


'''simple docstring'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''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,
)


_UpperCAmelCase				: Optional[Any]							    =				logging.getLogger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				self.layer[current_layer](__lowercase       ,			__lowercase       ,			head_mask[current_layer]		)

										__lowerCAmelCase							       =				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       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase							       =				BertEncoderWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0

										__lowerCAmelCase							       =				0




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				threshold




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				patience




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase							       =				(
										    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(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=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							       =				input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase							       =				inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

										__lowerCAmelCase							       =				input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__lowerCAmelCase							       =				torch.ones(__lowercase       ,			device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase							       =				torch.zeros(__lowercase       ,			dtype=torch.long       ,			device=__lowercase		)

										# 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							       =				self.get_extended_attention_mask(__lowercase       ,			__lowercase       ,			__lowercase		)

										# 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							       =				encoder_hidden_states.size()
																__lowerCAmelCase							       =				(encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																						__lowerCAmelCase							       =				torch.ones(__lowercase       ,			device=__lowercase		)
																__lowerCAmelCase							       =				self.invert_attention_mask(__lowercase		)
										else:
																__lowerCAmelCase							       =				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							       =				self.get_head_mask(__lowercase       ,			self.config.num_hidden_layers		)

										__lowerCAmelCase							       =				self.embeddings(
										    input_ids=__lowercase       ,			position_ids=__lowercase       ,			token_type_ids=__lowercase       ,			inputs_embeds=__lowercase		)
										__lowerCAmelCase							       =				embedding_output

										if self.training:
																__lowerCAmelCase							       =				[]
																for i in range(self.config.num_hidden_layers		):
																						__lowerCAmelCase							       =				self.encoder.adaptive_forward(
																						    __lowercase       ,			current_layer=__lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase		)

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

																						__lowerCAmelCase							       =				self.pooler(__lowercase		)
																						__lowerCAmelCase							       =				output_layers[i](__lowercase		)
																						if regression:
																												__lowerCAmelCase							       =				logits.detach()
																												if patient_result is not None:
																																		__lowerCAmelCase							       =				patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase							       =				0
																						else:
																												__lowerCAmelCase							       =				logits.detach().argmax(dim=1		)
																												if patient_result is not None:
																																		__lowerCAmelCase							       =				patient_result.detach().argmax(dim=1		)
																												if (patient_result is not None) and torch.all(labels.eq(__lowercase		)		):
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase							       =				0

																						__lowerCAmelCase							       =				logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase							       =				[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\n    the pooled output) e.g. for GLUE tasks. '							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase							       =				config.num_labels

										__lowerCAmelCase							       =				BertModelWithPabee(__lowercase		)
										__lowerCAmelCase							       =				nn.Dropout(config.hidden_dropout_prob		)
										__lowerCAmelCase							       =				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(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			):
										__lowerCAmelCase							       =				self.bert(
										    input_ids=__lowercase       ,			attention_mask=__lowercase       ,			token_type_ids=__lowercase       ,			position_ids=__lowercase       ,			head_mask=__lowercase       ,			inputs_embeds=__lowercase       ,			output_dropout=self.dropout       ,			output_layers=self.classifiers       ,			regression=self.num_labels == 1       ,			)

										__lowerCAmelCase							       =				(logits[-1],)

										if labels is not None:
																__lowerCAmelCase							       =				None
																__lowerCAmelCase							       =				0
																for ix, logits_item in enumerate(__lowercase		):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__lowerCAmelCase							       =				MSELoss()
																												__lowerCAmelCase							       =				loss_fct(logits_item.view(-1		)       ,			labels.view(-1		)		)
																						else:
																												__lowerCAmelCase							       =				CrossEntropyLoss()
																												__lowerCAmelCase							       =				loss_fct(logits_item.view(-1       ,			self.num_labels		)       ,			labels.view(-1		)		)
																						if total_loss is None:
																												__lowerCAmelCase							       =				loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__lowerCAmelCase							       =				(total_loss / total_weights,) + outputs

										return outputs

