
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class       A__  (		__lowerCAmelCase							):
							lowerCAmelCase__						:		str            =				"MarkupLMFeatureExtractor"
							lowerCAmelCase__						:		Union[str, Any]            =				("MarkupLMTokenizer", "MarkupLMTokenizerFast")
							lowerCAmelCase__						:		List[Any]            =				True





							def __call__(					self    :  int	,					_UpperCAmelCase    :  Optional[Any]=None	,					_UpperCAmelCase    :  Dict=None	,					_UpperCAmelCase    :  int=None	,					_UpperCAmelCase    :  Optional[Any]=None	,					_UpperCAmelCase    :  Optional[int]=None	,					_UpperCAmelCase    :  Optional[Any] = True	,					_UpperCAmelCase    :  Union[str, Any] = False	,					_UpperCAmelCase    :  Dict = None	,					_UpperCAmelCase    :  Tuple = None	,					_UpperCAmelCase    :  Tuple = 0	,					_UpperCAmelCase    :  List[str] = None	,					_UpperCAmelCase    :  str = None	,					_UpperCAmelCase    :  Any = None	,					_UpperCAmelCase    :  Optional[Any] = False	,					_UpperCAmelCase    :  Optional[int] = False	,					_UpperCAmelCase    :  List[Any] = False	,					_UpperCAmelCase    :  List[Any] = False	,					_UpperCAmelCase    :  List[Any] = True	,					_UpperCAmelCase    :  List[Any] = None	,					**_UpperCAmelCase    :  Tuple	,					)		->						BatchEncoding:



											"""simple docstring"""


											if self.parse_html:
															if html_strings is None:
																			raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`'							)

															if nodes is not None or xpaths is not None or node_labels is not None:
																			raise ValueError(
																			    'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'							)

															__lowercase								=   self.feature_extractor(lowercase_							)
															__lowercase								=   features['nodes']
															__lowercase								=   features['xpaths']
											else:
															if html_strings is not None:
																			raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.'							)
															if nodes is None or xpaths is None:
																			raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'							)

        # # second, apply the tokenizer
											if questions is not None and self.parse_html:
															if isinstance(lowercase_	,					lowercase_							):
																			__lowercase								=   [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

											__lowercase								=   self.tokenizer(
											    text=questions if questions is not None else nodes	,					text_pair=nodes if questions is not None else None	,					xpaths=lowercase_	,					node_labels=lowercase_	,					add_special_tokens=lowercase_	,					padding=lowercase_	,					truncation=lowercase_	,					max_length=lowercase_	,					stride=lowercase_	,					pad_to_multiple_of=lowercase_	,					return_token_type_ids=lowercase_	,					return_attention_mask=lowercase_	,					return_overflowing_tokens=lowercase_	,					return_special_tokens_mask=lowercase_	,					return_offsets_mapping=lowercase_	,					return_length=lowercase_	,					verbose=lowercase_	,					return_tensors=lowercase_	,					**lowercase_	,					)

											return encoded_inputs





							def      a__					(					self    :  Dict	,					*_UpperCAmelCase    :  Dict	,					**_UpperCAmelCase    :  Dict							)		->						Optional[int]:



											"""simple docstring"""


											return self.tokenizer.batch_decode(*lowercase_	,					**lowercase_							)





							def      a__					(					self    :  Optional[int]	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Any							)		->						Tuple:



											"""simple docstring"""


											return self.tokenizer.decode(*lowercase_	,					**lowercase_							)





							@property
							def      a__					(					self    :  Optional[int]							)		->						Any:



											"""simple docstring"""


											__lowercase								=   self.tokenizer.model_input_names
											return tokenizer_input_names








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `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(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





