



import argparse
import os
import shutil

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			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(SCREAMING_SNAKE_CASE_  ,					"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=SCREAMING_SNAKE_CASE_  ,					threshold=SCREAMING_SNAKE_CASE_					)
												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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
												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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
												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(SCREAMING_SNAKE_CASE_					)
												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(SCREAMING_SNAKE_CASE_					)  ,					f'''bertarized_{os.path.basename(SCREAMING_SNAKE_CASE_					)}'''					)

			if not os.path.isdir(SCREAMING_SNAKE_CASE_					):
						shutil.copytree(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						print(f'''\nCreated folder {target_model_path}'''					)

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


if __name__ == "__main__":
							lowercase_						=       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""",
							)

							lowercase_						=       parser.parse_args()

							main(args)









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							lowercase_						=       parser.parse_args()

							if args.model_type == "roberta":
														lowercase_						=       RobertaForMaskedLM.from_pretrained(args.model_name)
														lowercase_						=       """roberta"""
							elif args.model_type == "gpt2":
														lowercase_						=       GPTaLMHeadModel.from_pretrained(args.model_name)
														lowercase_						=       """transformer"""

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowercase_						=       state_dict[F'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowercase_						=       F'{prefix}.embeddings.{w}.weight'
																					lowercase_						=       state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowercase_						=       F'{prefix}.embeddings.LayerNorm.{w}'
																					lowercase_						=       state_dict[param_name]

    # Transformer Blocks #
							lowercase_						=       0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       state_dict["""lm_head.weight"""]

							print(F'N layers selected for distillation: {std_idx}')
							print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

							print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
							torch.save(compressed_sd, args.dump_checkpoint)




