



'''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.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


UpperCamelCase__	:     Any    =			logging.getLogger(__name__)






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			List[str]=None							,					lowerCAmelCase__					:			Union[str, Any]=None						):





					"""simple docstring"""






					if current_layer == 0:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.embedding_hidden_mapping_in(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		hidden_states[0]

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		int(self.config.num_hidden_layers / self.config.num_hidden_groups						)

					# Index of the hidden group
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.albert_layer_groups[group_idx](
					    lowerCAmelCase__							,					lowerCAmelCase__							,					head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]							,					)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		layer_group_output[0]

					return (hidden_states,)






@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''   , lowerCamelCase__   , )
class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def __init__(							self					:			List[str]							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		AlbertTransformerWithPabee(lowerCAmelCase__						)

					self.init_weights()
					__SCREAMING_SNAKE_CASE	:		Any   	=		0
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		0
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		0

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		0

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		threshold

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		patience

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		0
					__SCREAMING_SNAKE_CASE	:		Dict   	=		0

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.inference_layers_num / self.inference_instances_num
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    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(lowerCAmelCase__						)



			@add_start_docstrings_to_model_forward(lowerCAmelCase__						)
			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Any=None							,					lowerCAmelCase__					:			Union[str, Any]=None							,					lowerCAmelCase__					:			str=None							,					lowerCAmelCase__					:			str=None							,					lowerCAmelCase__					:			Dict=None							,					lowerCAmelCase__					:			Union[str, Any]=None							,					lowerCAmelCase__					:			Dict=None							,					lowerCAmelCase__					:			Optional[int]=None							,					lowerCAmelCase__					:			Any=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:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		input_ids.size()
					elif inputs_embeds is not None:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		inputs_embeds.size()[:-1]
					else:
							raise ValueError("""You have to specify either input_ids or inputs_embeds"""						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		input_ids.device if input_ids is not None else inputs_embeds.device

					if attention_mask is None:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.ones(lowerCAmelCase__							,					device=lowerCAmelCase__						)
					if token_type_ids is None:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.zeros(lowerCAmelCase__							,					dtype=torch.long							,					device=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		attention_mask.unsqueeze(1						).unsqueeze(2						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(1.0 - extended_attention_mask) * -1_00_00.0
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.get_head_mask(lowerCAmelCase__							,					self.config.num_hidden_layers						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.embeddings(
					    lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					token_type_ids=lowerCAmelCase__							,					inputs_embeds=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		embedding_output

					if self.training:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		[]
							for i in range(self.config.num_hidden_layers						):
									__SCREAMING_SNAKE_CASE	:		Dict   	=		self.encoder.adaptive_forward(
									    lowerCAmelCase__							,					current_layer=lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					head_mask=lowerCAmelCase__							,					)

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

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

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

					return res






@add_start_docstrings(
    '''Albert 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. '''   , lowerCamelCase__   , )
class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def __init__(							self					:			List[Any]							,					lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		config.num_labels

					__SCREAMING_SNAKE_CASE	:		Dict   	=		AlbertModelWithPabee(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		nn.Dropout(config.classifier_dropout_prob						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		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(lowerCAmelCase__						)
			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Union[str, Any]=None							,					lowerCAmelCase__					:			List[Any]=None							,					lowerCAmelCase__					:			Optional[int]=None							,					lowerCAmelCase__					:			str=None							,					lowerCAmelCase__					:			Optional[int]=None							,					lowerCAmelCase__					:			Any=None							,					lowerCAmelCase__					:			List[Any]=None							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.albert(
					    input_ids=lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					token_type_ids=lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					head_mask=lowerCAmelCase__							,					inputs_embeds=lowerCAmelCase__							,					output_dropout=self.dropout							,					output_layers=self.classifiers							,					regression=self.num_labels == 1							,					)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(logits[-1],)

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

					return outputs




'''simple docstring'''




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCamelCase__	:     str    =			TypeVar('''T''', bound=bool)






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		T   	=		value
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

			@property
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					return self._value



			@staticmethod
			def UpperCamelCase__ (							lowerCAmelCase__					:			RandomizedHeapNode[T] | None							,					lowerCAmelCase__					:			RandomizedHeapNode[T] | None						):





					"""simple docstring"""






					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		roota, roota

					if random.choice([True, False]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		roota.right, roota.left

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		RandomizedHeapNode.merge(roota.left							,					lowerCAmelCase__						)

					return roota






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			Iterable[T] | None = ()						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

					if data:
							for item in data:
									self.insert(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		RandomizedHeapNode.merge(self._root							,					RandomizedHeapNode(lowerCAmelCase__						)						)

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.top()

					if self._root is None:
							return None

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		RandomizedHeapNode.merge(self._root.left							,					self._root.right						)

					return result

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





					"""simple docstring"""






					if not self._root:
							raise IndexError("""Can't get top element for the empty heap."""						)
					return self._root.value

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		[]
					while self:
							result.append(self.pop()						)

					return result



			def __bool__(							self					:			List[Any]						):





					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()