import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def 					snake_case						(__lowercase=2					,			__lowercase=True					,			__lowercase=False						)       ->       str:

						'''simple docstring'''



						_snake_case						:     Optional[Any]				=    Accelerator(step_scheduler_with_optimizer=__lowercase					,			split_batches=__lowercase						)
						_snake_case						:     Dict				=    torch.nn.Linear(2					,			4						)
						_snake_case						:     Tuple				=    torch.optim.AdamW(model.parameters()					,			lr=1.0						)
						_snake_case						:     str				=    torch.optim.lr_scheduler.OneCycleLR(__lowercase					,			max_lr=0.01					,			steps_per_epoch=2					,			epochs=1						)
						_snake_case				,_snake_case				,_snake_case						:     List[Any]				=    accelerator.prepare(__lowercase					,			__lowercase					,			__lowercase						)

						# Optimizer has stepped
						scheduler.step()
						if step_scheduler_with_optimizer or (num_processes == 1):
												assert (
												    scheduler.scheduler.last_epoch == num_processes
												), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
						else:
												assert (
												    scheduler.scheduler.last_epoch != num_processes
												), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""
def 					snake_case						(__lowercase=2					,			__lowercase=True					,			__lowercase=False						)       ->       int:

						'''simple docstring'''



						_snake_case						:     int				=    Accelerator(step_scheduler_with_optimizer=__lowercase					,			split_batches=__lowercase						)
						_snake_case						:     Tuple				=    torch.nn.Linear(2					,			4						)
						_snake_case						:     str				=    torch.optim.AdamW(model.parameters()					,			lr=1.0						)
						_snake_case						:     Any				=    torch.optim.lr_scheduler.LambdaLR(__lowercase					,			lr_lambda=lambda __lowercase						: 1 - n / 10						)
						_snake_case				,_snake_case				,_snake_case						:     Optional[int]				=    accelerator.prepare(__lowercase					,			__lowercase					,			__lowercase						)

						# Optimizer has stepped
						_snake_case						:     List[Any]				=    False
						scheduler.step()
						_snake_case						:     int				=    1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
						assert (
						    scheduler.get_last_lr()[0] == expected_lr
						), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

						# Optimizer has not stepped
						_snake_case						:     Tuple				=    True
						scheduler.step()
						if not step_scheduler_with_optimizer:
												_snake_case						:     Dict				=    1 - 2 / 10
						assert (
						    scheduler.get_last_lr()[0] == expected_lr
						), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""
def 					snake_case						(__lowercase = 2						)       ->       Optional[int]:

						'''simple docstring'''



						from transformers import get_linear_schedule_with_warmup

						_snake_case						:     Optional[Any]				=    [1, 2, 4]
						for num_steps in steps:
												_snake_case						:     Optional[int]				=    GradientAccumulationPlugin(num_steps=__lowercase					,			adjust_scheduler=num_steps > 1						)
												_snake_case						:     Tuple				=    Accelerator(gradient_accumulation_plugin=__lowercase						)
												_snake_case						:     Tuple				=    torch.nn.Linear(2					,			4						)
												_snake_case						:     int				=    torch.optim.AdamW(model.parameters()					,			lr=10.0						)
												_snake_case						:     Dict				=    get_linear_schedule_with_warmup(optimizer=__lowercase					,			num_warmup_steps=0					,			num_training_steps=20						)

												_snake_case				,_snake_case				,_snake_case						:     Any				=    accelerator.prepare(__lowercase					,			__lowercase					,			__lowercase						)

												for i in range(10 * num_steps						):
																		with accelerator.accumulate(__lowercase						):
																								optimizer.step()
																								scheduler.step()

																		if i == (10 * num_steps - 2):
																								assert (
																								    scheduler.get_last_lr()[0] != 0
																								), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
												assert (
												    scheduler.get_last_lr()[0] == 0
												), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
												GradientState._reset_state()



@require_cpu
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													debug_launcher(partial(lowercase_      ,				num_processes=1				)      ,				num_processes=1				)
													debug_launcher(partial(lowercase_      ,				num_processes=1      ,				split_batches=lowercase_				)      ,				num_processes=1				)



							def        UpperCamelCase       (						self				):
													debug_launcher(partial(lowercase_      ,				num_processes=1				)      ,				num_processes=1				)
													debug_launcher(partial(lowercase_      ,				num_processes=1      ,				split_batches=lowercase_				)      ,				num_processes=1				)



							def        UpperCamelCase       (						self				):
													debug_launcher(partial(lowercase_      ,				num_processes=1      ,				step_scheduler_with_optimizer=lowercase_				)      ,				num_processes=1				)



							def        UpperCamelCase       (						self				):
													debug_launcher(partial(lowercase_      ,				num_processes=1      ,				step_scheduler_with_optimizer=lowercase_				)      ,				num_processes=1				)



							def        UpperCamelCase       (						self				):
													AcceleratorState._reset_state(lowercase_				)
													debug_launcher(lowercase_				)
													debug_launcher(partial(lowercase_      ,				num_processes=1      ,				split_batches=lowercase_				)      ,				num_processes=1				)



							def        UpperCamelCase       (						self				):
													AcceleratorState._reset_state(lowercase_				)
													debug_launcher(lowercase_				)
													debug_launcher(partial(lowercase_      ,				num_processes=1      ,				split_batches=lowercase_				)      ,				num_processes=1				)



							def        UpperCamelCase       (						self				):
													AcceleratorState._reset_state(lowercase_				)
													debug_launcher(partial(lowercase_      ,				step_scheduler_with_optimizer=lowercase_				)				)



							def        UpperCamelCase       (						self				):
													AcceleratorState._reset_state(lowercase_				)
													debug_launcher(partial(lowercase_      ,				step_scheduler_with_optimizer=lowercase_				)				)



							@require_huggingface_suite
							def        UpperCamelCase       (						self				):
													AcceleratorState._reset_state(lowercase_				)
													debug_launcher(partial(lowercase_      ,				num_processes=1				)				)
													debug_launcher(lowercase_				)
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

						_snake_case						:     Dict				=    0  # Number of remaining (not pruned) params in the encoder
						_snake_case						:     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":
																								_snake_case						:     Union[str, Any]				=    TopKBinarizer.apply(__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								_snake_case						:     str				=    ThresholdBinarizer.apply(__lowercase					,			__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "l0":
																								_snake_case				,_snake_case						:     List[str]				=    -0.1, 1.1
																								_snake_case						:     Any				=    torch.sigmoid(__lowercase						)
																								_snake_case						:     str				=    s * (r - l) + l
																								_snake_case						:     Dict				=    s_bar.clamp(min=0.0					,			max=1.0						)
																								_snake_case						:     Tuple				=    (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(__lowercase						)						)
												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__":
				__SCREAMING_SNAKE_CASE						:    str     = 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',
				)

				__SCREAMING_SNAKE_CASE						:    Any     = parser.parse_args()

				main(args)