

"""simple docstring"""

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EfficientNetForImageClassification, EfficientNetModel
    from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=32 ,				snake_case=3 ,				snake_case=[3, 3, 5] ,				snake_case=[32, 16, 24] ,				snake_case=[16, 24, 40] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 6, 6] ,				snake_case=True ,				snake_case=True ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=10 ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							parent
        lowerCAmelCase__      :							int    =							batch_size
        lowerCAmelCase__      :							Dict    =							image_size
        lowerCAmelCase__      :							str    =							num_channels
        lowerCAmelCase__      :							Union[str, Any]    =							kernel_sizes
        lowerCAmelCase__      :							int    =							in_channels
        lowerCAmelCase__      :							Dict    =							out_channels
        lowerCAmelCase__      :							str    =							strides
        lowerCAmelCase__      :							int    =							num_block_repeats
        lowerCAmelCase__      :							str    =							expand_ratios
        lowerCAmelCase__      :							List[Any]    =							is_training
        lowerCAmelCase__      :							Any    =							hidden_act
        lowerCAmelCase__      :							Any    =							num_labels
        lowerCAmelCase__      :							int    =							use_labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        lowerCAmelCase__      :							List[str]    =							None
        if self.use_labels:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.num_labels      )

        lowerCAmelCase__      :							List[str]    =							self.get_config()
        return config, pixel_values, labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return EfficientNetConfig(
            num_channels=self.num_channels ,				kernel_sizes=self.kernel_sizes ,				in_channels=self.in_channels ,				out_channels=self.out_channels ,				strides=self.strides ,				num_block_repeats=self.num_block_repeats ,				expand_ratios=self.expand_ratios ,				hidden_act=self.hidden_act ,				num_labels=self.num_labels ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							EfficientNetModel(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Tuple    =							model(snake_case      )
        # expected last hidden states: B, C, H // 4, W // 4
        self.parent.assertEqual(
            result.last_hidden_state.shape ,				(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4) ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							EfficientNetForImageClassification(snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							str    =							model(snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.prepare_config_and_inputs()
        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							config_and_inputs
        lowerCAmelCase__      :							Dict    =							{"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class  __a				(	__magic_name__					,  __magic_name__					,  unittest.TestCase					):





       """simple docstring"""


       __UpperCamelCase				:			Any          =	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
       __UpperCamelCase				:			Dict          =	(
           {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
           if is_torch_available()
           else {}
       )

       __UpperCamelCase				:			str          =	False
       __UpperCamelCase				:			List[Any]          =	False
       __UpperCamelCase				:			Dict          =	False
       __UpperCamelCase				:			Optional[int]          =	False
       __UpperCamelCase				:			List[Any]          =	False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							EfficientNetModelTester(self      )
        lowerCAmelCase__      :							Tuple    =							ConfigTester(
            self ,				config_class=snake_case ,				has_text_modality=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return






       @unittest.skip(reason="EfficientNet does not use inputs_embeds"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not support input and output embeddings"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not use feedforward chunking"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							Union[str, Any]    =							model_class(snake_case      )
         lowerCAmelCase__      :							Union[str, Any]    =							inspect.signature(model.forward      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase__      :							Union[str, Any]    =							[*signature.parameters.keys()]

         lowerCAmelCase__      :							List[str]    =							["pixel_values"]
         self.assertListEqual(arg_names[:1] ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        def check_hidden_states_output(snake_case ,				snake_case ,				snake_case      ):
         lowerCAmelCase__      :							Tuple    =							model_class(snake_case      )
         model.to(snake_case      )
         model.eval()

         with torch.no_grad():
          lowerCAmelCase__      :							List[str]    =							model(**self._prepare_for_class(snake_case ,				snake_case      )      )

         lowerCAmelCase__      :							Tuple    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
         lowerCAmelCase__      :							Any    =							sum(config.num_block_repeats      ) * 4
         self.assertEqual(len(snake_case      ) ,				snake_case      )

         # EfficientNet'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 // 2, self.model_tester.image_size // 2] ,				)

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							str    =							True
         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )

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

         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case      )






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__      :							str    =							EfficientNetModel.from_pretrained(snake_case      )
         self.assertIsNotNone(snake_case      )






       @is_pipeline_test
       @require_vision
       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        super().test_pipeline_image_classification()




def 			SCREAMING_SNAKE_CASE       (   ) ->	Dict:
 lowerCAmelCase__      :							List[Any]    =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
 return image






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





       """simple docstring"""


       @cached_property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return AutoImageProcessor.from_pretrained("google/efficientnet-b7"      ) if is_vision_available() else None






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7"      ).to(snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							self.default_image_processor
        lowerCAmelCase__      :							Tuple    =							prepare_img()
        lowerCAmelCase__      :							Dict    =							image_processor(images=snake_case ,				return_tensors="pt"      ).to(snake_case      )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__      :							str    =							model(**snake_case      )

        # verify the logits
        lowerCAmelCase__      :							Any    =							torch.Size((1, 1_000)      )
        self.assertEqual(outputs.logits.shape ,				snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([0.0_001, 0.0_002, 0.0_002]      ).to(snake_case      )
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				snake_case ,				atol=1e-4      )      )









"""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
_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, 2_0_4_8), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							Optional[Any]    =							arg

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

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

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






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        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(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

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






       def __call__(      self      ):





        """simple docstring"""

        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__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							self.model(
             snake_case ,				snake_case ,				scales_yx=snake_case ,				padding="max_detections" ,				max_detections=self.config.MAX_DETECTIONS ,				pad_value=0 ,				return_tensors="np" ,				location="cpu" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							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)






