


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__							:						Tuple							     =   logging.get_logger(__name__)


lowerCamelCase__							:						str							     =   {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase__							:						List[str]							     =   {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

lowerCamelCase__							:						str							     =   {"""facebook/blenderbot_small-90M""": 5_1_2}







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[int]:
  snake_case__						= set()
  snake_case__						= word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)				)
    snake_case__						= char

  snake_case__						= set(__lowerCAmelCase				)
  return pairs


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



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

     def __init__(					self:Dict     ,		_a:List[Any]     ,		_a:List[Any]     ,		_a:Dict="__start__"     ,		_a:Optional[Any]="__end__"     ,		_a:Tuple="__unk__"     ,		_a:int="__null__"     ,		**_a:Optional[Any]     ,		):
       super().__init__(unk_token=_a     ,		bos_token=_a     ,		eos_token=_a     ,		pad_token=_a     ,		**_a   )

       with open(_a     ,		encoding='''utf-8'''   ) as vocab_handle:
         snake_case__						= json.load(_a   )
       snake_case__						= {v: k for k, v in self.encoder.items()}
       with open(_a     ,		encoding='''utf-8'''   ) as merges_handle:
         snake_case__						= merges_handle.read().split('''\n'''   )[1:-1]
       snake_case__						= [tuple(merge.split()   ) for merge in merges]
       snake_case__						= dict(zip(_a     ,		range(len(_a   )   )   )   )
       snake_case__						= {}

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       return len(self.encoder   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return dict(self.encoder     ,		**self.added_tokens_encoder   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:str   ):
       if token in self.cache:
         return self.cache[token]
       snake_case__						= re.sub('''([.,!?()])'''     ,		r''' \1'''     ,		_a   )
       snake_case__						= re.sub('''(\')'''     ,		r''' \1 '''     ,		_a   )
       snake_case__						= re.sub(r'''\s{2,}'''     ,		''' '''     ,		_a   )
       if "\n" in token:
         snake_case__						= token.replace('''\n'''     ,		''' __newln__'''   )

       snake_case__						= token.split(''' '''   )
       snake_case__						= []
       for token in tokens:
         if not len(_a   ):
           continue

         snake_case__						= token.lower()
         snake_case__						= tuple(_a   )
         snake_case__						= tuple(list(word[:-1]   ) + [word[-1] + '''</w>''']   )
         snake_case__						= get_pairs(_a   )

         if not pairs:
           words.append(_a   )
           continue

         while True:
           snake_case__						= min(_a     ,		key=lambda _a   : self.bpe_ranks.get(_a     ,		float('''inf'''   )   )   )
           if bigram not in self.bpe_ranks:
             break
           snake_case__				,				snake_case__						= bigram
           snake_case__						= []
           snake_case__						= 0

           while i < len(_a   ):
             try:
               snake_case__						= word.index(_a     ,		_a   )
               new_word.extend(word[i:j]   )
               snake_case__						= j
             except ValueError:
               new_word.extend(word[i:]   )
               break

             if word[i] == first and i < len(_a   ) - 1 and word[i + 1] == second:
               new_word.append(first + second   )
               i += 2
             else:
               new_word.append(word[i]   )
               i += 1
           snake_case__						= tuple(_a   )
           snake_case__						= new_word
           if len(_a   ) == 1:
             break
           else:
             snake_case__						= get_pairs(_a   )
         snake_case__						= '''@@ '''.join(_a   )
         snake_case__						= word[:-4]

         snake_case__						= word
         words.append(_a   )
       return " ".join(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str   ):
       snake_case__						= []

       snake_case__						= re.findall(r'''\S+\n?'''     ,		_a   )

       for token in words:
         split_tokens.extend(list(self.bpe(_a   ).split(''' '''   )   )   )
       return split_tokens

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str   ):
       snake_case__						= token.lower()
       return self.encoder.get(_a     ,		self.encoder.get(self.unk_token   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int   ):
       return self.decoder.get(_a     ,		self.unk_token   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:List[str]   ):
       snake_case__						= ''' '''.join(_a   ).replace('''@@ '''     ,		''''''   ).strip()
       return out_string







     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''']   )
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

       with open(_a     ,		'''w'''     ,		encoding='''utf-8'''   ) as f:
         f.write(json.dumps(self.encoder     ,		indent=2     ,		sort_keys=_a     ,		ensure_ascii=_a   ) + '''\n'''   )

       snake_case__						= 0
       with open(_a     ,		'''w'''     ,		encoding='''utf-8'''   ) as writer:
         writer.write('''#version: 0.2\n'''   )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,		key=lambda _a   : kv[1]   ):
           if index != token_index:
             logger.warning(
                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                 ''' Please check that the tokenizer is not corrupted!'''   )
             snake_case__						= token_index
           writer.write(''' '''.join(_a   ) + '''\n'''   )
           index += 1

       return vocab_file, merge_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]

