



'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	int     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Any					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Dict       =		dataset
						_lowercase					:       Optional[int]       =		process
						_lowercase					:       Any       =		params




					def __len__(  self				:	List[str]					)  ->       str:



						'''simple docstring'''





						return len(self.dataset					)




					def __getitem__(  self				:	Dict     ,  UpperCamelCase_				:	List[str]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.dataset[i]
						_lowercase					:       Union[str, Any]       =		self.process(UpperCamelCase_     ,  **self.params					)
						return processed






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Tuple=None					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		loader
						_lowercase					:       List[str]       =		infer
						_lowercase					:       Optional[Any]       =		params
						if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							_lowercase					:       List[str]       =		None
						_lowercase					:       Any       =		loader_batch_size

						# Internal bookkeeping
						_lowercase					:       Union[str, Any]       =		None
						_lowercase					:       Dict       =		None




					def __len__(  self				:	Union[str, Any]					)  ->       Any:



						'''simple docstring'''





						return len(self.loader					)




					def __iter__(  self				:	Dict					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		iter(self.loader					)
						return self




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Dict:



						'''simple docstring'''





						if isinstance(self._loader_batch_data     ,  torch.Tensor					):
							# Batch data is simple tensor, just fetch the slice
							_lowercase					:       List[str]       =		self._loader_batch_data[self._loader_batch_index]
						else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							_lowercase					:       Dict       =		{}
							for k, element in self._loader_batch_data.items():
								if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
									# Convert ModelOutput to tuple first
									_lowercase					:       List[str]       =		element.to_tuple()
									if isinstance(element[0]     ,  torch.Tensor					):
										_lowercase					:       Dict       =		tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
									elif isinstance(element[0]     ,  np.ndarray					):
										_lowercase					:       List[Any]       =		tuple(np.expand_dims(el[self._loader_batch_index]     ,  0					) for el in element					)
									continue
								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]     ,  torch.Tensor					):
										_lowercase					:       Union[str, Any]       =		tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
									elif isinstance(element[0]     ,  np.ndarray					):
										_lowercase					:       List[str]       =		tuple(np.expand_dims(el[self._loader_batch_index]     ,  0					) for el in element					)
									continue
								if element is None:
									# This can happen for optional data that get passed around
									_lowercase					:       Optional[int]       =		None
								elif isinstance(element[self._loader_batch_index]     ,  torch.Tensor					):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers

									_lowercase					:       Union[str, Any]       =		element[self._loader_batch_index].unsqueeze(0					)
								elif isinstance(element[self._loader_batch_index]     ,  np.ndarray					):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers
									_lowercase					:       Any       =		np.expand_dims(element[self._loader_batch_index]     ,  0					)
								else:
									# This is typically a list, so no need to `unsqueeze`.
									_lowercase					:       Any       =		element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							_lowercase					:       Optional[int]       =		self._loader_batch_data.__class__(UpperCamelCase_					)
						self._loader_batch_index += 1
						return result




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Optional[Any]:



						'''simple docstring'''





						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

						# We're out of items within a batch
						_lowercase					:       Optional[int]       =		next(self.iterator					)
						_lowercase					:       Optional[Any]       =		self.infer(UpperCamelCase_     ,  **self.params					)
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(UpperCamelCase_     ,  torch.Tensor					):
								_lowercase					:       Tuple       =		processed
							else:
								_lowercase					:       Optional[int]       =		list(processed.keys()					)[0]
								_lowercase					:       Optional[int]       =		processed[key]
							if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       Optional[Any]       =		len(UpperCamelCase_					)
							else:
								_lowercase					:       Optional[Any]       =		first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								_lowercase					:       Any       =		observed_batch_size
							# Setting internal index to unwrap the batch
							_lowercase					:       str       =		processed
							_lowercase					:       Tuple       =		0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	int=None					)  ->       Dict:



						'''simple docstring'''





						super().__init__(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)




					def __iter__(  self				:	Optional[int]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		iter(self.loader					)
						_lowercase					:       str       =		None
						return self




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						if self.subiterator is None:
							_lowercase					:       Dict       =		self.infer(next(self.iterator					)     ,  **self.params					)
						try:
							# Try to return next item
							_lowercase					:       Tuple       =		next(self.subiterator					)
						except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							_lowercase					:       Optional[Any]       =		self.infer(next(self.iterator					)     ,  **self.params					)
							_lowercase					:       Optional[Any]       =		next(self.subiterator					)
						return processed






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __iter__(  self				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		iter(self.loader					)
						return self




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       int       =		False
						_lowercase					:       List[str]       =		[]
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
								_lowercase					:       Union[str, Any]       =		self.loader_batch_item()
								_lowercase					:       List[Any]       =		item.pop('is_last'					)
								accumulator.append(UpperCamelCase_					)
								if is_last:
									return accumulator

						while not is_last:
							_lowercase					:       Optional[int]       =		self.infer(next(self.iterator					)     ,  **self.params					)
							if self.loader_batch_size is not None:
								if isinstance(UpperCamelCase_     ,  torch.Tensor					):
									_lowercase					:       List[Any]       =		processed
								else:
									_lowercase					:       List[str]       =		list(processed.keys()					)[0]
									_lowercase					:       List[Any]       =		processed[key]
								if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
									_lowercase					:       int       =		len(UpperCamelCase_					)
								else:
									_lowercase					:       List[Any]       =		first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									_lowercase					:       Optional[Any]       =		observed_batch_size
								_lowercase					:       Tuple       =		processed
								_lowercase					:       Tuple       =		0
								while self._loader_batch_index < self.loader_batch_size:
									_lowercase					:       List[str]       =		self.loader_batch_item()
									_lowercase					:       Union[str, Any]       =		item.pop('is_last'					)
									accumulator.append(UpperCamelCase_					)
									if is_last:
										return accumulator
							else:
								_lowercase					:       Dict       =		processed
								_lowercase					:       Tuple       =		item.pop('is_last'					)
								accumulator.append(UpperCamelCase_					)
						return accumulator






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Any     ,  UpperCamelCase_				:	Dataset     ,  UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		dataset
						_lowercase					:       str       =		key




					def __len__(  self				:	int					)  ->       Any:



						'''simple docstring'''





						return len(self.dataset					)




					def __getitem__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Tuple					)  ->       str:



						'''simple docstring'''





						return self.dataset[i][self.key]






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	List[Any]     ,  UpperCamelCase_				:	Dataset     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	str					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		dataset
						_lowercase					:       int       =		keya
						_lowercase					:       List[str]       =		keya




					def __len__(  self				:	Union[str, Any]					)  ->       List[str]:



						'''simple docstring'''





						return len(self.dataset					)




					def __getitem__(  self				:	Tuple     ,  UpperCamelCase_				:	Any					)  ->       int:



						'''simple docstring'''





						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





'''simple docstring'''

import argparse
import os
import shutil

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







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       Tuple       =		args.pruning_method
	_lowercase					:       int       =		args.threshold

	_lowercase					:       str       =		args.model_name_or_path.rstrip('/'					)
	_lowercase					:       Dict       =		args.target_model_path

	print(f'''Load fine-pruned model from {model_name_or_path}'''					)
	_lowercase					:       str       =		torch.load(os.path.join(_lowercase,				'pytorch_model.bin'					)					)
	_lowercase					:       List[Any]       =		{}

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

	if target_model_path is None:
		_lowercase					:       List[Any]       =		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				:    Union[str, Any]     						=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				:    List[Any]     						=parser.parse_args()

		main(args)
