
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 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							@property
							def  A__      (				self):
												lowercase							=						1
												lowercase							=						3
												lowercase							=						(3_2, 3_2)

												lowercase							=						floats_tensor((batch_size, num_channels) + sizes   ,rng=random.Random(0)).to(A__)
												return image




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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   ,)
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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   ,)
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						RobertaSeriesConfig(
												    hidden_size=3_2   ,project_dim=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=5_0_0_6   ,)
												return RobertaSeriesModelWithTransformation(A__)




							@property
							def  A__      (				self):
												def extract(*A__   ,**A__):







																	class 				lowercase		:




																								def __init__(				self):
																													lowercase							=						torch.ones([0])





																								def  A__      (				self   ,A__):
																													self.pixel_values.to(A__)
																													return self


																	return Out()

												return extract




							def  A__      (				self):
												lowercase							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												lowercase							=						self.dummy_cond_unet
												lowercase							=						PNDMScheduler(skip_prk_steps=A__)
												lowercase							=						self.dummy_vae
												lowercase							=						self.dummy_text_encoder
												lowercase							=						XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
												lowercase							=						7_7

												lowercase							=						self.dummy_image.to(A__)
												lowercase							=						init_image / 2 + 0.5

												# make sure here that pndm scheduler skips prk
												lowercase							=						AltDiffusionImgaImgPipeline(
												    unet=A__   ,scheduler=A__   ,vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,safety_checker=A__   ,feature_extractor=self.dummy_extractor   ,)
												lowercase							=						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,do_normalize=A__)
												lowercase							=						alt_pipe.to(A__)
												alt_pipe.set_progress_bar_config(disable=A__)

												lowercase							=						'''A painting of a squirrel eating a burger'''
												lowercase							=						torch.Generator(device=A__).manual_seed(0)
												lowercase							=						alt_pipe(
												    [prompt]   ,generator=A__   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type='''np'''   ,image=A__   ,)

												lowercase							=						output.images

												lowercase							=						torch.Generator(device=A__).manual_seed(0)
												lowercase							=						alt_pipe(
												    [prompt]   ,generator=A__   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type='''np'''   ,image=A__   ,return_dict=A__   ,)[0]

												lowercase							=						image[0, -3:, -3:, -1]
												lowercase							=						image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 3_2, 3_2, 3)
												lowercase							=						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  A__      (				self):
												lowercase							=						self.dummy_cond_unet
												lowercase							=						PNDMScheduler(skip_prk_steps=A__)
												lowercase							=						self.dummy_vae
												lowercase							=						self.dummy_text_encoder
												lowercase							=						XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
												lowercase							=						7_7

												lowercase							=						self.dummy_image.to(A__)

												# put models in fp16
												lowercase							=						unet.half()
												lowercase							=						vae.half()
												lowercase							=						bert.half()

												# make sure here that pndm scheduler skips prk
												lowercase							=						AltDiffusionImgaImgPipeline(
												    unet=A__   ,scheduler=A__   ,vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,safety_checker=A__   ,feature_extractor=self.dummy_extractor   ,)
												lowercase							=						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,do_normalize=A__)
												lowercase							=						alt_pipe.to(A__)
												alt_pipe.set_progress_bar_config(disable=A__)

												lowercase							=						'''A painting of a squirrel eating a burger'''
												lowercase							=						torch.manual_seed(0)
												lowercase							=						alt_pipe(
												    [prompt]   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,image=A__   ,).images

												assert image.shape == (1, 3_2, 3_2, 3)





							@unittest.skipIf(torch_device != '''cuda'''   ,'''This test requires a GPU''')
							def  A__      (				self):
												lowercase							=						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
												lowercase							=						init_image.resize((7_6_0, 5_0_4))

												lowercase							=						'''BAAI/AltDiffusion'''
												lowercase							=						AltDiffusionImgaImgPipeline.from_pretrained(
												    A__   ,safety_checker=A__   ,)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)
												pipe.enable_attention_slicing()

												lowercase							=						'''A fantasy landscape, trending on artstation'''

												lowercase							=						torch.manual_seed(0)
												lowercase							=						pipe(
												    prompt=A__   ,image=A__   ,strength=0.75   ,guidance_scale=7.5   ,generator=A__   ,output_type='''np'''   ,)
												lowercase							=						output.images[0]

												lowercase							=						image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert image.shape == (5_0_4, 7_6_0, 3)
												lowercase							=						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 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





							def  A__      (				self):
												lowercase							=						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/img2img/sketch-mountains-input.jpg''')
												lowercase							=						init_image.resize((7_6_8, 5_1_2))
												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''')

												lowercase							=						'''BAAI/AltDiffusion'''
												lowercase							=						AltDiffusionImgaImgPipeline.from_pretrained(
												    A__   ,safety_checker=A__   ,)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)
												pipe.enable_attention_slicing()

												lowercase							=						'''A fantasy landscape, trending on artstation'''

												lowercase							=						torch.manual_seed(0)
												lowercase							=						pipe(
												    prompt=A__   ,image=A__   ,strength=0.75   ,guidance_scale=7.5   ,generator=A__   ,output_type='''np'''   ,)
												lowercase							=						output.images[0]

												assert image.shape == (5_1_2, 7_6_8, 3)
												# img2img is flaky across GPUs even in fp32, so using MAE here
												assert np.abs(expected_image - image).max() < 1E-2











"""simple docstring"""





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=_UpperCAmelCase					)
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      field(default='language-modeling'     ,  metadata={'include_in_asdict_even_if_is_default': True}					)

      _SCREAMING_SNAKE_CASE					=      Features({'text': Value('string'					)}					)
      _SCREAMING_SNAKE_CASE					=      Features({}					)
      _SCREAMING_SNAKE_CASE					=      "text"





      @property
      def     _snake_case			(       self )		->     Dict[str, str]:
            return {self.text_column: "text"}






