
import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowerCamelCase_  :       List[Any]					      =     logging.get_logger(__name__)







def 				A__    (  lowerCamelCase   ,			lowerCamelCase = True  )     ->		List[Any]:
				UpperCamelCase_:							Tuple		=  {}
				for name in model.attn_processors.keys():
								UpperCamelCase_:							int		=  name.endswith("""attn2.processor"""  ) and not (
								    name.startswith("""transformer_in"""  ) or """temp_attentions""" in name.split("""."""  )
								)
								UpperCamelCase_:							Union[str, Any]		=  model.config.cross_attention_dim if has_cross_attention else None
								if name.startswith("""mid_block"""  ):
												UpperCamelCase_:							List[str]		=  model.config.block_out_channels[-1]
								elif name.startswith("""up_blocks"""  ):
												UpperCamelCase_:							Union[str, Any]		=  int(name[len("""up_blocks."""  )]  )
												UpperCamelCase_:							Optional[Any]		=  list(reversed(model.config.block_out_channels  )  )[block_id]
								elif name.startswith("""down_blocks"""  ):
												UpperCamelCase_:							Optional[int]		=  int(name[len("""down_blocks."""  )]  )
												UpperCamelCase_:							List[Any]		=  model.config.block_out_channels[block_id]
								elif name.startswith("""transformer_in"""  ):
												# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
												UpperCamelCase_:							Any		=  8 * model.config.attention_head_dim

								UpperCamelCase_:							str		=  LoRAAttnProcessor(hidden_size=lowerCamelCase   ,			cross_attention_dim=lowerCamelCase  )
								UpperCamelCase_:							List[Any]		=  lora_attn_procs[name].to(model.device  )

								if mock_weights:
												# add 1 to weights to mock trained weights
												with torch.no_grad():
																lora_attn_procs[name].to_q_lora.up.weight += 1
																lora_attn_procs[name].to_k_lora.up.weight += 1
																lora_attn_procs[name].to_v_lora.up.weight += 1
																lora_attn_procs[name].to_out_lora.up.weight += 1

				return lora_attn_procs





@skip_mps
class 				_UpperCamelCase (    _A   ,				_A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  int    					=       UNetaDConditionModel
			__UpperCamelCase					:  Optional[Any]    					=       """sample"""

			@property
			def 					lowerCAmelCase__		(				self       :				Any					):
							UpperCamelCase_:							List[Any]		=  4
							UpperCamelCase_:							List[str]		=  4
							UpperCamelCase_:							Any		=  4
							UpperCamelCase_:							str		=  (32, 32)

							UpperCamelCase_:							List[Any]		=  floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(snake_case_					)
							UpperCamelCase_:							str		=  torch.tensor([10]					).to(snake_case_					)
							UpperCamelCase_:							Any		=  floats_tensor((batch_size, 4, 32)					).to(snake_case_					)

							return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

			@property
			def 					lowerCAmelCase__		(				self       :				List[str]					):
							return (4, 4, 32, 32)

			@property
			def 					lowerCAmelCase__		(				self       :				Tuple					):
							return (4, 4, 32, 32)

			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Optional[Any]		=  {
							    """block_out_channels""": (32, 64),
							    """down_block_types""": (
							        """CrossAttnDownBlock3D""",
							        """DownBlock3D""",
							    ),
							    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
							    """cross_attention_dim""": 32,
							    """attention_head_dim""": 8,
							    """out_channels""": 4,
							    """in_channels""": 4,
							    """layers_per_block""": 1,
							    """sample_size""": 32,
							}
							UpperCamelCase_:							List[Any]		=  self.dummy_input
							return init_dict, inputs_dict

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()  ,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,		)
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							UpperCamelCase_,      UpperCamelCase_:							List[str]		=  self.prepare_init_args_and_inputs_for_common()
							UpperCamelCase_:							Any		=  self.model_class(**snake_case_					)

							model.enable_xformers_memory_efficient_attention()

							assert (
							    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
							    == "XFormersAttnProcessor"
							), "xformers is not enabled"

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							Optional[Any]		=  32

							UpperCamelCase_:							Optional[int]		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)
							model.eval()

							with torch.no_grad():
											UpperCamelCase_:							Optional[Any]		=  model(**snake_case_					)

											if isinstance(snake_case_  ,		snake_case_					):
															UpperCamelCase_:							Union[str, Any]		=  output.sample

							self.assertIsNotNone(snake_case_					)
							UpperCamelCase_:							Optional[Any]		=  inputs_dict["""sample"""].shape
							self.assertEqual(output.shape  ,		snake_case_  ,		"""Input and output shapes do not match"""					)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_,      UpperCamelCase_:							int		=  self.prepare_init_args_and_inputs_for_common()
							UpperCamelCase_:							Optional[Any]		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)
							model.eval()

							with torch.no_grad():
											# Warmup pass when using mps (see #372)
											if torch_device == "mps" and isinstance(snake_case_  ,		snake_case_					):
															model(**self.dummy_input					)

											UpperCamelCase_:							Optional[Any]		=  model(**snake_case_					)
											if isinstance(snake_case_  ,		snake_case_					):
															UpperCamelCase_:							List[Any]		=  first.sample

											UpperCamelCase_:							Optional[int]		=  model(**snake_case_					)
											if isinstance(snake_case_  ,		snake_case_					):
															UpperCamelCase_:							int		=  second.sample

							UpperCamelCase_:							Tuple		=  first.cpu().numpy()
							UpperCamelCase_:							Dict		=  second.cpu().numpy()
							UpperCamelCase_:							Union[str, Any]		=  out_a[~np.isnan(snake_case_					)]
							UpperCamelCase_:							Any		=  out_a[~np.isnan(snake_case_					)]
							UpperCamelCase_:							Union[str, Any]		=  np.amax(np.abs(out_a - out_a					)					)
							self.assertLessEqual(snake_case_  ,		1e-5					)

			def 					lowerCAmelCase__		(				self       :				List[Any]					):
							UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							List[Any]		=  8

							UpperCamelCase_:							int		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)
							model.eval()

							model.set_attention_slice("""auto"""					)
							with torch.no_grad():
											UpperCamelCase_:							Any		=  model(**snake_case_					)
							assert output is not None

							model.set_attention_slice("""max"""					)
							with torch.no_grad():
											UpperCamelCase_:							Dict		=  model(**snake_case_					)
							assert output is not None

							model.set_attention_slice(2					)
							with torch.no_grad():
											UpperCamelCase_:							Union[str, Any]		=  model(**snake_case_					)
							assert output is not None

			def 					lowerCAmelCase__		(				self       :				Any					):
							UpperCamelCase_,      UpperCamelCase_:							str		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							List[str]		=  8

							UpperCamelCase_:							List[str]		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							List[str]		=  model(**snake_case_					).sample

							UpperCamelCase_:							Union[str, Any]		=  create_lora_layers(snake_case_					)

							# make sure we can set a list of attention processors
							model.set_attn_processor(snake_case_					)
							model.to(snake_case_					)

							# test that attn processors can be set to itself
							model.set_attn_processor(model.attn_processors					)

							with torch.no_grad():
											UpperCamelCase_:							List[str]		=  model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.0}					).sample
											UpperCamelCase_:							Tuple		=  model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.5}					).sample
											UpperCamelCase_:							List[Any]		=  model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.5}					).sample

							assert (samplea - samplea).abs().max() < 3e-3
							assert (samplea - samplea).abs().max() < 3e-3

							# sample 2 and sample 3 should be different
							assert (samplea - samplea).abs().max() > 3e-3

			def 					lowerCAmelCase__		(				self       :				Optional[int]					):
							UpperCamelCase_,      UpperCamelCase_:							int		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							Any		=  8

							torch.manual_seed(0					)
							UpperCamelCase_:							List[str]		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							Optional[Any]		=  model(**snake_case_					).sample

							UpperCamelCase_:							Any		=  create_lora_layers(snake_case_					)
							model.set_attn_processor(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							Optional[int]		=  model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.5}					).sample

							with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_attn_procs(snake_case_					)
											self.assertTrue(os.path.isfile(os.path.join(snake_case_  ,		"""pytorch_lora_weights.bin"""					)					)					)
											torch.manual_seed(0					)
											UpperCamelCase_:							Dict		=  self.model_class(**snake_case_					)
											new_model.to(snake_case_					)
											new_model.load_attn_procs(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							Any		=  new_model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.5}					).sample

							assert (sample - new_sample).abs().max() < 1e-3

							# LoRA and no LoRA should NOT be the same
							assert (sample - old_sample).abs().max() > 1e-4

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							UpperCamelCase_,      UpperCamelCase_:							Optional[Any]		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							str		=  8

							torch.manual_seed(0					)
							UpperCamelCase_:							int		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							List[str]		=  model(**snake_case_					).sample

							UpperCamelCase_:							Any		=  create_lora_layers(snake_case_					)
							model.set_attn_processor(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							str		=  model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.5}					).sample

							with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_attn_procs(snake_case_  ,		safe_serialization=snake_case_					)
											self.assertTrue(os.path.isfile(os.path.join(snake_case_  ,		"""pytorch_lora_weights.safetensors"""					)					)					)
											torch.manual_seed(0					)
											UpperCamelCase_:							Optional[int]		=  self.model_class(**snake_case_					)
											new_model.to(snake_case_					)
											new_model.load_attn_procs(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							List[str]		=  new_model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.5}					).sample

							assert (sample - new_sample).abs().max() < 3e-3

							# LoRA and no LoRA should NOT be the same
							assert (sample - old_sample).abs().max() > 1e-4

			def 					lowerCAmelCase__		(				self       :				Any					):
							# enable deterministic behavior for gradient checkpointing
							UpperCamelCase_,      UpperCamelCase_:							Any		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							Optional[Any]		=  8

							torch.manual_seed(0					)
							UpperCamelCase_:							Dict		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)

							UpperCamelCase_:							Any		=  create_lora_layers(snake_case_  ,		mock_weights=snake_case_					)
							model.set_attn_processor(snake_case_					)
							# Saving as torch, properly reloads with directly filename
							with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_attn_procs(snake_case_					)
											self.assertTrue(os.path.isfile(os.path.join(snake_case_  ,		"""pytorch_lora_weights.bin"""					)					)					)
											torch.manual_seed(0					)
											UpperCamelCase_:							Dict		=  self.model_class(**snake_case_					)
											new_model.to(snake_case_					)
											new_model.load_attn_procs(snake_case_  ,		weight_name="""pytorch_lora_weights.bin"""					)

			def 					lowerCAmelCase__		(				self       :				Optional[int]					):
							UpperCamelCase_,      UpperCamelCase_:							Any		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							List[Any]		=  8

							torch.manual_seed(0					)
							UpperCamelCase_:							Union[str, Any]		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)

							UpperCamelCase_:							Any		=  create_lora_layers(snake_case_  ,		mock_weights=snake_case_					)
							model.set_attn_processor(snake_case_					)
							# Saving as torch, properly reloads with directly filename
							with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_attn_procs(snake_case_					)
											self.assertTrue(os.path.isfile(os.path.join(snake_case_  ,		"""pytorch_lora_weights.bin"""					)					)					)
											torch.manual_seed(0					)
											UpperCamelCase_:							Optional[int]		=  self.model_class(**snake_case_					)
											new_model.to(snake_case_					)
											with self.assertRaises(snake_case_					) as e:
															new_model.load_attn_procs(snake_case_  ,		use_safetensors=snake_case_					)
											self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""  ,		str(e.exception					)					)

			def 					lowerCAmelCase__		(				self       :				str					):
							UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							Optional[int]		=  8

							torch.manual_seed(0					)
							UpperCamelCase_:							str		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							Any		=  model(**snake_case_					).sample

							UpperCamelCase_:							Tuple		=  create_lora_layers(snake_case_					)
							model.set_attn_processor(snake_case_					)

							with torch.no_grad():
											UpperCamelCase_:							List[Any]		=  model(**snake_case_  ,		cross_attention_kwargs={"""scale""": 0.0}					).sample

							model.set_attn_processor(AttnProcessor()					)

							with torch.no_grad():
											UpperCamelCase_:							int		=  model(**snake_case_					).sample

							assert (sample - new_sample).abs().max() < 1e-4
							assert (sample - old_sample).abs().max() < 3e-3

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()  ,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,		)
			def 					lowerCAmelCase__		(				self       :				int					):
							# enable deterministic behavior for gradient checkpointing
							UpperCamelCase_,      UpperCamelCase_:							Tuple		=  self.prepare_init_args_and_inputs_for_common()

							UpperCamelCase_:							List[Any]		=  4

							torch.manual_seed(0					)
							UpperCamelCase_:							str		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)
							UpperCamelCase_:							int		=  create_lora_layers(snake_case_					)
							model.set_attn_processor(snake_case_					)

							# default
							with torch.no_grad():
											UpperCamelCase_:							Any		=  model(**snake_case_					).sample

											model.enable_xformers_memory_efficient_attention()
											UpperCamelCase_:							Optional[Any]		=  model(**snake_case_					).sample

											model.disable_xformers_memory_efficient_attention()
											UpperCamelCase_:							List[Any]		=  model(**snake_case_					).sample

							assert (sample - on_sample).abs().max() < 1e-4
							assert (sample - off_sample).abs().max() < 1e-4




			def 					lowerCAmelCase__		(				self       :				List[Any]					):
							UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  self.prepare_init_args_and_inputs_for_common()
							UpperCamelCase_:							int		=  32

							UpperCamelCase_:							Optional[Any]		=  self.model_class(**snake_case_					)
							model.to(snake_case_					)
							model.eval()

							with torch.no_grad():
											UpperCamelCase_:							Dict		=  model(**snake_case_					)[0]

							model.enable_forward_chunking()
							with torch.no_grad():
											UpperCamelCase_:							Any		=  model(**snake_case_					)[0]

							self.assertEqual(output.shape  ,		output_a.shape  ,		"""Shape doesn't match"""					)
							assert np.abs(output.cpu() - output_a.cpu()					).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.








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_  :       Tuple					      =     False
lowerCamelCase_  :       int					      =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowerCamelCase_  :       Any					      =     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, 20_48), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

class 				_UpperCamelCase :






			'''simple docstring'''

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=sys.argv[1:]					):
							UpperCamelCase_:							str		=  None
							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Tuple		=  None
							UpperCamelCase_:							Tuple		=  1
							UpperCamelCase_,      UpperCamelCase_:							Optional[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"):
															UpperCamelCase_:							Dict		=  arg
											elif opt in ("-o", "--outfile"):
															UpperCamelCase_:							Optional[Any]		=  arg
											elif opt in ("-b", "--batch_size"):
															UpperCamelCase_:							List[str]		=  int(snake_case_					)
											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(snake_case_					), f'''{outputfile}'''
							if subset_list is not None:
											with open(os.path.realpath(snake_case_					)					) as f:
															UpperCamelCase_:							List[Any]		=  {self._vqa_file_split()[0] for x in tryload(snake_case_					)}
							else:
											UpperCamelCase_:							List[str]		=  None

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

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				int					):
							UpperCamelCase_:							int		=  int(file.split("""."""					)[0].split("""_"""					)[-1]					)
							UpperCamelCase_:							int		=  os.path.join(self.inputdir  ,		snake_case_					)
							return (img_id, filepath)

			@property
			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Dict		=  []
							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:
															UpperCamelCase_:							Dict		=  batch
															UpperCamelCase_:							List[Any]		=  []
															yield list(map(snake_case_  ,		zip(*snake_case_					)					)					)

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




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







def 				A__    (  lowerCamelCase  )     ->		Tuple:
				try:
								UpperCamelCase_:							str		=  json.load(lowerCamelCase  )
								try:
												UpperCamelCase_:							List[Any]		=  list(data.keys()  )
								except Exception:
												UpperCamelCase_:							Tuple		=  [d["""img_id"""] for d in data]
				except Exception:
								try:
												UpperCamelCase_:							int		=  eval(stream.read()  )
								except Exception:
												UpperCamelCase_:							Union[str, Any]		=  stream.read().split("""\n"""  )
				return data


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






