






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										=      logging.get_logger(__name__)

A										=      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										=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[int]   ):





				'''simple docstring'''







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

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

				for processor in PROCESSOR_MAPPING._extra_content.values():
								if getattr(lowerCAmelCase__     ,     '__name__'     ,     lowerCAmelCase__   ) == 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.
				SCREAMING_SNAKE_CASE_ :						int    				= importlib.import_module('transformers'   )
				if hasattr(lowerCAmelCase__     ,     lowerCAmelCase__   ):
								return getattr(lowerCAmelCase__     ,     lowerCAmelCase__   )

				return None


class 				__a				:





		'''simple docstring'''



		def __init__(    self							):
						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 	__snake_case      (    cls			,     UpperCamelCase__			,     **UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= kwargs.pop('config'			,     __A							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= kwargs.pop('trust_remote_code'			,     __A							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= True

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

						# First, let's see if we have a preprocessor config.
						# Filter the kwargs for `get_file_from_repo`.
						SCREAMING_SNAKE_CASE_ :						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
						SCREAMING_SNAKE_CASE_ :						List[str]    				= get_file_from_repo(
						    __A			,     __A			,     **__A							)
						if preprocessor_config_file is not None:
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= ImageProcessingMixin.get_image_processor_dict(__A			,     **__A							)
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= config_dict.get('processor_class'			,     __A							)
										if "AutoProcessor" in config_dict.get('auto_map'			,     {}							):
														SCREAMING_SNAKE_CASE_ :						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:
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= FeatureExtractionMixin.get_feature_extractor_dict(__A			,     **__A							)
										SCREAMING_SNAKE_CASE_ :						Dict    				= config_dict.get('processor_class'			,     __A							)
										if "AutoProcessor" in config_dict.get('auto_map'			,     {}							):
														SCREAMING_SNAKE_CASE_ :						Tuple    				= config_dict["auto_map"]["AutoProcessor"]

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

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

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

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

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

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

						if has_remote_code and trust_remote_code:
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= get_class_from_dynamic_module(
										    __A			,     __A			,     **__A							)
										SCREAMING_SNAKE_CASE_ :						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 	__snake_case      (    UpperCamelCase__			,     UpperCamelCase__							):
						PROCESSOR_MAPPING.register(__A			,     __A							)



'''simple docstring'''




import requests

try:
				from django.contrib.auth import authenticate, login
				from django.shortcuts import redirect, render
except ImportError:
				__lowercase	:   Dict   		=					print





def 				a_							(    lowercase     )      ->Any:






				'''simple docstring'''

				__lowerCamelCase		:   str           =       "secretKey"
				__lowerCamelCase		:   Optional[Any]           =       "https://www.google.com/recaptcha/api/siteverify"

				# when method is not POST, direct user to login page
				if request.method != "POST":
								return render(lowercase				,    "login.html"     )

				# from the frontend, get username, password, and client_key
				__lowerCamelCase		:   str           =       request.POST.get("username"     )
				__lowerCamelCase		:   Dict           =       request.POST.get("password"     )
				__lowerCamelCase		:   List[str]           =       request.POST.get("g-recaptcha-response"     )

				# post recaptcha response to Google's recaptcha api
				__lowerCamelCase		:   str           =       requests.post(lowercase				,    data={"secret": secret_key, "response": client_key}     )
				# if the recaptcha api verified our keys
				if response.json().get("success"				,    lowercase     ):
								# authenticate the user
								__lowerCamelCase		:   List[Any]           =       authenticate(lowercase				,    username=lowercase				,    password=lowercase     )
								if user_in_database:
												login(lowercase				,    lowercase     )
												return redirect("/your-webpage"     )
				return render(lowercase				,    "login.html"     )





