''simple docstring'''
									return dict(self.encoder							,							**self.added_tokens_encoder    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				List[str]:





									'''simple docstring'''
									if token in self.cache:
													return self.cache[token]
									A_													=	tuple(a__    )
									A_													=	tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
									A_													=	get_pairs(a__    )

									if not pairs:
													return token

									while True:
													A_													=	min(a__							,							key=lambda a__    : self.bpe_ranks.get(a__							,							float('''inf'''    )    )    )
													if bigram not in self.bpe_ranks:
																	break
													A_						,       A_													=	bigram
													A_													=	[]
													A_													=	0
													while i < len(a__    ):
																	try:
																					A_													=	word.index(a__							,							a__    )
																	except ValueError:
																					new_word.extend(word[i:]    )
																					break
																	else:
																					new_word.extend(word[i:j]    )
																					A_													=	j

																	if word[i] == first and i < len(a__    ) - 1 and word[i + 1] == second:
																					new_word.append(first + second    )
																					i += 2
																	else:
																					new_word.append(word[i]    )
																					i += 1
													A_													=	tuple(a__    )
													A_													=	new_word
													if len(a__    ) == 1:
																	break
													else:
																	A_													=	get_pairs(a__    )
									A_													=	'''@@ '''.join(a__    )
									A_													=	word[:-4]
									A_													=	word
									return word


					def 							lowerCAmelCase_     (						self							,							a__    )		->				Dict:





									'''simple docstring'''
									A_													=	[]

									A_													=	re.findall(r'''\S+\n?'''							,							a__    )

									for token in words:
													split_tokens.extend(list(self.bpe(a__    ).split(''' '''    )    )    )
									return split_tokens


					def 							lowerCAmelCase_     (						self							,							a__    )		->				List[Any]:





									'''simple docstring'''
									return self.encoder.get(a__							,							self.encoder.get(self.unk_token    )    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				List[Any]:





									'''simple docstring'''
									return self.decoder.get(a__							,							self.unk_token    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				Dict:





									'''simple docstring'''
									A_													=	''' '''.join(a__    ).replace('''@@ '''							,							''''''    ).strip()
									return out_string


					def 							lowerCAmelCase_     (						self							,							a__							,							a__ = None    )		->				Tuple[str]:





									'''simple docstring'''
									if not os.path.isdir(a__    ):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"    )
													return
									A_													=	os.path.join(
									    a__							,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
									A_													=	os.path.join(
									    a__							,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

									if os.path.abspath(self.vocab_file    ) != os.path.abspath(a__    ):
													copyfile(self.vocab_file							,							a__    )

									if os.path.abspath(self.merges_file    ) != os.path.abspath(a__    ):
													copyfile(self.merges_file							,							a__    )

									return out_vocab_file, out_merge_file


					def 							lowerCAmelCase_     (						self							,							a__    )		->				str:





									'''simple docstring'''
									if isinstance(a__							,							a__    ):
													try:
																	with open(a__							,							'''r'''							,							encoding='''utf-8'''    ) as fd:
																					self.add_from_file(a__    )
													except FileNotFoundError as fnfe:
																	raise fnfe
													except UnicodeError:
																	raise Exception(F"Incorrect encoding detected in {f}, please rebuild the dataset"    )
													return

									A_													=	f.readlines()
									for lineTmp in lines:
													A_													=	lineTmp.strip()
													A_													=	line.rfind(''' '''    )
													if idx == -1:
																	raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\''''    )
													A_													=	line[:idx]
													A_													=	len(self.encoder    )







'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase   :							Dict         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["pixel_values"]




      def __init__(	self			, lowerCamelCase = True			, lowerCamelCase = 32			, lowerCamelCase=PILImageResampling.BILINEAR			, lowerCamelCase = True			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         __a					  =     do_resize
         __a					  =     do_rescale
         __a					  =     size_divisor
         __a					  =     resample
         super().__init__(**lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         __a  ,      __a					  =     get_image_size(lowerCamelCase   )
         # Rounds the height and width down to the closest multiple of size_divisor
         __a					  =     height // size_divisor * size_divisor
         __a					  =     width // size_divisor * size_divisor
         __a					  =     resize(lowerCamelCase			, (new_h, new_w)			, resample=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )
         return image




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         return rescale(image=lowerCamelCase			, scale=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase=None			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = ChannelDimension.FIRST			, **lowerCamelCase			, )				->BatchFeature:



         '''simple docstring'''
         __a					  =     do_resize if do_resize is not None else self.do_resize
         __a					  =     do_rescale if do_rescale is not None else self.do_rescale
         __a					  =     size_divisor if size_divisor is not None else self.size_divisor
         __a					  =     resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
            raise ValueError('size_divisor is required for resizing'   )

         __a					  =     make_list_of_images(lowerCamelCase   )

         if not valid_images(lowerCamelCase   ):
            raise ValueError('Invalid image(s)'   )

         # All transformations expect numpy arrays.
         __a					  =     [to_numpy_array(lowerCamelCase   ) for img in images]

         if do_resize:
            __a					  =     [self.resize(lowerCamelCase			, size_divisor=lowerCamelCase			, resample=lowerCamelCase   ) for image in images]

         if do_rescale:
            __a					  =     [self.rescale(lowerCamelCase			, scale=1 / 255   ) for image in images]

         __a					  =     [to_channel_dimension_format(lowerCamelCase			, lowerCamelCase   ) for image in images]

         __a					  =     {'pixel_values': images}
         return BatchFeature(data=lowerCamelCase			, tensor_type=lowerCamelCase   )