
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,
)


__snake_case					:							List[str]           =							logging.getLogger(__name__)


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self  , A  , A  , A=None  , A=None	)  ->Any:
									if current_layer == 0:
												UpperCAmelCase__		:Dict 		=    self.embedding_hidden_mapping_in(A	)
									else:
												UpperCAmelCase__		:str 		=    hidden_states[0]

									UpperCAmelCase__		:Any 		=    int(self.config.num_hidden_layers / self.config.num_hidden_groups	)

									# Index of the hidden group
									UpperCAmelCase__		:Tuple 		=    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)	)

									UpperCAmelCase__		:Union[str, Any] 		=    self.albert_layer_groups[group_idx](
									    A  , A  , head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]  , )
									UpperCAmelCase__		:List[Any] 		=    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."""						,			UpperCAmelCase__						,			)
class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A	)  ->Optional[Any]:
									super().__init__(A	)

									UpperCAmelCase__		:List[str] 		=    AlbertTransformerWithPabee(A	)

									self.init_weights()
									UpperCAmelCase__		:Tuple 		=    0
									UpperCAmelCase__		:str 		=    0
									UpperCAmelCase__		:Optional[Any] 		=    0

									UpperCAmelCase__		:Union[str, Any] 		=    0


						def    A__       (  self  , A	)  ->Union[str, Any]:
									UpperCAmelCase__		:List[str] 		=    threshold


						def    A__       (  self  , A	)  ->List[str]:
									UpperCAmelCase__		:List[str] 		=    patience


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Any 		=    0
									UpperCAmelCase__		:Tuple 		=    0


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    self.inference_layers_num / self.inference_instances_num
									UpperCAmelCase__		: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    A__       (  self  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , A=False  , )  ->int:

									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:
												UpperCAmelCase__		:Optional[int] 		=    input_ids.size()
									elif inputs_embeds is not None:
												UpperCAmelCase__		:List[str] 		=    inputs_embeds.size()[:-1]
									else:
												raise ValueError('You have to specify either input_ids or inputs_embeds'	)

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

									if attention_mask is None:
												UpperCAmelCase__		:int 		=    torch.ones(A  , device=A	)
									if token_type_ids is None:
												UpperCAmelCase__		:Dict 		=    torch.zeros(A  , dtype=torch.long  , device=A	)

									UpperCAmelCase__		:Any 		=    attention_mask.unsqueeze(1	).unsqueeze(2	)
									UpperCAmelCase__		:Tuple 		=    extended_attention_mask.to(dtype=self.dtype	)  # fp16 compatibility
									UpperCAmelCase__		:Union[str, Any] 		=    (1.0 - extended_attention_mask) * -1_0000.0
									UpperCAmelCase__		:Optional[Any] 		=    self.get_head_mask(A  , self.config.num_hidden_layers	)

									UpperCAmelCase__		:Any 		=    self.embeddings(
									    A  , position_ids=A  , token_type_ids=A  , inputs_embeds=A	)
									UpperCAmelCase__		:Tuple 		=    embedding_output

									if self.training:
												UpperCAmelCase__		:Any 		=    []
												for i in range(self.config.num_hidden_layers	):
															UpperCAmelCase__		:List[Any] 		=    self.encoder.adaptive_forward(
															    A  , current_layer=A  , attention_mask=A  , head_mask=A  , )

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

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

															UpperCAmelCase__		:List[Any] 		=    logits
															if patient_counter == self.patience:
																		break
												UpperCAmelCase__		:List[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. """						,			UpperCAmelCase__						,			)
class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A	)  ->List[Any]:
									super().__init__(A	)
									UpperCAmelCase__		:str 		=    config.num_labels

									UpperCAmelCase__		:Any 		=    AlbertModelWithPabee(A	)
									UpperCAmelCase__		:List[Any] 		=    nn.Dropout(config.classifier_dropout_prob	)
									UpperCAmelCase__		: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(A	)
						def    A__       (  self  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , )  ->Dict:
									UpperCAmelCase__		:Optional[Any] 		=    self.albert(
									    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  , )

									UpperCAmelCase__		:Any 		=    (logits[-1],)

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

									return outputs







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    copy.deepcopy(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[Any] 		=    sorted(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    [bisect.bisect_right(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							) for y in x]
			return quantized




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    np.arange(start=3     ,						stop=SCREAMING_SNAKE_CASE     ,						step=4							).tolist() if k > 0 else [10]
			UpperCAmelCase__		:List[Any] 		=    _quantize(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
			# count number of elements per group
			UpperCAmelCase__		:Dict 		=    np.unique(SCREAMING_SNAKE_CASE     ,						return_counts=SCREAMING_SNAKE_CASE							)[1]
			UpperCAmelCase__		:Any 		=    [0] + bins + [np.inf]
			logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE							)							)
			logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE							)							)
			return groups




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A  , A  , A	)  ->Union[str, Any]:
									if not isinstance(A  , A	):
												raise ValueError(
												    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(A	)	)
									UpperCAmelCase__		:Tuple 		=    sampler
									UpperCAmelCase__		:Tuple 		=    group_ids
									UpperCAmelCase__		:str 		=    batch_size


						def __iter__(  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)

									UpperCAmelCase__		:Any 		=    0
									for idx in self.sampler:
												UpperCAmelCase__		:List[Any] 		=    self.group_ids[idx]
												buffer_per_group[group_id].append(A	)
												samples_per_group[group_id].append(A	)
												if len(buffer_per_group[group_id]	) == self.batch_size:
															yield buffer_per_group[group_id]  # TODO
															num_batches += 1
															del buffer_per_group[group_id]
												assert len(buffer_per_group[group_id]	) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									UpperCAmelCase__		:Union[str, Any] 		=    len(self	)
									UpperCAmelCase__		:Tuple 		=    expected_num_batches - num_batches
									if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												UpperCAmelCase__		:str 		=    []
												for group_id, idxs in sorted(buffer_per_group.items()  , key=lambda A	: x[0]	):
															batch_idx.extend(A	)
															if len(A	) >= self.batch_size:
																		yield batch_idx[: self.batch_size]
																		UpperCAmelCase__		:int 		=    batch_idx[self.batch_size :]
																		num_remaining -= 1
												if len(A	) > 0:
															yield batch_idx
															num_remaining -= 1
									assert num_remaining == 0


						def __len__(  self	)  ->Union[str, Any]:
									return (len(self.sampler	) + self.batch_size - 1) // self.batch_size





