




"""simple docstring"""





import warnings
from typing import Dict, List, Optional, Tuple

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


A				=							logging.get_logger(__name__)



class   a__					(						__magic_name__ ):
			lowercase_		=						["input_ids", "attention_mask"]


			def __init__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     List[Any]="</s>"   ,	UpperCamelCase_   :     Tuple="<unk>"   ,	UpperCamelCase_   :     List[str]="<pad>"   ,	UpperCamelCase_   :     Union[str, Any]=125   ,	UpperCamelCase_   :     Dict=None   ,	**UpperCamelCase_   :     Optional[Any]   ,	):


						"""simple docstring"""




						if extra_ids > 0 and additional_special_tokens is None:
									__UpperCAmelCase						:			int												=       [F"<extra_id_{i}>" for i in range(UpperCamelCase_)]
						elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra_id special tokens
									__UpperCAmelCase						:			Dict												=       len(set(filter(lambda UpperCamelCase_: bool("extra_id" in str(UpperCamelCase_))   ,	UpperCamelCase_)))
									if extra_tokens != extra_ids:
												raise ValueError(
												    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
												    " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
												    " extra_ids tokens")

						__UpperCAmelCase						:			List[str]												=       AddedToken(UpperCamelCase_   ,	lstrip=UpperCamelCase_   ,	rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_   ,	UpperCamelCase_) else pad_token
						__UpperCAmelCase						:			List[str]												=       AddedToken(UpperCamelCase_   ,	lstrip=UpperCamelCase_   ,	rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_   ,	UpperCamelCase_) else eos_token
						__UpperCAmelCase						:			Optional[int]												=       AddedToken(UpperCamelCase_   ,	lstrip=UpperCamelCase_   ,	rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_   ,	UpperCamelCase_) else unk_token

						super().__init__(
						    eos_token=UpperCamelCase_   ,	unk_token=UpperCamelCase_   ,	pad_token=UpperCamelCase_   ,	extra_ids=UpperCamelCase_   ,	additional_special_tokens=UpperCamelCase_   ,	**UpperCamelCase_   ,	)

						__UpperCAmelCase						:			List[str]												=       extra_ids

						__UpperCAmelCase						:			int												=       2**8  # utf is 8 bits

						# define special tokens dict
						__UpperCAmelCase						:			Dict[int, str]												=       {
						    self.pad_token: 0,
						    self.eos_token: 1,
						    self.unk_token: 2,
						}
						__UpperCAmelCase						:			Any												=       len(self.special_tokens_encoder)
						__UpperCAmelCase						:			List[Any]												=       len(UpperCamelCase_)
						for i, token in enumerate(UpperCamelCase_):
									__UpperCAmelCase						:			Union[str, Any]												=       self.vocab_size + i - n
						__UpperCAmelCase						:			Dict[str, int]												=       {v: k for k, v in self.special_tokens_encoder.items()}


			@property
			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						return self._utf_vocab_size + self._num_special_tokens + self._extra_ids


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     List[int]   ,	UpperCamelCase_   :     Optional[List[int]] = None   ,	UpperCamelCase_   :     bool = False):


						"""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_)

						# normal case: some special tokens
						if token_ids_a is None:
									return ([0] * len(UpperCamelCase_)) + [1]
						return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]


			def    a_  (   self   :     Optional[Any]   ,	UpperCamelCase_   :     List[int]):


						"""simple docstring"""




						if len(UpperCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
									warnings.warn(
									    F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
									    " eos tokens being added.")
									return token_ids
						else:
									return token_ids + [self.eos_token_id]


			def    a_  (   self   :     Dict   ,	UpperCamelCase_   :     List[int]   ,	UpperCamelCase_   :     Optional[List[int]] = None):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       [self.eos_token_id]

						if token_ids_a is None:
									return len(token_ids_a + eos) * [0]
						return len(token_ids_a + eos + token_ids_a + eos) * [0]


			def    a_  (   self   :     Optional[int]   ,	UpperCamelCase_   :     List[int]   ,	UpperCamelCase_   :     Optional[List[int]] = None):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self._add_eos_if_not_present(UpperCamelCase_)
						if token_ids_a is None:
									return token_ids_a
						else:
									__UpperCAmelCase						:			List[Any]												=       self._add_eos_if_not_present(UpperCamelCase_)
									return token_ids_a + token_ids_a


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       [chr(UpperCamelCase_) for i in text.encode("utf-8")]
						return tokens


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     List[Any]):


						"""simple docstring"""




						if token in self.special_tokens_encoder:
									__UpperCAmelCase						:			Any												=       self.special_tokens_encoder[token]
						elif token in self.added_tokens_encoder:
									__UpperCAmelCase						:			int												=       self.added_tokens_encoder[token]
						elif len(UpperCamelCase_) != 1:
									__UpperCAmelCase						:			Optional[Any]												=       self.unk_token_id
						else:
									__UpperCAmelCase						:			Any												=       ord(UpperCamelCase_) + self._num_special_tokens
						return token_id


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


						"""simple docstring"""




						if index in self.special_tokens_decoder:
									__UpperCAmelCase						:			Any												=       self.special_tokens_decoder[index]
						else:
									__UpperCAmelCase						:			List[str]												=       chr(index - self._num_special_tokens)
						return token


			def    a_  (   self   :     Dict   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       b""
						for token in tokens:
									if token in self.special_tokens_decoder:
												__UpperCAmelCase						:			Tuple												=       self.special_tokens_decoder[token].encode("utf-8")
									elif token in self.added_tokens_decoder:
												__UpperCAmelCase						:			Any												=       self.special_tokens_decoder[token].encode("utf-8")
									elif token in self.special_tokens_encoder:
												__UpperCAmelCase						:			Optional[int]												=       token.encode("utf-8")
									elif token in self.added_tokens_encoder:
												__UpperCAmelCase						:			Optional[Any]												=       token.encode("utf-8")
									else:
												__UpperCAmelCase						:			Any												=       bytes([ord(UpperCamelCase_)])
									bstring += tok_string
						__UpperCAmelCase						:			List[Any]												=       bstring.decode("utf-8"   ,	errors="ignore")
						return string


			def    a_  (   self   :     Optional[int]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Optional[str] = None):


						"""simple docstring"""




						return ()



'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`.""")
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.batch_decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor_class







			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor




