






"""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,
)


lowercase__          = logging.getLogger(__name__)


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	List[Any]						,			a_							:	Optional[int]						,			a_							:	List[Any]						,			a_							:	Tuple=None						,			a_							:	Any=None	):
				lowerCAmelCase_   :							str									=			self.layer[current_layer](a_						,			a_						,			head_mask[current_layer]	)

				lowerCAmelCase_   :							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.""" ,							A__ ,							)
class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		def __init__(   self							:	Optional[Any]						,			a_							:	Union[str, Any]	):
				super().__init__(a_	)

				lowerCAmelCase_   :							List[Any]									=			BertEncoderWithPabee(a_	)

				self.init_weights()
				lowerCAmelCase_   :							Any									=			0
				lowerCAmelCase_   :							List[Any]									=			0
				lowerCAmelCase_   :							str									=			0

				lowerCAmelCase_   :							Tuple									=			0





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	Tuple	):
				lowerCAmelCase_   :							Dict									=			threshold





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	str	):
				lowerCAmelCase_   :							List[str]									=			patience





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							Union[str, Any]									=			0
				lowerCAmelCase_   :							Dict									=			0





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							List[Any]									=			self.inference_layers_num / self.inference_instances_num
				lowerCAmelCase_   :							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(a_	)





		@add_start_docstrings_to_model_forward(a_	)
		def    lowerCamelCase       (   self							:	int						,			a_							:	List[str]=None						,			a_							:	int=None						,			a_							:	Union[str, Any]=None						,			a_							:	List[Any]=None						,			a_							:	Union[str, Any]=None						,			a_							:	List[str]=None						,			a_							:	List[Any]=None						,			a_							:	str=None						,			a_							:	int=None						,			a_							:	List[str]=None						,			a_							:	Optional[Any]=False						,			):

				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:
						lowerCAmelCase_   :							Tuple									=			input_ids.size()
				elif inputs_embeds is not None:
						lowerCAmelCase_   :							Optional[Any]									=			inputs_embeds.size()[:-1]
				else:
						raise ValueError("You have to specify either input_ids or inputs_embeds"	)

				lowerCAmelCase_   :							str									=			input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
						lowerCAmelCase_   :							Union[str, Any]									=			torch.ones(a_						,			device=a_	)
				if token_type_ids is None:
						lowerCAmelCase_   :							Optional[Any]									=			torch.zeros(a_						,			dtype=torch.long						,			device=a_	)

				# 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.
				lowerCAmelCase_   :							torch.Tensor									=			self.get_extended_attention_mask(a_						,			a_						,			a_	)

				# 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:
						lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_   :							Tuple									=			encoder_hidden_states.size()
						lowerCAmelCase_   :							int									=			(encoder_batch_size, encoder_sequence_length)
						if encoder_attention_mask is None:
								lowerCAmelCase_   :							Union[str, Any]									=			torch.ones(a_						,			device=a_	)
						lowerCAmelCase_   :							List[Any]									=			self.invert_attention_mask(a_	)
				else:
						lowerCAmelCase_   :							Optional[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]
				lowerCAmelCase_   :							List[Any]									=			self.get_head_mask(a_						,			self.config.num_hidden_layers	)

				lowerCAmelCase_   :							int									=			self.embeddings(
				    input_ids=a_						,			position_ids=a_						,			token_type_ids=a_						,			inputs_embeds=a_	)
				lowerCAmelCase_   :							List[Any]									=			embedding_output

				if self.training:
						lowerCAmelCase_   :							Dict									=			[]
						for i in range(self.config.num_hidden_layers	):
								lowerCAmelCase_   :							Optional[Any]									=			self.encoder.adaptive_forward(
								    a_						,			current_layer=a_						,			attention_mask=a_						,			head_mask=a_	)

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

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

								lowerCAmelCase_   :							Tuple									=			logits
								if patient_counter == self.patience:
										break
						lowerCAmelCase_   :							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. """ ,							A__ ,							)
class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		def __init__(   self							:	Any						,			a_							:	Any	):
				super().__init__(a_	)
				lowerCAmelCase_   :							Union[str, Any]									=			config.num_labels

				lowerCAmelCase_   :							List[Any]									=			BertModelWithPabee(a_	)
				lowerCAmelCase_   :							Tuple									=			nn.Dropout(config.hidden_dropout_prob	)
				lowerCAmelCase_   :							Optional[int]									=			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(a_	)
		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	Union[str, Any]=None						,			a_							:	str=None						,			a_							:	Union[str, Any]=None						,			a_							:	List[str]=None						,			a_							:	Tuple=None						,			a_							:	str=None						,			a_							:	Optional[Any]=None						,			):
				lowerCAmelCase_   :							Optional[Any]									=			self.bert(
				    input_ids=a_						,			attention_mask=a_						,			token_type_ids=a_						,			position_ids=a_						,			head_mask=a_						,			inputs_embeds=a_						,			output_dropout=self.dropout						,			output_layers=self.classifiers						,			regression=self.num_labels == 1						,			)

				lowerCAmelCase_   :							List[str]									=			(logits[-1],)

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

				return outputs








"""simple docstring"""

lowercase__          = 0  # The first color of the flag.
lowercase__          = 1  # The second color of the flag.
lowercase__          = 2  # The third color of the flag.
lowercase__          = (red, white, blue)

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    list:

		"""simple docstring"""







		if not sequence:
				return []
		if len(__UpperCamelCase       ) == 1:
				return list(__UpperCamelCase       )
		lowerCAmelCase_   :							Optional[Any]									=			0
		lowerCAmelCase_   :							List[Any]									=			len(__UpperCamelCase       ) - 1
		lowerCAmelCase_   :							Union[str, Any]									=			0
		while mid <= high:
				if sequence[mid] == colors[0]:
						lowerCAmelCase_				,	lowerCAmelCase_   :							Any									=			sequence[mid], sequence[low]
						low += 1
						mid += 1
				elif sequence[mid] == colors[1]:
						mid += 1
				elif sequence[mid] == colors[2]:
						lowerCAmelCase_				,	lowerCAmelCase_   :							str									=			sequence[high], sequence[mid]
						high -= 1
				else:
						lowerCAmelCase_   :							str									=			f'''The elements inside the sequence must contains only {colors} values'''
						raise ValueError(__UpperCamelCase       )
		return sequence


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowercase__          = input("""Enter numbers separated by commas:\n""").strip()
					lowercase__          = [int(item.strip()) for item in user_input.split(""",""")]
					print(F"""{dutch_national_flag_sort(unsorted)}""")
