





"""simple docstring"""


import argparse
import os
import shutil

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




def 					lowercase__    (					lowercase_			)					->		List[str]:




						"""simple docstring"""

						_UpperCamelCase      :	List[Any]						=       args.pruning_method
						_UpperCamelCase      :	int						=       args.threshold

						_UpperCamelCase      :	Optional[Any]						=       args.model_name_or_path.rstrip("/"			)
						_UpperCamelCase      :	str						=       args.target_model_path

						print(F'''Load fine-pruned model from {model_name_or_path}'''			)
						_UpperCamelCase      :	Dict						=       torch.load(os.path.join(lowercase_		,"pytorch_model.bin"			)			)
						_UpperCamelCase      :	Tuple						=       {}

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

						if target_model_path is None:
												_UpperCamelCase      :	int						=       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__				  =   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__				  =   parser.parse_args()

		main(args)







"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					"yolos"

  def __init__(		self   :       Dict					,   __a   :       Optional[Any]=768					,   __a   :       List[Any]=12					,   __a   :       Any=12					,   __a   :       List[Any]=3072					,   __a   :       Optional[int]="gelu"					,   __a   :       Dict=0.0					,   __a   :       Optional[Any]=0.0					,   __a   :       Any=0.02					,   __a   :       Optional[int]=1e-1_2					,   __a   :       List[Any]=[512, 864]					,   __a   :       List[str]=16					,   __a   :       str=3					,   __a   :       Optional[Any]=True					,   __a   :       Optional[Any]=100					,   __a   :       List[str]=True					,   __a   :       Any=False					,   __a   :       List[str]=1					,   __a   :       str=5					,   __a   :       Optional[Any]=2					,   __a   :       Tuple=5					,   __a   :       Any=2					,   __a   :       Union[str, Any]=0.1					,   **__a   :       List[str]					,   )		->				List[str]:
        super().__init__(**__a	)

        _UpperCamelCase      :	Dict						=       hidden_size
        _UpperCamelCase      :	Any						=       num_hidden_layers
        _UpperCamelCase      :	str						=       num_attention_heads
        _UpperCamelCase      :	Dict						=       intermediate_size
        _UpperCamelCase      :	List[str]						=       hidden_act
        _UpperCamelCase      :	List[str]						=       hidden_dropout_prob
        _UpperCamelCase      :	str						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Tuple						=       initializer_range
        _UpperCamelCase      :	List[str]						=       layer_norm_eps
        _UpperCamelCase      :	Tuple						=       image_size
        _UpperCamelCase      :	Tuple						=       patch_size
        _UpperCamelCase      :	Dict						=       num_channels
        _UpperCamelCase      :	Any						=       qkv_bias
        _UpperCamelCase      :	str						=       num_detection_tokens
        _UpperCamelCase      :	str						=       use_mid_position_embeddings
        _UpperCamelCase      :	List[str]						=       auxiliary_loss
        # Hungarian matcher
        _UpperCamelCase      :	List[Any]						=       class_cost
        _UpperCamelCase      :	int						=       bbox_cost
        _UpperCamelCase      :	Optional[int]						=       giou_cost
        # Loss coefficients
        _UpperCamelCase      :	List[Any]						=       bbox_loss_coefficient
        _UpperCamelCase      :	str						=       giou_loss_coefficient
        _UpperCamelCase      :	Dict						=       eos_coefficient





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]	     =					version.parse("1.11"   )

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				float:
        return 1e-4


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				int:
        return 12
