



import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


UpperCAmelCase_			      =  logging.getLogger(__name__)
def   UpperCAmelCase							(		A__     )							->					List[str]:
	random.seed(A__     )
	np.random.seed(A__     )
	torch.manual_seed(A__     )
	torch.cuda.manual_seed_all(A__     )
def   UpperCAmelCase							(		A__				,  A__				,  A__     )							->					Union[str, Any]:
	model.eval()
	_snake_case					:							Union[str, Any]					     =   next(model.parameters()     ).device
	_snake_case					:							Dict					     =   1
	_snake_case					:							List[Any]					     =   torch.zeros((eval_batch_size, context_len)				,  dtype=torch.long				,  device=A__     )
	_snake_case					:							Optional[Any]					     =   DataLoader(A__				,  shuffle=A__				,  batch_size=A__     )
	_snake_case					:							str					     =   torch.zeros(1				,  device=A__     )
	_snake_case					:							Optional[Any]					     =   0
	for batch in eval_dataloader:
		batch.to(A__     )
		# pad
		context.zero_()
		for i in range(A__     ):
			_snake_case					:							Any					     =   batch[i]
		_snake_case					:							int					     =   model(A__				,  labels=A__     )
		eval_loss += outputs[0].sum().item()
		nb_eval_examples += batch.size(0     )
	_snake_case					:							Optional[Any]					     =   eval_loss / nb_eval_examples
	_snake_case					:							Dict					     =   torch.exp(A__     )
	model.train()
	return perplexity
def   UpperCAmelCase							(		A__="gpt2"     )							->					int:
	_snake_case					:							Dict					     =   GPTaLMHeadModel.from_pretrained(A__				,  output_hidden_states=A__     )
	torch.save(model.state_dict()				,  model_name + """local.pt"""     )
	return model
def   UpperCAmelCase							(		A__				,  A__				,  A__     )							->					Dict:
	_snake_case					:							List[Any]					     =   copy.deepcopy(A__     )
	model.to(A__     )

	_snake_case					:							Optional[Any]					     =   ["""bias""", """LayerNorm.weight"""]
	_snake_case					:							int					     =   [
	    {
	        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
	        """weight_decay""": 0.0,
	    },
	    {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )], """weight_decay""": 0.0},
	]
	_snake_case					:							Any					     =   AdamW(A__				,  lr=5E-5				,  eps=1E-8     )
	_snake_case					:							str					     =   get_linear_schedule_with_warmup(A__				,  0				,  A__     )
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler
def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__     )							->					List[str]:
	_snake_case					:							Dict					     =   np.array(A__     ).mean()
	_snake_case					:							List[str]					     =   np.array(A__     ).std()
	_snake_case					:							Optional[int]					     =   (real_perps - avg) / std
	_snake_case					:							Union[str, Any]					     =   list(zip(A__				,  A__				,  A__     )     )
	joblib.dump(A__				,  A__     )
def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__				,  A__				,  A__				,  A__				,  A__="dev.jbl"				,  A__=recopy_gpta				,  )							->					Optional[int]:
	_snake_case					:							int					     =   []
	_snake_case					:							List[Any]					     =   []
	_snake_case					:							Dict					     =   []

	# Initialize the transformer model
	_snake_case					:							Optional[Any]					     =   copy.deepcopy(A__     )
	orig_model.to(device="""cpu"""     )
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	_snake_case     , _snake_case     , _snake_case					:							str					     =   recopy_model(A__				,  A__				,  A__     )

	for step in tqdm(range(A__     )     ):
		_snake_case					:							Tuple					     =   torch.zeros((1, context_len)				,  dtype=torch.long				,  device=A__     )
		_snake_case					:							Dict					     =   random.choice(A__     )
		_snake_case					:							Union[str, Any]					     =   random.randint(0				,  len(story[0]     ) - context_len - 1     )
		_snake_case					:							Tuple					     =   story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		_snake_case					:							Optional[int]					     =   model(A__				,  labels=A__     )
		_snake_case					:							List[Any]					     =   outputs[0]
		_snake_case					:							Optional[Any]					     =   compute_perplexity(A__				,  A__				,  A__     )
		model.train()
		lm_loss.backward()
		# Do LM backprop
		torch.nn.utils.clip_grad_norm_(model.parameters()				,  3.0     )
		lm_optimizer.step()
		lm_scheduler.step()  # Update learning rate schedule

		# Compute perplexity after back-propagating on the selected context
		_snake_case					:							str					     =   compute_perplexity(A__				,  A__				,  A__     )

		# Periodically save the stored (X, IG(X)) pairs
		if step % 10_00 == 0 and step > 1:
			intermittent_save(A__				,  A__				,  A__				,  A__     )

		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
		_snake_case     , _snake_case     , _snake_case					:							str					     =   recopy_model(A__				,  A__				,  A__     )

		past_perps.append(past_perp.item()     )
		real_perps.append(orig_perp - real_perp.item()     )
		contexts.append(np.array(context.cpu()     )     )

	intermittent_save(A__				,  A__				,  A__				,  A__     )
def   UpperCAmelCase							(		A__				,  A__="data/tokenized_stories_train_wikitext103.jbl"				,  A__=1_00				,  A__=10_26				,  A__=True     )							->					Optional[int]:
	_snake_case					:							Union[str, Any]					     =   joblib.load(A__     )
	print("""data loaded"""     )
	_snake_case					:							int					     =   []
	if trim:
		for i, example in enumerate(A__     ):
			if len(example[0]     ) > min_len:
				_snake_case					:							Union[str, Any]					     =   random.randint(0				,  len(example[0]     ) - context_len - 1     )
				objective_set.append(example[0, start : start + context_len]     )
			if len(A__     ) >= number:
				break
		_snake_case					:							str					     =   []
		for j in range(i + 1				,  len(A__     )     ):
			if len(data[j][0]     ) > min_len:
				train_data.append(data[j]     )
	else:
		_snake_case					:							Optional[int]					     =   data[0:number]
		_snake_case					:							int					     =   data[number:]

	joblib.dump(A__				,  """objective_set.jbl"""     )
	print("""objective set saved"""     )
	return train_data, objective_set
def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__				,  A__=50				,  A__="secondary_learner.pt"     )							->					Union[str, Any]:
	_snake_case					:							int					     =   torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )
	# We will use the first 512 pairs from our dataset as a test set for
	# our secondary learner and the rest to train
	_snake_case					:							Tuple					     =   train_dataset[:5_12]
	_snake_case					:							Dict					     =   train_dataset[5_12:]
	_snake_case					:							int					     =   DataLoader(A__				,  shuffle=A__				,  batch_size=A__     )
	_snake_case					:							Optional[Any]					     =   DataLoader(A__				,  shuffle=A__				,  batch_size=A__     )

	# secondary learner model set up
	_snake_case					:							Optional[int]					     =   nn.MSELoss()
	_snake_case					:							List[str]					     =   nn.MSELoss(reduction="""sum"""     )
	secondary_learner.to(A__     )
	_snake_case					:							List[str]					     =   torch.optim.Adam(secondary_learner.parameters()				,  lr=0.00001     )
	secondary_learner.train()

	# TODO in original code this is written as number of actual batches seen
	# not number of items seen but other places it is number of items instead.
	# improve consistency! changed this to epochs for clarity
	_snake_case					:							Any					     =   float("""inf"""     )
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(A__     )     ):
		_snake_case					:							List[Any]					     =   0.0
		secondary_learner.train()
		for step, batch in enumerate(A__     ):
			_snake_case					:							str					     =   batch[0].to(A__     )
			_snake_case					:							Optional[int]					     =   batch[1].to(A__     )
			_snake_case					:							Any					     =   secondary_learner(A__     )
			q_optimizer.zero_grad()
			_snake_case					:							Tuple					     =   loss(A__				,  real_q.float()     )
			q_loss.backward()
			q_optimizer.step()
			tr_q_loss += q_loss.item()

			# model trains fairly quickly so we won't wait for a full epoch
			# eval is triggered at eval_freq and end of epochs
			if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(A__     )):
				_snake_case					:							Union[str, Any]					     =   tr_q_loss / (step + 1)

				secondary_learner.eval()
				_snake_case					:							str					     =   0.0
				_snake_case					:							int					     =   0.0
				_snake_case					:							Dict					     =   []
				_snake_case					:							Optional[Any]					     =   []
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(A__     ):
					_snake_case					:							str					     =   batcha[0].to(A__     )
					_snake_case					:							List[Any]					     =   batcha[1].to(A__     )
					_snake_case					:							Optional[int]					     =   secondary_learner(A__     )
					q_lossa += test_loss(A__				,  A__     ).item()
					sum_qa += torch.sum(A__     ).item()
					for ei, i in enumerate(predicted_qa.cpu().detach().numpy()     ):
						predicted.append(i.item()     )
					for ei, i in enumerate(real_qa.cpu().detach().numpy()     ):
						actual.append(i.item()     )

				q_lossa /= len(A__     )
				print(
				    """Epoch: """				,  A__				,  """step: """				,  A__				,  """Avg. q:"""				,  sum_qa / len(A__     )				,  """Train Loss: """				,  A__				,  """Test Loss: """				,  A__				,  )
				if q_lossa < best_test_loss:
					joblib.dump((predicted, actual)				,  """pred_vs_actual.jbl"""     )
					torch.save(secondary_learner.state_dict()				,  A__     )
					_snake_case					:							Optional[int]					     =   q_lossa

			secondary_learner.train()
	return secondary_learner





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					super(SCREAMING_SNAKE_CASE__						,	self      ).__init__()
					_snake_case					:							int					     =   model.transformer.wte
					_snake_case					:							int					     =   copy.deepcopy(model.transformer.wte.weight      )
					_snake_case					:							Dict					     =   nn.Convad(self.embeddings.weight.size(1      )						,	2_56						,	3						,	padding=1      )
					_snake_case					:							Tuple					     =   nn.Sequential(nn.Linear(2_56						,	32      )						,	nn.Dropout(p=0.1      )						,	nn.Linear(32						,	32      )						,	nn.Linear(32						,	1      )      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							int					     =   torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE__      ).squeeze(1      ).transpose(1						,	2      )      )						,	2      )[0]
					_snake_case					:							Optional[Any]					     =   self.fc(SCREAMING_SNAKE_CASE__      )
					return qs.squeeze(1      )







				@classmethod
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   cls(SCREAMING_SNAKE_CASE__      )  # this calls __init__
					_snake_case					:							Union[str, Any]					     =   torch.load(SCREAMING_SNAKE_CASE__      )
					secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   model.transformer.wte
					_snake_case					:							Optional[Any]					     =   copy.deepcopy(model.transformer.wte.weight      )
					return secondary_learner










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


UpperCAmelCase_			      =  logging.get_logger(__name__)

UpperCAmelCase_			      =  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'''),
    ]
)

UpperCAmelCase_			      =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def   UpperCAmelCase							(		A__     )							->					Dict:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_snake_case					:							str					     =   model_type_to_module_name(A__     )

			_snake_case					:							Dict					     =   importlib.import_module(f'''.{module_name}'''				,  """transformers.models"""     )
			try:
				return getattr(A__				,  A__     )
			except AttributeError:
				continue

	for processor in PROCESSOR_MAPPING._extra_content.values():
		if getattr(A__				,  """__name__"""				,  A__     ) == 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.
	_snake_case					:							Optional[int]					     =   importlib.import_module("""transformers"""     )
	if hasattr(A__				,  A__     ):
		return getattr(A__				,  A__     )

	return None





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				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(SCREAMING_SNAKE_CASE__      )
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   kwargs.pop("""config"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   kwargs.pop("""trust_remote_code"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   True

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None

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

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

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

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

						# And check if the config contains the processor class.
						_snake_case					:							Optional[Any]					     =   getattr(SCREAMING_SNAKE_CASE__						,	"""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
						if hasattr(SCREAMING_SNAKE_CASE__						,	"""auto_map"""      ) and "AutoProcessor" in config.auto_map:
							_snake_case					:							List[str]					     =   config.auto_map["""AutoProcessor"""]

					if processor_class is not None:
						_snake_case					:							Any					     =   processor_class_from_name(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Optional[Any]					     =   processor_auto_map is not None
					_snake_case					:							Optional[int]					     =   processor_class is not None or type(SCREAMING_SNAKE_CASE__      ) in PROCESSOR_MAPPING
					_snake_case					:							List[Any]					     =   resolve_trust_remote_code(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

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

						try:
							return AutoFeatureExtractor.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_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 		__lowerCamelCase(				SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )





