code: 
  
  
  
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  import inspect
  import unittest
  from typing import List, Tuple
  
  from transformers import ConvNextConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
    import tensorflow as tf
  
    from transformers import TFConvNextForImageClassification, TFConvNextModel
  
  
  if is_vision_available():
    from PIL import Image
  
    from transformers import ConvNextImageProcessor
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :Optional[Any]		, lowercase_   :str		, lowercase_   :List[Any]=13		, lowercase_   :List[str]=32		, lowercase_   :Union[str, Any]=3		, lowercase_   :int=4		, lowercase_   :Any=[10, 20, 30, 40]		, lowercase_   :Optional[int]=[2, 2, 3, 2]		, lowercase_   :Optional[int]=True		, lowercase_   :int=True		, lowercase_   :Optional[Any]=37		, lowercase_   :Union[str, Any]="gelu"		, lowercase_   :Optional[int]=10		, lowercase_   :Union[str, Any]=0.0_2		, lowercase_   :List[Any]=3		, lowercase_   :str=None		, )->							Optional[int]:
       A__       =    parent
       A__       =    batch_size
       A__       =    image_size
       A__       =    num_channels
       A__       =    num_stages
       A__       =    hidden_sizes
       A__       =    depths
       A__       =    is_training
       A__       =    use_labels
       A__       =    intermediate_size
       A__       =    hidden_act
       A__       =    type_sequence_label_size
       A__       =    initializer_range
       A__       =    scope
   def   UpperCAmelCase_							(       self   :Tuple							)->							List[Any]:
       A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
  
       A__       =    None
       if self.use_labels:
           A__       =    ids_tensor([self.batch_size]		, self.type_sequence_label_size							)
  
       A__       =    self.get_config()
  
       return config, pixel_values, labels
   def   UpperCAmelCase_							(       self   :List[Any]							)->							List[Any]:
       return ConvNextConfig(
           num_channels=self.num_channels		, hidden_sizes=self.hidden_sizes		, depths=self.depths		, num_stages=self.num_stages		, hidden_act=self.hidden_act		, is_decoder=lowercase_		, initializer_range=self.initializer_range		, )
   def   UpperCAmelCase_							(       self   :str		, lowercase_   :str		, lowercase_   :Optional[Any]		, lowercase_   :Union[str, Any]							)->							Optional[Any]:
       A__       =    TFConvNextModel(config=lowercase_							)
       A__       =    model(lowercase_		, training=lowercase_							)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape		, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		, )
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Dict		, lowercase_   :List[str]		, lowercase_   :List[Any]							)->							Dict:
       A__       =    self.type_sequence_label_size
       A__       =    TFConvNextForImageClassification(lowercase_							)
       A__       =    model(lowercase_		, labels=lowercase_		, training=lowercase_							)
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)							)
  
  
  
   def   UpperCAmelCase_							(       self   :str							)->							List[Any]:
       A__       =    self.prepare_config_and_inputs()
       A__,			A__,			A__       =    config_and_inputs
       A__       =    {"pixel_values": pixel_values}
       return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase			( UpperCamelCase__    , UpperCamelCase__    , unittest.TestCase       ):
   __lowercase     	=       (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
   __lowercase     	=       (
       {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
       if is_tf_available()
       else {}
   )
  
   __lowercase     	=       False
   __lowercase     	=       False
   __lowercase     	=       False
   __lowercase     	=       False
   __lowercase     	=       False
   def   UpperCAmelCase_							(       self   :List[str]							)->							str:
       A__       =    TFConvNextModelTester(self							)
       A__       =    ConfigTester(
           self		, config_class=lowercase_		, has_text_modality=lowercase_		, hidden_size=37		, )
   @unittest.skip(reason="ConvNext does not use inputs_embeds"							)
   def   UpperCAmelCase_							(       self   :int							)->							int:
       pass
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("GPU"							)							) == 0		, reason="TF does not support backprop for grouped convolutions on CPU."		, )
   @slow
   def   UpperCAmelCase_							(       self   :str							)->							List[Any]:
       super().test_keras_fit()
   @unittest.skip(reason="ConvNext does not support input and output embeddings"							)
   def   UpperCAmelCase_							(       self   :int							)->							Optional[Any]:
       pass
   def   UpperCAmelCase_							(       self   :int							)->							Optional[Any]:
       A__,			A__       =    self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
           A__       =    model_class(lowercase_							)
           A__       =    inspect.signature(model.call							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           A__       =    [*signature.parameters.keys()]
  
           A__       =    ["pixel_values"]
           self.assertListEqual(arg_names[:1]		, lowercase_							)
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							List[str]:
       A__       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_							)
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("GPU"							)							) == 0		, reason="TF does not support backprop for grouped convolutions on CPU."		, )
   def   UpperCAmelCase_							(       self   :List[Any]							)->							Dict:
       super().test_dataset_conversion()
   def   UpperCAmelCase_							(       self   :List[str]							)->							Tuple:
       def check_hidden_states_output(lowercase_   :Dict		, lowercase_   :Dict		, lowercase_   :List[str]							):
           A__       =    model_class(lowercase_							)
  
           A__       =    model(**self._prepare_for_class(lowercase_		, lowercase_							)							)
           A__       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
           A__       =    self.model_tester.num_stages
           self.assertEqual(len(lowercase_							)		, expected_num_stages + 1							)
  
           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)		, [self.model_tester.image_size // 4, self.model_tester.image_size // 4]		, )
  
       A__,			A__       =    self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
           A__       =    True
           check_hidden_states_output(lowercase_		, lowercase_		, lowercase_							)
  
           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           A__       =    True
  
           check_hidden_states_output(lowercase_		, lowercase_		, lowercase_							)
   def   UpperCAmelCase_							(       self   :Any							)->							Any:
       A__,			A__       =    self.model_tester.prepare_config_and_inputs_for_common()
  
       def check_equivalence(lowercase_   :List[str]		, lowercase_   :List[str]		, lowercase_   :Union[str, Any]		, lowercase_   :Any={}							):
           A__       =    model(lowercase_		, return_dict=lowercase_		, **lowercase_							)
           A__       =    model(lowercase_		, return_dict=lowercase_		, **lowercase_							).to_tuple()
  
           def recursive_check(lowercase_   :str		, lowercase_   :Dict							):
               if isinstance(lowercase_		, (List, Tuple)							):
                   for tuple_iterable_value, dict_iterable_value in zip(lowercase_		, lowercase_							):
                       recursive_check(lowercase_		, lowercase_							)
               elif tuple_object is None:
                   return
               else:
                   self.assertTrue(
                       all(tf.equal(lowercase_		, lowercase_							)							)		, msg=(
                           "Tuple and dict output are not equal. Difference:"
                           F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}"
                       )		, )
  
               recursive_check(lowercase_		, lowercase_							)
  
       for model_class in self.all_model_classes:
           A__       =    model_class(lowercase_							)
  
           A__       =    self._prepare_for_class(lowercase_		, lowercase_							)
           A__       =    self._prepare_for_class(lowercase_		, lowercase_							)
           check_equivalence(lowercase_		, lowercase_		, lowercase_							)
  
           A__       =    self._prepare_for_class(lowercase_		, lowercase_		, return_labels=lowercase_							)
           A__       =    self._prepare_for_class(lowercase_		, lowercase_		, return_labels=lowercase_							)
           check_equivalence(lowercase_		, lowercase_		, lowercase_							)
  
           A__       =    self._prepare_for_class(lowercase_		, lowercase_							)
           A__       =    self._prepare_for_class(lowercase_		, lowercase_							)
           check_equivalence(lowercase_		, lowercase_		, lowercase_		, {"output_hidden_states": True}							)
  
           A__       =    self._prepare_for_class(lowercase_		, lowercase_		, return_labels=lowercase_							)
           A__       =    self._prepare_for_class(lowercase_		, lowercase_		, return_labels=lowercase_							)
           check_equivalence(lowercase_		, lowercase_		, lowercase_		, {"output_hidden_states": True}							)
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[Any]:
       A__       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_							)
  
  
  
   @slow
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Union[str, Any]:
       A__       =    TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"							)
       self.assertIsNotNone(lowercase_							)
  
  
  
  
  
  def 		UpperCamelCase       (							):
      A__       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
      return image
  
  
  
  
  
  
  @require_tf
  @require_vision
  class      UpperCAmelCase			( unittest.TestCase       ):
   @cached_property
   def   UpperCAmelCase_							(       self   :List[Any]							)->							Dict:
       return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"							) if is_vision_available() else None
  
  
  
   @slow
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							List[str]:
       A__       =    TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"							)
  
       A__       =    self.default_image_processor
       A__       =    prepare_img()
       A__       =    image_processor(images=lowercase_		, return_tensors="tf"							)
  
       # forward pass
       A__       =    model(**lowercase_							)
  
       # verify the logits
       A__       =    tf.TensorShape((1, 10_00)							)
       self.assertEqual(outputs.logits.shape		, lowercase_							)
  
       A__       =    tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]							)
  
       tf.debugging.assert_near(outputs.logits[0, :3]		, lowercase_		, atol=1E-4							)
  
  
  
  
style_context: 
  
  
  
  
  
  '''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
  __lowerCAmelCase							:			str				=False
  __lowerCAmelCase							:			Union[str, Any]				=Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  __lowerCAmelCase							:			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      UpperCAmelCase			:
   def __init__(       self   :List[str]		, lowercase_   :List[Any]=sys.argv[1:]							)->							Dict:
       A__       =    None
       A__       =    None
       A__       =    None
       A__       =    1
       A__,			A__       =    getopt.getopt(lowercase_		, "i:o:b:s"		, ["inputdir=", "outfile=", "batch_size=", "subset_list="]							)
       for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
               A__       =    arg
           elif opt in ("-o", "--outfile"):
               A__       =    arg
           elif opt in ("-b", "--batch_size"):
               A__       =    int(lowercase_							)
           elif opt in ("-s", "--subset_list"):
               A__       =    arg
  
       assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
       assert outputfile is not None and not os.path.isfile(lowercase_							), F"{outputfile}"
       if subset_list is not None:
           with open(os.path.realpath(lowercase_							)							) as f:
               A__       =    {self._vqa_file_split()[0] for x in tryload(lowercase_							)}
       else:
           A__       =    None
  
       A__       =    CONFIG
       if torch.cuda.is_available():
           A__       =    "cuda"
       A__       =    os.path.realpath(lowercase_							)
       A__       =    os.path.realpath(lowercase_							)
       A__       =    Preprocess(self.config							)
       A__       =    GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"		, config=self.config							)
       A__       =    batch_size if batch_size != 0 else 1
       A__       =    DEFAULT_SCHEMA
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Dict:
       A__       =    int(file.split("."							)[0].split("_"							)[-1]							)
       A__       =    os.path.join(self.inputdir		, lowercase_							)
       return (img_id, filepath)
   @property
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
       A__       =    []
       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(lowercase_							)							)
           if len(lowercase_							) == self.batch:
               A__       =    batch
               A__       =    []
               yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
       for i in range(1							):
           yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
  
  
   def __call__(       self   :Optional[int]							)->							List[str]:
       # make writer
       if not TEST:
           A__       =    datasets.ArrowWriter(features=self.schema		, path=self.outputfile							)
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator							):
           A__,			A__,			A__       =    self.preprocess(lowercase_							)
           A__       =    self.model(
               lowercase_		, lowercase_		, scales_yx=lowercase_		, padding="max_detections"		, max_detections=self.config.MAX_DETECTIONS		, pad_value=0		, return_tensors="np"		, location="cpu"		, )
           A__       =    output_dict.pop("normalized_boxes"							)
           if not TEST:
               A__       =    np.array(lowercase_							)
               A__       =    self.schema.encode_batch(lowercase_							)
               writer.write_batch(lowercase_							)
           if TEST:
               break
           # finalizer the writer
       if not TEST:
           A__,			A__       =    writer.finalize()
           print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
      try:
          A__       =    json.load(_lowerCamelCase )
          try:
              A__       =    list(data.keys() )
          except Exception:
              A__       =    [d["img_id"] for d in data]
      except Exception:
          try:
              A__       =    eval(stream.read() )
          except Exception:
              A__       =    stream.read().split("\n" )
      return data
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			int				=Extract(sys.argv[1:])
    extract()
    if not TEST:
      __lowerCAmelCase							:			Optional[Any]				=datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  