


'''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
__lowercase:		Optional[int]    						=  False
__lowercase:		Union[str, Any]    						=  Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
__lowercase:		Tuple    						=  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_048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)







class 			UpperCAmelCase			:

 def __init__(			self			:						Optional[int],       a_			:						Optional[Any]=sys.argv[1:]       ):





    """simple docstring"""



    UpperCamelCase__			    =    None
    UpperCamelCase__			    =    None
    UpperCamelCase__			    =    None
    UpperCamelCase__			    =    1
    UpperCamelCase__    ,		UpperCamelCase__			    =    getopt.getopt(a_,       "i:o:b:s",       ["inputdir=", "outfile=", "batch_size=", "subset_list="]       )
    for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
          UpperCamelCase__			    =    arg
       elif opt in ("-o", "--outfile"):
          UpperCamelCase__			    =    arg
       elif opt in ("-b", "--batch_size"):
          UpperCamelCase__			    =    int(a_       )
       elif opt in ("-s", "--subset_list"):
          UpperCamelCase__			    =    arg

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

    UpperCamelCase__			    =    CONFIG
    if torch.cuda.is_available():
       UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    os.path.realpath(a_       )
    UpperCamelCase__			    =    os.path.realpath(a_       )
    UpperCamelCase__			    =    Preprocess(self.config       )
    UpperCamelCase__			    =    GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned",       config=self.config       )
    UpperCamelCase__			    =    batch_size if batch_size != 0 else 1
    UpperCamelCase__			    =    DEFAULT_SCHEMA

 def 		lowercase_    (			self			:						Dict,       a_			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    int(file.split("."       )[0].split("_"       )[-1]       )
    UpperCamelCase__			    =    os.path.join(self.inputdir,       a_       )
    return (img_id, filepath)

 @property
 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    []
    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(a_       )       )
       if len(a_       ) == self.batch:
          UpperCamelCase__			    =    batch
          UpperCamelCase__			    =    []
          yield list(map(a_,       zip(*a_       )       )       )

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

 def __call__(			self			:						Tuple       ):





    """simple docstring"""



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







def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any )		->       Union[str, Any]:





   '''simple docstring'''







   try:
      UpperCamelCase__			    =    json.load(_UpperCamelCase )
      try:
         UpperCamelCase__			    =    list(data.keys() )
      except Exception:
         UpperCamelCase__			    =    [d["img_id"] for d in data]
   except Exception:
      try:
         UpperCamelCase__			    =    eval(stream.read() )
      except Exception:
         UpperCamelCase__			    =    stream.read().split("\n" )
   return data


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







"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
    import torch

    from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer

@require_torch
class 			a	(  unittest.TestCase      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'			)
      model.to(_lowerCamelCase			)

      # encoder-decoder outputs
      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]			).to(_lowerCamelCase			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			torch.Size((1, 1_4, 2_5_0_0_1_2)			)
      self.assertEqual(output_predited_logis.shape				,						_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]			).to(_lowerCamelCase			)
      self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)

      # encoder outputs
      lowercase				       =			model.prophetnet.encoder(_lowerCamelCase			)[0]
      lowercase				       =			torch.tensor(
          [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]			).to(_lowerCamelCase			)
      lowercase				       =			torch.Size((1, 4, 1_0_2_4)			)
      self.assertEqual(encoder_outputs.shape				,						_lowerCamelCase			)
      self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)

      # decoder outputs
      lowercase				       =			model.prophetnet.decoder(
          _lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						)
      lowercase				       =			decoder_outputs[1].view(1				,						model.config.ngram				,						1_4				,						-1			)
      lowercase				       =			model.lm_head(_lowerCamelCase			)
      lowercase				       =			predicting_streams_logits[:, 0]
      self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
      model.to(_lowerCamelCase			)

      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]			).to(_lowerCamelCase			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			torch.Size((1, 1_4, 2_5_0_0_1_2)			)
      self.assertEqual(output_predited_logis.shape				,						_lowerCamelCase			)
      # compare the actual values for a slice.
      lowercase				       =			torch.tensor(
          [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]			).to(_lowerCamelCase			)

      self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
      model.to(_lowerCamelCase			)
      lowercase				       =			5_1_2

      lowercase				       =			XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)

      lowercase				       =			(
          'Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'
          ' January 14, 2020, according to the official portal of the organization. From that day, users of this'
          ' system will not be able to receive security updates, which could make their computers vulnerable to'
          ' cyber attacks.'
      )
      lowercase				       =			(
          'орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'
          ' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'
          ' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'
          ' к кибератакам.'
      )
      lowercase				       =			(
          '根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'
      )

      lowercase				       =			tokenizer(
          [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]				,						padding=_lowerCamelCase				,						max_length=2_5_5				,						return_tensors='pt'			).input_ids
      lowercase				       =			input_ids.to(_lowerCamelCase			)

      lowercase				       =			model.generate(
          _lowerCamelCase				,						num_beams=1_0				,						length_penalty=1.0				,						no_repeat_ngram_size=3				,						early_stopping=_lowerCamelCase			)
      lowercase				       =			[tokenizer.decode(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase			) for g in summary_ids]
      lowercase				       =			'Microsoft to end Windows 7 free support after January 14, 2020'
      lowercase				       =			'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
      lowercase				       =			'微软打算终止对Windows 7操作系统的免费支持'
      self.assertListEqual(
          [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]				,						_lowerCamelCase				,						)

      lowercase				       =			model.generate(
          _lowerCamelCase				,						num_beams=1				,						length_penalty=1.0				,						no_repeat_ngram_size=3				,						early_stopping=_lowerCamelCase			)
      lowercase				       =			[
          tokenizer.convert_ids_to_tokens(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase			) for g in summary_ids_beama
      ]
      lowercase				       =			'▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '			)
      lowercase				       =			'▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
          ' '			)
      lowercase				       =			'微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'.split(' '			)
      self.assertListEqual(
          [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]				,						_lowerCamelCase				,						)


