


'''simple docstring'''




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	:   int   		=					logging.get_logger(__name__)

__lowercase	:   str   		=					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 				a_							(    lowercase     )      ->List[str]:






				'''simple docstring'''

				for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
								if class_name in processors:
												__lowerCamelCase		:   Tuple           =       model_type_to_module_name(lowercase     )

												__lowerCamelCase		:   Optional[int]           =       importlib.import_module(F'.{module_name}'				,    "transformers.models"     )
												try:
																return getattr(lowercase				,    lowercase     )
												except AttributeError:
																continue

				for processor in PROCESSOR_MAPPING._extra_content.values():
								if getattr(lowercase				,    "__name__"				,    lowercase     ) == 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.
				__lowerCamelCase		:   int           =       importlib.import_module("transformers"     )
				if hasattr(lowercase				,    lowercase     ):
								return getattr(lowercase				,    lowercase     )

				return None





class _snake_case :
							"""simple docstring"""





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



											"""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(__A						)
							def 		__SCREAMING_SNAKE_CASE( cls      ,					__A      ,					**__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       kwargs.pop("config"      ,					__A						)
											__lowerCamelCase		:   List[Any]           =       kwargs.pop("trust_remote_code"      ,					__A						)
											__lowerCamelCase		:   Optional[int]           =       True

											__lowerCamelCase		:   Optional[Any]           =       None
											__lowerCamelCase		:   int           =       None

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

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

																			__lowerCamelCase		:   Optional[int]           =       config_dict.get("processor_class"      ,					__A						)
																			if "AutoProcessor" in config_dict.get("auto_map"      ,					{}						):
																							__lowerCamelCase		:   int           =       config_dict["auto_map"]["AutoProcessor"]

											if processor_class is None:
															# Otherwise, load config, if it can be loaded.
															if not isinstance(__A      ,					__A						):
																			__lowerCamelCase		:   Optional[Any]           =       AutoConfig.from_pretrained(
																			    __A      ,					trust_remote_code=__A      ,					**__A						)

															# And check if the config contains the processor class.
															__lowerCamelCase		:   int           =       getattr(__A      ,					"processor_class"      ,					__A						)
															if hasattr(__A      ,					"auto_map"						) and "AutoProcessor" in config.auto_map:
																			__lowerCamelCase		:   int           =       config.auto_map["AutoProcessor"]

											if processor_class is not None:
															__lowerCamelCase		:   Dict           =       processor_class_from_name(__A						)

											__lowerCamelCase		:   int           =       processor_auto_map is not None
											__lowerCamelCase		:   int           =       processor_class is not None or type(__A						) in PROCESSOR_MAPPING
											__lowerCamelCase		:   Dict           =       resolve_trust_remote_code(
											    __A      ,					__A      ,					__A      ,					__A						)

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

											# 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(
															    __A      ,					trust_remote_code=__A      ,					**__A						)
											except Exception:
															try:
																			return AutoImageProcessor.from_pretrained(
																			    __A      ,					trust_remote_code=__A      ,					**__A						)
															except Exception:
																			pass

															try:
																			return AutoFeatureExtractor.from_pretrained(
																			    __A      ,					trust_remote_code=__A      ,					**__A						)
															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( __A      ,					__A						)  ->List[str]:



											"""simple docstring"""







											PROCESSOR_MAPPING.register(__A      ,					__A						)









'''simple docstring'''




from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       word
											__lowerCamelCase		:   Optional[Any]           =       count







							def __eq__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count == other.count







							def __lt__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count < other.count





def 				a_							(    lowercase				,    lowercase     )      ->list[str]:






				'''simple docstring'''

				__lowerCamelCase		:   Heap[WordCount]           =       Heap()
				__lowerCamelCase		:   int           =       Counter(lowercase     )
				heap.build_max_heap(
				    [WordCount(lowercase				,    lowercase     ) for word, count in count_by_word.items()]     )
				return [heap.extract_max().word for _ in range(min(lowercase				,    len(lowercase     )     )     )]


if __name__ == "__main__":
				import doctest

				doctest.testmod()





