
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE :		Union[str, Any]       			=							{
    """configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE :		Optional[Any]       			=							["""ConvNextFeatureExtractor"""]
						SCREAMING_SNAKE_CASE :		Dict       			=							["""ConvNextImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE :		List[str]       			=							[
						    """CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ConvNextForImageClassification""",
						    """ConvNextModel""",
						    """ConvNextPreTrainedModel""",
						    """ConvNextBackbone""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE :		str       			=							[
						    """TFConvNextForImageClassification""",
						    """TFConvNextModel""",
						    """TFConvNextPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_convnext import ConvNextFeatureExtractor
												from .image_processing_convnext import ConvNextImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convnext import (
												    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvNextBackbone,
												    ConvNextForImageClassification,
												    ConvNextModel,
												    ConvNextPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
						import sys

						SCREAMING_SNAKE_CASE :		Tuple       			=							_LazyModule(__name__, globals()["__file__"], _import_structure)







'''simple docstring'''



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

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


__UpperCamelCase   :							int         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["input_ids", "attention_mask"]




      def __init__(	self			, lowerCamelCase="</s>"			, lowerCamelCase="<unk>"			, lowerCamelCase="<pad>"			, lowerCamelCase=125			, lowerCamelCase=None			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
            __a					  =     [F"""<extra_id_{i}>""" for i in range(lowerCamelCase   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            __a					  =     len(set(filter(lambda lowerCamelCase   : bool('extra_id' in str(lowerCamelCase   )   )			, lowerCamelCase   )   )   )
            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'   )

         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else pad_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else eos_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else unk_token

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

         __a					  =     extra_ids

         __a					  =     2**8  # utf is 8 bits

         # define special tokens dict
         __a					  =     {
             self.pad_token: 0,
             self.eos_token: 1,
             self.unk_token: 2,
         }
         __a					  =     len(self.special_tokens_encoder   )
         __a					  =     len(lowerCamelCase   )
         for i, token in enumerate(lowerCamelCase   ):
            __a					  =     self.vocab_size + i - n
         __a					  =     {v: k for k, v in self.special_tokens_encoder.items()}




      @property
      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




      def 			__UpperCamelCase				(	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   )

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




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[int]:



         '''simple docstring'''
         if len(lowerCamelCase   ) > 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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     [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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
         if token_ids_a is None:
            return token_ids_a
         else:
            __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
            return token_ids_a + token_ids_a




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



         '''simple docstring'''
         __a					  =     [chr(lowerCamelCase   ) for i in text.encode('utf-8'   )]
         return tokens




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



         '''simple docstring'''
         if token in self.special_tokens_encoder:
            __a					  =     self.special_tokens_encoder[token]
         elif token in self.added_tokens_encoder:
            __a					  =     self.added_tokens_encoder[token]
         elif len(lowerCamelCase   ) != 1:
            __a					  =     self.unk_token_id
         else:
            __a					  =     ord(lowerCamelCase   ) + self._num_special_tokens
         return token_id




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         if index in self.special_tokens_decoder:
            __a					  =     self.special_tokens_decoder[index]
         else:
            __a					  =     chr(index - self._num_special_tokens   )
         return token




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



         '''simple docstring'''
         __a					  =     B''
         for token in tokens:
            if token in self.special_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.added_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.special_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            elif token in self.added_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            else:
               __a					  =     bytes([ord(lowerCamelCase   )]   )
            bstring += tok_string
         __a					  =     bstring.decode('utf-8'			, errors='ignore'   )
         return string






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->Tuple[str]:



         '''simple docstring'''
         return ()