
import flax.linen as nn
import jax
import jax.numpy as jnp





class 							UpperCAmelCase      (  nn.Module  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     jnp.floataa






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= nn.Conv(
					    self.out_channels							,kernel_size=(3, 3)							,strides=(1, 1)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)







	def __call__(       self					: List[str]							,A					: str ):
					__A			,							__A			,							__A			,							__A   			= hidden_states.shape
					__A   			= jax.image.resize(
					    A							,shape=(batch, height * 2, width * 2, channels)							,method="nearest"							,)
					__A   			= self.conv(A )
					return hidden_states





class 							UpperCAmelCase      (  nn.Module  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     jnp.floataa






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= nn.Conv(
					    self.out_channels							,kernel_size=(3, 3)							,strides=(2, 2)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)







	def __call__(       self					: Any							,A					: str ):
					# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
					# hidden_states = jnp.pad(hidden_states, pad_width=pad)
					__A   			= self.conv(A )
					return hidden_states





class 							UpperCAmelCase      (  nn.Module  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     None
	snake_case_					    =     0.0
	snake_case_					    =     None
	snake_case_					    =     jnp.floataa






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.in_channels if self.out_channels is None else self.out_channels

					__A   			= nn.GroupNorm(num_groups=32							,epsilon=1E-5 )
					__A   			= nn.Conv(
					    A							,kernel_size=(3, 3)							,strides=(1, 1)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)

					__A   			= nn.Dense(A							,dtype=self.dtype )

					__A   			= nn.GroupNorm(num_groups=32							,epsilon=1E-5 )
					__A   			= nn.Dropout(self.dropout_prob )
					__A   			= nn.Conv(
					    A							,kernel_size=(3, 3)							,strides=(1, 1)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)

					__A   			= self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

					__A   			= None
					if use_nin_shortcut:
									__A   			= nn.Conv(
									    A							,kernel_size=(1, 1)							,strides=(1, 1)							,padding="VALID"							,dtype=self.dtype							,)







	def __call__(       self					: List[Any]							,A					: List[str]							,A					: str							,A					: Optional[int]=True ):
					__A   			= hidden_states
					__A   			= self.norma(A )
					__A   			= nn.swish(A )
					__A   			= self.conva(A )

					__A   			= self.time_emb_proj(nn.swish(A ) )
					__A   			= jnp.expand_dims(jnp.expand_dims(A							,1 )							,1 )
					__A   			= hidden_states + temb

					__A   			= self.norma(A )
					__A   			= nn.swish(A )
					__A   			= self.dropout(A							,A )
					__A   			= self.conva(A )

					if self.conv_shortcut is not None:
									__A   			= self.conv_shortcut(A )

					return hidden_states + residual


import argparse
import os
import shutil

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


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

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

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

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				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',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
