




import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand



def       UpperCAmelCase       (     _snake_case							):
							return EnvironmentCommand()



def       UpperCAmelCase       (     _snake_case							):
							return EnvironmentCommand(args.accelerate_config_file							)


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				@staticmethod
				def       __snake_case		( UpperCAmelCase_						):
											lowerCAmelCase   =		parser.add_parser('''env'''						)
											download_parser.set_defaults(func=UpperCAmelCase_						)
											download_parser.add_argument(
											    '''--accelerate-config_file'''       , default=UpperCAmelCase_       , help='''The accelerate config file to use for the default values in the launching script.'''       , )
											download_parser.set_defaults(func=UpperCAmelCase_						)



				def __init__( self       , UpperCAmelCase_       , *UpperCAmelCase_						):
											lowerCAmelCase   =		accelerate_config_file



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''not installed'''
											if is_safetensors_available():
																		import safetensors

																		lowerCAmelCase   =		safetensors.__version__
											elif importlib.util.find_spec('''safetensors'''						) is not None:
																		import safetensors

																		lowerCAmelCase   =		F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

											lowerCAmelCase   =		'''not installed'''
											lowerCAmelCase   =		lowerCAmelCase   =		'''not found'''
											if is_accelerate_available():
																		import accelerate
																		from accelerate.commands.config import default_config_file, load_config_from_file

																		lowerCAmelCase   =		accelerate.__version__
																		# Get the default from the config file.
																		if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase_						):
																									lowerCAmelCase   =		load_config_from_file(self._accelerate_config_file						).to_dict()

																		lowerCAmelCase   =		(
																		    '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
																		    if isinstance(UpperCAmelCase_       , UpperCAmelCase_						)
																		    else F"""\t{accelerate_config}"""
																		)

											lowerCAmelCase   =		'''not installed'''
											lowerCAmelCase   =		'''NA'''
											if is_torch_available():
																		import torch

																		lowerCAmelCase   =		torch.__version__
																		lowerCAmelCase   =		torch.cuda.is_available()

											lowerCAmelCase   =		'''not installed'''
											lowerCAmelCase   =		'''NA'''
											if is_tf_available():
																		import tensorflow as tf

																		lowerCAmelCase   =		tf.__version__
																		try:
																									# deprecated in v2.1
																									lowerCAmelCase   =		tf.test.is_gpu_available()
																		except AttributeError:
																									# returns list of devices, convert to bool
																									lowerCAmelCase   =		bool(tf.config.list_physical_devices('''GPU'''						)						)

											lowerCAmelCase   =		'''not installed'''
											lowerCAmelCase   =		'''not installed'''
											lowerCAmelCase   =		'''not installed'''
											lowerCAmelCase   =		'''NA'''
											if is_flax_available():
																		import flax
																		import jax
																		import jaxlib

																		lowerCAmelCase   =		flax.__version__
																		lowerCAmelCase   =		jax.__version__
																		lowerCAmelCase   =		jaxlib.__version__
																		lowerCAmelCase   =		jax.lib.xla_bridge.get_backend().platform

											lowerCAmelCase   =		{
											    '''`transformers` version''': version,
											    '''Platform''': platform.platform(),
											    '''Python version''': platform.python_version(),
											    '''Huggingface_hub version''': huggingface_hub.__version__,
											    '''Safetensors version''': F"""{safetensors_version}""",
											    '''Accelerate version''': F"""{accelerate_version}""",
											    '''Accelerate config''': F"""{accelerate_config_str}""",
											    '''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
											    '''Tensorflow version (GPU?)''': F"""{tf_version} ({tf_cuda_available})""",
											    '''Flax version (CPU?/GPU?/TPU?)''': F"""{flax_version} ({jax_backend})""",
											    '''Jax version''': F"""{jax_version}""",
											    '''JaxLib version''': F"""{jaxlib_version}""",
											    '''Using GPU in script?''': '''<fill in>''',
											    '''Using distributed or parallel set-up in script?''': '''<fill in>''',
											}

											print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''						)
											print(self.format_dict(UpperCAmelCase_						)						)

											return info






				@staticmethod
				def       __snake_case		( UpperCAmelCase_						):
											return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"









import argparse
import os
import shutil

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



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		args.pruning_method
							lowerCAmelCase   =		args.threshold

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

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

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

							if target_model_path is None:
														lowerCAmelCase   =		os.path.join(
														    os.path.dirname(_snake_case							)						, F"""bertarized_{os.path.basename(_snake_case							)}"""							)

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

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


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

		UpperCAmelCase_						=parser.parse_args()

		main(args)



