

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   CycleDiffusionPipeline
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        '''negative_prompt''',
        '''height''',
        '''width''',
        '''negative_prompt_embeds''',
    }
    UpperCAmelCase__													=   PipelineTesterMixin.required_optional_params - {'''latents'''}
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}  )
    UpperCAmelCase__													=   IMAGE_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase__													=   IMAGE_TO_IMAGE_IMAGE_PARAMS


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->int:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					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      ,				)
           A__   				=					DDIMScheduler(
               beta_start=0.00085      ,				beta_end=0.012      ,				beta_schedule='''scaled_linear'''      ,				num_train_timesteps=1_000      ,				clip_sample=_SCREAMING_SNAKE_CASE      ,				set_alpha_to_one=_SCREAMING_SNAKE_CASE      ,				)
           torch.manual_seed(0)
           A__   				=					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      ,				)
           torch.manual_seed(0)
           A__   				=					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=1_000      ,				)
           A__   				=					CLIPTextModel(_SCREAMING_SNAKE_CASE)
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					{
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[Any]=0)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
           A__   				=					image / 2 + 0.5
           if str(_SCREAMING_SNAKE_CASE).startswith('''mps'''):
                  A__   				=					torch.manual_seed(_SCREAMING_SNAKE_CASE)
           else:
                  A__   				=					torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)
           A__   				=					{
               '''prompt''': '''An astronaut riding an elephant''',
               '''source_prompt''': '''An astronaut riding a horse''',
               '''image''': image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''eta''': 0.1,
               '''strength''': 0.8,
               '''guidance_scale''': 3,
               '''source_guidance_scale''': 1,
               '''output_type''': '''numpy''',
           }
           return inputs


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

           A__   				=					self.get_dummy_components()
           A__   				=					CycleDiffusionPipeline(**_SCREAMING_SNAKE_CASE)
           A__   				=					pipe.to(_SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

           A__   				=					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)
           A__   				=					pipe(**_SCREAMING_SNAKE_CASE)
           A__   				=					output.images

           A__   				=					images[0, -3:, -3:, -1]

           assert images.shape == (1, 32, 32, 3)
           A__   				=					np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179])

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


    @unittest.skipIf(torch_device != '''cuda'''      ,				'''This test requires a GPU''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[str]:





           '''simple docstring'''

           A__   				=					self.get_dummy_components()
           for name, module in components.items():
                  if hasattr(_SCREAMING_SNAKE_CASE      ,				'''half'''):
                         A__   				=					module.half()
           A__   				=					CycleDiffusionPipeline(**_SCREAMING_SNAKE_CASE)
           A__   				=					pipe.to(_SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

           A__   				=					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)
           A__   				=					pipe(**_SCREAMING_SNAKE_CASE)
           A__   				=					output.images

           A__   				=					images[0, -3:, -3:, -1]

           assert images.shape == (1, 32, 32, 3)
           A__   				=					np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116])

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


    @skip_mps
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           return super().test_save_load_local()


    @unittest.skip('''non-deterministic pipeline''')
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           return super().test_inference_batch_single_identical()


    @skip_mps
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->int:





           '''simple docstring'''

           return super().test_dict_tuple_outputs_equivalent()


    @skip_mps
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Optional[int]:





           '''simple docstring'''

           return super().test_save_load_optional_components()





    @skip_mps
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Any:





           '''simple docstring'''

           return super().test_attention_slicing_forward_pass()


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

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[int]:





           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/cycle-diffusion/black_colored_car.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''')
           A__   				=					init_image.resize((512, 512))

           A__   				=					'''CompVis/stable-diffusion-v1-4'''
           A__   				=					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE      ,				subfolder='''scheduler''')
           A__   				=					CycleDiffusionPipeline.from_pretrained(
               _SCREAMING_SNAKE_CASE      ,				scheduler=_SCREAMING_SNAKE_CASE      ,				safety_checker=_SCREAMING_SNAKE_CASE      ,				torch_dtype=torch.floataa      ,				revision='''fp16''')

           pipe.to(_SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           A__   				=					'''A black colored car'''
           A__   				=					'''A blue colored car'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=_SCREAMING_SNAKE_CASE      ,				source_prompt=_SCREAMING_SNAKE_CASE      ,				image=_SCREAMING_SNAKE_CASE      ,				num_inference_steps=100      ,				eta=0.1      ,				strength=0.85      ,				guidance_scale=3      ,				source_guidance_scale=1      ,				generator=_SCREAMING_SNAKE_CASE      ,				output_type='''np'''      ,				)
           A__   				=					output.images

           # the values aren't exactly equal, but the images look the same visually
           assert np.abs(image - expected_image).max() < 5e-1





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/cycle-diffusion/black_colored_car.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''')
           A__   				=					init_image.resize((512, 512))

           A__   				=					'''CompVis/stable-diffusion-v1-4'''
           A__   				=					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE      ,				subfolder='''scheduler''')
           A__   				=					CycleDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE      ,				scheduler=_SCREAMING_SNAKE_CASE      ,				safety_checker=_SCREAMING_SNAKE_CASE)

           pipe.to(_SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           A__   				=					'''A black colored car'''
           A__   				=					'''A blue colored car'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=_SCREAMING_SNAKE_CASE      ,				source_prompt=_SCREAMING_SNAKE_CASE      ,				image=_SCREAMING_SNAKE_CASE      ,				num_inference_steps=100      ,				eta=0.1      ,				strength=0.85      ,				guidance_scale=3      ,				source_guidance_scale=1      ,				generator=_SCREAMING_SNAKE_CASE      ,				output_type='''np'''      ,				)
           A__   				=					output.images

           assert np.abs(image - expected_image).max() < 2e-2








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

import cva
import numpy as np

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







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__				,							A__   				=					get_dataset(lowercase_				,       lowercase_		)
       for index in range(lowercase_		):
              A__   				=					random.sample(range(len(lowercase_		)		)				,       4		)
              A__				,							A__				,							A__   				=					update_image_and_anno(
                  lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       filter_scale=lowercase_				,       )

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

       A__   				=					[]
       A__   				=					[]
       for label_file in glob.glob(os.path.join(lowercase_				,       '''*.txt'''		)		):
              A__   				=					label_file.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              with open(lowercase_		) as in_file:
                     A__   				=					in_file.readlines()
              A__   				=					os.path.join(lowercase_				,       f"""{label_name}.jpg"""		)

              A__   				=					[]
              for obj_list in obj_lists:
                     A__   				=					obj_list.rstrip('''\n'''		).split(''' '''		)
                     A__   				=					float(obj[1]		) - float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) - float(obj[4]		) / 2
                     A__   				=					float(obj[1]		) + float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) + float(obj[4]		) / 2

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

       A__   				=					np.zeros([output_size[0], output_size[1], 3]				,       dtype=np.uinta		)
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					int(scale_x * output_size[1]		)
       A__   				=					int(scale_y * output_size[0]		)

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



       """simple docstring"""

       assert number_char > 1, "The number of character should greater than 1"
       A__   				=					ascii_lowercase + digits
       return "".join(random.choice(lowercase_		) for _ in range(lowercase_		)		)


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


