import argparse
import os
import shutil

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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							args.pruning_method
				__lowercase 			=							args.threshold

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

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

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

				if target_model_path is None:
								__lowercase 			=							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__":
		__a  :     int       =  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""",
		)

		__a  :     Dict       =  parser.parse_args()

		main(args)
import requests





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''Content-Type''': '''application/json'''}
				__lowercase 			=							requests.post(lowercase							,			json={'''text''': message_body}							,			headers=lowercase							)
				if response.status_code != 200:
								__lowercase 			=							(
								    '''Request to slack returned an error '''
								    F"{response.status_code}, the response is:\n{response.text}"
								)
								raise ValueError(lowercase							)


if __name__ == "__main__":
		# Set the slack url to the one provided by Slack when you create the webhook at
		# https://my.slack.com/services/new/incoming-webhook/
		send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")