
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,
)


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							: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'),
    ]
)

SCREAMING_SNAKE_CASE							:Optional[int]							 =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def 						UpperCAmelCase			(	a_						)		->	Tuple:







				"""simple docstring"""



				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												__A   			= model_type_to_module_name(a_						)

												__A   			= 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.
				__A   			= importlib.import_module("transformers"						)
				if hasattr(a_		,   a_						):
								return getattr(a_		,   a_						)

				return None


def 						UpperCAmelCase			(	a_		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   **a_		,   )		->	List[str]:







				"""simple docstring"""



				__A   			= 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 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: Optional[int] ):
					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(A )
	def 				UpperCamelCase_  (       cls					: Union[str, Any]							,A					: Tuple							,**A					: Optional[int] ):
					__A   			= kwargs.pop("config"							,A )
					__A   			= kwargs.pop("trust_remote_code"							,A )
					__A   			= True

					__A			,							__A   			= ImageProcessingMixin.get_image_processor_dict(A							,**A )
					__A   			= config_dict.get("image_processor_type"							,A )
					__A   			= None
					if "AutoImageProcessor" in config_dict.get("auto_map"							,{} ):
									__A   			= 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:
									__A   			= config_dict.pop("feature_extractor_type"							,A )
									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." )
													__A   			= feature_extractor_class.replace("FeatureExtractor"							,"ImageProcessor" )
									if "AutoFeatureExtractor" in config_dict.get("auto_map"							,{} ):
													__A   			= config_dict["auto_map"]["AutoFeatureExtractor"]
													__A   			= 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(A							,A ):
													__A   			= AutoConfig.from_pretrained(A							,**A )
									# It could be in `config.image_processor_type``
									__A   			= getattr(A							,"image_processor_type"							,A )
									if hasattr(A							,"auto_map" ) and "AutoImageProcessor" in config.auto_map:
													__A   			= config.auto_map["AutoImageProcessor"]

					if image_processor_class is not None:
									__A   			= image_processor_class_from_name(A )

					__A   			= image_processor_auto_map is not None
					__A   			= image_processor_class is not None or type(A ) in IMAGE_PROCESSOR_MAPPING
					__A   			= resolve_trust_remote_code(
					    A							,A							,A							,A )

					if has_remote_code and trust_remote_code:
									__A   			= get_class_from_dynamic_module(
									    A							,A							,**A )
									__A   			= kwargs.pop("code_revision"							,A )
									if os.path.isdir(A ):
													image_processor_class.register_for_auto_class()
									return image_processor_class.from_dict(A							,**A )
					elif image_processor_class is not None:
									return image_processor_class.from_dict(A							,**A )
					# Last try: we use the IMAGE_PROCESSOR_MAPPING.
					elif type(A ) in IMAGE_PROCESSOR_MAPPING:
									__A   			= IMAGE_PROCESSOR_MAPPING[type(A )]
									return image_processor_class.from_dict(A							,**A )

					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 				UpperCamelCase_  (       A					: Optional[Any]							,A					: Any ):
					IMAGE_PROCESSOR_MAPPING.register(A							,A )


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_snake_case		=			args.pruning_method
			_snake_case		=			args.threshold

			_snake_case		=			args.model_name_or_path.rstrip('/' )
			_snake_case		=			args.target_model_path

			print(f'''Load fine-pruned model from {model_name_or_path}''' )
			_snake_case		=			torch.load(os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			_snake_case		=			{}

			for name, tensor in model.items():
						if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "classifier" in name or "qa_output" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "bias" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						else:
									if pruning_method == "magnitude":
												_snake_case		=			MagnitudeBinarizer.apply(inputs=__lowercase       ,					threshold=__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "topK":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			TopKBinarizer.apply(__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "sigmoied_threshold":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			ThresholdBinarizer.apply(__lowercase       ,					__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "l0":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case	,					_snake_case		=			-0.1, 1.1
												_snake_case		=			torch.sigmoid(__lowercase )
												_snake_case		=			s * (r - l) + l
												_snake_case		=			s_bar.clamp(min=0.0       ,					max=1.0 )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									else:
												raise ValueError('Unknown pruning method' )

			if target_model_path is None:
						_snake_case		=			os.path.join(
						    os.path.dirname(__lowercase )       ,					f'''bertarized_{os.path.basename(__lowercase )}''' )

			if not os.path.isdir(__lowercase ):
						shutil.copytree(__lowercase       ,					__lowercase )
						print(f'''\nCreated folder {target_model_path}''' )

			torch.save(__lowercase       ,					os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			print('\nPruned model saved! See you later!' )


if __name__ == "__main__":
			_lowerCamelCase       :				Dict       =			argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
			        ''' sigmoied_threshold = Soft movement pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
			        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
			        '''Not needed for `l0`'''
			    ),
			)
			parser.add_argument(
			    '''--model_name_or_path''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)
			parser.add_argument(
			    '''--target_model_path''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			_lowerCamelCase       :				int       =			parser.parse_args()

			main(args)