

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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_ta import TaTokenizer
else:
   UpperCAmelCase      =   None


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase      =   {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase      =   {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  VOCAB_FILES_NAMES
 __snake_case												=  PRETRAINED_VOCAB_FILES_MAP
 __snake_case												=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case												=  ["input_ids", "attention_mask"]
 __snake_case												=  TaTokenizer

 __snake_case												=  []



 def __init__(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase="</s>"		,				_UpperCAmelCase="<unk>"		,				_UpperCAmelCase="<pad>"		,				_UpperCAmelCase=1_00		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
             snake_case_	=				[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 special tokens
             snake_case_	=				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 T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                       ''' tokens''' )

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

       snake_case_	=				vocab_file
       snake_case_	=				False if not self.vocab_file else True
       snake_case_	=				extra_ids



 @staticmethod
 def 						UpperCamelCase__					(     _UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             snake_case_	=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
             if init_max_model_length is not None and init_max_model_length != max_model_length:
                   return init_max_model_length
             elif init_max_model_length is None:
                   warnings.warn(
                       '''This tokenizer was incorrectly instantiated with a model max length of'''
                       F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                       ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                       ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                       F''' {pretrained_model_name_or_path} automatically truncating your input to'''
                       F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                       F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                       ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                       ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''		,				_UpperCAmelCase		,				)

       return max_model_length



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.''' )

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

       if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
             copyfile(self.vocab_file		,				_UpperCAmelCase )
             logger.info(F'''Copy vocab file to {out_vocab_file}''' )

       return (out_vocab_file,)



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       snake_case_	=				token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
       else:
             snake_case_	=				token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       snake_case_	=				[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 ):
       return list(
           set(filter(lambda _UpperCAmelCase : bool(re.search(R'''<extra_id_\d+>'''		,				_UpperCAmelCase ) ) is not None		,				self.additional_special_tokens ) ) )





 def 						UpperCamelCase__					(     self ):
       return [self.convert_tokens_to_ids(_UpperCAmelCase ) for token in self.get_sentinel_tokens()]



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "convnextv2"



 def __init__(     self		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=4		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-12		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=2_24		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase )

       snake_case_	=				num_channels
       snake_case_	=				patch_size
       snake_case_	=				num_stages
       snake_case_	=				[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
       snake_case_	=				[3, 3, 9, 3] if depths is None else depths
       snake_case_	=				hidden_act
       snake_case_	=				initializer_range
       snake_case_	=				layer_norm_eps
       snake_case_	=				drop_path_rate
       snake_case_	=				image_size
       snake_case_	=				['''stem'''] + [F'''stage{idx}''' for idx in range(1		,				len(self.depths ) + 1 )]
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(
           out_features=_UpperCAmelCase		,				out_indices=_UpperCAmelCase		,				stage_names=self.stage_names )