






'''simple docstring'''





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 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	@property
	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Optional[int]         =   1
					snake_case__      :Any         =   3
					snake_case__      :Union[str, Any]         =   (32, 32)

					snake_case__      :str         =   floats_tensor((batch_size, num_channels) + sizes					,rng=random.Random(0       )       ).to(_UpperCAmelCase       )
					return image






	@property
	def    lowerCAmelCase_							(		self       )						->    Dict:
					torch.manual_seed(0       )
					snake_case__      :Optional[Any]         =   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    lowerCAmelCase_							(		self       )						->    str:
					torch.manual_seed(0       )
					snake_case__      :Any         =   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    lowerCAmelCase_							(		self       )						->    Tuple:
					torch.manual_seed(0       )
					snake_case__      :List[str]         =   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=5_006					,)
					return RobertaSeriesModelWithTransformation(_UpperCAmelCase       )






	@property
	def    lowerCAmelCase_							(		self       )						->    str:




					def extract(*UpperCamelCase					,**UpperCamelCase       ):


									class 					_snake_case						:






										def __init__(		self       )						->    str:
														snake_case__      :Union[str, Any]         =   torch.ones([0]       )






										def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    List[Any]:
														self.pixel_values.to(_UpperCAmelCase       )
														return self




									return Out()

					return extract






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

					snake_case__      :int         =   self.dummy_image.to(_UpperCAmelCase       )
					snake_case__      :str         =   init_image / 2 + 0.5

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

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

					snake_case__      :Optional[int]         =   output.images

					snake_case__      :List[str]         =   torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
					snake_case__      :Optional[Any]         =   alt_pipe(
					    [prompt]					,generator=_UpperCAmelCase					,guidance_scale=6.0					,num_inference_steps=2					,output_type="np"					,image=_UpperCAmelCase					,return_dict=_UpperCAmelCase					,)[0]

					snake_case__      :Dict         =   image[0, -3:, -3:, -1]
					snake_case__      :List[Any]         =   image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					snake_case__      :int         =   np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]       )

					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    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[str]         =   self.dummy_cond_unet
					snake_case__      :Tuple         =   PNDMScheduler(skip_prk_steps=_UpperCAmelCase       )
					snake_case__      :List[str]         =   self.dummy_vae
					snake_case__      :List[Any]         =   self.dummy_text_encoder
					snake_case__      :int         =   XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
					snake_case__      :List[Any]         =   77

					snake_case__      :Optional[int]         =   self.dummy_image.to(_UpperCAmelCase       )

					# put models in fp16
					snake_case__      :Dict         =   unet.half()
					snake_case__      :List[Any]         =   vae.half()
					snake_case__      :List[Any]         =   bert.half()

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

					snake_case__      :Tuple         =   "A painting of a squirrel eating a burger"
					snake_case__      :Dict         =   torch.manual_seed(0       )
					snake_case__      :List[Any]         =   alt_pipe(
					    [prompt]					,generator=_UpperCAmelCase					,num_inference_steps=2					,output_type="np"					,image=_UpperCAmelCase					,).images

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






	@unittest.skipIf(torch_device != "cuda"					,"This test requires a GPU"       )
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Optional[int]         =   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__      :Tuple         =   init_image.resize((760, 504)       )

					snake_case__      :Dict         =   "BAAI/AltDiffusion"
					snake_case__      :str         =   AltDiffusionImgaImgPipeline.from_pretrained(
					    _UpperCAmelCase					,safety_checker=_UpperCAmelCase					,)
					pipe.to(_UpperCAmelCase       )
					pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
					pipe.enable_attention_slicing()

					snake_case__      :List[str]         =   "A fantasy landscape, trending on artstation"

					snake_case__      :List[str]         =   torch.manual_seed(0       )
					snake_case__      :Optional[int]         =   pipe(
					    prompt=_UpperCAmelCase					,image=_UpperCAmelCase					,strength=0.75					,guidance_scale=7.5					,generator=_UpperCAmelCase					,output_type="np"					,)
					snake_case__      :str         =   output.images[0]

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

					assert image.shape == (504, 760, 3)
					snake_case__      :Tuple         =   np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]       )

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




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






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






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

					snake_case__      :str         =   "BAAI/AltDiffusion"
					snake_case__      :int         =   AltDiffusionImgaImgPipeline.from_pretrained(
					    _UpperCAmelCase					,safety_checker=_UpperCAmelCase					,)
					pipe.to(_UpperCAmelCase       )
					pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
					pipe.enable_attention_slicing()

					snake_case__      :Any         =   "A fantasy landscape, trending on artstation"

					snake_case__      :Any         =   torch.manual_seed(0       )
					snake_case__      :Dict         =   pipe(
					    prompt=_UpperCAmelCase					,image=_UpperCAmelCase					,strength=0.75					,guidance_scale=7.5					,generator=_UpperCAmelCase					,output_type="np"					,)
					snake_case__      :List[str]         =   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 os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

__UpperCAmelCase							:   List[Any]							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCAmelCase							:   int							=  [file for file in filepaths if file != file.lower()]
if upper_files:
	print(F'''{len(upper_files)} files contain uppercase characters:''')
	print("\n".join(upper_files) + "\n")

__UpperCAmelCase							:   Any							=  [file for file in filepaths if " " in file]
if space_files:
	print(F'''{len(space_files)} files contain space characters:''')
	print("\n".join(space_files) + "\n")

__UpperCAmelCase							:   str							=  [file for file in filepaths if "-" in file]
if hyphen_files:
	print(F'''{len(hyphen_files)} files contain hyphen characters:''')
	print("\n".join(hyphen_files) + "\n")

__UpperCAmelCase							:   Dict							=  [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(F'''{len(nodir_files)} files are not in a directory:''')
	print("\n".join(nodir_files) + "\n")

__UpperCAmelCase							:   int							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)