

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							:						List[Any]     			=		logging.getLogger(__name__)



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  List[Any]     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  Optional[int]=None     ,		lowerCAmelCase__						:  Any=None):
									if current_layer == 0:
															SCREAMING_SNAKE_CASE_:				int							= self.embedding_hidden_mapping_in(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= hidden_states[0]

									SCREAMING_SNAKE_CASE_:				Dict							= int(self.config.num_hidden_layers / self.config.num_hidden_groups)

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

									SCREAMING_SNAKE_CASE_:				Tuple							= self.albert_layer_groups[group_idx](
									    lowerCAmelCase__     ,		lowerCAmelCase__     ,		head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]     ,		)
									SCREAMING_SNAKE_CASE_:				Tuple							= 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 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= AlbertTransformerWithPabee(lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_:				Dict							= 0



			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Tuple):
									SCREAMING_SNAKE_CASE_:				List[str]							= threshold



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				int							= patience



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				Any							= 0
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= 0



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

									SCREAMING_SNAKE_CASE_:				int							= 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_:				Tuple							= torch.zeros(lowerCAmelCase__     ,		dtype=torch.long     ,		device=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Optional[int]							= attention_mask.unsqueeze(1).unsqueeze(2)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= (1.0 - extended_attention_mask) * -1_0000.0
									SCREAMING_SNAKE_CASE_:				List[str]							= 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[int]							= embedding_output

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

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

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




			"""simple docstring"""







			def __init__(				self						:  str     ,		lowerCAmelCase__						:  Optional[Any]):
									super().__init__(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Any							= config.num_labels

									SCREAMING_SNAKE_CASE_:				List[Any]							= AlbertModelWithPabee(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[str]							= nn.Dropout(config.classifier_dropout_prob)
									SCREAMING_SNAKE_CASE_:				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(lowerCAmelCase__)
			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]     ,		lowerCAmelCase__						:  Any=None     ,		lowerCAmelCase__						:  str=None     ,		lowerCAmelCase__						:  int=None     ,		lowerCAmelCase__						:  List[Any]=None     ,		lowerCAmelCase__						:  Union[str, Any]=None     ,		lowerCAmelCase__						:  Union[str, Any]=None     ,		lowerCAmelCase__						:  List[Any]=None     ,		):
									SCREAMING_SNAKE_CASE_:				Union[str, 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_:				Optional[int]							= (logits[-1],)

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

									return outputs









import io
import json
import subprocess


lowerCAmelCase							:						Dict     			=		[
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

lowerCAmelCase							:						List[str]     			=		8







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Dict							= {}
						for src, tgt in pairs:
												SCREAMING_SNAKE_CASE_:				Tuple							= f"{src}-{tgt}"
												SCREAMING_SNAKE_CASE_:				List[str]							= f"sacrebleu -t wmt19 -l {pair} --echo src".split()
												SCREAMING_SNAKE_CASE_:				int							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
												SCREAMING_SNAKE_CASE_:				Dict							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Tuple							= {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
						return text


lowerCAmelCase							:						Tuple     			=		get_all_data(pairs, n_objs)
lowerCAmelCase							:						Optional[Any]     			=		"""./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
						lowerCAmelCase							:						List[str]     			=		json.dump(text, f, indent=2, ensure_ascii=False)






