




'''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__						:       Dict										=						logging.get_logger(__name__)

lowerCAmelCase__						:       int										=						{
    '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     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """yolos"""







						def __init__(							self					:      str			,			snake_case_					:      Optional[Any]=7_6_8			,			snake_case_					:      int=1_2			,			snake_case_					:      Any=1_2			,			snake_case_					:      Union[str, Any]=3_0_7_2			,			snake_case_					:      List[Any]="gelu"			,			snake_case_					:      Dict=0.0			,			snake_case_					:      str=0.0			,			snake_case_					:      str=0.0_2			,			snake_case_					:      int=1e-12			,			snake_case_					:      Union[str, Any]=[5_1_2, 8_6_4]			,			snake_case_					:      Any=1_6			,			snake_case_					:      str=3			,			snake_case_					:      Optional[int]=True			,			snake_case_					:      str=1_0_0			,			snake_case_					:      Tuple=True			,			snake_case_					:      Optional[int]=False			,			snake_case_					:      str=1			,			snake_case_					:      Union[str, Any]=5			,			snake_case_					:      str=2			,			snake_case_					:      Dict=5			,			snake_case_					:      Optional[Any]=2			,			snake_case_					:      List[Any]=0.1			,			**snake_case_					:      Tuple			,			):



								'''simple docstring'''



								super().__init__(**__A  )

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





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    version.parse("""1.11"""       )







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]  )







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								return 1e-4







						@property
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								return 1_2






'''simple docstring'''


import argparse
import os
import shutil

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




def 	_a       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       int       						=  args.pruning_method
		snake_case__						:       Dict       						=  args.threshold

		snake_case__						:       str       						=  args.model_name_or_path.rstrip('''/'''     )
		snake_case__						:       int       						=  args.target_model_path

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

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

		if target_model_path is None:
				snake_case__						:       Optional[int]       						=  os.path.join(
				    os.path.dirname(__lowerCAmelCase     )   ,							F"""bertarized_{os.path.basename(__lowerCAmelCase     )}"""     )

		if not os.path.isdir(__lowerCAmelCase     ):
				shutil.copytree(__lowerCAmelCase   ,							__lowerCAmelCase     )
				print(F"""\nCreated folder {target_model_path}"""     )

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


if __name__ == "__main__":
					lowerCAmelCase__						:       str										=						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__						:       Optional[Any]										=						parser.parse_args()

					main(args)



