

'''simple docstring'''


import copy
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
from ..auto import CONFIG_MAPPING


lowerCAmelCase__						:       str										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Optional[int]										=						{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """conditional_detr"""
						__UpperCAmelCase			     =    ["""past_key_values"""]
						__UpperCAmelCase			     =    {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}







						def __init__(							self					:      Union[str, Any]			,			snake_case_					:      str=True			,			snake_case_					:      Optional[Any]=None			,			snake_case_					:      Optional[int]=3			,			snake_case_					:      str=3_0_0			,			snake_case_					:      int=6			,			snake_case_					:      List[str]=2_0_4_8			,			snake_case_					:      Optional[Any]=8			,			snake_case_					:      Optional[Any]=6			,			snake_case_					:      Dict=2_0_4_8			,			snake_case_					:      List[str]=8			,			snake_case_					:      Dict=0.0			,			snake_case_					:      Any=0.0			,			snake_case_					:      int=True			,			snake_case_					:      Any="relu"			,			snake_case_					:      Union[str, Any]=2_5_6			,			snake_case_					:      List[str]=0.1			,			snake_case_					:      str=0.0			,			snake_case_					:      List[Any]=0.0			,			snake_case_					:      Union[str, Any]=0.0_2			,			snake_case_					:      int=1.0			,			snake_case_					:      Optional[int]=False			,			snake_case_					:      Optional[Any]="sine"			,			snake_case_					:      Tuple="resnet50"			,			snake_case_					:      int=True			,			snake_case_					:      Optional[Any]=False			,			snake_case_					:      int=2			,			snake_case_					:      List[Any]=5			,			snake_case_					:      Dict=2			,			snake_case_					:      str=1			,			snake_case_					:      Optional[Any]=1			,			snake_case_					:      Optional[Any]=2			,			snake_case_					:      Tuple=5			,			snake_case_					:      Tuple=2			,			snake_case_					:      int=0.2_5			,			**snake_case_					:      Optional[Any]			,			):



								'''simple docstring'''



								if backbone_config is not None and use_timm_backbone:
										raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''  )

								if not use_timm_backbone:
										if backbone_config is None:
												logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''  )
												snake_case__						:       Any       						=  CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']  )
										elif isinstance(snake_case_			,			snake_case_  ):
												snake_case__						:       Union[str, Any]       						=  backbone_config.get('''model_type'''  )
												snake_case__						:       Optional[int]       						=  CONFIG_MAPPING[backbone_model_type]
												snake_case__						:       List[Any]       						=  config_class.from_dict(snake_case_  )

								snake_case__						:       Union[str, Any]       						=  use_timm_backbone
								snake_case__						:       Union[str, Any]       						=  backbone_config
								snake_case__						:       Dict       						=  num_channels
								snake_case__						:       Union[str, Any]       						=  num_queries
								snake_case__						:       Optional[Any]       						=  d_model
								snake_case__						:       Any       						=  encoder_ffn_dim
								snake_case__						:       Optional[Any]       						=  encoder_layers
								snake_case__						:       Optional[Any]       						=  encoder_attention_heads
								snake_case__						:       Optional[int]       						=  decoder_ffn_dim
								snake_case__						:       Dict       						=  decoder_layers
								snake_case__						:       str       						=  decoder_attention_heads
								snake_case__						:       str       						=  dropout
								snake_case__						:       Optional[int]       						=  attention_dropout
								snake_case__						:       str       						=  activation_dropout
								snake_case__						:       Tuple       						=  activation_function
								snake_case__						:       Union[str, Any]       						=  init_std
								snake_case__						:       Union[str, Any]       						=  init_xavier_std
								snake_case__						:       str       						=  encoder_layerdrop
								snake_case__						:       Union[str, Any]       						=  decoder_layerdrop
								snake_case__						:       Tuple       						=  encoder_layers
								snake_case__						:       Any       						=  auxiliary_loss
								snake_case__						:       List[str]       						=  position_embedding_type
								snake_case__						:       Dict       						=  backbone
								snake_case__						:       Optional[int]       						=  use_pretrained_backbone
								snake_case__						:       Optional[int]       						=  dilation
								# Hungarian matcher
								snake_case__						:       List[Any]       						=  class_cost
								snake_case__						:       List[Any]       						=  bbox_cost
								snake_case__						:       Optional[Any]       						=  giou_cost
								# Loss coefficients
								snake_case__						:       str       						=  mask_loss_coefficient
								snake_case__						:       int       						=  dice_loss_coefficient
								snake_case__						:       Any       						=  cls_loss_coefficient
								snake_case__						:       List[str]       						=  bbox_loss_coefficient
								snake_case__						:       str       						=  giou_loss_coefficient
								snake_case__						:       Dict       						=  focal_alpha
								super().__init__(is_encoder_decoder=snake_case_			,			**snake_case_  )







						@property
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								return self.encoder_attention_heads







						@property
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								return self.d_model







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  copy.deepcopy(self.__dict__  )
								if self.backbone_config is not None:
										snake_case__						:       int       						=  self.backbone_config.to_dict()
								snake_case__						:       str       						=  self.__class__.model_type
								return output





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




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







						@property
						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



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







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								return 1e-5







						@property
						def       __magic_name__							(							self					:      int  ):



								'''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)



