



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__A   			={"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A   			=["ViTFeatureExtractor"]
				__A   			=["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A   			=[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A   			=[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A   			=[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

				__A   			=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










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 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""input_ids""", """attention_mask"""]




			def __init__(   self							: List[str]       ,       a_							: List[Any]="</s>"       ,       a_							: str="<unk>"       ,       a_							: Tuple="<pad>"       ,       a_							: List[Any]=1_25       ,       a_							: Any=None       ,       **a_							: Tuple       ,       ):
					'''simple docstring'''





					if extra_ids > 0 and additional_special_tokens is None:
							__UpperCAmelCase    :     Optional[Any]													=	[F'<extra_id_{i}>' for i in range(a_ )]
					elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							__UpperCAmelCase    :     Optional[Any]													=	len(set(filter(lambda a_ : bool('''extra_id''' in str(a_ ) )       ,       a_ ) ) )
							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    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else pad_token
					__UpperCAmelCase    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else eos_token
					__UpperCAmelCase    :     int													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else unk_token

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

					__UpperCAmelCase    :     Dict													=	extra_ids

					__UpperCAmelCase    :     Any													=	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    :     Optional[int]													=	len(self.special_tokens_encoder )
					__UpperCAmelCase    :     Union[str, Any]													=	len(a_ )
					for i, token in enumerate(a_ ):
							__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  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: Any       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None       ,       a_							: bool = False ):
					'''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_ )

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




			def  snake_case__     (   self							: Any       ,       a_							: List[int] ):
					'''simple docstring'''





					if len(a_ ) > 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  snake_case__     (   self							: Union[str, Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[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  snake_case__     (   self							: Optional[Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self._add_eos_if_not_present(a_ )
					if token_ids_a is None:
							return token_ids_a
					else:
							__UpperCAmelCase    :     List[str]													=	self._add_eos_if_not_present(a_ )
							return token_ids_a + token_ids_a




			def  snake_case__     (   self							: List[Any]       ,       a_							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[chr(a_ ) for i in text.encode('''utf-8''' )]
					return tokens




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Optional[int] ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: int       ,       a_							: Dict ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: Tuple       ,       a_							: str       ,       a_							: Optional[str] = None ):
					'''simple docstring'''





					return ()





