






"""simple docstring"""
import argparse
import os
import shutil

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






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						str	    =   args.pruning_method
	A_		:						Optional[Any]	    =   args.threshold

	A_		:						str	    =   args.model_name_or_path.rstrip("""/"""					)
	A_		:						int	    =   args.target_model_path

	print(F"""Load fine-pruned model from {model_name_or_path}"""					)
	A_		:						Any	    =   torch.load(os.path.join(snake_case__   ,   """pytorch_model.bin"""					)					)
	A_		:						str	    =   {}

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

	if target_model_path is None:
		A_		:						List[Any]	    =   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__":
						_lowerCAmelCase         =							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",
						)

						_lowerCAmelCase         =							parser.parse_args()

						main(args)









"""simple docstring"""
import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				FunnelTokenizer
	_A       :				Union[str, Any]    =				FunnelTokenizerFast
	_A       :				List[str]    =				True
	_A       :				List[Any]    =				True





	def    lowerCamelCase(self							):
		super().setUp()

		A_		:						Tuple	    =   [
		    """<unk>""",
		    """<cls>""",
		    """<sep>""",
		    """want""",
		    """##want""",
		    """##ed""",
		    """wa""",
		    """un""",
		    """runn""",
		    """##ing""",
		    """,""",
		    """low""",
		    """lowest""",
		]
		A_		:						str	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""							) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizer.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   """UNwant\u00E9d,running"""
		A_		:						int	    =   """unwanted, running"""
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   self.tokenizer_class(self.vocab_file							)

		A_		:						Any	    =   tokenizer.tokenize("""UNwant\u00E9d,running"""							)
		self.assertListEqual(lowerCAmelCase_     ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)     ,     [7, 4, 5, 10, 8, 9]							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_tokenizers(do_lower_case=lowerCAmelCase_							)
		for tokenizer in tokenizers:
			A_		:						Any	    =   tokenizer("""UNwant\u00E9d,running"""							)
			A_		:						Dict	    =   len(inputs["""input_ids"""]							) - 1
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len							)

			A_		:						Tuple	    =   tokenizer("""UNwant\u00E9d,running"""     ,     """UNwant\u00E9d,running"""							)
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len + [1] * sentence_len							)

