



'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
							raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
							raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowerCAmelCase_     :       str								=						logging.get_logger(__name__)

lowerCAmelCase_     :       List[str]								=						'Hello, World!'
lowerCAmelCase_     :       int								=						'en_XX'
def 					UpperCAmelCase   (						A  :				str					,		A  :				str					,		A  :				bool				):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			Path('''data_bin'''				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(A				).parent				)					,		checkpoint_file=Path(A				).name					,		_name='''xmod_base'''					,		arch='''xmod_base'''					,		task='''multilingual_masked_lm'''					,		data_name_or_path=str(A				)					,		bpe='''sentencepiece'''					,		sentencepiece_model=str(Path(A				).parent / '''sentencepiece.bpe.model'''				)					,		src_dict=str(data_dir / '''dict.txt'''				)					,		)
							xmod.eval()  # disable dropout
							print(A				)

							SCREAMING_SNAKE_CASE					:      List[Any]     					=			xmod.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE					:      List[str]     					=			XmodConfig(
							    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings					,		hidden_size=xmod.cfg.model.encoder_embed_dim					,		num_hidden_layers=xmod.cfg.model.encoder_layers					,		num_attention_heads=xmod.cfg.model.encoder_attention_heads					,		intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim					,		max_position_embeddings=514					,		type_vocab_size=1					,		layer_norm_eps=1e-5					,		pre_norm=xmod.cfg.model.encoder_normalize_before					,		adapter_reduction_factor=getattr(xmod.cfg.model					,		'''bottleneck'''					,		2				)					,		adapter_layer_norm=xmod.cfg.model.adapter_layer_norm					,		adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm					,		ln_before_adapter=xmod.cfg.model.ln_before_adapter					,		languages=xmod.cfg.model.languages					,		)
							if classification_head:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

							print('''Our X-MOD config:'''					,		A				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			XmodForSequenceClassification(A				) if classification_head else XmodForMaskedLM(A				)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							SCREAMING_SNAKE_CASE					:      int     					=			xmod_sent_encoder.embed_tokens.weight
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			xmod_sent_encoder.embed_positions.weight
							SCREAMING_SNAKE_CASE					:      int     					=			torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c xmod doesn't use them.

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			xmod_sent_encoder.layernorm_embedding.weight
							SCREAMING_SNAKE_CASE					:      int     					=			xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers				):
														# Encoder: start of layer
														SCREAMING_SNAKE_CASE					:      List[str]     					=			model.roberta.encoder.layer[i]
														SCREAMING_SNAKE_CASE					:      Tuple     					=			xmod_sent_encoder.layers[i]

														# self attention
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			layer.attention.self
														if not (
														    xmod_layer.self_attn.k_proj.weight.data.shape
														    == xmod_layer.self_attn.q_proj.weight.data.shape
														    == xmod_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)				)
														):
																					raise AssertionError('''Dimensions of self-attention weights do not match.'''				)

														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			xmod_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE					:      str     					=			xmod_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			xmod_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			xmod_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			xmod_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE					:      int     					=			xmod_layer.self_attn.v_proj.bias

														# self-attention output
														SCREAMING_SNAKE_CASE					:      int     					=			layer.attention.output
														if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
																					raise AssertionError('''Dimensions of self-attention output weights do not match.'''				)
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			xmod_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE					:      Any     					=			xmod_layer.self_attn.out_proj.bias
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			xmod_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Dict     					=			xmod_layer.self_attn_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE					:      Any     					=			layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
														SCREAMING_SNAKE_CASE					:      int     					=			xmod_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Dict     					=			xmod_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of feed-forward weights do not match.'''				)
														SCREAMING_SNAKE_CASE					:      int     					=			xmod_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			xmod_layer.fca.bias
														SCREAMING_SNAKE_CASE					:      int     					=			xmod_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			xmod_layer.adapter_layer_norm.weight
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			xmod_layer.adapter_layer_norm.bias

														if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
																					raise AssertionError('''Lists of language adapters do not match.'''				)
														for lang_code, adapter in xmod_layer.adapter_modules.items():
																					SCREAMING_SNAKE_CASE					:      Dict     					=			bert_output.adapter_modules[lang_code]
																					SCREAMING_SNAKE_CASE					:      str     					=			xmod_layer.adapter_modules[lang_code]
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			from_adapter.fca.weight
																					SCREAMING_SNAKE_CASE					:      str     					=			from_adapter.fca.bias
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			from_adapter.fca.weight
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														SCREAMING_SNAKE_CASE					:      Tuple     					=			xmod_sent_encoder.layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Tuple     					=			xmod_sent_encoder.layer_norm.bias

							if classification_head:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			xmod.model.classification_heads['''mnli'''].dense.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			xmod.model.classification_heads['''mnli'''].dense.bias
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			xmod.model.classification_heads['''mnli'''].out_proj.weight
														SCREAMING_SNAKE_CASE					:      List[str]     					=			xmod.model.classification_heads['''mnli'''].out_proj.bias
							else:
														# LM Head
														SCREAMING_SNAKE_CASE					:      Any     					=			xmod.model.encoder.lm_head.dense.weight
														SCREAMING_SNAKE_CASE					:      Tuple     					=			xmod.model.encoder.lm_head.dense.bias
														SCREAMING_SNAKE_CASE					:      int     					=			xmod.model.encoder.lm_head.layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			xmod.model.encoder.lm_head.layer_norm.bias
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			xmod.model.encoder.lm_head.weight
														SCREAMING_SNAKE_CASE					:      Any     					=			xmod.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			xmod.encode(A				).unsqueeze(0				)  # batch of size 1
							model.roberta.set_default_language(A				)

							SCREAMING_SNAKE_CASE					:      Dict     					=			model(A				)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(A				)				)
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			xmod.model(A					,		lang_id=[SAMPLE_LANGUAGE]				)[0]
							print(our_output.shape					,		their_output.shape				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.max(torch.abs(our_output - their_output				)				).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
							SCREAMING_SNAKE_CASE					:      Dict     					=			torch.allclose(A					,		A					,		atol=1e-3				)
							print('''Do both models output the same tensors?'''					,		'''🔥''' if success else '''💩'''				)
							if not success:
														raise Exception('''Something went wRoNg'''				)

							Path(A				).mkdir(parents=A					,		exist_ok=A				)
							print(F"""Saving model to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(A				)


if __name__ == "__main__":
							lowerCAmelCase_     :       Dict								=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
							)
							lowerCAmelCase_     :       List[Any]								=						parser.parse_args()
							convert_xmod_checkpoint_to_pytorch(
							    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)









'''simple docstring'''



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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,
)


lowerCAmelCase_     :       str								=						logging.get_logger(__name__)

lowerCAmelCase_     :       List[Any]								=						OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

lowerCAmelCase_     :       List[str]								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 					UpperCAmelCase   (						A  :				str				):
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			model_type_to_module_name(A				)

																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			importlib.import_module(F""".{module_name}"""					,		'''transformers.models'''				)
																					try:
																												return getattr(A					,		A				)
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(A					,		'''__name__'''					,		A				) == class_name:
																					return extractor

    # 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					:      List[str]     					=			importlib.import_module('''transformers'''				)
							if hasattr(A					,		A				):
														return getattr(A					,		A				)

							return None
def 					UpperCAmelCase   (						A  :				Union[str, os.PathLike]					,		A  :				Optional[Union[str, os.PathLike]] = None					,		A  :				bool = False					,		A  :				bool = False					,		A  :				Optional[Dict[str, str]] = None					,		A  :				Optional[Union[bool, str]] = None					,		A  :				Optional[str] = None					,		A  :				bool = False					,		**A  :				List[str]					,		):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_file_from_repo(
							    A					,		A					,		cache_dir=A					,		force_download=A					,		resume_download=A					,		proxies=A					,		use_auth_token=A					,		revision=A					,		local_files_only=A					,		)
							if resolved_config_file is None:
														logger.info(
														    '''Could not locate the image processor configuration file, will try to use the model config instead.'''				)
														return {}

							with open(A					,		encoding='''utf-8'''				) as reader:
														return json.load(A				)






class 	lowerCamelCase_							:







						def __init__(  self   :      Tuple				):

													"""simple docstring"""
													raise EnvironmentError(
													    '''AutoImageProcessor is designed to be instantiated '''
													    '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''				)







						@classmethod
						@replace_list_option_in_docstrings(lowerCAmelCase__				)
						def 					__lowercase    (  cls   :      int				,    lowerCAmelCase__   :      Optional[int]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			kwargs.pop('''config'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			kwargs.pop('''trust_remote_code'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			True

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			config_dict.get('''image_processor_type'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			None
													if "AutoImageProcessor" in config_dict.get('''auto_map'''				,    {}				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			config_dict['''auto_map''']['''AutoImageProcessor''']

													# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
													# and if so, infer the image processor class from there.
													if image_processor_class is None and image_processor_auto_map is None:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			config_dict.pop('''feature_extractor_type'''				,    lowerCAmelCase__				)
																				if feature_extractor_class is not None:
																											logger.warning(
																											    '''Could not find image processor class in the image processor config or the model config. Loading'''
																											    ''' based on pattern matching with the model\'s feature extractor configuration.'''				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			feature_extractor_class.replace('''FeatureExtractor'''				,    '''ImageProcessor'''				)
																				if "AutoFeatureExtractor" in config_dict.get('''auto_map'''				,    {}				):
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			config_dict['''auto_map''']['''AutoFeatureExtractor''']
																											SCREAMING_SNAKE_CASE					:      str     					=			feature_extractor_auto_map.replace('''FeatureExtractor'''				,    '''ImageProcessor'''				)
																											logger.warning(
																											    '''Could not find image processor auto map in the image processor config or the model config.'''
																											    ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''				)

        # If we don't find the image processor class in the image processor config, let's try the model config.
													if image_processor_class is None and image_processor_auto_map is None:
																				if not isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			AutoConfig.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																				# It could be in `config.image_processor_type``
																				SCREAMING_SNAKE_CASE					:      Any     					=			getattr(lowerCAmelCase__				,    '''image_processor_type'''				,    lowerCAmelCase__				)
																				if hasattr(lowerCAmelCase__				,    '''auto_map'''				) and "AutoImageProcessor" in config.auto_map:
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			config.auto_map['''AutoImageProcessor''']

													if image_processor_class is not None:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processor_class_from_name(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_processor_auto_map is not None
													SCREAMING_SNAKE_CASE					:      str     					=			image_processor_class is not None or type(lowerCAmelCase__				) in IMAGE_PROCESSOR_MAPPING
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			resolve_trust_remote_code(
													    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

													if has_remote_code and trust_remote_code:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_class_from_dynamic_module(
																				    lowerCAmelCase__				,    lowerCAmelCase__				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			kwargs.pop('''code_revision'''				,    lowerCAmelCase__				)
																				if os.path.isdir(lowerCAmelCase__				):
																											image_processor_class.register_for_auto_class()
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													elif image_processor_class is not None:
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													# Last try: we use the IMAGE_PROCESSOR_MAPPING.
													elif type(lowerCAmelCase__				) in IMAGE_PROCESSOR_MAPPING:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			IMAGE_PROCESSOR_MAPPING[type(lowerCAmelCase__				)]
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)

													raise ValueError(
													    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
													    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
													    F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()				)}"""				)







						@staticmethod
						def 					__lowercase    (  lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase__				,    lowerCAmelCase__				)




