
import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class __snake_case	(   unittest.TestCase  ):
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   1
           lowercase							:  Dict 			=   3
           lowercase							:  Union[str, Any] 			=   (32, 32)

           lowercase							:  List[str] 			=   floats_tensor((batch_size, num_channels) + sizes	,rng=random.Random(0   )   ).to(snake_case   )
           return image
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )
           lowercase							:  Optional[Any] 			=   UNetaDConditionModel(
               block_out_channels=(32, 64)	,layers_per_block=2	,sample_size=32	,in_channels=4	,out_channels=4	,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,cross_attention_dim=32	,)
           return model
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )
           lowercase							:  int 			=   AutoencoderKL(
               block_out_channels=[32, 64]	,in_channels=3	,out_channels=3	,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,latent_channels=4	,)
           return model
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )
           lowercase							:  int 			=   CLIPTextConfig(
               bos_token_id=0	,eos_token_id=2	,hidden_size=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=1000	,)
           return CLIPTextModel(snake_case   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           def extract(*snake_case	,**snake_case   ):

                  class __snake_case	:
                      def __init__(						self   ):



                             '''simple docstring'''







                             lowercase							:  Union[str, Any] 			=   torch.ones([0]   )
                      def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



                             '''simple docstring'''







                             self.pixel_values.to(snake_case   )
                             return self






                  return Out()

           return extract
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
           lowercase							:  Union[str, Any] 			=   self.dummy_cond_unet
           lowercase							:  Dict 			=   DDIMScheduler(
               beta_start=0.00_085	,beta_end=0.012	,beta_schedule="""scaled_linear"""	,clip_sample=snake_case	,set_alpha_to_one=snake_case	,)

           lowercase							:  List[Any] 			=   self.dummy_vae
           lowercase							:  List[str] 			=   self.dummy_text_encoder
           lowercase							:  str 			=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

           # make sure here that pndm scheduler skips prk
           lowercase							:  str 			=   StableDiffusionPipeline(
               unet=snake_case	,scheduler=snake_case	,vae=snake_case	,text_encoder=snake_case	,tokenizer=snake_case	,safety_checker=snake_case	,feature_extractor=self.dummy_extractor	,)
           lowercase							:  List[Any] 			=   sd_pipe.to(snake_case   )
           sd_pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  List[str] 			=   """A painting of a squirrel eating a burger"""

           lowercase							:  List[Any] 			=   torch.Generator(device=snake_case   ).manual_seed(0   )
           lowercase							:  Dict 			=   sd_pipe([prompt]	,generator=snake_case	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""   )
           lowercase							:  Union[str, Any] 			=   output.images

           lowercase							:  Dict 			=   torch.Generator(device=snake_case   ).manual_seed(0   )
           lowercase							:  List[Any] 			=   sd_pipe(
               [prompt]	,generator=snake_case	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""	,return_dict=snake_case	,)[0]

           lowercase							:  Optional[int] 			=   image[0, -3:, -3:, -1]
           lowercase							:  str 			=   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           lowercase							:  Tuple 			=   np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
           lowercase							:  Optional[int] 			=   self.dummy_cond_unet
           lowercase							:  List[str] 			=   PNDMScheduler(skip_prk_steps=snake_case   )
           lowercase							:  str 			=   self.dummy_vae
           lowercase							:  List[Any] 			=   self.dummy_text_encoder
           lowercase							:  Tuple 			=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

           # make sure here that pndm scheduler skips prk
           lowercase							:  List[Any] 			=   StableDiffusionPipeline(
               unet=snake_case	,scheduler=snake_case	,vae=snake_case	,text_encoder=snake_case	,tokenizer=snake_case	,safety_checker=snake_case	,feature_extractor=self.dummy_extractor	,)
           lowercase							:  Union[str, Any] 			=   sd_pipe.to(snake_case   )
           sd_pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  Tuple 			=   """A painting of a squirrel eating a burger"""
           lowercase							:  List[Any] 			=   torch.Generator(device=snake_case   ).manual_seed(0   )
           lowercase							:  Optional[int] 			=   sd_pipe([prompt]	,generator=snake_case	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""   )

           lowercase							:  Dict 			=   output.images

           lowercase							:  int 			=   torch.Generator(device=snake_case   ).manual_seed(0   )
           lowercase							:  str 			=   sd_pipe(
               [prompt]	,generator=snake_case	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""	,return_dict=snake_case	,)[0]

           lowercase							:  Union[str, Any] 			=   image[0, -3:, -3:, -1]
           lowercase							:  Tuple 			=   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           lowercase							:  List[Any] 			=   np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   StableDiffusionPipeline.from_pretrained(
               """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""	,safety_checker=snake_case   )
           assert isinstance(snake_case	,snake_case   )
           assert isinstance(pipe.scheduler	,snake_case   )
           assert pipe.safety_checker is None

           lowercase							:  int 			=   pipe("""example prompt"""	,num_inference_steps=2   ).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
                  pipe.save_pretrained(snake_case   )
                  lowercase							:  Union[str, Any] 			=   StableDiffusionPipeline.from_pretrained(snake_case   )

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           lowercase							:  List[str] 			=   pipe("""example prompt"""	,num_inference_steps=2   ).images[0]
           assert image is not None
    @unittest.skipIf(torch_device != """cuda"""	,"""This test requires a GPU"""   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.dummy_cond_unet
           lowercase							:  Optional[Any] 			=   PNDMScheduler(skip_prk_steps=snake_case   )
           lowercase							:  Dict 			=   self.dummy_vae
           lowercase							:  str 			=   self.dummy_text_encoder
           lowercase							:  Any 			=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

           # put models in fp16
           lowercase							:  List[str] 			=   unet.half()
           lowercase							:  Dict 			=   vae.half()
           lowercase							:  Optional[int] 			=   bert.half()

           # make sure here that pndm scheduler skips prk
           lowercase							:  Optional[int] 			=   StableDiffusionPipeline(
               unet=snake_case	,scheduler=snake_case	,vae=snake_case	,text_encoder=snake_case	,tokenizer=snake_case	,safety_checker=snake_case	,feature_extractor=self.dummy_extractor	,)
           lowercase							:  Tuple 			=   sd_pipe.to(snake_case   )
           sd_pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  Any 			=   """A painting of a squirrel eating a burger"""
           lowercase							:  Dict 			=   sd_pipe([prompt]	,num_inference_steps=2	,output_type="""np"""   ).images

           assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class __snake_case	(   unittest.TestCase  ):
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   """cuda"""
           lowercase							:  str 			=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
           lowercase							:  Optional[int] 			=   pipe.to(snake_case   )
           pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  List[Any] 			=   """a photo of a cat"""
           lowercase							:  Tuple 			=   {
               """editing_prompt""": ["""sunglasses"""],
               """reverse_editing_direction""": [False],
               """edit_warmup_steps""": 10,
               """edit_guidance_scale""": 6,
               """edit_threshold""": 0.95,
               """edit_momentum_scale""": 0.5,
               """edit_mom_beta""": 0.6,
           }

           lowercase							:  Optional[int] 			=   3
           lowercase							:  List[Any] 			=   7

           # no sega enabled
           lowercase							:  int 			=   torch.Generator(snake_case   )
           generator.manual_seed(snake_case   )
           lowercase							:  List[str] 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

           lowercase							:  Tuple 			=   output.images
           lowercase							:  Union[str, Any] 			=   image[0, -3:, -3:, -1]
           lowercase							:  Tuple 			=   [
               0.34_673_113,
               0.38_492_733,
               0.37_597_352,
               0.34_086_335,
               0.35_650_748,
               0.35_579_205,
               0.3_384_763,
               0.34_340_236,
               0.3_573_271,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case   )
           lowercase							:  Optional[Any] 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**snake_case	,)

           lowercase							:  List[str] 			=   output.images
           lowercase							:  List[Any] 			=   image[0, -3:, -3:, -1]
           lowercase							:  Tuple 			=   [
               0.41_887_826,
               0.37_728_766,
               0.30_138_272,
               0.41_416_335,
               0.41_664_985,
               0.36_283_392,
               0.36_191_246,
               0.43_364_465,
               0.43_001_732,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   """cuda"""
           lowercase							:  List[Any] 			=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
           lowercase							:  Optional[Any] 			=   pipe.to(snake_case   )
           pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  List[str] 			=   """an image of a crowded boulevard, realistic, 4k"""
           lowercase							:  List[Any] 			=   {
               """editing_prompt""": """crowd, crowded, people""",
               """reverse_editing_direction""": True,
               """edit_warmup_steps""": 10,
               """edit_guidance_scale""": 8.3,
               """edit_threshold""": 0.9,
               """edit_momentum_scale""": 0.5,
               """edit_mom_beta""": 0.6,
           }

           lowercase							:  Optional[int] 			=   9
           lowercase							:  Dict 			=   7

           # no sega enabled
           lowercase							:  Optional[Any] 			=   torch.Generator(snake_case   )
           generator.manual_seed(snake_case   )
           lowercase							:  Any 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

           lowercase							:  Optional[int] 			=   output.images
           lowercase							:  Optional[Any] 			=   image[0, -3:, -3:, -1]
           lowercase							:  List[Any] 			=   [
               0.43_497_998,
               0.91_814_065,
               0.7_540_739,
               0.55_580_205,
               0.8_467_265,
               0.5_389_691,
               0.62_574_506,
               0.58_897_763,
               0.50_926_757,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case   )
           lowercase							:  Optional[int] 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**snake_case	,)

           lowercase							:  List[str] 			=   output.images
           lowercase							:  Dict 			=   image[0, -3:, -3:, -1]
           lowercase							:  Optional[Any] 			=   [
               0.3_089_719,
               0.30_500_144,
               0.29_016_042,
               0.30_630_964,
               0.325_687,
               0.29_419_225,
               0.2_908_091,
               0.28_723_598,
               0.27_696_294,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   """cuda"""
           lowercase							:  int 			=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
           lowercase							:  Dict 			=   pipe.to(snake_case   )
           pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  Any 			=   """a castle next to a river"""
           lowercase							:  Tuple 			=   {
               """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
               """reverse_editing_direction""": False,
               """edit_warmup_steps""": [15, 18],
               """edit_guidance_scale""": 6,
               """edit_threshold""": [0.9, 0.8],
               """edit_momentum_scale""": 0.5,
               """edit_mom_beta""": 0.6,
           }

           lowercase							:  Tuple 			=   48
           lowercase							:  List[Any] 			=   7

           # no sega enabled
           lowercase							:  Tuple 			=   torch.Generator(snake_case   )
           generator.manual_seed(snake_case   )
           lowercase							:  Union[str, Any] 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

           lowercase							:  str 			=   output.images
           lowercase							:  List[Any] 			=   image[0, -3:, -3:, -1]
           lowercase							:  Optional[Any] 			=   [
               0.75_163_555,
               0.76_037_145,
               0.61_785,
               0.9_189_673,
               0.8_627_701,
               0.85_189_694,
               0.8_512_813,
               0.87_012_076,
               0.8_312_857,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case   )
           lowercase							:  Any 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**snake_case	,)

           lowercase							:  Optional[int] 			=   output.images
           lowercase							:  str 			=   image[0, -3:, -3:, -1]
           lowercase							:  Union[str, Any] 			=   [
               0.73_553_365,
               0.7_537_271,
               0.74_341_905,
               0.66_480_356,
               0.6_472_925,
               0.63_039_416,
               0.64_812_905,
               0.6_749_717,
               0.6_517_102,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   """cuda"""
           lowercase							:  Optional[int] 			=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,torch_dtype=torch.floataa   )
           lowercase							:  Optional[int] 			=   pipe.to(snake_case   )
           pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  List[Any] 			=   """a photo of a cat"""
           lowercase							:  Optional[int] 			=   {
               """editing_prompt""": ["""sunglasses"""],
               """reverse_editing_direction""": [False],
               """edit_warmup_steps""": 10,
               """edit_guidance_scale""": 6,
               """edit_threshold""": 0.95,
               """edit_momentum_scale""": 0.5,
               """edit_mom_beta""": 0.6,
           }

           lowercase							:  List[Any] 			=   3
           lowercase							:  int 			=   7

           # no sega enabled
           lowercase							:  Any 			=   torch.Generator(snake_case   )
           generator.manual_seed(snake_case   )
           lowercase							:  Any 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

           lowercase							:  int 			=   output.images
           lowercase							:  Dict 			=   image[0, -3:, -3:, -1]
           lowercase							:  int 			=   [
               0.34_887_695,
               0.3_876_953,
               0.375,
               0.34_423_828,
               0.3_581_543,
               0.35_717_773,
               0.3_383_789,
               0.34_570_312,
               0.359_375,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case   )
           lowercase							:  str 			=   pipe(
               [prompt]	,generator=snake_case	,guidance_scale=snake_case	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**snake_case	,)

           lowercase							:  Tuple 			=   output.images
           lowercase							:  Dict 			=   image[0, -3:, -3:, -1]
           lowercase							:  Any 			=   [
               0.42_285_156,
               0.36_914_062,
               0.29_077_148,
               0.42_041_016,
               0.41_918_945,
               0.35_498_047,
               0.3_618_164,
               0.4_423_828,
               0.43_115_234,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


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						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							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	:
    def __init__(						self	,snake_case=sys.argv[1:]   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   None
           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Tuple 			=   None
           lowercase							:  Optional[int] 			=   1
           lowercase			,    lowercase							:  str 			=   getopt.getopt(snake_case	,"""i:o:b:s"""	,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
           for opt, arg in opts:
                  if opt in ("-i", "--inputdir"):
                         lowercase							:  List[Any] 			=   arg
                  elif opt in ("-o", "--outfile"):
                         lowercase							:  Any 			=   arg
                  elif opt in ("-b", "--batch_size"):
                         lowercase							:  Any 			=   int(snake_case   )
                  elif opt in ("-s", "--subset_list"):
                         lowercase							:  str 			=   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:
                         lowercase							:  Dict 			=   {self._vqa_file_split()[0] for x in tryload(snake_case   )}
           else:
                  lowercase							:  Optional[Any] 			=   None

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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
           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:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         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:
                  lowercase							:  Any 			=   datasets.ArrowWriter(features=self.schema	,path=self.outputfile   )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                  lowercase			,    lowercase			,    lowercase							:  Tuple 			=   self.preprocess(snake_case   )
                  lowercase							:  Dict 			=   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"""	,)
                  lowercase							:  int 			=   output_dict.pop("""normalized_boxes"""   )
                  if not TEST:
                         lowercase							:  Optional[int] 			=   np.array(snake_case   )
                         lowercase							:  Tuple 			=   self.schema.encode_batch(snake_case   )
                         writer.write_batch(snake_case   )
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  lowercase			,    lowercase							:  Dict 			=   writer.finalize()
                  print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


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