

"""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.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


_UpperCamelCase		    =						logging.getLogger(__name__)






class  __a				(	__magic_name__					):





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case=None ,				snake_case=None      ):





        """simple docstring"""

        if current_layer == 0:
         lowerCAmelCase__      :							Tuple    =							self.embedding_hidden_mapping_in(snake_case      )
        else:
         lowerCAmelCase__      :							Any    =							hidden_states[0]

        lowerCAmelCase__      :							Optional[int]    =							int(self.config.num_hidden_layers / self.config.num_hidden_groups      )

        # Index of the hidden group
        lowerCAmelCase__      :							Optional[int]    =							int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)      )

        lowerCAmelCase__      :							int    =							self.albert_layer_groups[group_idx](
            snake_case ,				snake_case ,				head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group] ,				)
        lowerCAmelCase__      :							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.'					,  __magic_name__					,  )
class  __a				(	__magic_name__					):





       """simple docstring"""


       def __init__(      self ,				snake_case      ):





        """simple docstring"""

        super().__init__(snake_case      )

        lowerCAmelCase__      :							List[Any]    =							AlbertTransformerWithPabee(snake_case      )

        self.init_weights()
        lowerCAmelCase__      :							Union[str, Any]    =							0
        lowerCAmelCase__      :							Optional[Any]    =							0
        lowerCAmelCase__      :							str    =							0

        lowerCAmelCase__      :							Any    =							0






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							threshold






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							patience






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							0
        lowerCAmelCase__      :							str    =							0






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							self.inference_layers_num / self.inference_instances_num
        lowerCAmelCase__      :							Optional[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(snake_case      )






       @add_start_docstrings_to_model_forward(snake_case      )
       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=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:
         lowerCAmelCase__      :							List[str]    =							input_ids.size()
        elif inputs_embeds is not None:
         lowerCAmelCase__      :							str    =							inputs_embeds.size()[:-1]
        else:
         raise ValueError("You have to specify either input_ids or inputs_embeds"      )

        lowerCAmelCase__      :							Union[str, Any]    =							input_ids.device if input_ids is not None else inputs_embeds.device

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

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

        lowerCAmelCase__      :							Dict    =							self.embeddings(
            snake_case ,				position_ids=snake_case ,				token_type_ids=snake_case ,				inputs_embeds=snake_case      )
        lowerCAmelCase__      :							str    =							embedding_output

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

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

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

          lowerCAmelCase__      :							Any    =							logits
          if patient_counter == self.patience:
           break
         lowerCAmelCase__      :							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\n    the pooled output) e.g. for GLUE tasks. '					,  __magic_name__					,  )
class  __a				(	__magic_name__					):





       """simple docstring"""


       def __init__(      self ,				snake_case      ):





        """simple docstring"""

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

        lowerCAmelCase__      :							Any    =							AlbertModelWithPabee(snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							nn.Dropout(config.classifier_dropout_prob      )
        lowerCAmelCase__      :							List[Any]    =							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(snake_case      )
       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							self.albert(
            input_ids=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				position_ids=snake_case ,				head_mask=snake_case ,				inputs_embeds=snake_case ,				output_dropout=self.dropout ,				output_layers=self.classifiers ,				regression=self.num_labels == 1 ,				)

        lowerCAmelCase__      :							int    =							(logits[-1],)

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

        return outputs









"""simple docstring"""

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
_UpperCamelCase		    =						False
_UpperCamelCase		    =						Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_UpperCamelCase		    =						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  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							Optional[Any]    =							arg

        assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
        assert outputfile is not None and not os.path.isfile(snake_case      ), F"""{outputfile}"""
        if subset_list is not None:
         with open(os.path.realpath(snake_case      )      ) as f:
          lowerCAmelCase__      :							Union[str, Any]    =							{self._vqa_file_split()[0] for x in tryload(snake_case      )}
        else:
         lowerCAmelCase__      :							Optional[int]    =							None

        lowerCAmelCase__      :							Optional[int]    =							CONFIG
        if torch.cuda.is_available():
         lowerCAmelCase__      :							Union[str, Any]    =							"cuda"
        lowerCAmelCase__      :							Dict    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Tuple    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Any    =							Preprocess(self.config      )
        lowerCAmelCase__      :							str    =							GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned" ,				config=self.config      )
        lowerCAmelCase__      :							int    =							batch_size if batch_size != 0 else 1
        lowerCAmelCase__      :							Optional[int]    =							DEFAULT_SCHEMA






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							[]
        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(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

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






       def __call__(      self      ):





        """simple docstring"""

        if not TEST:
         lowerCAmelCase__      :							Tuple    =							datasets.ArrowWriter(features=self.schema ,				path=self.outputfile      )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
         lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							self.model(
             snake_case ,				snake_case ,				scales_yx=snake_case ,				padding="max_detections" ,				max_detections=self.config.MAX_DETECTIONS ,				pad_value=0 ,				return_tensors="np" ,				location="cpu" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							stream.read().split("\n"      )
 return data


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






