'''simple docstring'''





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							["image_processor", "tokenizer"]
   lowerCAmelCase_														=							"AutoImageProcessor"
   lowerCAmelCase_														=							"AutoTokenizer"




   def __init__(					self    :       Tuple ,			_lowercase    :       Any=None ,			_lowercase    :       int=None ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   None
     if "feature_extractor" in kwargs:
       warnings.warn(
           """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
           """ instead.""" ,			_lowercase ,			)
       SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""feature_extractor"""   )

     SCREAMING_SNAKE_CASE__    							=   image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
       raise ValueError("""You need to specify an `image_processor`."""   )
     if tokenizer is None:
       raise ValueError("""You need to specify a `tokenizer`."""   )

     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.image_processor
     SCREAMING_SNAKE_CASE__    							=   False




   def __call__(					self    :       Tuple ,			*_lowercase    :       List[Any] ,			**_lowercase    :       Optional[int]   ):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase ,			**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""images""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if images is None and text is None:
       raise ValueError("""You need to specify either an `images` or `text` input to process."""   )

     if images is not None:
       SCREAMING_SNAKE_CASE__    							=   self.image_processor(_lowercase ,			*_lowercase ,			**_lowercase   )
     if text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if text is None:
       return inputs
     elif images is None:
       return encodings
     else:
       SCREAMING_SNAKE_CASE__    							=   encodings["""input_ids"""]
       return inputs




   def 			__a		(					self    :       List[Any] ,			*_lowercase    :       str ,			**_lowercase    :       Dict   ):



     """simple docstring"""






     return self.tokenizer.batch_decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       Optional[Any] ,			*_lowercase    :       Optional[Any] ,			**_lowercase    :       List[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   @contextmanager
   def 			__a		(					self    :       int   ):



     """simple docstring"""






     warnings.warn(
         """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
         """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
         """your images inputs, or in a separate call."""   )
     SCREAMING_SNAKE_CASE__    							=   True
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer
     yield
     SCREAMING_SNAKE_CASE__    							=   self.image_processor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Optional[Any]=False ,			_lowercase    :       int=None   ):



     """simple docstring"""






     if added_vocab is None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer.get_added_vocab()

     SCREAMING_SNAKE_CASE__    							=   {}

     while tokens:
       SCREAMING_SNAKE_CASE__    							=   re.search(R"""<s_(.*?)>""" ,			_lowercase ,			re.IGNORECASE   )
       if start_token is None:
         break
       SCREAMING_SNAKE_CASE__    							=   start_token.group(1   )
       SCREAMING_SNAKE_CASE__    							=   re.search(Rf"""</s_{key}>""" ,			_lowercase ,			re.IGNORECASE   )
       SCREAMING_SNAKE_CASE__    							=   start_token.group()
       if end_token is None:
         SCREAMING_SNAKE_CASE__    							=   tokens.replace(_lowercase ,			""""""   )
       else:
         SCREAMING_SNAKE_CASE__    							=   end_token.group()
         SCREAMING_SNAKE_CASE__    							=   re.escape(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   re.escape(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""" ,			_lowercase ,			re.IGNORECASE   )
         if content is not None:
           SCREAMING_SNAKE_CASE__    							=   content.group(1   ).strip()
           if r"<s_" in content and r"</s_" in content:  # non-leaf node
             SCREAMING_SNAKE_CASE__    							=   self.tokenajson(_lowercase ,			is_inner_value=_lowercase ,			added_vocab=_lowercase   )
             if value:
               if len(_lowercase   ) == 1:
                 SCREAMING_SNAKE_CASE__    							=   value[0]
               SCREAMING_SNAKE_CASE__    							=   value
           else:  # leaf nodes
             SCREAMING_SNAKE_CASE__    							=   []
             for leaf in content.split(R"""<sep/>"""   ):
               SCREAMING_SNAKE_CASE__    							=   leaf.strip()
               if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                 SCREAMING_SNAKE_CASE__    							=   leaf[1:-2]  # for categorical special tokens
               output[key].append(_lowercase   )
             if len(output[key]   ) == 1:
               SCREAMING_SNAKE_CASE__    							=   output[key][0]

         SCREAMING_SNAKE_CASE__    							=   tokens[tokens.find(_lowercase   ) + len(_lowercase   ) :].strip()
         if tokens[:6] == r"<sep/>":  # non-leaf nodes
           return [output] + self.tokenajson(tokens[6:] ,			is_inner_value=_lowercase ,			added_vocab=_lowercase   )

     if len(_lowercase   ):
       return [output] if is_inner_value else output
     else:
       return [] if is_inner_value else {"text_sequence": tokens}




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     warnings.warn(
         """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,			_lowercase ,			)
     return self.image_processor_class




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     warnings.warn(
         """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,			_lowercase ,			)
     return self.image_processor

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
