





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowercase		:  Dict			       =(720, 1280)  # Height, Width
_lowercase		:  List[str]			       =(0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowercase		:  Tuple			       =1 / 100
_lowercase		:  Any			       =""""""
_lowercase		:  List[str]			       =""""""
_lowercase		:  Dict			       =""""""
_lowercase		:  Dict			       =250


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_						,      lowerCamelCase_	:     int 						=      get_dataset(lowerCAmelCase__							,lowerCAmelCase__						)
		for index in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Optional[Any] 						=      random.sample(range(len(lowerCAmelCase__						)						)							,4						)
				lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Any 						=      update_image_and_anno(
				    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,filter_scale=lowerCAmelCase__							,)

				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				lowerCamelCase_	:     Optional[int] 						=      random_chars(32						)
				lowerCamelCase_	:     str 						=      path.split(os.sep						)[-1].rsplit('.'							,1						)[0]
				lowerCamelCase_	:     int 						=      F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
				cva.imwrite(F"{file_root}.jpg"							,lowerCAmelCase__							,[cva.IMWRITE_JPEG_QUALITY, 85]						)
				print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"						)
				lowerCamelCase_	:     Tuple 						=      []
				for anno in new_annos:
						lowerCamelCase_	:     List[Any] 						=      anno[3] - anno[1]
						lowerCamelCase_	:     Union[str, Any] 						=      anno[4] - anno[2]
						lowerCamelCase_	:     Dict 						=      anno[1] + width / 2
						lowerCamelCase_	:     Any 						=      anno[2] + height / 2
						lowerCamelCase_	:     List[str] 						=      F"{anno[0]} {x_center} {y_center} {width} {height}"
						annos_list.append(lowerCAmelCase__						)
				with open(F"{file_root}.txt"							,'w'						) as outfile:
						outfile.write('\n'.join(line for line in annos_list						)						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      []
		lowerCamelCase_	:     Tuple 						=      []
		for label_file in glob.glob(os.path.join(lowerCAmelCase__							,'*.txt'						)						):
				lowerCamelCase_	:     Optional[int] 						=      label_file.split(os.sep						)[-1].rsplit('.'							,1						)[0]
				with open(lowerCAmelCase__						) as in_file:
						lowerCamelCase_	:     Optional[Any] 						=      in_file.readlines()
				lowerCamelCase_	:     Optional[Any] 						=      os.path.join(lowerCAmelCase__							,F"{label_name}.jpg"						)

				lowerCamelCase_	:     Union[str, Any] 						=      []
				for obj_list in obj_lists:
						lowerCamelCase_	:     Optional[int] 						=      obj_list.rstrip('\n'						).split(' '						)
						lowerCamelCase_	:     Union[str, Any] 						=      float(obj[1]						) - float(obj[3]						) / 2
						lowerCamelCase_	:     int 						=      float(obj[2]						) - float(obj[4]						) / 2
						lowerCamelCase_	:     Optional[int] 						=      float(obj[1]						) + float(obj[3]						) / 2
						lowerCamelCase_	:     int 						=      float(obj[2]						) + float(obj[4]						) / 2

						boxes.append([int(obj[0]						), xmin, ymin, xmax, ymax]						)
				if not boxes:
						continue
				img_paths.append(lowerCAmelCase__						)
				labels.append(lowerCAmelCase__						)
		return img_paths, labels


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__ = 0.0							,):
		lowerCamelCase_	:     Any 						=      np.zeros([output_size[0], output_size[1], 3]							,dtype=np.uinta						)
		lowerCamelCase_	:     Union[str, Any] 						=      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		lowerCamelCase_	:     str 						=      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		lowerCamelCase_	:     Union[str, Any] 						=      int(scale_x * output_size[1]						)
		lowerCamelCase_	:     Any 						=      int(scale_y * output_size[0]						)

		lowerCamelCase_	:     str 						=      []
		lowerCamelCase_	:     Optional[int] 						=      []
		for i, index in enumerate(lowerCAmelCase__						):
				lowerCamelCase_	:     List[Any] 						=      all_img_list[index]
				path_list.append(lowerCAmelCase__						)
				lowerCamelCase_	:     Any 						=      all_annos[index]
				lowerCamelCase_	:     str 						=      cva.imread(lowerCAmelCase__						)
				if i == 0:  # top-left
						lowerCamelCase_	:     Any 						=      cva.resize(lowerCAmelCase__							,(divid_point_x, divid_point_y)						)
						lowerCamelCase_	:     List[str] 						=      img
						for bbox in img_annos:
								lowerCamelCase_	:     int 						=      bbox[1] * scale_x
								lowerCamelCase_	:     Any 						=      bbox[2] * scale_y
								lowerCamelCase_	:     Optional[Any] 						=      bbox[3] * scale_x
								lowerCamelCase_	:     str 						=      bbox[4] * scale_y
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]						)
				elif i == 1:  # top-right
						lowerCamelCase_	:     List[str] 						=      cva.resize(lowerCAmelCase__							,(output_size[1] - divid_point_x, divid_point_y)						)
						lowerCamelCase_	:     Any 						=      img
						for bbox in img_annos:
								lowerCamelCase_	:     Tuple 						=      scale_x + bbox[1] * (1 - scale_x)
								lowerCamelCase_	:     Tuple 						=      bbox[2] * scale_y
								lowerCamelCase_	:     Union[str, Any] 						=      scale_x + bbox[3] * (1 - scale_x)
								lowerCamelCase_	:     Optional[int] 						=      bbox[4] * scale_y
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]						)
				elif i == 2:  # bottom-left
						lowerCamelCase_	:     List[Any] 						=      cva.resize(lowerCAmelCase__							,(divid_point_x, output_size[0] - divid_point_y)						)
						lowerCamelCase_	:     str 						=      img
						for bbox in img_annos:
								lowerCamelCase_	:     Tuple 						=      bbox[1] * scale_x
								lowerCamelCase_	:     List[Any] 						=      scale_y + bbox[2] * (1 - scale_y)
								lowerCamelCase_	:     List[str] 						=      bbox[3] * scale_x
								lowerCamelCase_	:     Tuple 						=      scale_y + bbox[4] * (1 - scale_y)
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]						)
				else:  # bottom-right
						lowerCamelCase_	:     Optional[int] 						=      cva.resize(
						    lowerCAmelCase__							,(output_size[1] - divid_point_x, output_size[0] - divid_point_y)						)
						lowerCamelCase_	:     Any 						=      img
						for bbox in img_annos:
								lowerCamelCase_	:     Optional[int] 						=      scale_x + bbox[1] * (1 - scale_x)
								lowerCamelCase_	:     List[Any] 						=      scale_y + bbox[2] * (1 - scale_y)
								lowerCamelCase_	:     str 						=      scale_x + bbox[3] * (1 - scale_x)
								lowerCamelCase_	:     Optional[int] 						=      scale_y + bbox[4] * (1 - scale_y)
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]						)

    # Remove bounding box small than scale of filter
		if filter_scale > 0:
				lowerCamelCase_	:     Dict 						=      [
				    anno
				    for anno in new_anno
				    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
				]

		return output_img, new_anno, path_list[0]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		assert number_char > 1, "The number of character should greater than 1"
		lowerCamelCase_	:     str 						=      ascii_lowercase + digits
		return "".join(random.choice(lowerCAmelCase__						) for _ in range(lowerCAmelCase__						)						)


if __name__ == "__main__":
		main()
		print("""DONE ✅""")







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

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


_lowercase		:  Any			       =False






@skip_mps
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						Optional[int]						    =					StableDiffusionAttendAndExcitePipeline
					_a      :						Union[str, Any]						    =					False
					_a      :						Dict						    =					TEXT_TO_IMAGE_PARAMS
					_a      :						List[str]						    =					TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}							)
					_a      :						Optional[int]						    =					TEXT_TO_IMAGE_IMAGE_PARAMS
					_a      :						List[Any]						    =					TEXT_TO_IMAGE_IMAGE_PARAMS



					@classmethod
					def   __a     (		cls     :     Tuple						):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					@classmethod
					def   __a     (		cls     :     Tuple						):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					def   __a     (		self     :     Dict						):
							torch.manual_seed(0						)
							lowerCamelCase_	:     int 						=      UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,      layers_per_block=1       ,      sample_size=32       ,      in_channels=4       ,      out_channels=4       ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,      cross_attention_dim=32       ,      attention_head_dim=(2, 4)       ,      use_linear_projection=lowerCamelCase       ,      )
							lowerCamelCase_	:     Dict 						=      DDIMScheduler(
							    beta_start=0.00_085       ,      beta_end=0.012       ,      beta_schedule='scaled_linear'       ,      clip_sample=lowerCamelCase       ,      set_alpha_to_one=lowerCamelCase       ,      )
							torch.manual_seed(0						)
							lowerCamelCase_	:     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       ,      sample_size=1_28       ,      )
							torch.manual_seed(0						)
							lowerCamelCase_	:     List[Any] 						=      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=10_00       ,      hidden_act='gelu'       ,      projection_dim=5_12       ,      )
							lowerCamelCase_	:     Any 						=      CLIPTextModel(lowerCamelCase						)
							lowerCamelCase_	:     int 						=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

							lowerCamelCase_	:     Optional[Any] 						=      {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[Any]=0						):
							if str(lowerCamelCase						).startswith('mps'						):
									lowerCamelCase_	:     Union[str, Any] 						=      torch.manual_seed(lowerCamelCase						)
							else:
									lowerCamelCase_	:     Optional[Any] 						=      torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
							lowerCamelCase_	:     Dict 						=      {
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     List[Any] 						=      'cpu'

							lowerCamelCase_	:     List[Any] 						=      self.get_dummy_components()
							lowerCamelCase_	:     List[str] 						=      self.pipeline_class(**lowerCamelCase						)
							pipe.to(lowerCamelCase						)
							pipe.set_progress_bar_config(disable=lowerCamelCase						)

							lowerCamelCase_	:     List[Any] 						=      self.get_dummy_inputs(lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      pipe(**lowerCamelCase						).images
							lowerCamelCase_	:     Optional[Any] 						=      image[0, -3:, -3:, -1]

							self.assertEqual(image.shape       ,      (1, 64, 64, 3)						)
							lowerCamelCase_	:     Dict 						=      np.array(
							    [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]						)
							lowerCamelCase_	:     Any 						=      np.abs(image_slice.flatten() - expected_slice						).max()
							self.assertLessEqual(lowerCamelCase       ,      1E-3						)



					def   __a     (		self     :     Tuple						):
							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4						)



					def   __a     (		self     :     Dict						):
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]						)



					def   __a     (		self     :     List[str]						):
							self._test_inference_batch_single_identical(batch_size=2       ,      expected_max_diff=7E-4						)



					def   __a     (		self     :     str						):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)



					def   __a     (		self     :     Optional[Any]						):
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4						)



					def   __a     (		self     :     Tuple						):
							super().test_save_load_local(expected_max_difference=5E-4						)





					def   __a     (		self     :     List[Any]						):
							super().test_save_load_optional_components(expected_max_difference=4E-4						)






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



					@classmethod
					def   __a     (		cls     :     Union[str, Any]						):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					@classmethod
					def   __a     (		cls     :     List[Any]						):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					def   __a     (		self     :     List[str]						):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





					def   __a     (		self     :     str						):
							lowerCamelCase_	:     Optional[Any] 						=      torch.manual_seed(51						)

							lowerCamelCase_	:     Any 						=      StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,      safety_checker=lowerCamelCase       ,      torch_dtype=torch.floataa						)
							pipe.to('cuda'						)

							lowerCamelCase_	:     List[Any] 						=      'a painting of an elephant with glasses'
							lowerCamelCase_	:     Tuple 						=      [5, 7]

							lowerCamelCase_	:     List[str] 						=      pipe(
							    prompt=lowerCamelCase       ,      token_indices=lowerCamelCase       ,      guidance_scale=7.5       ,      generator=lowerCamelCase       ,      num_inference_steps=5       ,      max_iter_to_alter=5       ,      output_type='numpy'       ,      ).images[0]

							lowerCamelCase_	:     Optional[Any] 						=      load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'						)
							assert np.abs((expected_image - image).max()						) < 5E-1
