



'''simple docstring'''



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowerCAmelCase_     :       List[Any]								=						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowerCAmelCase_     :       int								=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 					UpperCAmelCase   (						A  :				Any					,		A  :				Optional[int]					,		A  :				Tuple					,		A  :				Any					,		A  :				Dict				):
							for attribute in key.split('''.'''				):
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			getattr(A					,		A				)

							if weight_type is not None:
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			getattr(A					,		A				).shape
							else:
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			hf_pointer.shape

							assert hf_shape == value.shape, (
							    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
							    F""" {value.shape} for {full_name}"""
							)

							if weight_type == "weight":
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE					:      List[str]     					=			value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE					:      Any     					=			value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			value
							else:
														SCREAMING_SNAKE_CASE					:      Any     					=			value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""				)
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				List[Any]				):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE					:      int     					=			hf_model.feature_extractor
							SCREAMING_SNAKE_CASE					:      Tuple     					=			hf_model.adapter

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE					:      List[str]     					=			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A					,		A					,		A					,		A					,		hf_model.config.feat_extract_norm == '''group'''					,		)
																					SCREAMING_SNAKE_CASE					:      int     					=			True
														elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']				):
																					load_adapter(A					,		A					,		A					,		A				)
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
																																			SCREAMING_SNAKE_CASE					:      Dict     					=			True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			name.split(A				)[0].split('''.'''				)[-2]
																																										SCREAMING_SNAKE_CASE					:      Tuple     					=			mapped_key.replace('''*'''					,		A				)
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE					:      Optional[int]     					=			'''weight_g'''
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''weight_v'''
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE					:      Any     					=			'''bias'''
																																			elif "weight" in name:
																																										SCREAMING_SNAKE_CASE					:      int     					=			'''weight'''
																																			else:
																																										SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
																																			set_recursively(A					,		A					,		A					,		A					,		A				)
																												continue
														if not is_used:
																					unused_weights.append(A				)

							logger.warning(F"""Unused weights: {unused_weights}"""				)
def 					UpperCAmelCase   (						A  :				int					,		A  :				Tuple					,		A  :				int					,		A  :				Any					,		A  :				Dict				):
							SCREAMING_SNAKE_CASE					:      str     					=			full_name.split('''conv_layers.'''				)[-1]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			name.split('''.'''				)
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(items[0]				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			int(items[1]				)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																					)
																					SCREAMING_SNAKE_CASE					:      Dict     					=			value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""				)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																					)
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""				)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																					    " found."
																					)
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																					)
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(A				)
def 					UpperCAmelCase   (						A  :				Optional[int]					,		A  :				Tuple					,		A  :				Dict					,		A  :				List[Any]				):
							SCREAMING_SNAKE_CASE					:      str     					=			full_name.split('''adaptor.'''				)[-1]
							SCREAMING_SNAKE_CASE					:      Any     					=			name.split('''.'''				)

							if items[1].isdigit():
														SCREAMING_SNAKE_CASE					:      Any     					=			int(items[1]				)
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																												SCREAMING_SNAKE_CASE					:      Optional[int]     					=			value
																												logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""				)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																												SCREAMING_SNAKE_CASE					:      Optional[int]     					=			value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																												SCREAMING_SNAKE_CASE					:      List[Any]     					=			value
																												logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""				)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																												SCREAMING_SNAKE_CASE					:      str     					=			value
																												logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""				)
							elif isinstance(A					,		A				):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			value
																					logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""				)
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			value
																					logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""				)
							else:
														unused_weights.append(A				)
def 					UpperCAmelCase   (						A  :				Dict				):
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			emb.weight.shape
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			nn.Linear(A					,		A					,		bias=A				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			emb.weight.data
							return lin_layer
@torch.no_grad()
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				Tuple					,		A  :				Union[str, Any]					,		A  :				Dict					,		A  :				Union[str, Any]					,		A  :				Any					,		A  :				List[Any]					,		A  :				str					,		A  :				Optional[int]					,		A  :				int					,		A  :				Tuple					,		):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			WavaVecaConfig.from_pretrained(
							    A					,		add_adapter=A					,		adapter_stride=A					,		adapter_kernel_size=A					,		use_auth_token=A					,		output_hidden_size=A					,		)
							SCREAMING_SNAKE_CASE					:      Any     					=			MBartConfig.from_pretrained(A				)

							# load model
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]					,		arg_overrides={
							        '''config_yaml''': config_yaml_path,
							        '''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				),
							        '''w2v_path''': checkpoint_path,
							        '''load_pretrained_decoder_from''': None,
							    }					,		)
							SCREAMING_SNAKE_CASE					:      Dict     					=			model[0].eval()

							# load feature extractor
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			WavaVecaFeatureExtractor.from_pretrained(A					,		use_auth_token=A				)

							# set weights for wav2vec2 encoder
							SCREAMING_SNAKE_CASE					:      int     					=			WavaVecaModel(A				)

							recursively_load_weights_wavaveca(model.encoder					,		A				)

							# load decoder weights
							SCREAMING_SNAKE_CASE					:      List[str]     					=			MBartForCausalLM(A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Any     					=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,		strict=A				)
							logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""				)
							logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			SpeechEncoderDecoderModel(encoder=A					,		decoder=A				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			False

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			MBartaaTokenizer(A				)
							tokenizer.save_pretrained(A				)

							SCREAMING_SNAKE_CASE					:      int     					=			hf_wavavec.config.to_dict()
							SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.pad_token_id
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer.bos_token_id
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.eos_token_id
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''mbart50'''
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''wav2vec2'''

							SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.eos_token_id
							SCREAMING_SNAKE_CASE					:      int     					=			250004
							SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.eos_token_id

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			SpeechEncoderDecoderConfig.from_dict(A				)

							hf_wavavec.save_pretrained(A				)
							feature_extractor.save_pretrained(A				)


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[Any]								=						argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
							parser.add_argument(
							    '--encoder_config_path',
							    default='facebook/wav2vec2-xls-r-1b',
							    type=str,
							    help='Path to hf encoder wav2vec2 checkpoint config',
							)
							parser.add_argument(
							    '--decoder_config_path',
							    default='facebook/mbart-large-50-one-to-many-mmt',
							    type=str,
							    help='Path to hf decoder checkpoint config',
							)
							parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
							parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
							parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
							parser.add_argument('--encoder_output_dim', default=1024, type=int, help='encoder output dim')
							parser.add_argument('--start_token_id', default=25_0004, type=int, help='`decoder_start_token_id` of model config')

							lowerCAmelCase_     :       List[str]								=						parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)









'''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__				)




