
import torch

from accelerate import Accelerator, DistributedType

class __snake_case	:







    def __enter__(						self   ):



           '''simple docstring'''







           if self.enabled:
                  lowercase							:  Optional[int] 			=   self.model.no_sync()
                  self.model_sync_obj.__enter__()

           return self







    def __exit__(						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           if self.enabled:
                  # Average all models on exit
                  self._sync_and_avg_model_params()
                  self.model_sync_obj.__exit__(lowerCamelCase__	,lowerCamelCase__	,lowerCamelCase__   )







    def __init__(						self	,snake_case	,snake_case	,snake_case	,snake_case = True   ):



           '''simple docstring'''







           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)"""   )
           lowercase							:  List[Any] 			=   enabled and accelerator.distributed_type != DistributedType.NO
           lowercase							:  Dict 			=   0
           if self.enabled:
                  lowercase							:  List[Any] 			=   accelerator
                  lowercase							:  Optional[int] 			=   model
                  lowercase							:  str 			=   local_sgd_steps







    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                  for param in self.model.parameters():
                         lowercase							:  Any 			=   self.accelerator.reduce(param.data	,reduction="""mean"""   )


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowercase						:      Tuple							=							logging.get_logger(__name__)

lowercase						:      Optional[int]							=							OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

lowercase						:      str							=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                     lowercase							:  Optional[int] 			=   model_type_to_module_name(SCREAMING_SNAKE_CASE__       )

                     lowercase							:  str 			=   importlib.import_module(f".{module_name}"      ,			"""transformers.models"""       )
                     try:
                            return getattr(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
                     except AttributeError:
                            continue

       for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(SCREAMING_SNAKE_CASE__      ,			"""__name__"""      ,			SCREAMING_SNAKE_CASE__       ) == class_name:
                     return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
       lowercase							:  Dict 			=   importlib.import_module("""transformers"""       )
       if hasattr(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       ):
              return getattr(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )

       return None




class __snake_case	:
    def __init__(						self   ):



           '''simple docstring'''







           raise EnvironmentError(
               """AutoProcessor is designed to be instantiated """
               """using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."""   )
    @classmethod
    @replace_list_option_in_docstrings(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						cls	,snake_case	,**snake_case   ):



           '''simple docstring'''







           lowercase							:  Any 			=   kwargs.pop("""config"""	,snake_case   )
           lowercase							:  Optional[int] 			=   kwargs.pop("""trust_remote_code"""	,snake_case   )
           lowercase							:  Union[str, Any] 			=   True

           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Optional[Any] 			=   None

           # First, let's see if we have a preprocessor config.
           # Filter the kwargs for `get_file_from_repo`.
           lowercase							:  str 			=   {
               key: kwargs[key] for key in inspect.signature(snake_case   ).parameters.keys() if key in kwargs
           }
           # Let's start by checking whether the processor class is saved in an image processor
           lowercase							:  Dict 			=   get_file_from_repo(
               snake_case	,snake_case	,**snake_case   )
           if preprocessor_config_file is not None:
                  lowercase			,    lowercase							:  Dict 			=   ImageProcessingMixin.get_image_processor_dict(snake_case	,**snake_case   )
                  lowercase							:  Dict 			=   config_dict.get("""processor_class"""	,snake_case   )
                  if "AutoProcessor" in config_dict.get("""auto_map"""	,{}   ):
                         lowercase							:  List[str] 			=   config_dict["""auto_map"""]["""AutoProcessor"""]

        # If not found, let's check whether the processor class is saved in a feature extractor config
           if preprocessor_config_file is not None and processor_class is None:
                  lowercase			,    lowercase							:  Dict 			=   FeatureExtractionMixin.get_feature_extractor_dict(snake_case	,**snake_case   )
                  lowercase							:  Dict 			=   config_dict.get("""processor_class"""	,snake_case   )
                  if "AutoProcessor" in config_dict.get("""auto_map"""	,{}   ):
                         lowercase							:  Any 			=   config_dict["""auto_map"""]["""AutoProcessor"""]

           if processor_class is None:
                  # Next, let's check whether the processor class is saved in a tokenizer
                  lowercase							:  Any 			=   get_file_from_repo(
                      snake_case	,snake_case	,**snake_case   )
                  if tokenizer_config_file is not None:
                         with open(snake_case	,encoding="""utf-8"""   ) as reader:
                                lowercase							:  Optional[int] 			=   json.load(snake_case   )

                         lowercase							:  Union[str, Any] 			=   config_dict.get("""processor_class"""	,snake_case   )
                         if "AutoProcessor" in config_dict.get("""auto_map"""	,{}   ):
                                lowercase							:  Optional[Any] 			=   config_dict["""auto_map"""]["""AutoProcessor"""]

           if processor_class is None:
                  # Otherwise, load config, if it can be loaded.
                  if not isinstance(snake_case	,snake_case   ):
                         lowercase							:  Union[str, Any] 			=   AutoConfig.from_pretrained(
                             snake_case	,trust_remote_code=snake_case	,**snake_case   )

                  # And check if the config contains the processor class.
                  lowercase							:  str 			=   getattr(snake_case	,"""processor_class"""	,snake_case   )
                  if hasattr(snake_case	,"""auto_map"""   ) and "AutoProcessor" in config.auto_map:
                         lowercase							:  Tuple 			=   config.auto_map["""AutoProcessor"""]

           if processor_class is not None:
                  lowercase							:  List[str] 			=   processor_class_from_name(snake_case   )

           lowercase							:  str 			=   processor_auto_map is not None
           lowercase							:  str 			=   processor_class is not None or type(snake_case   ) in PROCESSOR_MAPPING
           lowercase							:  List[Any] 			=   resolve_trust_remote_code(
               snake_case	,snake_case	,snake_case	,snake_case   )

           if has_remote_code and trust_remote_code:
                  lowercase							:  Dict 			=   get_class_from_dynamic_module(
                      snake_case	,snake_case	,**snake_case   )
                  lowercase							:  str 			=   kwargs.pop("""code_revision"""	,snake_case   )
                  if os.path.isdir(snake_case   ):
                         processor_class.register_for_auto_class()
                  return processor_class.from_pretrained(
                      snake_case	,trust_remote_code=snake_case	,**snake_case   )
           elif processor_class is not None:
                  return processor_class.from_pretrained(
                      snake_case	,trust_remote_code=snake_case	,**snake_case   )
           # Last try: we use the PROCESSOR_MAPPING.
           elif type(snake_case   ) in PROCESSOR_MAPPING:
                  return PROCESSOR_MAPPING[type(snake_case   )].from_pretrained(snake_case	,**snake_case   )

           # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
           # tokenizer.
           try:
                  return AutoTokenizer.from_pretrained(
                      snake_case	,trust_remote_code=snake_case	,**snake_case   )
           except Exception:
                  try:
                         return AutoImageProcessor.from_pretrained(
                             snake_case	,trust_remote_code=snake_case	,**snake_case   )
                  except Exception:
                         pass

                  try:
                         return AutoFeatureExtractor.from_pretrained(
                             snake_case	,trust_remote_code=snake_case	,**snake_case   )
                  except Exception:
                         pass

           raise ValueError(
               f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
               """tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"""
               """the files of at least one of those processing classes."""   )
    @staticmethod
    def    _SCREAMING_SNAKE_CASE (						snake_case	,snake_case   ):



           '''simple docstring'''







           PROCESSOR_MAPPING.register(snake_case	,snake_case   )
