'''simple docstring'''






import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_UpperCAmelCase						:    Any     					=					logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_UpperCAmelCase						:    Any     					=					direct_transformers_import(Path(__file__).parent)


_UpperCAmelCase						:    int     					=					{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      ["feature_extractor", "tokenizer"]
   # Names need to be attr_class for attr in attributes
   __lowerCAmelCase    						=      None
   __lowerCAmelCase    						=      None
   __lowerCAmelCase    						=      None
   def __init__(			self			: Union[str, Any],   *UpperCamelCase__			: List[Any],   **UpperCamelCase__			: Optional[int]    )     -> List[Any]:
        # Sanitize args and kwargs
        for key in kwargs:
             if key not in self.attributes:
                  raise TypeError(f'Unexpected keyword argument {key}.'    )
        for arg, attribute_name in zip(UpperCamelCase__,   self.attributes    ):
             if attribute_name in kwargs:
                  raise TypeError(f'Got multiple values for argument {attribute_name}.'    )
             else:
                  _A								=     arg

        if len(UpperCamelCase__    ) != len(self.attributes    ):
             raise ValueError(
                 f'This processor requires {len(self.attributes    )} arguments: {", ".join(self.attributes    )}. Got '
                 f'{len(UpperCamelCase__    )} arguments instead.'    )

        # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
        for attribute_name, arg in kwargs.items():
             _A								=     getattr(self,   f'{attribute_name}_class'    )
             # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
             _A								=     AUTO_TO_BASE_CLASS_MAPPING.get(UpperCamelCase__,   UpperCamelCase__    )
             if isinstance(UpperCamelCase__,   UpperCamelCase__    ):
                  _A								=     tuple(getattr(UpperCamelCase__,   UpperCamelCase__    ) for n in class_name if n is not None    )
             else:
                  _A								=     getattr(UpperCamelCase__,   UpperCamelCase__    )

             if not isinstance(UpperCamelCase__,   UpperCamelCase__    ):
                  raise ValueError(
                      f'Received a {type(UpperCamelCase__    ).__name__} for argument {attribute_name}, but a {class_name} was expected.'    )

             setattr(self,   UpperCamelCase__,   UpperCamelCase__    )
   def __repr__(			self			: Optional[Any]    )     -> Dict:
        _A								=     [f'- {name}: {repr(getattr(self,   UpperCamelCase__    )    )}' for name in self.attributes]
        _A								=     '\n'.join(UpperCamelCase__    )
        return f'{self.__class__.__name__}:\n{attributes_repr}'
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: Any,   UpperCamelCase__			: bool = False,   **UpperCamelCase__			: Tuple    )     -> Tuple:
        os.makedirs(UpperCamelCase__,   exist_ok=UpperCamelCase__    )

        if push_to_hub:
             _A								=     kwargs.pop('commit_message',   UpperCamelCase__    )
             _A								=     kwargs.pop('repo_id',   save_directory.split(os.path.sep    )[-1]    )
             _A								=     self._create_repo(UpperCamelCase__,   **UpperCamelCase__    )
             _A								=     self._get_files_timestamps(UpperCamelCase__    )
        # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
        # loaded from the Hub.
        if self._auto_class is not None:
             _A								=     [getattr(self,   UpperCamelCase__    ) for attribute_name in self.attributes]
             _A								=     [(a.init_kwargs if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else a) for a in attrs]
             custom_object_save(self,   UpperCamelCase__,   config=UpperCamelCase__    )

        for attribute_name in self.attributes:
             _A								=     getattr(self,   UpperCamelCase__    )
             # Include the processor class in the attribute config so this processor can then be reloaded with the
             # `AutoProcessor` API.
             if hasattr(UpperCamelCase__,   '_set_processor_class'    ):
                  attribute._set_processor_class(self.__class__.__name__    )
             attribute.save_pretrained(UpperCamelCase__    )

        if self._auto_class is not None:
             # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
             for attribute_name in self.attributes:
                  _A								=     getattr(self,   UpperCamelCase__    )
                  if isinstance(UpperCamelCase__,   UpperCamelCase__    ):
                       del attribute.init_kwargs["auto_map"]

        if push_to_hub:
             self._upload_modified_files(
                 UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   commit_message=UpperCamelCase__,   token=kwargs.get('use_auth_token'    ),   )
   @classmethod
   def   __UpperCAmelCase      (			cls			: Optional[int],   UpperCamelCase__			: Union[str, os.PathLike],   UpperCamelCase__			: Optional[Union[str, os.PathLike]] = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[Union[str, bool]] = None,   UpperCamelCase__			: str = "main",   **UpperCamelCase__			: Optional[int],   )     -> Union[str, Any]:
        _A								=     cache_dir
        _A								=     force_download
        _A								=     local_files_only
        _A								=     revision

        _A								=     kwargs.pop('use_auth_token',   UpperCamelCase__    )
        if use_auth_token is not None:
             warnings.warn(
                 'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.',   UpperCamelCase__    )
             if token is not None:
                  raise ValueError(
                      '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'    )
             _A								=     use_auth_token

        if token is not None:
             # change to `token` in a follow-up PR
             _A								=     token

        _A								=     cls._get_arguments_from_pretrained(UpperCamelCase__,   **UpperCamelCase__    )
        return cls(*UpperCamelCase__    )
   @classmethod
   def   __UpperCAmelCase      (			cls			: int,   UpperCamelCase__			: int="AutoProcessor"    )     -> Tuple:
        if not isinstance(UpperCamelCase__,   UpperCamelCase__    ):
             _A								=     auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(UpperCamelCase__,   UpperCamelCase__    ):
             raise ValueError(f'{auto_class} is not a valid auto class.'    )

        _A								=     auto_class
   @classmethod
   def   __UpperCAmelCase      (			cls			: int,   UpperCamelCase__			: Union[str, Any],   **UpperCamelCase__			: Tuple    )     -> Any:
        _A								=     []
        for attribute_name in cls.attributes:
             _A								=     getattr(cls,   f'{attribute_name}_class'    )
             if isinstance(UpperCamelCase__,   UpperCamelCase__    ):
                  _A								=     tuple(getattr(UpperCamelCase__,   UpperCamelCase__    ) if n is not None else None for n in class_name    )
                  _A								=     kwargs.get('use_fast',   UpperCamelCase__    )
                  if use_fast and classes[1] is not None:
                       _A								=     classes[1]
                  else:
                       _A								=     classes[0]
             else:
                  _A								=     getattr(UpperCamelCase__,   UpperCamelCase__    )

             args.append(attribute_class.from_pretrained(UpperCamelCase__,   **UpperCamelCase__    )    )
        return args

   @property
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     getattr(self,   self.attributes[0]    )
        return getattr(UpperCamelCase__,   'model_input_names',   UpperCamelCase__    )






_UpperCAmelCase						:    List[str]     					=					copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    _UpperCAmelCase						:    str     					=					ProcessorMixin.push_to_hub.__doc__.format(
        object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
    )




'''simple docstring'''






import torch

from accelerate import Accelerator, DistributedType







class lowercase_					:







   """simple docstring"""

   def __enter__(			self			: Optional[int]    )     -> Union[str, Any]:
        if self.enabled:
             _A								=     self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: int,   UpperCamelCase__			: Any    )     -> str:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
   def __init__(			self			: Optional[int],   UpperCamelCase__			: Accelerator,   UpperCamelCase__			: torch.nn.Module,   UpperCamelCase__			: int,   UpperCamelCase__			: bool = True    )     -> str:
        if accelerator.distributed_type not in [
            DistributedType.NO,
            DistributedType.MULTI_CPU,
            DistributedType.MULTI_GPU,
        ]:
             raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
        _A								=     enabled and accelerator.distributed_type != DistributedType.NO
        _A								=     0
        if self.enabled:
             _A								=     accelerator
             _A								=     model
             _A								=     local_sgd_steps
   def   __UpperCAmelCase      (			self			: Any    )     -> int:
        self.num_steps += 1
        if not self.enabled:
             return

        if self.num_steps % self.local_sgd_steps == 0:
             self._sync_and_avg_model_params()

   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  _A								=     self.accelerator.reduce(param.data,   reduction='mean'    )



