

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   UpperCamelCase		(     self							)	->    int:
							'''simple docstring'''






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


			@property
			def   UpperCamelCase		(     self							)	->    str:
							'''simple docstring'''






							A__          =      1
							A__          =      3
							A__          =      (32, 32)

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


			@property
			def   UpperCamelCase		(     self							)	->    Union[str, Any]:
							'''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   ,   )
							return model


			@property
			def   UpperCamelCase		(     self							)	->    Tuple:
							'''simple docstring'''






							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   ,   )
							return model


			@property
			def   UpperCamelCase		(     self							)	->    int:
							'''simple docstring'''






							torch.manual_seed(0							)
							A__          =      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   UpperCamelCase		(     self							)	->    str:
							'''simple docstring'''






							def extract(*lowercase   ,   **lowercase							):

											class        a__    :
														"""simple docstring"""







														def __init__(     self							)	->    Optional[int]:
																		'''simple docstring'''






																		A__          =      torch.ones([0]							)


														def   UpperCamelCase		(     self   ,   lowercase							)	->    Any:
																		'''simple docstring'''






																		self.pixel_values.to(__lowerCamelCase							)
																		return self

											return Out()

							return extract


			def   UpperCamelCase		(     self							)	->    Optional[int]:
							'''simple docstring'''






							A__          =      "cpu"  # ensure determinism for the device-dependent torch.Generator
							A__          =      self.dummy_cond_unet
							A__          =      PNDMScheduler(skip_prk_steps=__lowerCamelCase							)
							A__          =      self.dummy_vae
							A__          =      self.dummy_text_encoder
							A__          =      XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"							)
							A__          =      77

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

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

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

							A__          =      output.images

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

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

							assert image.shape == (1, 32, 32, 3)
							A__          =      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   UpperCamelCase		(     self							)	->    Optional[Any]:
							'''simple docstring'''






							A__          =      self.dummy_cond_unet
							A__          =      PNDMScheduler(skip_prk_steps=__lowerCamelCase							)
							A__          =      self.dummy_vae
							A__          =      self.dummy_text_encoder
							A__          =      XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"							)
							A__          =      77

							A__          =      self.dummy_image.to(__lowerCamelCase							)

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

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

							A__          =      "A painting of a squirrel eating a burger"
							A__          =      torch.manual_seed(0							)
							A__          =      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   UpperCamelCase		(     self							)	->    int:
							'''simple docstring'''






							A__          =      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
							A__          =      init_image.resize((760, 504)							)

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

							A__          =      "A fantasy landscape, trending on artstation"

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

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

							assert image.shape == (504, 760, 3)
							A__          =      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        a__    (       unittest.TestCase      ):
			"""simple docstring"""







			def   UpperCamelCase		(     self							)	->    List[str]:
							'''simple docstring'''






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


			def   UpperCamelCase		(     self							)	->    Union[str, Any]:
							'''simple docstring'''






							A__          =      load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"							)
							A__          =      init_image.resize((768, 512)							)
							A__          =      load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"							)

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

							A__          =      "A fantasy landscape, trending on artstation"

							A__          =      torch.manual_seed(0							)
							A__          =      pipe(
							    prompt=__lowerCamelCase   ,   image=__lowerCamelCase   ,   strength=0.75   ,   guidance_scale=7.5   ,   generator=__lowerCamelCase   ,   output_type="np"   ,   )
							A__          =      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 logging

from transformers.configuration_utils import PretrainedConfig


__magic_name__  :  Tuple					=       logging.getLogger(__name__)





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Tuple				= "masked_bert"
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Optional[Any]=3_05_22       ,						__lowerCamelCase	:		Dict=7_68       ,						__lowerCamelCase	:		Tuple=12       ,						__lowerCamelCase	:		int=12       ,						__lowerCamelCase	:		Tuple=30_72       ,						__lowerCamelCase	:		List[Any]="gelu"       ,						__lowerCamelCase	:		Union[str, Any]=0.1       ,						__lowerCamelCase	:		Dict=0.1       ,						__lowerCamelCase	:		Optional[int]=5_12       ,						__lowerCamelCase	:		List[Any]=2       ,						__lowerCamelCase	:		Union[str, Any]=0.02       ,						__lowerCamelCase	:		Optional[int]=1e-12       ,						__lowerCamelCase	:		Optional[int]=0       ,						__lowerCamelCase	:		List[str]="topK"       ,						__lowerCamelCase	:		Union[str, Any]="constant"       ,						__lowerCamelCase	:		Union[str, Any]=0.0       ,						**__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       super().__init__(pad_token_id=__lowerCamelCase       ,						**__lowerCamelCase						)

       lowerCAmelCase__										=      vocab_size
       lowerCAmelCase__										=      hidden_size
       lowerCAmelCase__										=      num_hidden_layers
       lowerCAmelCase__										=      num_attention_heads
       lowerCAmelCase__										=      hidden_act
       lowerCAmelCase__										=      intermediate_size
       lowerCAmelCase__										=      hidden_dropout_prob
       lowerCAmelCase__										=      attention_probs_dropout_prob
       lowerCAmelCase__										=      max_position_embeddings
       lowerCAmelCase__										=      type_vocab_size
       lowerCAmelCase__										=      initializer_range
       lowerCAmelCase__										=      layer_norm_eps
       lowerCAmelCase__										=      pruning_method
       lowerCAmelCase__										=      mask_init
       lowerCAmelCase__										=      mask_scale

