

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_a			=       logging.get_logger(__name__)






@dataclass
class 							A_			:
					_lowercase      :					str
					_lowercase      :					str
					_lowercase      :					Optional[str]       =     None
					_lowercase      :					Optional[str]       =     None


					def   UpperCAmelCase  (							self      :		int )							->     int:
						return json.dumps(dataclasses.asdict(self )      ,  indent=2 ) + "\n"







@dataclass(frozen=snake_case__ )
class 							A_			:
					_lowercase      :					List[int]
					_lowercase      :					Optional[List[int]]       =     None
					_lowercase      :					Optional[List[int]]       =     None
					_lowercase      :					Optional[Union[int, float]]       =     None


					def   UpperCAmelCase  (							self      :		Tuple )							->     str:
						return json.dumps(dataclasses.asdict(self ) ) + "\n"







class 							A_			:


					def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Optional[Any] )							->     Optional[int]:
						raise NotImplementedError()


					def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Dict )							->     Optional[Any]:
						raise NotImplementedError()


					def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Dict )							->     Union[str, Any]:
						raise NotImplementedError()


					def   UpperCAmelCase  (							self      :		Dict      ,  UpperCAmelCase      :		Dict )							->     str:
						raise NotImplementedError()


					def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[Any]:
						raise NotImplementedError()


					def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Dict )							->     Tuple:
						if len(self.get_labels() ) > 1:
							__lowerCAmelCase:       str            =				self.get_labels()[int(example.label )]
						return example


					@classmethod
					def   UpperCAmelCase  (							cls      :		str      ,  UpperCAmelCase      :		Dict      ,  UpperCAmelCase      :		Union[str, Any]=None )							->     Union[str, Any]:
						with open(UpperCAmelCase      ,  'r'      ,  encoding='utf-8-sig' ) as f:
							return list(csv.reader(UpperCAmelCase      ,  delimiter='\t'      ,  quotechar=UpperCAmelCase ) )







class 							A_			(		snake_case__ ):


					def __init__(							self      :		Union[str, Any]      ,  UpperCAmelCase      :		str=None      ,  UpperCAmelCase      :		int=None      ,  UpperCAmelCase      :		List[str]="classification"      ,  UpperCAmelCase      :		Tuple=False )							->     List[Any]:
						__lowerCAmelCase:       Optional[Any]            =				[] if labels is None else labels
						__lowerCAmelCase:       List[str]            =				[] if examples is None else examples
						__lowerCAmelCase:       Optional[Any]            =				mode
						__lowerCAmelCase:       Optional[int]            =				verbose


					def __len__(							self      :		Any )							->     str:
						return len(self.examples )


					def __getitem__(							self      :		Any      ,  UpperCAmelCase      :		str )							->     int:
						if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
							return SingleSentenceClassificationProcessor(labels=self.labels      ,  examples=self.examples[idx] )
						return self.examples[idx]


					@classmethod
					def   UpperCAmelCase  (							cls      :		Any      ,  UpperCAmelCase      :		List[Any]      ,  UpperCAmelCase      :		List[Any]=""      ,  UpperCAmelCase      :		List[Any]=0      ,  UpperCAmelCase      :		List[str]=1      ,  UpperCAmelCase      :		Any=None      ,  UpperCAmelCase      :		Optional[Any]=False      ,  **UpperCAmelCase      :		List[Any] )							->     Dict:
						__lowerCAmelCase:       Dict            =				cls(**UpperCAmelCase )
						processor.add_examples_from_csv(
						    UpperCAmelCase      ,  split_name=UpperCAmelCase      ,  column_label=UpperCAmelCase      ,  column_text=UpperCAmelCase      ,  column_id=UpperCAmelCase      ,  skip_first_row=UpperCAmelCase      ,  overwrite_labels=UpperCAmelCase      ,  overwrite_examples=UpperCAmelCase      ,  )
						return processor


					@classmethod
					def   UpperCAmelCase  (							cls      :		List[Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		List[str]=None      ,  **UpperCAmelCase      :		Dict )							->     List[str]:
						__lowerCAmelCase:       List[str]            =				cls(**UpperCAmelCase )
						processor.add_examples(UpperCAmelCase      ,  labels=UpperCAmelCase )
						return processor


					def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		List[Any]      ,  UpperCAmelCase      :		Any=""      ,  UpperCAmelCase      :		List[Any]=0      ,  UpperCAmelCase      :		Dict=1      ,  UpperCAmelCase      :		Optional[Any]=None      ,  UpperCAmelCase      :		Dict=False      ,  UpperCAmelCase      :		List[str]=False      ,  UpperCAmelCase      :		Any=False      ,  )							->     Any:
						__lowerCAmelCase:       Union[str, Any]            =				self._read_tsv(UpperCAmelCase )
						if skip_first_row:
							__lowerCAmelCase:       Optional[Any]            =				lines[1:]
						__lowerCAmelCase:       Union[str, Any]            =				[]
						__lowerCAmelCase:       int            =				[]
						__lowerCAmelCase:       List[str]            =				[]
						for i, line in enumerate(UpperCAmelCase ):
							texts.append(line[column_text] )
							labels.append(line[column_label] )
							if column_id is not None:
								ids.append(line[column_id] )
							else:
								__lowerCAmelCase:       Any            =				F'''{split_name}-{i}''' if split_name else str(UpperCAmelCase )
								ids.append(UpperCAmelCase )

						return self.add_examples(
						    UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  overwrite_labels=UpperCAmelCase      ,  overwrite_examples=UpperCAmelCase )


					def   UpperCAmelCase  (							self      :		Dict      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Optional[Any]=None      ,  UpperCAmelCase      :		Optional[int]=None      ,  UpperCAmelCase      :		Union[str, Any]=False      ,  UpperCAmelCase      :		Optional[int]=False )							->     Union[str, Any]:
						if labels is not None and len(UpperCAmelCase ) != len(UpperCAmelCase ):
							raise ValueError(
							    F'''Text and labels have mismatched lengths {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
						if ids is not None and len(UpperCAmelCase ) != len(UpperCAmelCase ):
							raise ValueError(F'''Text and ids have mismatched lengths {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
						if ids is None:
							__lowerCAmelCase:       str            =				[None] * len(UpperCAmelCase )
						if labels is None:
							__lowerCAmelCase:       Optional[int]            =				[None] * len(UpperCAmelCase )
						__lowerCAmelCase:       int            =				[]
						__lowerCAmelCase:       Optional[Any]            =				set()
						for text_or_text_and_label, label, guid in zip(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase ):
							if isinstance(UpperCAmelCase      ,  (tuple, list) ) and label is None:
								__lowerCAmelCase:       Union[str, Any]            =				text_or_text_and_label
							else:
								__lowerCAmelCase:       List[str]            =				text_or_text_and_label
							added_labels.add(UpperCAmelCase )
							examples.append(InputExample(guid=UpperCAmelCase      ,  text_a=UpperCAmelCase      ,  text_b=UpperCAmelCase      ,  label=UpperCAmelCase ) )

						# Update examples
						if overwrite_examples:
							__lowerCAmelCase:       Optional[int]            =				examples
						else:
							self.examples.extend(UpperCAmelCase )

						# Update labels
						if overwrite_labels:
							__lowerCAmelCase:       int            =				list(UpperCAmelCase )
						else:
							__lowerCAmelCase:       str            =				list(set(self.labels ).union(UpperCAmelCase ) )

						return self.examples


					def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		List[Any]=None      ,  UpperCAmelCase      :		str=False      ,  UpperCAmelCase      :		List[Any]=0      ,  UpperCAmelCase      :		List[Any]=True      ,  UpperCAmelCase      :		Optional[int]=None      ,  )							->     List[str]:
						if max_length is None:
							__lowerCAmelCase:       Optional[int]            =				tokenizer.max_len

						__lowerCAmelCase:       List[Any]            =				{label: i for i, label in enumerate(self.labels )}

						__lowerCAmelCase:       Dict            =				[]
						for ex_index, example in enumerate(self.examples ):
							if ex_index % 1_0_0_0_0 == 0:
								logger.info(F'''Tokenizing example {ex_index}''' )

							__lowerCAmelCase:       Tuple            =				tokenizer.encode(
							    example.text_a      ,  add_special_tokens=UpperCAmelCase      ,  max_length=min(UpperCAmelCase      ,  tokenizer.max_len )      ,  )
							all_input_ids.append(UpperCAmelCase )

						__lowerCAmelCase:       Dict            =				max(len(UpperCAmelCase ) for input_ids in all_input_ids )

						__lowerCAmelCase:       Optional[int]            =				[]
						for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase      ,  self.examples ) ):
							if ex_index % 1_0_0_0_0 == 0:
								logger.info(F'''Writing example {ex_index}/{len(self.examples )}''' )
							# The mask has 1 for real tokens and 0 for padding tokens. Only real
							# tokens are attended to.
							__lowerCAmelCase:       Any            =				[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase )

							# Zero-pad up to the sequence length.
							__lowerCAmelCase:       Union[str, Any]            =				batch_length - len(UpperCAmelCase )
							if pad_on_left:
								__lowerCAmelCase:       Tuple            =				([pad_token] * padding_length) + input_ids
								__lowerCAmelCase:       Tuple            =				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
							else:
								__lowerCAmelCase:       str            =				input_ids + ([pad_token] * padding_length)
								__lowerCAmelCase:       Dict            =				attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

							if len(UpperCAmelCase ) != batch_length:
								raise ValueError(F'''Error with input length {len(UpperCAmelCase )} vs {batch_length}''' )
							if len(UpperCAmelCase ) != batch_length:
								raise ValueError(F'''Error with input length {len(UpperCAmelCase )} vs {batch_length}''' )

							if self.mode == "classification":
								__lowerCAmelCase:       int            =				label_map[example.label]
							elif self.mode == "regression":
								__lowerCAmelCase:       Dict            =				float(example.label )
							else:
								raise ValueError(self.mode )

							if ex_index < 5 and self.verbose:
								logger.info('*** Example ***' )
								logger.info(F'''guid: {example.guid}''' )
								logger.info(F'''input_ids: {' '.join([str(UpperCAmelCase ) for x in input_ids] )}''' )
								logger.info(F'''attention_mask: {' '.join([str(UpperCAmelCase ) for x in attention_mask] )}''' )
								logger.info(F'''label: {example.label} (id = {label})''' )

							features.append(InputFeatures(input_ids=UpperCAmelCase      ,  attention_mask=UpperCAmelCase      ,  label=UpperCAmelCase ) )

						if return_tensors is None:
							return features
						elif return_tensors == "tf":
							if not is_tf_available():
								raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported' )
							import tensorflow as tf

							def gen():
								for ex in features:
									yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

							__lowerCAmelCase:       List[Any]            =				tf.data.Dataset.from_generator(
							    UpperCAmelCase      ,  ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)      ,  ({'input_ids': tf.TensorShape([None] ), 'attention_mask': tf.TensorShape([None] )}, tf.TensorShape([] ))      ,  )
							return dataset
						elif return_tensors == "pt":
							if not is_torch_available():
								raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported' )
							import torch
							from torch.utils.data import TensorDataset

							__lowerCAmelCase:       Tuple            =				torch.tensor([f.input_ids for f in features]      ,  dtype=torch.long )
							__lowerCAmelCase:       Dict            =				torch.tensor([f.attention_mask for f in features]      ,  dtype=torch.long )
							if self.mode == "classification":
								__lowerCAmelCase:       Optional[int]            =				torch.tensor([f.label for f in features]      ,  dtype=torch.long )
							elif self.mode == "regression":
								__lowerCAmelCase:       Optional[Any]            =				torch.tensor([f.label for f in features]      ,  dtype=torch.float )

							__lowerCAmelCase:       Optional[int]            =				TensorDataset(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
							return dataset
						else:
							raise ValueError('return_tensors should be one of \'tf\' or \'pt\'' )








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 							A_			:


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

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

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


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Dict )							->     Dict:
      __lowerCAmelCase:       str            =				int(file.split('.' )[0].split('_' )[-1] )
      __lowerCAmelCase:       Union[str, Any]            =				os.path.join(self.inputdir      ,  UpperCAmelCase )
      return (img_id, filepath)


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __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(UpperCAmelCase ) )
       if len(UpperCAmelCase ) == self.batch:
        __lowerCAmelCase:       Optional[int]            =				batch
        __lowerCAmelCase:       Tuple            =				[]
        yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )

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


     def __call__(							self      :		Tuple )							->     Dict:
      # make writer
      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:       List[Any]            =				self.preprocess(UpperCAmelCase )
       __lowerCAmelCase:       str            =				self.model(
           UpperCAmelCase      ,  UpperCAmelCase      ,  scales_yx=UpperCAmelCase      ,  padding='max_detections'      ,  max_detections=self.config.MAX_DETECTIONS      ,  pad_value=0      ,  return_tensors='np'      ,  location='cpu'      ,  )
       __lowerCAmelCase:       str            =				output_dict.pop('normalized_boxes' )
       if not TEST:
        __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase )
        __lowerCAmelCase:       Optional[int]            =				self.schema.encode_batch(UpperCAmelCase )
        writer.write_batch(UpperCAmelCase )
       if TEST:
        break
       # finalizer the writer
      if not TEST:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb''' )







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Any:

 """simple docstring"""






 try:
  __lowerCAmelCase:       Optional[Any]            =				json.load(SCREAMING_SNAKE_CASE		)
  try:
   __lowerCAmelCase:       Tuple            =				list(data.keys()		)
  except Exception:
   __lowerCAmelCase:       int            =				[d['img_id'] for d in data]
 except Exception:
  try:
   __lowerCAmelCase:       Dict            =				eval(stream.read()		)
  except Exception:
   __lowerCAmelCase:       Dict            =				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)





