


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCamelCase__							:						int							     =   logging.get_logger(__name__)

lowerCamelCase__							:						str							     =   {"""vocab_file""": """spiece.model"""}

lowerCamelCase__							:						Optional[Any]							     =   {
    """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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase__							:						str							     =   {
    """t5-small""": 5_1_2,
    """t5-base""": 5_1_2,
    """t5-large""": 5_1_2,
    """t5-3b""": 5_1_2,
    """t5-11b""": 5_1_2,
}

lowerCamelCase__							:						Union[str, Any]							     =   """▁"""
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							str	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							List[str]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowercase				:							Dict	   =       ['input_ids', 'attention_mask']

     def __init__(					self:int     ,		_a:Any     ,		_a:List[str]="</s>"     ,		_a:Union[str, Any]="<unk>"     ,		_a:List[Any]="<pad>"     ,		_a:Optional[Any]=1_00     ,		_a:List[str]=None     ,		_a:Optional[Dict[str, Any]] = None     ,		_a:int=True     ,		**_a:int     ,		):
       # 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(_a   )]
       elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra_id special tokens
         snake_case__						= 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 T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
               ''' tokens'''   )
       if legacy:
         logger.warning_once(
             F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
             ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''   )

       snake_case__						= legacy
       snake_case__						= {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=_a     ,		unk_token=_a     ,		pad_token=_a     ,		extra_ids=_a     ,		additional_special_tokens=_a     ,		sp_model_kwargs=self.sp_model_kwargs     ,		legacy=_a     ,		**_a     ,		)

       snake_case__						= vocab_file
       snake_case__						= extra_ids

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_a   )

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:Optional[int]     ,		_a:Union[str, Any]     ,		_a:str   ):
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
         snake_case__						= TaTokenizer.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.'''     ,		_a     ,		)

       return max_model_length

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return self.sp_model.get_piece_size() + self._extra_ids

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= {self.convert_ids_to_tokens(_a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       return list(
           set(filter(lambda _a   : bool(re.search(r'''<extra_id_\d+>'''     ,		_a   )   ) is not None     ,		self.additional_special_tokens   )   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       return [self._convert_token_to_id(_a   ) for token in self.get_sentinel_tokens()]

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:List[int]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[int]     ,		_a:Optional[List[int]] = 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 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= self._add_eos_if_not_present(_a   )
       if token_ids_a is None:
         return token_ids_a
       else:
         snake_case__						= self._add_eos_if_not_present(_a   )
         return token_ids_a + token_ids_a

     def __getstate__(					self:Dict   ):
       snake_case__						= self.__dict__.copy()
       snake_case__						= None
       return state

     def __setstate__(					self:Tuple     ,		_a:Union[str, Any]   ):
       snake_case__						= d

       # for backward compatibility
       if not hasattr(self     ,		'''sp_model_kwargs'''   ):
         snake_case__						= {}

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:"TextInput"     ,		**_a:Tuple   ):
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
         snake_case__						= SPIECE_UNDERLINE + text.replace(_a     ,		''' '''   )
       return super().tokenize(_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]     ,		**_a:str   ):
       if not self.legacy:
         snake_case__						= text.startswith(_a   )
         if is_first:
           snake_case__						= text[1:]

       snake_case__						= self.sp_model.encode(_a     ,		out_type=_a   )

       if not self.legacy and not is_first and not text.startswith(''' '''   ) and tokens[0].startswith(_a   ):
         snake_case__						= ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
       return tokens

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int   ):
       if token.startswith('''<extra_id_'''   ):
         snake_case__						= re.match(r'''<extra_id_(\d+)>'''     ,		_a   )
         snake_case__						= int(match.group(1   )   )
         return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[Any]   ):
       if index < self.sp_model.get_piece_size():
         snake_case__						= self.sp_model.IdToPiece(_a   )
       else:
         snake_case__						= F"""<extra_id_{self.vocab_size - 1 - index}>"""
       return token

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:Dict   ):
       snake_case__						= []
       snake_case__						= ''''''
       snake_case__						= False
       for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
           if not prev_is_special:
             out_string += " "
           out_string += self.sp_model.decode(_a   ) + token
           snake_case__						= True
           snake_case__						= []
         else:
           current_sub_tokens.append(_a   )
           snake_case__						= False
       out_string += self.sp_model.decode(_a   )
       return out_string.strip()







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(_a   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file     ,		_a   )
       elif not os.path.isfile(self.vocab_file   ):
         with open(_a     ,		'''wb'''   ) as fi:
           snake_case__						= self.sp_model.serialized_model_proto()
           fi.write(_a   )

       return (out_vocab_file,)





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= 0

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
       self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= Path(_a   ) / '''config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
         json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Ensure we can load the image processor from the feature extractor config
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= Path(_a   ) / '''config.json'''
         json.dump(
             {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
         json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= CLIPConfig()

         # Create a dummy config file with image_proceesor_type
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= Path(_a   ) / '''config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
         json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

         # remove image_processor_type to make sure config.json alone is enough to load image processor locally
         snake_case__						= AutoImageProcessor.from_pretrained(_a   ).to_dict()

         config_dict.pop('''image_processor_type'''   )
         snake_case__						= CLIPImageProcessor(**_a   )

         # save in new folder
         model_config.save_pretrained(_a   )
         config.save_pretrained(_a   )

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )

         # make sure private variable is not incorrectly saved
         snake_case__						= json.loads(config.to_json_string()   )
         self.assertTrue('''_processor_class''' not in dict_as_saved   )

       self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       with self.assertRaisesRegex(
           _a     ,		'''clip-base is not a local folder and is not a valid model identifier'''   ):
         snake_case__						= AutoImageProcessor.from_pretrained('''clip-base'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       with self.assertRaisesRegex(
           _a     ,		r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''   ):
         snake_case__						= AutoImageProcessor.from_pretrained(_a     ,		revision='''aaaaaa'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       with self.assertRaisesRegex(
           _a     ,		'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''     ,		):
         snake_case__						= AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_a   ):
         snake_case__						= AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''   )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_a   ):
         snake_case__						= AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )

       snake_case__						= AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )
       self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )

       # Test image processor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(_a   )
         snake_case__						= AutoImageProcessor.from_pretrained(_a     ,		trust_remote_code=_a   )
       self.assertEqual(reloaded_image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       try:
         AutoConfig.register('''custom'''     ,		_a   )
         AutoImageProcessor.register(_a     ,		_a   )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(_a   ):
           AutoImageProcessor.register(_a     ,		_a   )

         with tempfile.TemporaryDirectory() as tmpdirname:
           snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
           snake_case__						= Path(_a   ) / '''config.json'''
           json.dump(
               {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
           json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

           snake_case__						= CustomImageProcessor.from_pretrained(_a   )

         # Now that the config is registered, it can be used as any other config with the auto-API
         with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(_a   )
           snake_case__						= AutoImageProcessor.from_pretrained(_a   )
           self.assertIsInstance(_a     ,		_a   )

       finally:
         if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       class 		__magic_name__						(snake_case_				):





            '''simple docstring'''



            __lowercase				:							List[str]	   =       True

       try:
         AutoConfig.register('''custom'''     ,		_a   )
         AutoImageProcessor.register(_a     ,		_a   )
         # If remote code is not set, the default is to use local
         snake_case__						= AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''   )
         self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )
         self.assertTrue(image_processor.is_local   )

         # If remote code is disabled, we load the local one.
         snake_case__						= AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )
         self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )
         self.assertTrue(image_processor.is_local   )

         # If remote is enabled, we load from the Hub
         snake_case__						= AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )
         self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )
         self.assertTrue(not hasattr(_a     ,		'''is_local'''   )   )

       finally:
         if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]

