
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,
)


__a        = logging.getLogger(__name__)
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Tuple=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	)	->					Any:
					if current_layer == 0:
									lowercase_					  = self.embedding_hidden_mapping_in(SCREAMING_SNAKE_CASE_	)
					else:
									lowercase_					  = hidden_states[0]

					lowercase_					  = int(self.config.num_hidden_layers / self.config.num_hidden_groups	)

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

					lowercase_					  = self.albert_layer_groups[group_idx](
					    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]	,							)
					lowercase_					  = 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  :      Any	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					Tuple:
					super().__init__(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = AlbertTransformerWithPabee(SCREAMING_SNAKE_CASE_	)

					self.init_weights()
					lowercase_					  = 0
					lowercase_					  = 0
					lowercase_					  = 0

					lowercase_					  = 0
	def 						_lowercase  (	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      List[str]	)	->					int:
					lowercase_					  = threshold
	def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	)	->					List[Any]:
					lowercase_					  = patience
	def 						_lowercase  (	self  :      Tuple	)	->					Dict:
					lowercase_					  = 0
					lowercase_					  = 0
	def 						_lowercase  (	self  :      Tuple	)	->					Tuple:
					lowercase_					  = self.inference_layers_num / self.inference_instances_num
					lowercase_					  = (
					    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(SCREAMING_SNAKE_CASE_	)




	@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=None	,							SCREAMING_SNAKE_CASE_  :      List[Any]=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      Tuple=None	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=None	,							SCREAMING_SNAKE_CASE_  :      List[Any]=None	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=False	,							)	->					List[Any]:

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

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

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

					lowercase_					  = attention_mask.unsqueeze(1	).unsqueeze(2	)
					lowercase_					  = extended_attention_mask.to(dtype=self.dtype	)  # fp16 compatibility
					lowercase_					  = (1.0 - extended_attention_mask) * -1_00_00.0
					lowercase_					  = self.get_head_mask(SCREAMING_SNAKE_CASE_	,							self.config.num_hidden_layers	)

					lowercase_					  = self.embeddings(
					    SCREAMING_SNAKE_CASE_	,							position_ids=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							inputs_embeds=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = embedding_output

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

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

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

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



	"""simple docstring"""




	def __init__(	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      List[Any]	)	->					Tuple:
					super().__init__(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = config.num_labels

					lowercase_					  = AlbertModelWithPabee(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = nn.Dropout(config.classifier_dropout_prob	)
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Dict=None	,							SCREAMING_SNAKE_CASE_  :      int=None	,							SCREAMING_SNAKE_CASE_  :      List[Any]=None	,							SCREAMING_SNAKE_CASE_  :      Dict=None	,							SCREAMING_SNAKE_CASE_  :      Tuple=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      List[Any]=None	,							)	->					Tuple:
					lowercase_					  = self.albert(
					    input_ids=SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							position_ids=SCREAMING_SNAKE_CASE_	,							head_mask=SCREAMING_SNAKE_CASE_	,							inputs_embeds=SCREAMING_SNAKE_CASE_	,							output_dropout=self.dropout	,							output_layers=self.classifiers	,							regression=self.num_labels == 1	,							)

					lowercase_					  = (logits[-1],)

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

					return outputs






import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__a        = False
__a        = Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__a        = datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=sys.argv[1:]	)	->					int:
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = 1
					lowercase_  ,     lowercase_					  = getopt.getopt(SCREAMING_SNAKE_CASE_	,							'''i:o:b:s'''	,							['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
					for opt, arg in opts:
									if opt in ("-i", "--inputdir"):
													lowercase_					  = arg
									elif opt in ("-o", "--outfile"):
													lowercase_					  = arg
									elif opt in ("-b", "--batch_size"):
													lowercase_					  = int(SCREAMING_SNAKE_CASE_	)
									elif opt in ("-s", "--subset_list"):
													lowercase_					  = arg

					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
					assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE_	), f'''{outputfile}'''
					if subset_list is not None:
									with open(os.path.realpath(SCREAMING_SNAKE_CASE_	)	) as f:
													lowercase_					  = {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_	)}
					else:
									lowercase_					  = None

					lowercase_					  = CONFIG
					if torch.cuda.is_available():
									lowercase_					  = '''cuda'''
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = Preprocess(self.config	)
					lowercase_					  = GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''	,							config=self.config	)
					lowercase_					  = batch_size if batch_size != 0 else 1
					lowercase_					  = DEFAULT_SCHEMA
	def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	)	->					Any:
					lowercase_					  = int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
					lowercase_					  = os.path.join(self.inputdir	,							SCREAMING_SNAKE_CASE_	)
					return (img_id, filepath)
	@property
	def 						_lowercase  (	self  :      List[str]	)	->					List[Any]:
					lowercase_					  = []
					for i, file in enumerate(os.listdir(self.inputdir	)	):
									if self.subset_list is not None and i not in self.subset_list:
													continue
									batch.append(self._vqa_file_split(SCREAMING_SNAKE_CASE_	)	)
									if len(SCREAMING_SNAKE_CASE_	) == self.batch:
													lowercase_					  = batch
													lowercase_					  = []
													yield list(map(SCREAMING_SNAKE_CASE_	,							zip(*SCREAMING_SNAKE_CASE_	)	)	)

					for i in range(1	):
									yield list(map(SCREAMING_SNAKE_CASE_	,							zip(*SCREAMING_SNAKE_CASE_	)	)	)




	def __call__(	self  :      Any	)	->					List[str]:
					# make writer
					if not TEST:
									lowercase_					  = datasets.ArrowWriter(features=self.schema	,							path=self.outputfile	)
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator	):
									lowercase_  ,     lowercase_  ,     lowercase_					  = self.preprocess(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = self.model(
									    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							scales_yx=SCREAMING_SNAKE_CASE_	,							padding='''max_detections'''	,							max_detections=self.config.MAX_DETECTIONS	,							pad_value=0	,							return_tensors='''np'''	,							location='''cpu'''	,							)
									lowercase_					  = output_dict.pop('''normalized_boxes'''	)
									if not TEST:
													lowercase_					  = np.array(SCREAMING_SNAKE_CASE_	)
													lowercase_					  = self.schema.encode_batch(SCREAMING_SNAKE_CASE_	)
													writer.write_batch(SCREAMING_SNAKE_CASE_	)
									if TEST:
													break
									# finalizer the writer
					if not TEST:
									lowercase_  ,     lowercase_					  = writer.finalize()
									print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''	)




def     a						(							snake_case__: Optional[Any] ):


				'''simple docstring'''
				try:
								lowercase_					  = json.load(snake_case__ )
								try:
												lowercase_					  = list(data.keys() )
								except Exception:
												lowercase_					  = [d['''img_id'''] for d in data]
				except Exception:
								try:
												lowercase_					  = eval(stream.read() )
								except Exception:
												lowercase_					  = stream.read().split('''\n''' )
				return data


if __name__ == "__main__":
						__a        = Extract(sys.argv[1:])
						extract()
						if not TEST:
												__a        = datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)




