






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					:						Optional[Any]         =     False
_UpperCAmelCase					:						str         =     Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_UpperCAmelCase					:						int         =     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, 2048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase=sys.argv[1:]  )		->				Optional[int]:
        lowerCamelCase									=None
        lowerCamelCase									=None
        lowerCamelCase									=None
        lowerCamelCase									=1
        lowerCamelCase				,       lowerCamelCase									=getopt.getopt(_UpperCAmelCase							, """i:o:b:s"""							, ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]  )
        for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
              lowerCamelCase									=arg
           elif opt in ("-o", "--outfile"):
              lowerCamelCase									=arg
           elif opt in ("-b", "--batch_size"):
              lowerCamelCase									=int(_UpperCAmelCase  )
           elif opt in ("-s", "--subset_list"):
              lowerCamelCase									=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									={self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase  )}
        else:
           lowerCamelCase									=None

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





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





     @property
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=[]
        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									=batch
              lowerCamelCase									=[]
              yield list(map(_UpperCAmelCase							, zip(*_UpperCAmelCase  )  )  )

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





     def __call__(	self  )		->				List[str]:
        # make writer
        if not TEST:
           lowerCamelCase									=datasets.ArrowWriter(features=self.schema							, path=self.outputfile  )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator  ):
           lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=self.preprocess(_UpperCAmelCase  )
           lowerCamelCase									=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									=output_dict.pop("""normalized_boxes"""  )
           if not TEST:
              lowerCamelCase									=np.array(_UpperCAmelCase  )
              lowerCamelCase									=self.schema.encode_batch(_UpperCAmelCase  )
              writer.write_batch(_UpperCAmelCase  )
           if TEST:
              break
           # finalizer the writer
        if not TEST:
           lowerCamelCase				,       lowerCamelCase									=writer.finalize()
           print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''  )






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]    ):
   try:
      lowerCamelCase									=json.load(lowerCamelCase    )
      try:
         lowerCamelCase									=list(data.keys()    )
      except Exception:
         lowerCamelCase									=[d["""img_id"""] for d in data]
   except Exception:
      try:
         lowerCamelCase									=eval(stream.read()    )
      except Exception:
         lowerCamelCase									=stream.read().split("""\n"""    )
   return data


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







import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import (
       MODEL_MAPPING,
       DataaVecVisionForImageClassification,
       DataaVecVisionForSemanticSegmentation,
       DataaVecVisionModel,
   )
   from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor


class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=100							, _UpperCAmelCase=13							, _UpperCAmelCase=30							, _UpperCAmelCase=2							, _UpperCAmelCase=3							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=32							, _UpperCAmelCase=4							, _UpperCAmelCase=4							, _UpperCAmelCase=37							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.1							, _UpperCAmelCase=0.1							, _UpperCAmelCase=10							, _UpperCAmelCase=0.02							, _UpperCAmelCase=3							, _UpperCAmelCase=None							, _UpperCAmelCase=[0, 1, 2, 3]							, )		->				Dict:
        lowerCamelCase									=parent
        lowerCamelCase									=100
        lowerCamelCase									=batch_size
        lowerCamelCase									=image_size
        lowerCamelCase									=patch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=is_training
        lowerCamelCase									=use_labels
        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=type_sequence_label_size
        lowerCamelCase									=initializer_range
        lowerCamelCase									=scope
        lowerCamelCase									=out_indices
        lowerCamelCase									=num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(image_size // patch_size) ** 2
        lowerCamelCase									=num_patches + 1





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        lowerCamelCase									=None
        lowerCamelCase									=None
        if self.use_labels:
           lowerCamelCase									=ids_tensor([self.batch_size]							, self.type_sequence_label_size  )
           lowerCamelCase									=ids_tensor([self.batch_size, self.image_size, self.image_size]							, self.num_labels  )

        lowerCamelCase									=self.get_config()

        return config, pixel_values, labels, pixel_labels





     def 						lowerCamelCase							(	self  )		->				str:
        return DataaVecVisionConfig(
            vocab_size=self.vocab_size							, image_size=self.image_size							, patch_size=self.patch_size							, num_channels=self.num_channels							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, is_decoder=_UpperCAmelCase							, initializer_range=self.initializer_range							, out_indices=self.out_indices							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=DataaVecVisionModel(config=_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, num_patches + 1, self.hidden_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Any:
        lowerCamelCase									=self.type_sequence_label_size
        lowerCamelCase									=DataaVecVisionForImageClassification(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        lowerCamelCase									=self.num_labels
        lowerCamelCase									=DataaVecVisionForSemanticSegmentation(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=self.prepare_config_and_inputs()
        lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=config_and_inputs
        lowerCamelCase									={"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
class 		lowerCAmelCase_				(  UpperCamelCase				,     UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[int] 	=     (
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __lowerCamelCase       :    str 	=     (
         {
             '''feature-extraction''': DataaVecVisionModel,
             '''image-classification''': DataaVecVisionForImageClassification,
             '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __lowerCamelCase       :    Any 	=     False
     __lowerCamelCase       :    List[str] 	=     False
     __lowerCamelCase       :    int 	=     False





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=DataaVecVisionModelTester(self  )
        lowerCamelCase									=ConfigTester(
            self							, config_class=_UpperCAmelCase							, has_text_modality=_UpperCAmelCase							, hidden_size=37  )





     def 						lowerCamelCase							(	self  )		->				int:
        self.config_tester.run_common_tests()





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Data2VecVision does not use inputs_embeds
        pass





     @require_torch_multi_gpu
     @unittest.skip(
         reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
     def 						lowerCamelCase							(	self  )		->				Dict:
        pass





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           self.assertIsInstance(model.get_input_embeddings()							, (nn.Module)  )
           lowerCamelCase									=model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_UpperCAmelCase							, nn.Linear  )  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           lowerCamelCase									=inspect.signature(model.forward  )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowerCamelCase									=[*signature.parameters.keys()]

           lowerCamelCase									=["""pixel_values"""]
           self.assertListEqual(arg_names[:1]							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        if not self.model_tester.is_training:
           return

        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )]:
              continue

           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
           return

        lowerCamelCase									=False
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )] or not model_class.supports_gradient_checkpointing:
              continue
           # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
           # this can then be incorporated into _prepare_for_class in test_modeling_common.py
           elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
              lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=inputs_dict["""pixel_values"""].shape
              lowerCamelCase									=torch.zeros(
                  [self.model_tester.batch_size, height, width]							, device=_UpperCAmelCase  ).long()
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.gradient_checkpointing_enable()
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        lowerCamelCase									=_config_zero_init(_UpperCAmelCase  )
        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(config=_UpperCAmelCase  )
           for name, param in model.named_parameters():
              # we skip lambda parameters as these require special initial values
              # determined by config.layer_scale_init_value
              if "lambda" in name:
                 continue
              if param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9).round() / 1E9).item()							, [0.0, 1.0]							, msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase=2E-4							, _UpperCAmelCase="outputs"							, _UpperCAmelCase=None  )		->				int:
        # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
        super().check_pt_tf_outputs(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCamelCase									=DataaVecVisionModel.from_pretrained(_UpperCAmelCase  )
           self.assertIsNotNone(_UpperCAmelCase  )






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
   return image







@require_torch
@require_vision
class 		lowerCAmelCase_				(  unittest.TestCase ):





     @cached_property
     def 						lowerCamelCase							(	self  )		->				List[str]:
        return (
            BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ) if is_vision_available() else None
        )





     @slow
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ).to(
            _UpperCAmelCase  )

        lowerCamelCase									=self.default_image_processor
        lowerCamelCase									=prepare_img()
        lowerCamelCase									=image_processor(images=_UpperCAmelCase							, return_tensors="""pt"""  ).to(_UpperCAmelCase  )

        # forward pass
        with torch.no_grad():
           lowerCamelCase									=model(**_UpperCAmelCase  )
        lowerCamelCase									=outputs.logits

        # verify the logits
        lowerCamelCase									=torch.Size((1, 1000)  )
        self.assertEqual(logits.shape							, _UpperCAmelCase  )

        lowerCamelCase									=torch.tensor([0.3277, -0.1395, 0.0911]  ).to(_UpperCAmelCase  )

        self.assertTrue(torch.allclose(logits[0, :3]							, _UpperCAmelCase							, atol=1E-4  )  )

        lowerCamelCase									=[model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
        self.assertEqual(logits[0].topk(2  ).indices.cpu().tolist()							, _UpperCAmelCase  )