




'''simple docstring'''


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowerCAmelCase__						:       Union[str, Any]										=						logging.getLogger(__name__)




class     a			(				lowercase_       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      List[Any]			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      str=None  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.layer[current_layer](UpperCamelCase__			,			UpperCamelCase__			,			head_mask[current_layer]  )

								snake_case__						:       Optional[Any]       						=  layer_outputs[0]

								return hidden_states





@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."""			,			lowercase_			,			)
class     a			(				lowercase_       ):






						"""simple docstring"""




						def __init__(							self					:      Optional[int]			,			snake_case_					:      int  ):



								'''simple docstring'''



								super().__init__(UpperCamelCase__  )

								snake_case__						:       List[Any]       						=  BertEncoderWithPabee(UpperCamelCase__  )

								self.init_weights()
								snake_case__						:       Tuple       						=  0
								snake_case__						:       List[Any]       						=  0
								snake_case__						:       Tuple       						=  0

								snake_case__						:       Union[str, Any]       						=  0







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  threshold







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  patience







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  0
								snake_case__						:       List[str]       						=  0







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.inference_layers_num / self.inference_instances_num
								snake_case__						:       Optional[int]       						=  (
								    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
								    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
								)
								print(UpperCamelCase__  )







						@add_start_docstrings_to_model_forward(UpperCamelCase__  )
						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Union[str, Any]=None			,			snake_case_					:      Tuple=None			,			snake_case_					:      Tuple=None			,			snake_case_					:      str=None			,			snake_case_					:      str=None			,			snake_case_					:      List[Any]=None			,			snake_case_					:      List[Any]=None			,			snake_case_					:      List[str]=None			,			snake_case_					:      Dict=None			,			snake_case_					:      Optional[Any]=None			,			snake_case_					:      Tuple=False			,			):



								'''simple docstring'''



								if input_ids is not None and inputs_embeds is not None:
										raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''  )
								elif input_ids is not None:
										snake_case__						:       str       						=  input_ids.size()
								elif inputs_embeds is not None:
										snake_case__						:       Tuple       						=  inputs_embeds.size()[:-1]
								else:
										raise ValueError('''You have to specify either input_ids or inputs_embeds'''  )

								snake_case__						:       Optional[int]       						=  input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
										snake_case__						:       Optional[int]       						=  torch.ones(UpperCamelCase__			,			device=UpperCamelCase__  )
								if token_type_ids is None:
										snake_case__						:       Dict       						=  torch.zeros(UpperCamelCase__			,			dtype=torch.long			,			device=UpperCamelCase__  )

								# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
								# ourselves in which case we just need to make it broadcastable to all heads.
								snake_case__						:       Tuple       						=  self.get_extended_attention_mask(UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__  )

								# If a 2D ou 3D attention mask is provided for the cross-attention
								# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
								if self.config.is_decoder and encoder_hidden_states is not None:
										snake_case__						,       snake_case__						,       snake_case__						:       Dict       						=  encoder_hidden_states.size()
										snake_case__						:       str       						=  (encoder_batch_size, encoder_sequence_length)
										if encoder_attention_mask is None:
												snake_case__						:       List[Any]       						=  torch.ones(UpperCamelCase__			,			device=UpperCamelCase__  )
										snake_case__						:       Tuple       						=  self.invert_attention_mask(UpperCamelCase__  )
								else:
										snake_case__						:       Any       						=  None

								# Prepare head mask if needed
								# 1.0 in head_mask indicate we keep the head
								# attention_probs has shape bsz x n_heads x N x N
								# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
								# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
								snake_case__						:       List[str]       						=  self.get_head_mask(UpperCamelCase__			,			self.config.num_hidden_layers  )

								snake_case__						:       Optional[Any]       						=  self.embeddings(
								    input_ids=UpperCamelCase__			,			position_ids=UpperCamelCase__			,			token_type_ids=UpperCamelCase__			,			inputs_embeds=UpperCamelCase__  )
								snake_case__						:       List[Any]       						=  embedding_output

								if self.training:
										snake_case__						:       Tuple       						=  []
										for i in range(self.config.num_hidden_layers  ):
												snake_case__						:       Optional[int]       						=  self.encoder.adaptive_forward(
												    UpperCamelCase__			,			current_layer=UpperCamelCase__			,			attention_mask=UpperCamelCase__			,			head_mask=UpperCamelCase__  )

												snake_case__						:       Dict       						=  self.pooler(UpperCamelCase__  )
												snake_case__						:       Dict       						=  output_layers[i](output_dropout(UpperCamelCase__  )  )
												res.append(UpperCamelCase__  )
								elif self.patience == 0:  # Use all layers for inference
										snake_case__						:       Optional[int]       						=  self.encoder(
										    UpperCamelCase__			,			attention_mask=UpperCamelCase__			,			head_mask=UpperCamelCase__			,			encoder_hidden_states=UpperCamelCase__			,			encoder_attention_mask=UpperCamelCase__			,			)
										snake_case__						:       Optional[Any]       						=  self.pooler(encoder_outputs[0]  )
										snake_case__						:       List[str]       						=  [output_layers[self.config.num_hidden_layers - 1](UpperCamelCase__  )]
								else:
										snake_case__						:       Any       						=  0
										snake_case__						:       List[str]       						=  None
										snake_case__						:       str       						=  0
										for i in range(self.config.num_hidden_layers  ):
												calculated_layer_num += 1
												snake_case__						:       Optional[Any]       						=  self.encoder.adaptive_forward(
												    UpperCamelCase__			,			current_layer=UpperCamelCase__			,			attention_mask=UpperCamelCase__			,			head_mask=UpperCamelCase__  )

												snake_case__						:       Dict       						=  self.pooler(UpperCamelCase__  )
												snake_case__						:       int       						=  output_layers[i](UpperCamelCase__  )
												if regression:
														snake_case__						:       Tuple       						=  logits.detach()
														if patient_result is not None:
																snake_case__						:       Tuple       						=  patient_result.detach()
														if (patient_result is not None) and torch.abs(patient_result - labels  ) < self.regression_threshold:
																patient_counter += 1
														else:
																snake_case__						:       Optional[int]       						=  0
												else:
														snake_case__						:       str       						=  logits.detach().argmax(dim=1  )
														if patient_result is not None:
																snake_case__						:       Optional[Any]       						=  patient_result.detach().argmax(dim=1  )
														if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase__  )  ):
																patient_counter += 1
														else:
																snake_case__						:       List[str]       						=  0

												snake_case__						:       int       						=  logits
												if patient_counter == self.patience:
														break
										snake_case__						:       Optional[Any]       						=  [patient_result]
										self.inference_layers_num += calculated_layer_num
										self.inference_instances_num += 1

								return res





@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """			,			lowercase_			,			)
class     a			(				lowercase_       ):






						"""simple docstring"""




						def __init__(							self					:      Dict			,			snake_case_					:      str  ):



								'''simple docstring'''



								super().__init__(UpperCamelCase__  )
								snake_case__						:       str       						=  config.num_labels

								snake_case__						:       int       						=  BertModelWithPabee(UpperCamelCase__  )
								snake_case__						:       Dict       						=  nn.Dropout(config.hidden_dropout_prob  )
								snake_case__						:       List[str]       						=  nn.ModuleList(
								    [nn.Linear(config.hidden_size			,			self.config.num_labels  ) for _ in range(config.num_hidden_layers  )]  )

								self.init_weights()







						@add_start_docstrings_to_model_forward(UpperCamelCase__  )
						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      Dict=None			,			snake_case_					:      Tuple=None			,			snake_case_					:      int=None			,			snake_case_					:      int=None			,			snake_case_					:      Union[str, Any]=None			,			snake_case_					:      Dict=None			,			snake_case_					:      Union[str, Any]=None			,			):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.bert(
								    input_ids=UpperCamelCase__			,			attention_mask=UpperCamelCase__			,			token_type_ids=UpperCamelCase__			,			position_ids=UpperCamelCase__			,			head_mask=UpperCamelCase__			,			inputs_embeds=UpperCamelCase__			,			output_dropout=self.dropout			,			output_layers=self.classifiers			,			regression=self.num_labels == 1			,			)

								snake_case__						:       Optional[int]       						=  (logits[-1],)

								if labels is not None:
										snake_case__						:       str       						=  None
										snake_case__						:       Union[str, Any]       						=  0
										for ix, logits_item in enumerate(UpperCamelCase__  ):
												if self.num_labels == 1:
														#  We are doing regression
														snake_case__						:       int       						=  MSELoss()
														snake_case__						:       Union[str, Any]       						=  loss_fct(logits_item.view(-1  )			,			labels.view(-1  )  )
												else:
														snake_case__						:       Tuple       						=  CrossEntropyLoss()
														snake_case__						:       Optional[Any]       						=  loss_fct(logits_item.view(-1			,			self.num_labels  )			,			labels.view(-1  )  )
												if total_loss is None:
														snake_case__						:       List[str]       						=  loss
												else:
														total_loss += loss * (ix + 1)
												total_weights += ix + 1
										snake_case__						:       Any       						=  (total_loss / total_weights,) + outputs

								return outputs






'''simple docstring'''


import argparse
import os
import shutil

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




def 	_a       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       int       						=  args.pruning_method
		snake_case__						:       Dict       						=  args.threshold

		snake_case__						:       str       						=  args.model_name_or_path.rstrip('''/'''     )
		snake_case__						:       int       						=  args.target_model_path

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

		for name, tensor in model.items():
				if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
						snake_case__						:       List[Any]       						=  tensor
						print(F"""Copied layer {name}"""     )
				elif "classifier" in name or "qa_output" in name:
						snake_case__						:       int       						=  tensor
						print(F"""Copied layer {name}"""     )
				elif "bias" in name:
						snake_case__						:       Union[str, Any]       						=  tensor
						print(F"""Copied layer {name}"""     )
				else:
						if pruning_method == "magnitude":
								snake_case__						:       Dict       						=  MagnitudeBinarizer.apply(inputs=__lowerCAmelCase   ,							threshold=__lowerCAmelCase     )
								snake_case__						:       Optional[int]       						=  tensor * mask
								print(F"""Pruned layer {name}"""     )
						elif pruning_method == "topK":
								if "mask_scores" in name:
										continue
								snake_case__						:       List[str]       						=  name[:-6]
								snake_case__						:       List[Any]       						=  model[F"""{prefix_}mask_scores"""]
								snake_case__						:       Optional[int]       						=  TopKBinarizer.apply(__lowerCAmelCase   ,							__lowerCAmelCase     )
								snake_case__						:       Any       						=  tensor * mask
								print(F"""Pruned layer {name}"""     )
						elif pruning_method == "sigmoied_threshold":
								if "mask_scores" in name:
										continue
								snake_case__						:       Optional[Any]       						=  name[:-6]
								snake_case__						:       Dict       						=  model[F"""{prefix_}mask_scores"""]
								snake_case__						:       Dict       						=  ThresholdBinarizer.apply(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )
								snake_case__						:       int       						=  tensor * mask
								print(F"""Pruned layer {name}"""     )
						elif pruning_method == "l0":
								if "mask_scores" in name:
										continue
								snake_case__						:       Any       						=  name[:-6]
								snake_case__						:       Tuple       						=  model[F"""{prefix_}mask_scores"""]
								snake_case__						,       snake_case__						:       Tuple       						=  -0.1, 1.1
								snake_case__						:       List[str]       						=  torch.sigmoid(__lowerCAmelCase     )
								snake_case__						:       Optional[int]       						=  s * (r - l) + l
								snake_case__						:       Optional[int]       						=  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__						:       Optional[int]       						=  os.path.join(
				    os.path.dirname(__lowerCAmelCase     )   ,							F"""bertarized_{os.path.basename(__lowerCAmelCase     )}"""     )

		if not os.path.isdir(__lowerCAmelCase     ):
				shutil.copytree(__lowerCAmelCase   ,							__lowerCAmelCase     )
				print(F"""\nCreated folder {target_model_path}"""     )

		torch.save(__lowerCAmelCase   ,							os.path.join(__lowerCAmelCase   ,							'''pytorch_model.bin'''     )     )
		print('''\nPruned model saved! See you later!'''     )


if __name__ == "__main__":
					lowerCAmelCase__						:       str										=						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__						:       Optional[Any]										=						parser.parse_args()

					main(args)



