

'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Optional[Any]        =       args.serialization_dir
					UpperCAmelCase		:							List[str]        =       args.pruning_method
					UpperCAmelCase		:							int        =       args.threshold

					UpperCAmelCase		:							Optional[int]        =       torch.load(os.path.join(__magic_name__			,   "pytorch_model.bin"							)			,   map_location="cpu"							)

					UpperCAmelCase		:							Any        =       0  # Number of remaining (not pruned) params in the encoder
					UpperCAmelCase		:							int        =       0  # Number of params in the encoder

					print("name".ljust(60			,   " "							)			,   "Remaining Weights %"			,   "Remaining Weight"							)
					for name, param in st.items():
										if "encoder" not in name:
															continue

										if "mask_scores" in name:
															if pruning_method == "topK":
																				UpperCAmelCase		:							List[Any]        =       TopKBinarizer.apply(__magic_name__			,   __magic_name__							).sum().item()
															elif pruning_method == "sigmoied_threshold":
																				UpperCAmelCase		:							int        =       ThresholdBinarizer.apply(__magic_name__			,   __magic_name__			,   __magic_name__							).sum().item()
															elif pruning_method == "l0":
																				UpperCAmelCase		:							Optional[Any]        =       -0.1, 1.1
																				UpperCAmelCase		:							str        =       torch.sigmoid(__magic_name__							)
																				UpperCAmelCase		:							Optional[int]        =       s * (r - l) + l
																				UpperCAmelCase		:							List[Any]        =       s_bar.clamp(min=0.0			,   max=1.0							)
																				UpperCAmelCase		:							int        =       (mask > 0.0).sum().item()
															else:
																				raise ValueError("Unknown pruning method"							)
															remaining_count += mask_ones
															print(name.ljust(60			,   " "							)			,   str(round(100 * mask_ones / param.numel()			,   3							)							).ljust(20			,   " "							)			,   str(__magic_name__							)							)
										else:
															encoder_count += param.numel()
															if "bias" in name or "LayerNorm" in name:
																				remaining_count += param.numel()

					print(""							)
					print("Remaining Weights (global) %: "			,   100 * remaining_count / encoder_count							)


if __name__ == "__main__":
				a       :      Tuple  	=			argparse.ArgumentParser()

				parser.add_argument(
				    "--pruning_method",
				    choices=["l0", "topK", "sigmoied_threshold"],
				    type=str,
				    required=True,
				    help=(
				        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
				        " pruning)"
				    ),
				)
				parser.add_argument(
				    "--threshold",
				    type=float,
				    required=False,
				    help=(
				        "For `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(
				    "--serialization_dir",
				    type=str,
				    required=True,
				    help="Folder containing the model that was previously fine-pruned",
				)

				a       :      Optional[int]  	=			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


a       :      List[Any]  	=			logging.get_logger(__name__)

a       :      Dict  	=			{
    "apple/mobilevitv2-1.0": "https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json",
}







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							"mobilevitv2"







		def __init__(			self   ,      snake_case=3   ,      snake_case=2_5_6   ,      snake_case=2   ,      snake_case=2.0   ,      snake_case="swish"   ,      snake_case=3   ,      snake_case=3_2   ,      snake_case=0.1   ,      snake_case=0.02   ,      snake_case=1e-5   ,      snake_case=5_1_2   ,      snake_case=[6, 1_2, 1_8]   ,      snake_case=0.1   ,      snake_case=2_5_5   ,      snake_case=[2, 4, 3]   ,      snake_case=[1_2_8, 1_9_2, 2_5_6]   ,      snake_case=1.0   ,      snake_case=2   ,      snake_case=0.0   ,      snake_case=0.0   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(**snake_case      )

							UpperCAmelCase		:							Tuple        =       num_channels
							UpperCAmelCase		:							Optional[int]        =       image_size
							UpperCAmelCase		:							Optional[int]        =       patch_size
							UpperCAmelCase		:							Optional[int]        =       expand_ratio
							UpperCAmelCase		:							List[Any]        =       hidden_act
							UpperCAmelCase		:							Optional[Any]        =       conv_kernel_size
							UpperCAmelCase		:							str        =       output_stride
							UpperCAmelCase		:							Optional[Any]        =       initializer_range
							UpperCAmelCase		:							Optional[Any]        =       layer_norm_eps
							UpperCAmelCase		:							List[str]        =       n_attn_blocks
							UpperCAmelCase		:							Any        =       base_attn_unit_dims
							UpperCAmelCase		:							str        =       width_multiplier
							UpperCAmelCase		:							Optional[int]        =       ffn_multiplier
							UpperCAmelCase		:							List[Any]        =       ffn_dropout
							UpperCAmelCase		:							List[str]        =       attn_dropout
							UpperCAmelCase		:							Optional[Any]        =       classifier_dropout_prob

							# decode head attributes for semantic segmentation
							UpperCAmelCase		:							Optional[Any]        =       aspp_out_channels
							UpperCAmelCase		:							Dict        =       atrous_rates
							UpperCAmelCase		:							int        =       aspp_dropout_prob
							UpperCAmelCase		:							List[str]        =       semantic_loss_ignore_index







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							version.parse("1.11"       )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

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







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							if self.task == "image-classification":
												return OrderedDict([("logits", {0: "batch"})]      )
							else:
												return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return 1e-4
