

'''simple docstring'''




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



@skip_mps
class     lowercase_		(						a	,    a	,    a	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	Union[str, Any]      					=				StableDiffusionModelEditingPipeline
  __lowerCAmelCase   :	List[Any]      					=				TEXT_TO_IMAGE_PARAMS
  __lowerCAmelCase   :	List[str]      					=				TEXT_TO_IMAGE_BATCH_PARAMS
  __lowerCAmelCase   :	Any      					=				TEXT_TO_IMAGE_IMAGE_PARAMS
  __lowerCAmelCase   :	Union[str, Any]      					=				TEXT_TO_IMAGE_IMAGE_PARAMS
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    torch.manual_seed(0       )
    UpperCAmelCase  							=		UNetaDConditionModel(
        block_out_channels=(3_2, 6_4)				,	layers_per_block=2				,	sample_size=3_2				,	in_channels=4				,	out_channels=4				,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,	cross_attention_dim=3_2				,	)
    UpperCAmelCase  							=		DDIMScheduler()
    torch.manual_seed(0       )
    UpperCAmelCase  							=		AutoencoderKL(
        block_out_channels=[3_2, 6_4]				,	in_channels=3				,	out_channels=3				,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,	latent_channels=4				,	)
    torch.manual_seed(0       )
    UpperCAmelCase  							=		CLIPTextConfig(
        bos_token_id=0				,	eos_token_id=2				,	hidden_size=3_2				,	intermediate_size=3_7				,	layer_norm_eps=1E-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=1_0_0_0				,	)
    UpperCAmelCase  							=		CLIPTextModel(a_       )
    UpperCAmelCase  							=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

    UpperCAmelCase  							=		{
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components
  def   snake_case_     (       self				,	a_				,	a_=0       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		torch.manual_seed(a_       )
    UpperCAmelCase  							=		{
        'prompt': 'A field of roses',
        'generator': generator,
        # Setting height and width to None to prevent OOMs on CPU.
        'height': None,
        'width': None,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
    }
    return inputs
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    UpperCAmelCase  							=		sd_pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase  							=		np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    UpperCAmelCase  							=		'french fries'
    UpperCAmelCase  							=		sd_pipe(**a_				,	negative_prompt=a_       )
    UpperCAmelCase  							=		output.images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase  							=		np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		EulerAncestralDiscreteScheduler(
        beta_start=0.0_0085				,	beta_end=0.012				,	beta_schedule='scaled_linear'       )
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    UpperCAmelCase  							=		sd_pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase  							=		np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		PNDMScheduler()
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    # the pipeline does not expect pndm so test if it raises error.
    with self.assertRaises(a_       ):
      UpperCAmelCase  							=		sd_pipe(**a_       ).images
  def   snake_case_     (       self       )							->			Optional[Any]:


    """simple docstring"""
    super().test_inference_batch_single_identical(expected_max_diff=5E-3       )





  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )




@slow
@require_torch_gpu
class     lowercase_		(						unittest.TestCase	):

  '''simple docstring'''





  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()
  def   snake_case_     (       self				,	a_=0       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		torch.manual_seed(a_       )
    UpperCAmelCase  							=		{
        'prompt': 'A field of roses',
        'generator': generator,
        'num_inference_steps': 3,
        'guidance_scale': 7.5,
        'output_type': 'numpy',
    }
    return inputs
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		'CompVis/stable-diffusion-v1-4'
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline.from_pretrained(a_				,	safety_checker=a_       )
    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )
    pipe.enable_attention_slicing()

    UpperCAmelCase  							=		self.get_inputs()
    UpperCAmelCase  							=		pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    UpperCAmelCase  							=		np.array(
        [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]       )

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

    # make sure image changes after editing
    pipe.edit_model('A pack of roses'				,	'A pack of blue roses'       )

    UpperCAmelCase  							=		pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(expected_slice - image_slice       ).max() > 1E-1





  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    UpperCAmelCase  							=		'CompVis/stable-diffusion-v1-4'
    UpperCAmelCase  							=		DDIMScheduler.from_pretrained(a_				,	subfolder='scheduler'       )
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline.from_pretrained(
        a_				,	scheduler=a_				,	safety_checker=a_       )
    UpperCAmelCase  							=		pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )
    pipe.enable_attention_slicing(1       )
    pipe.enable_sequential_cpu_offload()

    UpperCAmelCase  							=		self.get_inputs()
    UpperCAmelCase  							=		pipe(**a_       )

    UpperCAmelCase  							=		torch.cuda.max_memory_allocated()
    # make sure that less than 4.4 GB is allocated
    assert mem_bytes < 4.4 * 1_0**9





'''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
_a			:	Any								=  False
_a			:	Union[str, Any]								=  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a			:	Dict								=  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_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_=sys.argv[1:]       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		1
    UpperCAmelCase							, UpperCAmelCase  							=		getopt.getopt(a_				,	'i:o:b:s'				,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        UpperCAmelCase  							=		arg
      elif opt in ("-o", "--outfile"):
        UpperCAmelCase  							=		arg
      elif opt in ("-b", "--batch_size"):
        UpperCAmelCase  							=		int(a_       )
      elif opt in ("-s", "--subset_list"):
        UpperCAmelCase  							=		arg

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

    UpperCAmelCase  							=		CONFIG
    if torch.cuda.is_available():
      UpperCAmelCase  							=		'cuda'
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		Preprocess(self.config       )
    UpperCAmelCase  							=		GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'				,	config=self.config       )
    UpperCAmelCase  							=		batch_size if batch_size != 0 else 1
    UpperCAmelCase  							=		DEFAULT_SCHEMA
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		int(file.split('.'       )[0].split('_'       )[-1]       )
    UpperCAmelCase  							=		os.path.join(self.inputdir				,	a_       )
    return (img_id, filepath)
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    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(a_       )       )
      if len(a_       ) == self.batch:
        UpperCAmelCase  							=		batch
        UpperCAmelCase  							=		[]
        yield list(map(a_				,	zip(*a_       )       )       )

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





  def __call__(       self       )							->			str:


    """simple docstring"""
    # make writer
    if not TEST:
      UpperCAmelCase  							=		datasets.ArrowWriter(features=self.schema				,	path=self.outputfile       )
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator       ):
      UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		self.preprocess(a_       )
      UpperCAmelCase  							=		self.model(
          a_				,	a_				,	scales_yx=a_				,	padding='max_detections'				,	max_detections=self.config.MAX_DETECTIONS				,	pad_value=0				,	return_tensors='np'				,	location='cpu'				,	)
      UpperCAmelCase  							=		output_dict.pop('normalized_boxes'       )
      if not TEST:
        UpperCAmelCase  							=		np.array(a_       )
        UpperCAmelCase  							=		self.schema.encode_batch(a_       )
        writer.write_batch(a_       )
      if TEST:
        break
      # finalizer the writer
    if not TEST:
      UpperCAmelCase							, UpperCAmelCase  							=		writer.finalize()
      print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''       )




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  try:
    UpperCAmelCase  							=		json.load(SCREAMING_SNAKE_CASE  )
    try:
      UpperCAmelCase  							=		list(data.keys()  )
    except Exception:
      UpperCAmelCase  							=		[d['img_id'] for d in data]
  except Exception:
    try:
      UpperCAmelCase  							=		eval(stream.read()  )
    except Exception:
      UpperCAmelCase  							=		stream.read().split('\n'  )
  return data


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


