



import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
      from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import GLPNImageProcessor







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Optional[int]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Tuple  		=					self.config_class(**self.inputs_dict							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""hidden_sizes"""							)							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""num_attention_heads"""							)							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""num_encoder_blocks"""							)							)







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     int  ,lowerCamelCase_:     int  ,lowerCamelCase_:     List[str]=13  ,lowerCamelCase_:     Dict=64  ,lowerCamelCase_:     str=3  ,lowerCamelCase_:     List[str]=4  ,lowerCamelCase_:     Optional[int]=[2, 2, 2, 2]  ,lowerCamelCase_:     Optional[int]=[8, 4, 2, 1]  ,lowerCamelCase_:     Any=[16, 32, 64, 128]  ,lowerCamelCase_:     int=[1, 4, 8, 16]  ,lowerCamelCase_:     Union[str, Any]=[1, 2, 4, 8]  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Any=True  ,lowerCamelCase_:     Optional[Any]="gelu"  ,lowerCamelCase_:     str=0.1  ,lowerCamelCase_:     str=0.1  ,lowerCamelCase_:     List[str]=0.0_2  ,lowerCamelCase_:     Optional[Any]=16  ,lowerCamelCase_:     str=3  ,lowerCamelCase_:     int=None  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					parent
       UpperCAmelCase_     :       Tuple  		=					batch_size
       UpperCAmelCase_     :       Optional[int]  		=					image_size
       UpperCAmelCase_     :       Dict  		=					num_channels
       UpperCAmelCase_     :       Union[str, Any]  		=					num_encoder_blocks
       UpperCAmelCase_     :       int  		=					sr_ratios
       UpperCAmelCase_     :       int  		=					depths
       UpperCAmelCase_     :       str  		=					hidden_sizes
       UpperCAmelCase_     :       Union[str, Any]  		=					downsampling_rates
       UpperCAmelCase_     :       List[str]  		=					num_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					is_training
       UpperCAmelCase_     :       int  		=					use_labels
       UpperCAmelCase_     :       Optional[Any]  		=					hidden_act
       UpperCAmelCase_     :       Optional[int]  		=					hidden_dropout_prob
       UpperCAmelCase_     :       List[str]  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       Any  		=					initializer_range
       UpperCAmelCase_     :       List[str]  		=					decoder_hidden_size
       UpperCAmelCase_     :       Any  		=					num_labels
       UpperCAmelCase_     :       Optional[int]  		=					scope


   def       A__    (    self:     List[str]							)	->      int:
       UpperCAmelCase_     :       Dict  		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCAmelCase_     :       Optional[Any]  		=					None
       if self.use_labels:
           UpperCAmelCase_     :       Any  		=					ids_tensor([self.batch_size, self.image_size, self.image_size]  ,self.num_labels							)

       UpperCAmelCase_     :       str  		=					self.get_config()
       return config, pixel_values, labels


   def       A__    (    self:     Union[str, Any]							)	->      int:
       return GLPNConfig(
           image_size=self.image_size  ,num_channels=self.num_channels  ,num_encoder_blocks=self.num_encoder_blocks  ,depths=self.depths  ,hidden_sizes=self.hidden_sizes  ,num_attention_heads=self.num_attention_heads  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,initializer_range=self.initializer_range  ,decoder_hidden_size=self.decoder_hidden_size  ,)


   def       A__    (    self:     Any  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      Any:
       UpperCAmelCase_     :       Optional[Any]  		=					GLPNModel(config=lowerCamelCase_							)
       model.to(lowerCamelCase_							)
       model.eval()
       UpperCAmelCase_     :       Any  		=					model(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					self.image_size // (self.downsampling_rates[-1] * 2)
       self.parent.assertEqual(
           result.last_hidden_state.shape  ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str							)	->      List[str]:
       UpperCAmelCase_     :       Optional[Any]  		=					self.num_labels
       UpperCAmelCase_     :       Any  		=					GLPNForDepthEstimation(lowerCamelCase_							)
       model.to(lowerCamelCase_							)
       model.eval()
       UpperCAmelCase_     :       Optional[int]  		=					model(lowerCamelCase_							)
       self.parent.assertEqual(result.predicted_depth.shape  ,(self.batch_size, self.image_size, self.image_size)							)
       UpperCAmelCase_     :       Optional[int]  		=					model(lowerCamelCase_  ,labels=lowerCamelCase_							)
       self.parent.assertEqual(result.predicted_depth.shape  ,(self.batch_size, self.image_size, self.image_size)							)






   def       A__    (    self:     Tuple							)	->      Tuple:
       UpperCAmelCase_     :       Optional[int]  		=					self.prepare_config_and_inputs()
       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					config_and_inputs
       UpperCAmelCase_     :       Optional[Any]  		=					{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class       _snake_case				(     __snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : int       =						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   A__      : Any       =						(
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   A__      : List[str]       =						False
   A__      : Optional[int]       =						False
   A__      : str       =						False


   def       A__    (    self:     Union[str, Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       str  		=					GLPNModelTester(self							)
       UpperCAmelCase_     :       Optional[Any]  		=					GLPNConfigTester(self  ,config_class=lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      Optional[Any]:
       self.config_tester.run_common_tests()


   def       A__    (    self:     Union[str, Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_							)


   def       A__    (    self:     List[str]							)	->      Optional[int]:
       UpperCAmelCase_     :       str  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_							)


   @unittest.skip("""GLPN does not use inputs_embeds"""							)
   def       A__    (    self:     List[str]							)	->      List[Any]:
       pass


   @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""							)
   def       A__    (    self:     Optional[Any]							)	->      Optional[Any]:
       pass


   def       A__    (    self:     Union[str, Any]							)	->      List[str]:
       UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCAmelCase_     :       str  		=					["""pixel_values"""]
           self.assertListEqual(arg_names[:1]  ,lowerCamelCase_							)


   def       A__    (    self:     str							)	->      List[Any]:
       UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       str  		=					True

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       int  		=					True
           UpperCAmelCase_     :       Dict  		=					False
           UpperCAmelCase_     :       Optional[int]  		=					True
           UpperCAmelCase_     :       List[str]  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()
           with torch.no_grad():
               UpperCAmelCase_     :       Tuple  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)
           UpperCAmelCase_     :       List[Any]  		=					outputs.attentions

           UpperCAmelCase_     :       str  		=					sum(self.model_tester.depths							)
           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           UpperCAmelCase_     :       int  		=					True
           UpperCAmelCase_     :       Optional[Any]  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()
           with torch.no_grad():
               UpperCAmelCase_     :       List[str]  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)
           UpperCAmelCase_     :       Optional[Any]  		=					outputs.attentions

           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # verify the first attentions (first block, first layer)
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // 4) ** 2
           UpperCAmelCase_     :       List[Any]  		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
           self.assertListEqual(
               list(attentions[0].shape[-3:]							)  ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,)

           # verify the last attentions (last block, last layer)
           UpperCAmelCase_     :       Tuple  		=					(self.model_tester.image_size // 32) ** 2
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
           self.assertListEqual(
               list(attentions[-1].shape[-3:]							)  ,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]  ,)
           UpperCAmelCase_     :       Tuple  		=					len(lowerCamelCase_							)

           # Check attention is always last and order is fine
           UpperCAmelCase_     :       Optional[Any]  		=					True
           UpperCAmelCase_     :       List[str]  		=					True
           UpperCAmelCase_     :       str  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()
           with torch.no_grad():
               UpperCAmelCase_     :       str  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)

           self.assertEqual(out_len + 1  ,len(lowerCamelCase_							)							)

           UpperCAmelCase_     :       List[str]  		=					outputs.attentions

           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)
           # verify the first attentions (first block, first layer)
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // 4) ** 2
           UpperCAmelCase_     :       List[Any]  		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
           self.assertListEqual(
               list(self_attentions[0].shape[-3:]							)  ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,)


   def       A__    (    self:     str							)	->      List[Any]:
       def check_hidden_states_output(lowerCamelCase_:     Any  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     List[str]							):
           UpperCAmelCase_     :       Optional[int]  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()

           with torch.no_grad():
               UpperCAmelCase_     :       Optional[int]  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)

           UpperCAmelCase_     :       List[str]  		=					outputs.hidden_states

           UpperCAmelCase_     :       Union[str, Any]  		=					self.model_tester.num_encoder_blocks
           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # verify the first hidden states (first block)
           self.assertListEqual(
               list(hidden_states[0].shape[-3:]							)  ,[
                   self.model_tester.hidden_sizes[0],
                   self.model_tester.image_size // 4,
                   self.model_tester.image_size // 4,
               ]  ,)

       UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Optional[Any]  		=					True
           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCAmelCase_     :       Tuple  		=					True

           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      List[str]:
       if not self.model_tester.is_training:
           return

       UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       Dict  		=					True

       for model_class in self.all_model_classes:
           if model_class in get_values(lowerCamelCase_							):
               continue
           # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
           # this can then be incorporated into _prepare_for_class in test_modeling_common.py
           if model_class.__name__ == "GLPNForDepthEstimation":
               UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					inputs_dict["""pixel_values"""].shape
               UpperCAmelCase_     :       Optional[int]  		=					torch.zeros(
                   [self.model_tester.batch_size, height, width]  ,device=lowerCamelCase_							).long()
           UpperCAmelCase_     :       int  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.train()
           UpperCAmelCase_     :       Union[str, Any]  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_  ,return_labels=lowerCamelCase_							)
           UpperCAmelCase_     :       Optional[Any]  		=					model(**lowerCamelCase_							).loss
           loss.backward()






   @slow
   def       A__    (    self:     List[str]							)	->      Optional[int]:
       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_     :       List[Any]  		=					GLPNModel.from_pretrained(lowerCamelCase_							)
           self.assertIsNotNone(lowerCamelCase_							)







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    return image







@require_torch
@require_vision
@slow
class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   @slow
   def       A__    (    self:     Any							)	->      List[str]:
       UpperCAmelCase_     :       str  		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
       UpperCAmelCase_     :       Tuple  		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(lowerCamelCase_							)

       UpperCAmelCase_     :       Any  		=					prepare_img()
       UpperCAmelCase_     :       List[Any]  		=					image_processor(images=lowerCamelCase_  ,return_tensors="""pt"""							).to(lowerCamelCase_							)

       # forward pass
       with torch.no_grad():
           UpperCAmelCase_     :       Union[str, Any]  		=					model(**lowerCamelCase_							)

       # verify the predicted depth
       UpperCAmelCase_     :       Tuple  		=					torch.Size([1, 480, 640]							)
       self.assertEqual(outputs.predicted_depth.shape  ,lowerCamelCase_							)

       UpperCAmelCase_     :       List[str]  		=					torch.tensor(
           [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]							).to(lowerCamelCase_							)

       self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]  ,lowerCamelCase_  ,atol=1e-4							)							)





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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)







class       _snake_case				:






   '''simple docstring'''






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

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

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


   def       A__    (    self:     str  ,lowerCamelCase_:     Dict							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					int(file.split("""."""							)[0].split("""_"""							)[-1]							)
       UpperCAmelCase_     :       int  		=					os.path.join(self.inputdir  ,lowerCamelCase_							)
       return (img_id, filepath)


   @property
   def       A__    (    self:     Tuple							)	->      List[str]:
       UpperCAmelCase_     :       int  		=					[]
       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(lowerCamelCase_							)							)
           if len(lowerCamelCase_							) == self.batch:
               UpperCAmelCase_     :       Tuple  		=					batch
               UpperCAmelCase_     :       Union[str, Any]  		=					[]
               yield list(map(lowerCamelCase_  ,zip(*lowerCamelCase_							)							)							)

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






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







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       Optional[int]  		=					json.load(_a    )
        try:
            UpperCAmelCase_     :       List[Any]  		=					list(data.keys()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					[d["""img_id"""] for d in data]
    except Exception:
        try:
            UpperCAmelCase_     :       str  		=					eval(stream.read()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					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)
