



"""simple docstring"""

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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 SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					StableDiffusionXLImgaImgPipeline
						_lowerCAmelCase							=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
						_lowerCAmelCase							=					PipelineTesterMixin.required_optional_params - {"latents"}
						_lowerCAmelCase							=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						_lowerCAmelCase							=					IMAGE_TO_IMAGE_IMAGE_PARAMS
						_lowerCAmelCase							=					IMAGE_TO_IMAGE_IMAGE_PARAMS



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		str									=       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""")						,     attention_head_dim=(2, 4)						,     use_linear_projection=_lowercase						,     addition_embed_type="""text_time"""						,     addition_time_embed_dim=8						,     transformer_layers_per_block=(1, 2)						,     projection_class_embeddings_input_dim=80						,     cross_attention_dim=64						,     )
													__a		:		Dict									=       EulerDiscreteScheduler(
													    beta_start=0.0_0085						,     beta_end=0.012						,     steps_offset=1						,     beta_schedule="""scaled_linear"""						,     timestep_spacing="""leading"""						,     )
													torch.manual_seed(0							)
													__a		:		Tuple									=       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						,     sample_size=128						,     )
													torch.manual_seed(0							)
													__a		:		str									=       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=1000						,     hidden_act="""gelu"""						,     projection_dim=32						,     )
													__a		:		Optional[Any]									=       CLIPTextModel(_lowercase							)
													__a		:		Tuple									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						,     local_files_only=_lowercase							)

													__a		:		List[Any]									=       CLIPTextModelWithProjection(_lowercase							)
													__a		:		Optional[int]									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						,     local_files_only=_lowercase							)

													__a		:		Optional[int]									=       {
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """vae""": vae,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """text_encoder_2""": text_encoder_a,
													    """tokenizer_2""": tokenizer_a,
													    # "safety_checker": None,
													    # "feature_extractor": None,
													}
													return components



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=0							):





													'''simple docstring'''

													__a		:		str									=       floats_tensor((1, 3, 32, 32)						,     rng=random.Random(_lowercase							)							).to(_lowercase							)
													__a		:		List[Any]									=       image / 2 + 0.5
													if str(_lowercase							).startswith("""mps"""							):
																				__a		:		Tuple									=       torch.manual_seed(_lowercase							)
													else:
																				__a		:		Optional[Any]									=       torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
													__a		:		List[Any]									=       {
													    """prompt""": """A painting of a squirrel eating a burger""",
													    """image""": image,
													    """generator""": generator,
													    """num_inference_steps""": 2,
													    """guidance_scale""": 5.0,
													    """output_type""": """numpy""",
													    """strength""": 0.75,
													}
													return inputs



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
													__a		:		int									=       self.get_dummy_components()
													__a		:		Optional[int]									=       StableDiffusionXLImgaImgPipeline(**_lowercase							)
													__a		:		Any									=       sd_pipe.to(_lowercase							)
													sd_pipe.set_progress_bar_config(disable=_lowercase							)

													__a		:		Optional[Any]									=       self.get_dummy_inputs(_lowercase							)
													__a		:		Optional[int]									=       sd_pipe(**_lowercase							).images
													__a		:		Union[str, Any]									=       image[0, -3:, -3:, -1]

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

													__a		:		str									=       np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]							)

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().test_inference_batch_single_identical(expected_max_diff=3e-3							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       self.get_dummy_components()
													__a		:		List[Any]									=       StableDiffusionXLImgaImgPipeline(**_lowercase							)
													__a		:		Union[str, Any]									=       sd_pipe.to(_lowercase							)
													__a		:		List[Any]									=       sd_pipe.to(_lowercase							)
													sd_pipe.set_progress_bar_config(disable=_lowercase							)

													# forward without prompt embeds
													__a		:		Dict									=       self.get_dummy_inputs(_lowercase							)
													__a		:		str									=       3 * ["""this is a negative prompt"""]
													__a		:		Union[str, Any]									=       negative_prompt
													__a		:		str									=       3 * [inputs["""prompt"""]]

													__a		:		Union[str, Any]									=       sd_pipe(**_lowercase							)
													__a		:		Any									=       output.images[0, -3:, -3:, -1]

													# forward with prompt embeds
													__a		:		Optional[int]									=       self.get_dummy_inputs(_lowercase							)
													__a		:		List[str]									=       3 * ["""this is a negative prompt"""]
													__a		:		Dict									=       3 * [inputs.pop("""prompt"""							)]

													(
													    (
													    __a
													)							,     (
													    __a
													)							,     (
													    __a
													)							,     (
													    __a
													)							,     
													)		:		str									=       sd_pipe.encode_prompt(_lowercase						,     negative_prompt=_lowercase							)

													__a		:		List[str]									=       sd_pipe(
													    **_lowercase						,     prompt_embeds=_lowercase						,     negative_prompt_embeds=_lowercase						,     pooled_prompt_embeds=_lowercase						,     negative_pooled_prompt_embeds=_lowercase						,     )
													__a		:		Any									=       output.images[0, -3:, -3:, -1]

													# make sure that it's equal
													assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1e-4







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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase="cpu"						,     _lowercase=torch.floataa						,     _lowercase=0							):





													'''simple docstring'''

													__a		:		List[Any]									=       torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
													__a		:		Any									=       np.random.RandomState(_lowercase							).standard_normal((1, 4, 64, 64)							)
													__a		:		List[Any]									=       torch.from_numpy(_lowercase							).to(device=_lowercase						,     dtype=_lowercase							)
													__a		:		str									=       {
													    """prompt""": """a photograph of an astronaut riding a horse""",
													    """latents""": latents,
													    """generator""": generator,
													    """num_inference_steps""": 3,
													    """guidance_scale""": 7.5,
													    """output_type""": """numpy""",
													}
													return inputs





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""							)
													pipe.to(_lowercase							)
													pipe.set_progress_bar_config(disable=_lowercase							)

													__a		:		List[Any]									=       self.get_inputs(_lowercase							)
													__a		:		Optional[Any]									=       pipe(**_lowercase							).images
													__a		:		List[str]									=       image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 512, 512, 3)
													__a		:		Tuple									=       np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]							)
													assert np.abs(image_slice - expected_slice							).max() < 7e-3






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

