


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()

class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Optional[int]							)      ->       int:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							@property
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Tuple:
														__snake_case						:     List[Any]									=  1
														__snake_case						:     Any									=  3
														__snake_case						:     str									=  (32, 32)

														__snake_case						:     Optional[Any]									=  floats_tensor((batch_size, num_channels) + sizes       ,      rng=random.Random(0							)							).to(lowerCamelCase							)
														return image

							@property
							def        __snake_case   (				self			:    List[Any]							)      ->       Union[str, Any]:
														torch.manual_seed(0							)
														__snake_case						:     int									=  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        __snake_case   (				self			:    List[str]							)      ->       Optional[Any]:
														torch.manual_seed(0							)
														__snake_case						:     Optional[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        __snake_case   (				self			:    int							)      ->       Any:
														torch.manual_seed(0							)
														__snake_case						:     Optional[Any]									=  RobertaSeriesConfig(
														    hidden_size=32       ,      project_dim=32       ,      intermediate_size=37       ,      layer_norm_eps=1E-05       ,      num_attention_heads=4       ,      num_hidden_layers=5       ,      pad_token_id=1       ,      vocab_size=5006       ,      )
														return RobertaSeriesModelWithTransformation(lowerCamelCase							)

							@property
							def        __snake_case   (				self			:    int							)      ->       Any:
														def extract(*lowerCamelCase			:    int       ,      **lowerCamelCase			:    Any							):

																					class 							a						:




																												"""simple docstring"""


																												def __init__(				self			:    Tuple							)      ->       int:
																																			__snake_case						:     List[Any]									=  torch.ones([0]							)






																												def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    List[str]							)      ->       Dict:
																																			self.pixel_values.to(lowerCamelCase							)
																																			return self
																					return Out()

														return extract

							def        __snake_case   (				self			:    Tuple							)      ->       Tuple:
														__snake_case						:     List[Any]									=  "cpu"  # ensure determinism for the device-dependent torch.Generator
														__snake_case						:     Optional[Any]									=  self.dummy_cond_unet
														__snake_case						:     Dict									=  PNDMScheduler(skip_prk_steps=lowerCamelCase							)
														__snake_case						:     List[Any]									=  self.dummy_vae
														__snake_case						:     Optional[int]									=  self.dummy_text_encoder
														__snake_case						:     int									=  XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"							)
														__snake_case						:     List[Any]									=  77

														__snake_case						:     int									=  self.dummy_image.to(lowerCamelCase							)
														__snake_case						:     str									=  init_image / 2 + 0.5

														# make sure here that pndm scheduler skips prk
														__snake_case						:     Dict									=  AltDiffusionImgaImgPipeline(
														    unet=lowerCamelCase       ,      scheduler=lowerCamelCase       ,      vae=lowerCamelCase       ,      text_encoder=lowerCamelCase       ,      tokenizer=lowerCamelCase       ,      safety_checker=lowerCamelCase       ,      feature_extractor=self.dummy_extractor       ,      )
														__snake_case						:     List[str]									=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,      do_normalize=lowerCamelCase							)
														__snake_case						:     Any									=  alt_pipe.to(lowerCamelCase							)
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase							)

														__snake_case						:     str									=  "A painting of a squirrel eating a burger"
														__snake_case						:     Optional[int]									=  torch.Generator(device=lowerCamelCase							).manual_seed(0							)
														__snake_case						:     Any									=  alt_pipe(
														    [prompt]       ,      generator=lowerCamelCase       ,      guidance_scale=6.0       ,      num_inference_steps=2       ,      output_type="np"       ,      image=lowerCamelCase       ,      )

														__snake_case						:     Dict									=  output.images

														__snake_case						:     Tuple									=  torch.Generator(device=lowerCamelCase							).manual_seed(0							)
														__snake_case						:     Dict									=  alt_pipe(
														    [prompt]       ,      generator=lowerCamelCase       ,      guidance_scale=6.0       ,      num_inference_steps=2       ,      output_type="np"       ,      image=lowerCamelCase       ,      return_dict=lowerCamelCase       ,      )[0]

														__snake_case						:     int									=  image[0, -3:, -3:, -1]
														__snake_case						:     int									=  image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														__snake_case						:     Tuple									=  np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99]							)

														assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-3
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 5E-3

							@unittest.skipIf(torch_device != "cuda"       ,      "This test requires a GPU"							)
							def        __snake_case   (				self			:    int							)      ->       Optional[int]:
														__snake_case						:     Optional[int]									=  self.dummy_cond_unet
														__snake_case						:     Union[str, Any]									=  PNDMScheduler(skip_prk_steps=lowerCamelCase							)
														__snake_case						:     Optional[int]									=  self.dummy_vae
														__snake_case						:     Optional[int]									=  self.dummy_text_encoder
														__snake_case						:     Union[str, Any]									=  XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"							)
														__snake_case						:     List[str]									=  77

														__snake_case						:     Optional[int]									=  self.dummy_image.to(lowerCamelCase							)

														# put models in fp16
														__snake_case						:     str									=  unet.half()
														__snake_case						:     Dict									=  vae.half()
														__snake_case						:     int									=  bert.half()

														# make sure here that pndm scheduler skips prk
														__snake_case						:     int									=  AltDiffusionImgaImgPipeline(
														    unet=lowerCamelCase       ,      scheduler=lowerCamelCase       ,      vae=lowerCamelCase       ,      text_encoder=lowerCamelCase       ,      tokenizer=lowerCamelCase       ,      safety_checker=lowerCamelCase       ,      feature_extractor=self.dummy_extractor       ,      )
														__snake_case						:     List[str]									=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,      do_normalize=lowerCamelCase							)
														__snake_case						:     Dict									=  alt_pipe.to(lowerCamelCase							)
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase							)

														__snake_case						:     int									=  "A painting of a squirrel eating a burger"
														__snake_case						:     int									=  torch.manual_seed(0							)
														__snake_case						:     int									=  alt_pipe(
														    [prompt]       ,      generator=lowerCamelCase       ,      num_inference_steps=2       ,      output_type="np"       ,      image=lowerCamelCase       ,      ).images

														assert image.shape == (1, 32, 32, 3)






							@unittest.skipIf(torch_device != "cuda"       ,      "This test requires a GPU"							)
							def        __snake_case   (				self			:    Any							)      ->       List[str]:
														__snake_case						:     str									=  load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/img2img/sketch-mountains-input.jpg"							)
														# resize to resolution that is divisible by 8 but not 16 or 32
														__snake_case						:     Any									=  init_image.resize((760, 504)							)

														__snake_case						:     Optional[Any]									=  "BAAI/AltDiffusion"
														__snake_case						:     List[str]									=  AltDiffusionImgaImgPipeline.from_pretrained(
														    lowerCamelCase       ,      safety_checker=lowerCamelCase       ,      )
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)
														pipe.enable_attention_slicing()

														__snake_case						:     Dict									=  "A fantasy landscape, trending on artstation"

														__snake_case						:     int									=  torch.manual_seed(0							)
														__snake_case						:     Dict									=  pipe(
														    prompt=lowerCamelCase       ,      image=lowerCamelCase       ,      strength=0.75       ,      guidance_scale=7.5       ,      generator=lowerCamelCase       ,      output_type="np"       ,      )
														__snake_case						:     Optional[Any]									=  output.images[0]

														__snake_case						:     Dict									=  image[255:258, 383:386, -1]

														assert image.shape == (504, 760, 3)
														__snake_case						:     Any									=  np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00]							)

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

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




							"""simple docstring"""


							def        __snake_case   (				self			:    List[Any]							)      ->       List[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def        __snake_case   (				self			:    Optional[Any]							)      ->       int:
														__snake_case						:     Optional[int]									=  load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/img2img/sketch-mountains-input.jpg"							)
														__snake_case						:     str									=  init_image.resize((768, 512)							)
														__snake_case						:     Dict									=  load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"							)

														__snake_case						:     Any									=  "BAAI/AltDiffusion"
														__snake_case						:     Any									=  AltDiffusionImgaImgPipeline.from_pretrained(
														    lowerCamelCase       ,      safety_checker=lowerCamelCase       ,      )
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)
														pipe.enable_attention_slicing()

														__snake_case						:     Optional[Any]									=  "A fantasy landscape, trending on artstation"

														__snake_case						:     Optional[Any]									=  torch.manual_seed(0							)
														__snake_case						:     Optional[Any]									=  pipe(
														    prompt=lowerCamelCase       ,      image=lowerCamelCase       ,      strength=0.75       ,      guidance_scale=7.5       ,      generator=lowerCamelCase       ,      output_type="np"       ,      )
														__snake_case						:     Union[str, Any]									=  output.images[0]

														assert image.shape == (512, 768, 3)
														# img2img is flaky across GPUs even in fp32, so using MAE here
														assert np.abs(expected_image - image							).max() < 1E-2



import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


