

'''simple docstring'''

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCAmelCase_						   =			logging.get_logger(__name__)







@dataclass
class lowerCamelCase      :
				snake_case_           =   42
				snake_case_           =   42
				snake_case_           =   None
				snake_case_           =   None

				def 				_lowerCamelCase						(					self   )						->       List[str]:
					return json.dumps(dataclasses.asdict(self   ),	indent=2   ) + "\n"







@dataclass(frozen=__lowerCAmelCase							)
class lowerCamelCase      :
				snake_case_           =   42
				snake_case_           =   None
				snake_case_           =   None
				snake_case_           =   None

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					return json.dumps(dataclasses.asdict(self   )   ) + "\n"







class lowerCamelCase      :

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Union[str, Any]:
					raise NotImplementedError()

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Tuple:
					raise NotImplementedError()

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       List[Any]:
					raise NotImplementedError()

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Dict:
					raise NotImplementedError()

				def 				_lowerCamelCase						(					self   )						->       str:
					raise NotImplementedError()

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       int:
					if len(self.get_labels()   ) > 1:
						snake_case							 =				self.get_labels()[int(example.label   )]
					return example






				@classmethod
				def 				_lowerCamelCase						(					cls,	lowercase_,	lowercase_=None   )						->       Tuple:
					with open(lowercase_,	'r',	encoding='utf-8-sig'   ) as f:
						return list(csv.reader(lowercase_,	delimiter='\t',	quotechar=lowercase_   )   )







class lowerCamelCase      (						__lowerCAmelCase							):

				def __init__(					self,	lowercase_=None,	lowercase_=None,	lowercase_="classification",	lowercase_=False   )						->       Optional[Any]:
					snake_case							 =				[] if labels is None else labels
					snake_case							 =				[] if examples is None else examples
					snake_case							 =				mode
					snake_case							 =				verbose

				def __len__(					self   )						->       Dict:
					return len(self.examples   )

				def __getitem__(					self,	lowercase_   )						->       int:
					if isinstance(lowercase_,	lowercase_   ):
						return SingleSentenceClassificationProcessor(labels=self.labels,	examples=self.examples[idx]   )
					return self.examples[idx]

				@classmethod
				def 				_lowerCamelCase						(					cls,	lowercase_,	lowercase_="",	lowercase_=0,	lowercase_=1,	lowercase_=None,	lowercase_=False,	**lowercase_   )						->       str:
					snake_case							 =				cls(**lowercase_   )
					processor.add_examples_from_csv(
					    lowercase_,	split_name=lowercase_,	column_label=lowercase_,	column_text=lowercase_,	column_id=lowercase_,	skip_first_row=lowercase_,	overwrite_labels=lowercase_,	overwrite_examples=lowercase_,	)
					return processor

				@classmethod
				def 				_lowerCamelCase						(					cls,	lowercase_,	lowercase_=None,	**lowercase_   )						->       Optional[int]:
					snake_case							 =				cls(**lowercase_   )
					processor.add_examples(lowercase_,	labels=lowercase_   )
					return processor

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_="",	lowercase_=0,	lowercase_=1,	lowercase_=None,	lowercase_=False,	lowercase_=False,	lowercase_=False,	)						->       List[str]:
					snake_case							 =				self._read_tsv(lowercase_   )
					if skip_first_row:
						snake_case							 =				lines[1:]
					snake_case							 =				[]
					snake_case							 =				[]
					snake_case							 =				[]
					for i, line in enumerate(lowercase_   ):
						texts.append(line[column_text]   )
						labels.append(line[column_label]   )
						if column_id is not None:
							ids.append(line[column_id]   )
						else:
							snake_case							 =				F'''{split_name}-{i}''' if split_name else str(lowercase_   )
							ids.append(lowercase_   )

					return self.add_examples(
					    lowercase_,	lowercase_,	lowercase_,	overwrite_labels=lowercase_,	overwrite_examples=lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_=None,	lowercase_=None,	lowercase_=False,	lowercase_=False   )						->       List[Any]:
					if labels is not None and len(lowercase_   ) != len(lowercase_   ):
						raise ValueError(
						    F'''Text and labels have mismatched lengths {len(lowercase_   )} and {len(lowercase_   )}'''   )
					if ids is not None and len(lowercase_   ) != len(lowercase_   ):
						raise ValueError(F'''Text and ids have mismatched lengths {len(lowercase_   )} and {len(lowercase_   )}'''   )
					if ids is None:
						snake_case							 =				[None] * len(lowercase_   )
					if labels is None:
						snake_case							 =				[None] * len(lowercase_   )
					snake_case							 =				[]
					snake_case							 =				set()
					for text_or_text_and_label, label, guid in zip(lowercase_,	lowercase_,	lowercase_   ):
						if isinstance(lowercase_,	(tuple, list)   ) and label is None:
							snake_case , snake_case							 =				text_or_text_and_label
						else:
							snake_case							 =				text_or_text_and_label
						added_labels.add(lowercase_   )
						examples.append(InputExample(guid=lowercase_,	text_a=lowercase_,	text_b=lowercase_,	label=lowercase_   )   )

					# Update examples
					if overwrite_examples:
						snake_case							 =				examples
					else:
						self.examples.extend(lowercase_   )

					# Update labels
					if overwrite_labels:
						snake_case							 =				list(lowercase_   )
					else:
						snake_case							 =				list(set(self.labels   ).union(lowercase_   )   )

					return self.examples






				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_=None,	lowercase_=False,	lowercase_=0,	lowercase_=True,	lowercase_=None,	)						->       List[str]:
					if max_length is None:
						snake_case							 =				tokenizer.max_len

					snake_case							 =				{label: i for i, label in enumerate(self.labels   )}

					snake_case							 =				[]
					for ex_index, example in enumerate(self.examples   ):
						if ex_index % 10000 == 0:
							logger.info(F'''Tokenizing example {ex_index}'''   )

						snake_case							 =				tokenizer.encode(
						    example.text_a,	add_special_tokens=lowercase_,	max_length=min(lowercase_,	tokenizer.max_len   ),	)
						all_input_ids.append(lowercase_   )

					snake_case							 =				max(len(lowercase_   ) for input_ids in all_input_ids   )

					snake_case							 =				[]
					for ex_index, (input_ids, example) in enumerate(zip(lowercase_,	self.examples   )   ):
						if ex_index % 10000 == 0:
							logger.info(F'''Writing example {ex_index}/{len(self.examples   )}'''   )
						# The mask has 1 for real tokens and 0 for padding tokens. Only real
						# tokens are attended to.
						snake_case							 =				[1 if mask_padding_with_zero else 0] * len(lowercase_   )

						# Zero-pad up to the sequence length.
						snake_case							 =				batch_length - len(lowercase_   )
						if pad_on_left:
							snake_case							 =				([pad_token] * padding_length) + input_ids
							snake_case							 =				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
						else:
							snake_case							 =				input_ids + ([pad_token] * padding_length)
							snake_case							 =				attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

						if len(lowercase_   ) != batch_length:
							raise ValueError(F'''Error with input length {len(lowercase_   )} vs {batch_length}'''   )
						if len(lowercase_   ) != batch_length:
							raise ValueError(F'''Error with input length {len(lowercase_   )} vs {batch_length}'''   )

						if self.mode == "classification":
							snake_case							 =				label_map[example.label]
						elif self.mode == "regression":
							snake_case							 =				float(example.label   )
						else:
							raise ValueError(self.mode   )

						if ex_index < 5 and self.verbose:
							logger.info('*** Example ***'   )
							logger.info(F'''guid: {example.guid}'''   )
							logger.info(F'''input_ids: {" ".join([str(lowercase_   ) for x in input_ids]   )}'''   )
							logger.info(F'''attention_mask: {" ".join([str(lowercase_   ) for x in attention_mask]   )}'''   )
							logger.info(F'''label: {example.label} (id = {label})'''   )

						features.append(InputFeatures(input_ids=lowercase_,	attention_mask=lowercase_,	label=lowercase_   )   )

					if return_tensors is None:
						return features
					elif return_tensors == "tf":
						if not is_tf_available():
							raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'   )
						import tensorflow as tf

						def gen():
							for ex in features:
								yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

						snake_case							 =				tf.data.Dataset.from_generator(
						    lowercase_,	({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa),	({'input_ids': tf.TensorShape([None]   ), 'attention_mask': tf.TensorShape([None]   )}, tf.TensorShape([]   )),	)
						return dataset
					elif return_tensors == "pt":
						if not is_torch_available():
							raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'   )
						import torch
						from torch.utils.data import TensorDataset

						snake_case							 =				torch.tensor([f.input_ids for f in features],	dtype=torch.long   )
						snake_case							 =				torch.tensor([f.attention_mask for f in features],	dtype=torch.long   )
						if self.mode == "classification":
							snake_case							 =				torch.tensor([f.label for f in features],	dtype=torch.long   )
						elif self.mode == "regression":
							snake_case							 =				torch.tensor([f.label for f in features],	dtype=torch.float   )

						snake_case							 =				TensorDataset(lowercase_,	lowercase_,	lowercase_   )
						return dataset
					else:
						raise ValueError('return_tensors should be one of \'tf\' or \'pt\''   )








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def     __magic_name__       ( A							)			->		Dict:
	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)





