
"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  A__       (	_lowerCamelCase       ,     unittest.TestCase):
		A_						:       Optional[int]				   =	None
		A_						:       int				   =	BloomTokenizerFast
		A_						:       List[Any]				   =	BloomTokenizerFast
		A_						:       Dict				   =	True
		A_						:       str				   =	False
		A_						:       str				   =	'tokenizer_file'
		A_						:       Any				   =	{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}

		def 							__lowerCamelCase							(      self ):
			super().setUp()
			__lowerCAmelCase			:						Optional[Any]									=      BloomTokenizerFast.from_pretrained('bigscience/tokenizer' )
			tokenizer.save_pretrained(self.tmpdirname )

		def 							__lowerCamelCase							(      self       ,		**_SCREAMING_SNAKE_CASE ):
			kwargs.update(self.special_tokens_map )
			return BloomTokenizerFast.from_pretrained(self.tmpdirname       ,		**_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      self.get_rust_tokenizer()

			__lowerCAmelCase			:						str									=      ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
			__lowerCAmelCase			:						List[Any]									=      [[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

			__lowerCAmelCase			:						int									=      tokenizer.batch_encode_plus(_SCREAMING_SNAKE_CASE )['input_ids']
			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[int]									=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=6 ):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
					__lowerCAmelCase			:						Union[str, Any]									=      self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )
					# tokenizer_r.pad_token = None # Hotfixing padding = None
					# Simple input
					__lowerCAmelCase			:						Union[str, Any]									=      'This is a simple input'
					__lowerCAmelCase			:						List[Any]									=      ['This is a simple input 1', 'This is a simple input 2']
					__lowerCAmelCase			:						Union[str, Any]									=      ('This is a simple input', 'This is a pair')
					__lowerCAmelCase			:						str									=      [
					    ('This is a simple input 1', 'This is a simple input 2'),
					    ('This is a simple pair 1', 'This is a simple pair 2'),
					]

					# Simple input tests
					try:
						tokenizer_r.encode(_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE )
						tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE )

						tokenizer_r.batch_encode_plus(_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE )
						tokenizer_r.encode(_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE )
						tokenizer_r.batch_encode_plus(_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE )
					except ValueError:
						self.fail('Bloom Tokenizer should be able to deal with padding' )

					__lowerCAmelCase			:						str									=      None  # Hotfixing padding = None
					self.assertRaises(_SCREAMING_SNAKE_CASE       ,		tokenizer_r.encode       ,		_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE       ,		padding='max_length' )

					# Simple input
					self.assertRaises(_SCREAMING_SNAKE_CASE       ,		tokenizer_r.encode_plus       ,		_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE       ,		padding='max_length' )

					# Simple input
					self.assertRaises(
					    _SCREAMING_SNAKE_CASE       ,		tokenizer_r.batch_encode_plus       ,		_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE       ,		padding='max_length'       ,		)

					# Pair input
					self.assertRaises(_SCREAMING_SNAKE_CASE       ,		tokenizer_r.encode       ,		_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE       ,		padding='max_length' )

					# Pair input
					self.assertRaises(_SCREAMING_SNAKE_CASE       ,		tokenizer_r.encode_plus       ,		_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE       ,		padding='max_length' )

					# Pair input
					self.assertRaises(
					    _SCREAMING_SNAKE_CASE       ,		tokenizer_r.batch_encode_plus       ,		_SCREAMING_SNAKE_CASE       ,		max_length=_SCREAMING_SNAKE_CASE       ,		padding='max_length'       ,		)

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      self.get_rust_tokenizer()
			__lowerCAmelCase			:						Optional[Any]									=      load_dataset('xnli'       ,		'all_languages'       ,		split='test'       ,		streaming=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Union[str, Any]									=      next(iter(_SCREAMING_SNAKE_CASE ) )['premise']  # pick up one data
			__lowerCAmelCase			:						Optional[int]									=      list(sample_data.values() )

			__lowerCAmelCase			:						Union[str, Any]									=      list(map(tokenizer.encode       ,		_SCREAMING_SNAKE_CASE ) )
			__lowerCAmelCase			:						Optional[int]									=      [tokenizer.decode(_SCREAMING_SNAKE_CASE       ,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE ) for x in output_tokens]
			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )







		def 							__lowerCamelCase							(      self ):
			# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
			# any sequence length constraints. This test of the parent class will fail since it relies on the
			# maximum sequence length of the positoonal embeddings.
			self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map )       ,		1 )
			self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] )       ,		1 )

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)
