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


__a  :					Optional[int]          =  logging.get_logger(__name__)

__a  :					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"""),
    ]
)

__a  :					List[Any]          =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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

												__lowercase			      =							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.
				__lowercase			      =							importlib.import_module('''transformers'''							)
				if hasattr(lowercase							,      lowercase							):
								return getattr(lowercase							,      lowercase							)

				return None




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self						)					-> int:

							'''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(lowerCAmelCase__						)
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       **lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							__lowercase			      =							kwargs.pop('''config'''				,       lowerCAmelCase__						)
							__lowercase			      =							kwargs.pop('''trust_remote_code'''				,       lowerCAmelCase__						)
							__lowercase			      =							True

							__lowercase			      =							None
							__lowercase			      =							None

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

							if processor_class is None:
											# Next, let's check whether the processor class is saved in a tokenizer
											__lowercase			      =							get_file_from_repo(
											    lowerCAmelCase__				,       lowerCAmelCase__				,       **lowerCAmelCase__						)
											if tokenizer_config_file is not None:
															with open(lowerCAmelCase__				,       encoding='''utf-8'''						) as reader:
																			__lowercase			      =							json.load(lowerCAmelCase__						)

															__lowercase			      =							config_dict.get('''processor_class'''				,       lowerCAmelCase__						)
															if "AutoProcessor" in config_dict.get('''auto_map'''				,       {}						):
																			__lowercase			      =							config_dict['''auto_map''']['''AutoProcessor''']

							if processor_class is None:
											# Otherwise, load config, if it can be loaded.
											if not isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
															__lowercase			      =							AutoConfig.from_pretrained(
															    lowerCAmelCase__				,       trust_remote_code=lowerCAmelCase__				,       **lowerCAmelCase__						)

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

							if processor_class is not None:
											__lowercase			      =							processor_class_from_name(lowerCAmelCase__						)

							__lowercase			      =							processor_auto_map is not None
							__lowercase			      =							processor_class is not None or type(lowerCAmelCase__						) in PROCESSOR_MAPPING
							__lowercase			      =							resolve_trust_remote_code(
							    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)

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

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

											try:
															return AutoFeatureExtractor.from_pretrained(
															    lowerCAmelCase__				,       trust_remote_code=lowerCAmelCase__				,       **lowerCAmelCase__						)
											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      (       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							PROCESSOR_MAPPING.register(lowerCAmelCase__				,       lowerCAmelCase__						)
__a  :					Union[str, Any]          =  [[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class        _UpperCamelCase				:


			"""simple docstring"""






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

							'''simple docstring'''


							__lowercase			      =							name
							__lowercase			      =							[]
							__lowercase			      =							[]





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

							'''simple docstring'''


							self.inbound.append(lowerCAmelCase__						)





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

							'''simple docstring'''


							self.outbound.append(lowerCAmelCase__						)





			def __repr__(       self						)					-> Tuple:

							'''simple docstring'''


							return F"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"





def     UpperCAmelCase						(   lowercase							,      lowercase=3							,      lowercase=0.85							):







				"""simple docstring"""



				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							1

				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							len(node.outbound							)

				for i in range(lowercase							):
								print(F"======= Iteration {i + 1} ======="							)
								for _, node in enumerate(lowercase							):
												__lowercase			      =							(1 - d) + d * sum(
												    ranks[ib] / outbounds[ib] for ib in node.inbound							)
								print(lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							list(input('''Enter Names of the Nodes: '''							).split()							)

				__lowercase			      =							[Node(lowercase							) for name in names]

				for ri, row in enumerate(lowercase							):
								for ci, col in enumerate(lowercase							):
												if col == 1:
																nodes[ci].add_inbound(names[ri]							)
																nodes[ri].add_outbound(names[ci]							)

				print('''======= Nodes ======='''							)
				for node in nodes:
								print(lowercase							)

				page_rank(lowercase							)


if __name__ == "__main__":
		main()