


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,
)


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







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None    ):





										"""simple docstring"""




										if current_layer == 0:
													SCREAMING_SNAKE_CASE_						:			Dict       						= self.embedding_hidden_mapping_in(lowerCAmelCase__    )
										else:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= hidden_states[0]

										SCREAMING_SNAKE_CASE_						:			List[str]       						= int(self.config.num_hidden_layers / self.config.num_hidden_groups    )

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

										SCREAMING_SNAKE_CASE_						:			List[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_						:			List[str]       						= 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."""     ,    __SCREAMING_SNAKE_CASE     ,    )
class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= AlbertTransformerWithPabee(lowerCAmelCase__    )

										self.init_weights()
										SCREAMING_SNAKE_CASE_						:			str       						= 0
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 0
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 0

										SCREAMING_SNAKE_CASE_						:			Any       						= 0






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= threshold






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= patience






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= 0
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 0






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= self.inference_layers_num / self.inference_instances_num
										SCREAMING_SNAKE_CASE_						:			Tuple       						= (
										    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       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=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_						:			Optional[Any]       						= inputs_embeds.size()[:-1]
										else:
													raise ValueError('You have to specify either input_ids or inputs_embeds'    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= input_ids.device if input_ids is not None else inputs_embeds.device

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

										SCREAMING_SNAKE_CASE_						:			Dict       						= attention_mask.unsqueeze(1    ).unsqueeze(2    )
										SCREAMING_SNAKE_CASE_						:			int       						= extended_attention_mask.to(dtype=self.dtype    )  # fp16 compatibility
										SCREAMING_SNAKE_CASE_						:			Tuple       						= (1.0 - extended_attention_mask) * -10_000.0
										SCREAMING_SNAKE_CASE_						:			int       						= self.get_head_mask(lowerCAmelCase__       ,		self.config.num_hidden_layers    )

										SCREAMING_SNAKE_CASE_						:			Any       						= self.embeddings(
										    lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		token_type_ids=lowerCAmelCase__       ,		inputs_embeds=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= embedding_output

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

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

																SCREAMING_SNAKE_CASE_						:			Any       						= self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]    )    )
																SCREAMING_SNAKE_CASE_						:			List[str]       						= output_layers[i](lowerCAmelCase__    )
																if regression:
																			SCREAMING_SNAKE_CASE_						:			int       						= logits.detach()
																			if patient_result is not None:
																						SCREAMING_SNAKE_CASE_						:			Dict       						= 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_						:			str       						= 0
																else:
																			SCREAMING_SNAKE_CASE_						:			List[str]       						= logits.detach().argmax(dim=1    )
																			if patient_result is not None:
																						SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 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_						:			Optional[Any]       						= 0

																SCREAMING_SNAKE_CASE_						:			List[str]       						= logits
																if patient_counter == self.patience:
																			break
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= [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. """     ,    __SCREAMING_SNAKE_CASE     ,    )
class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= config.num_labels

										SCREAMING_SNAKE_CASE_						:			str       						= AlbertModelWithPabee(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= nn.Dropout(config.classifier_dropout_prob    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 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       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[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_						:			Dict       						= (logits[-1],)

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

										return outputs









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__       :	List[Any]     				={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Optional[Any]     				=['MBart50Tokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Any     				=['MBart50TokenizerFast']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__       :	Optional[int]     				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





