






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

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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						=	{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

SCREAMING_SNAKE_CASE						=	'</w>'
SCREAMING_SNAKE_CASE						=	'@@ '


def 							lowercase_				(    __A :       List[str]			)       ->      Any:



 """simple docstring"""




 lowercase					:   int				  =set()
 lowercase					:   str				  =word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)			)
  lowercase					:   str				  =char
 return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE						=	{'facebook/s2t-wav2vec2-large-en-de': 1_024}





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			VOCAB_FILES_NAMES
      UpperCamelCase_								=			PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_								=			['''input_ids''', '''attention_mask''']






      def __init__( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		List[Any]="<s>"     ,      UpperCAmelCase			:		Tuple="<pad>"     ,      UpperCAmelCase			:		Optional[int]="</s>"     ,      UpperCAmelCase			:		Dict="<unk>"     ,      UpperCAmelCase			:		Union[str, Any]=False     ,      UpperCAmelCase			:		str=None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  Any:


       '''simple docstring'''

       super().__init__(
           unk_token=__A     ,      bos_token=__A     ,      eos_token=__A     ,      pad_token=__A     ,      do_lower_case=__A     ,      **__A     ,      )

       lowercase					:   int				  =do_lower_case

       with open(__A     ,      encoding='''utf-8'''		) as vocab_handle:
        lowercase					:   List[Any]				  =json.load(__A		)
       lowercase					:   str				  ={v: k for k, v in self.encoder.items()}

       if merges_file is None:
        logger.info(f'No merges files provided. {self.__class__.__name__} can only be used for decoding.'		)

        lowercase					:   List[Any]				  =None
        lowercase					:   str				  =None
       else:
        with open(__A     ,      encoding='''utf-8'''		) as merges_handle:
         lowercase					:   Optional[Any]				  =merges_handle.read().split('''\n'''		)[:-1]

        lowercase					:   List[Any]				  =[tuple(merge.split()[:2]		) for merge in merges]
        lowercase					:   List[Any]				  =dict(zip(__A     ,      range(len(__A		)		)		)		)
        lowercase					:   Tuple				  ={}






      @property
      def 				A__      ( self			:		Optional[int]		)  ->  List[str]:


       '''simple docstring'''

       return len(self.decoder		)






      def 				A__      ( self			:		Any		)  ->  Optional[int]:


       '''simple docstring'''

       return dict(self.encoder     ,      **self.added_tokens_encoder		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Tuple				  =tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
        return self.cache[token]
       lowercase					:   Optional[Any]				  =get_pairs(__A		)

       if not pairs:
        return token

       while True:
        lowercase					:   Any				  =min(__A     ,      key=lambda UpperCAmelCase		: self.bpe_ranks.get(__A     ,      float('''inf'''		)		)		)
        if bigram not in self.bpe_ranks:
         break
        lowercase					:   Optional[Any]				  =bigram
        lowercase					:   Optional[Any]				  =[]
        lowercase					:   Any				  =0
        while i < len(__A		):
         try:
          lowercase					:   Tuple				  =word.index(__A     ,      __A		)
         except ValueError:
          new_word.extend(word[i:]		)
          break
         else:
          new_word.extend(word[i:j]		)
          lowercase					:   Union[str, Any]				  =j

         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
        lowercase					:   List[str]				  =tuple(__A		)
        lowercase					:   Dict				  =new_word
        if len(__A		) == 1:
         break
        else:
         lowercase					:   List[Any]				  =get_pairs(__A		)
       lowercase					:   Dict				  =" ".join(__A		)
       if word == "\n  " + BPE_TOKEN_MERGES:
        lowercase					:   Any				  ="\n" + BPE_TOKEN_MERGES

       if word.endswith(__A		):
        lowercase					:   List[str]				  =word.replace(__A     ,      ''''''		)

       lowercase					:   List[Any]				  =word.replace(''' '''     ,      __A		)
       lowercase					:   Tuple				  =word
       return word






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[int]		)  ->  Tuple:


       '''simple docstring'''

       if self.bpe_ranks is None:
        raise ValueError(
            '''This tokenizer was instantiated without a `merges.txt` file, so'''
            ''' that it can only be used for decoding, not for encoding.'''
            '''Make sure to provide `merges.txt` file at instantiation to enable '''
            '''encoding.'''		)

       if self.do_lower_case:
        lowercase					:   List[Any]				  =text.lower()

       lowercase					:   Optional[int]				  =text.split()

       lowercase					:   Optional[int]				  =[]
       for token in text:
        if token:
         split_tokens.extend(list(self.bpe(__A		).split(''' '''		)		)		)

       return split_tokens






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str		)  ->  Any:


       '''simple docstring'''

       return self.encoder.get(__A     ,      self.encoder.get(self.unk_token		)		)






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		int		)  ->  int:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.decoder.get(__A     ,      self.unk_token		)
       return result






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   List[str]				  =" ".join(__A		)

       # make sure @@ tokens are concatenated
       lowercase					:   int				  ="".join(string.split(__A		)		)

       return string






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Optional[str] = None		)  ->  Optional[Any]:


       '''simple docstring'''

       if not os.path.isdir(__A		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase					:   Union[str, Any]				  =os.path.join(
           __A     ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
       lowercase					:   Any				  =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'''		)

       lowercase					:   Dict				  =0
       if self.bpe_ranks is None:
        return (vocab_file,)

       with open(__A     ,      '''w'''     ,      encoding='''utf-8'''		) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,      key=lambda UpperCAmelCase		: kv[1]		):
         if index != token_index:
          logger.warning(
              f'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
              ''' Please check that the tokenizer is not corrupted!'''		)
          lowercase					:   Any				  =token_index
         writer.write(''' '''.join(__A		) + '''\n'''		)
         index += 1

       return (vocab_file, merges_file)











'''simple docstring'''



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 UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =0






      def 				A__      ( self			:		str		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Tuple				  =AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
       self.assertIsInstance(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		Any		)  ->  int:


       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase					:   int				  =Path(UpperCAmelCase		) / '''preprocessor_config.json'''
        lowercase					:   str				  =Path(UpperCAmelCase		) / '''config.json'''
        json.dump(
            {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,      open(UpperCAmelCase     ,      '''w'''		)     ,      )
        json.dump({'''model_type''': '''clip'''}     ,      open(UpperCAmelCase     ,      '''w'''		)		)

        lowercase					:   str				  =AutoImageProcessor.from_pretrained(UpperCAmelCase		)
        self.assertIsInstance(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		Any		)  ->  Union[str, Any]:


       '''simple docstring'''

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

        lowercase					:   Optional[Any]				  =AutoImageProcessor.from_pretrained(UpperCAmelCase		)
        self.assertIsInstance(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		Optional[Any]		)  ->  Any:


       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase					:   str				  =CLIPConfig()

        # Create a dummy config file with image_proceesor_type
        lowercase					:   Optional[int]				  =Path(UpperCAmelCase		) / '''preprocessor_config.json'''
        lowercase					:   Optional[Any]				  =Path(UpperCAmelCase		) / '''config.json'''
        json.dump(
            {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,      open(UpperCAmelCase     ,      '''w'''		)     ,      )
        json.dump({'''model_type''': '''clip'''}     ,      open(UpperCAmelCase     ,      '''w'''		)		)

        # remove image_processor_type to make sure config.json alone is enough to load image processor locally
        lowercase					:   Dict				  =AutoImageProcessor.from_pretrained(UpperCAmelCase		).to_dict()

        config_dict.pop('''image_processor_type'''		)
        lowercase					:   str				  =CLIPImageProcessor(**UpperCAmelCase		)

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

        lowercase					:   Optional[int]				  =AutoImageProcessor.from_pretrained(UpperCAmelCase		)

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

       self.assertIsInstance(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		str		)  ->  List[str]:


       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase					:   Dict				  =Path(UpperCAmelCase		) / '''preprocessor_config.json'''
        json.dump(
            {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,      open(UpperCAmelCase     ,      '''w'''		)     ,      )

        lowercase					:   Optional[Any]				  =AutoImageProcessor.from_pretrained(UpperCAmelCase		)
        self.assertIsInstance(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		int		)  ->  List[str]:


       '''simple docstring'''

       with self.assertRaisesRegex(
           UpperCAmelCase     ,      '''clip-base is not a local folder and is not a valid model identifier'''		):
        lowercase					:   Union[str, Any]				  =AutoImageProcessor.from_pretrained('''clip-base'''		)






      def 				A__      ( self			:		List[Any]		)  ->  List[str]:


       '''simple docstring'''

       with self.assertRaisesRegex(
           UpperCAmelCase     ,      R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
        lowercase					:   Any				  =AutoImageProcessor.from_pretrained(UpperCAmelCase     ,      revision='''aaaaaa'''		)






      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       with self.assertRaisesRegex(
           UpperCAmelCase     ,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''     ,      ):
        lowercase					:   Optional[int]				  =AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''		)






      def 				A__      ( self			:		List[str]		)  ->  str:


       '''simple docstring'''

       with self.assertRaises(UpperCAmelCase		):
        lowercase					:   Dict				  =AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''		)
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(UpperCAmelCase		):
        lowercase					:   List[str]				  =AutoImageProcessor.from_pretrained(
            '''hf-internal-testing/test_dynamic_image_processor'''     ,      trust_remote_code=UpperCAmelCase		)

       lowercase					:   Union[str, Any]				  =AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/test_dynamic_image_processor'''     ,      trust_remote_code=UpperCAmelCase		)
       self.assertEqual(image_processor.__class__.__name__     ,      '''NewImageProcessor'''		)

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






      def 				A__      ( self			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

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

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

         lowercase					:   Optional[int]				  =CustomImageProcessor.from_pretrained(UpperCAmelCase		)

        # 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(UpperCAmelCase		)
         lowercase					:   Dict				  =AutoImageProcessor.from_pretrained(UpperCAmelCase		)
         self.assertIsInstance(UpperCAmelCase     ,      UpperCAmelCase		)

       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 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       class UpperCAmelCase_					(    __A				):




             """simple docstring"""




             UpperCamelCase_								=			True

       try:
        AutoConfig.register('''custom'''     ,      UpperCAmelCase		)
        AutoImageProcessor.register(UpperCAmelCase     ,      UpperCAmelCase		)
        # If remote code is not set, the default is to use local
        lowercase					:   List[str]				  =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.
        lowercase					:   Tuple				  =AutoImageProcessor.from_pretrained(
            '''hf-internal-testing/test_dynamic_image_processor'''     ,      trust_remote_code=UpperCAmelCase		)
        self.assertEqual(image_processor.__class__.__name__     ,      '''NewImageProcessor'''		)
        self.assertTrue(image_processor.is_local		)

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



