"""simple docstring"""




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		XLMTokenizer
							_lowerCamelCase						=		False



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

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													_snake_case						:					Optional[int]    =   [
													    "l",
													    "o",
													    "w",
													    "e",
													    "r",
													    "s",
													    "t",
													    "i",
													    "d",
													    "n",
													    "w</w>",
													    "r</w>",
													    "t</w>",
													    "lo",
													    "low",
													    "er</w>",
													    "low</w>",
													    "lowest</w>",
													    "newer</w>",
													    "wider</w>",
													    "<unk>",
													]
													_snake_case						:					int    =   dict(zip(lowercase_				,    range(len(lowercase_    )    )    )    )
													_snake_case						:					Optional[Any]    =   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

													_snake_case						:					int    =   os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["vocab_file"]    )
													_snake_case						:					Optional[Any]    =   os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["merges_file"]    )
													with open(self.vocab_file				,    "w"    ) as fp:
																			fp.write(json.dumps(lowercase_    )    )
													with open(self.merges_file				,    "w"    ) as fp:
																			fp.write("\n".join(lowercase_    )    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Any    =   "lower newer"
													_snake_case						:					Union[str, Any]    =   "lower newer"
													return input_text, output_text



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   XLMTokenizer(self.vocab_file				,    self.merges_file    )

													_snake_case						:					Dict    =   "lower"
													_snake_case						:					List[str]    =   ["low", "er</w>"]
													_snake_case						:					Optional[int]    =   tokenizer.tokenize(lowercase_    )
													self.assertListEqual(lowercase_				,    lowercase_    )

													_snake_case						:					int    =   tokens + ["<unk>"]
													_snake_case						:					Union[str, Any]    =   [14, 15, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_    )				,    lowercase_    )



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   XLMTokenizer.from_pretrained("xlm-mlm-en-2048"    )

													_snake_case						:					Tuple    =   tokenizer.encode("sequence builders"				,    add_special_tokens=lowercase_    )
													_snake_case						:					Optional[Any]    =   tokenizer.encode("multi-sequence build"				,    add_special_tokens=lowercase_    )

													_snake_case						:					Tuple    =   tokenizer.build_inputs_with_special_tokens(lowercase_    )
													_snake_case						:					List[str]    =   tokenizer.build_inputs_with_special_tokens(lowercase_				,    lowercase_    )

													assert encoded_sentence == [0] + text + [1]
													assert encoded_pair == [0] + text + [1] + text_a + [1]
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

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

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

						if target_model_path is None:
												_snake_case						:					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__":
				__SCREAMING_SNAKE_CASE						:   List[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',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)