





'''simple docstring'''






import gc
import unittest

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

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

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class    _A   (				__lowercase	,				__lowercase	,				unittest.TestCase		):
		lowercase__:      Tuple							    =      StableDiffusionXLPipeline
		lowercase__:      List[Any]							    =      TEXT_TO_IMAGE_PARAMS
		lowercase__:      Dict							    =      TEXT_TO_IMAGE_BATCH_PARAMS
		lowercase__:      Optional[int]							    =      TEXT_TO_IMAGE_IMAGE_PARAMS
		lowercase__:      Any							    =      TEXT_TO_IMAGE_IMAGE_PARAMS

		def   lowercase__    (      self				:      Union[str, Any] )				->						int:


					"""simple docstring"""

					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""")     ,			attention_head_dim=(2, 4)     ,			use_linear_projection=__magic_name__     ,			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     ,			)
					__snake_case				:			Dict       			=    EulerDiscreteScheduler(
					    beta_start=0.00085     ,			beta_end=0.012     ,			steps_offset=1     ,			beta_schedule="""scaled_linear"""     ,			timestep_spacing="""leading"""     ,			)
					torch.manual_seed(0 )
					__snake_case				:			List[str]       			=    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=1_28     ,			)
					torch.manual_seed(0 )
					__snake_case				:			List[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=10_00     ,			hidden_act="""gelu"""     ,			projection_dim=32     ,			)
					__snake_case				:			List[Any]       			=    CLIPTextModel(__magic_name__ )
					__snake_case				:			Optional[Any]       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,			local_files_only=__magic_name__ )

					__snake_case				:			Dict       			=    CLIPTextModelWithProjection(__magic_name__ )
					__snake_case				:			List[Any]       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,			local_files_only=__magic_name__ )

					__snake_case				:			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   lowercase__    (      self				:      Any     ,			__magic_name__				:      Optional[int]     ,			__magic_name__				:      Dict=0 )				->						Optional[int]:


					"""simple docstring"""

					if str(__magic_name__ ).startswith("""mps""" ):
								__snake_case				:			int       			=    torch.manual_seed(__magic_name__ )
					else:
								__snake_case				:			Any       			=    torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
					__snake_case				:			int       			=    {
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 5.0,
					    """output_type""": """numpy""",
					}
					return inputs

		def   lowercase__    (      self				:      Dict )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			Any       			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
					__snake_case				:			List[Any]       			=    self.get_dummy_components()
					__snake_case				:			Dict       			=    StableDiffusionXLPipeline(**__magic_name__ )
					__snake_case				:			int       			=    sd_pipe.to(__magic_name__ )
					sd_pipe.set_progress_bar_config(disable=__magic_name__ )

					__snake_case				:			Union[str, Any]       			=    self.get_dummy_inputs(__magic_name__ )
					__snake_case				:			Tuple       			=    sd_pipe(**__magic_name__ ).images
					__snake_case				:			Any       			=    image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					__snake_case				:			str       			=    np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747] )

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

		def   lowercase__    (      self				:      Tuple )				->						Optional[Any]:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    self.get_dummy_components()
					__snake_case				:			List[Any]       			=    StableDiffusionXLPipeline(**__magic_name__ )
					__snake_case				:			int       			=    sd_pipe.to(__magic_name__ )
					__snake_case				:			Optional[Any]       			=    sd_pipe.to(__magic_name__ )
					sd_pipe.set_progress_bar_config(disable=__magic_name__ )

					# forward without prompt embeds
					__snake_case				:			int       			=    self.get_dummy_inputs(__magic_name__ )
					__snake_case				:			Dict       			=    3 * ["""this is a negative prompt"""]
					__snake_case				:			Optional[int]       			=    negative_prompt
					__snake_case				:			Dict       			=    3 * [inputs["""prompt"""]]

					__snake_case				:			Any       			=    sd_pipe(**__magic_name__ )
					__snake_case				:			int       			=    output.images[0, -3:, -3:, -1]

					# forward with prompt embeds
					__snake_case				:			List[Any]       			=    self.get_dummy_inputs(__magic_name__ )
					__snake_case				:			List[Any]       			=    3 * ["""this is a negative prompt"""]
					__snake_case				:			Dict       			=    3 * [inputs.pop("""prompt""" )]

					(
					    __snake_case    
					)				:			Any       			=    sd_pipe.encode_prompt(__magic_name__     ,			negative_prompt=__magic_name__ )

					__snake_case				:			Optional[Any]       			=    sd_pipe(
					    **__magic_name__     ,			prompt_embeds=__magic_name__     ,			negative_prompt_embeds=__magic_name__     ,			pooled_prompt_embeds=__magic_name__     ,			negative_pooled_prompt_embeds=__magic_name__     ,			)
					__snake_case				:			Tuple       			=    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

		def   lowercase__    (      self				:      Any )				->						str:


					"""simple docstring"""

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

		def   lowercase__    (      self				:      Dict )				->						Tuple:


					"""simple docstring"""

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





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

		def   lowercase__    (      self				:      Optional[int] )				->						Optional[Any]:


					"""simple docstring"""

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

		def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      str     ,			__magic_name__				:      str="cpu"     ,			__magic_name__				:      int=torch.floataa     ,			__magic_name__				:      List[Any]=0 )				->						str:


					"""simple docstring"""

					__snake_case				:			Dict       			=    torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
					__snake_case				:			List[str]       			=    np.random.RandomState(__magic_name__ ).standard_normal((1, 4, 64, 64) )
					__snake_case				:			Optional[int]       			=    torch.from_numpy(__magic_name__ ).to(device=__magic_name__     ,			dtype=__magic_name__ )
					__snake_case				:			Tuple       			=    {
					    """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   lowercase__    (      self				:      Dict )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
					pipe.to(__magic_name__ )
					pipe.set_progress_bar_config(disable=__magic_name__ )

					__snake_case				:			str       			=    self.get_inputs(__magic_name__ )
					__snake_case				:			Union[str, Any]       			=    pipe(**__magic_name__ ).images
					__snake_case				:			Optional[int]       			=    image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case				:			str       			=    np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506] )
					assert np.abs(image_slice - expected_slice ).max() < 7E-3













'''simple docstring'''






from __future__ import annotations

from typing import Any




class    _A   :

		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      Any     ,			__magic_name__				:      Any )				->						None:  # noqa: A002


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    item
					__snake_case				:			List[Any]       			=    next





class    _A   :

		def __init__(      self				:      str )				->						None:


					"""simple docstring"""

					__snake_case				:			Node | None       			=    None
					__snake_case				:			Optional[Any]       			=    0

		def   lowercase__    (      self				:      Optional[Any]     ,			__magic_name__				:      Any )				->						None:


					"""simple docstring"""

					__snake_case				:			int       			=    Node(__magic_name__     ,			self.head )
					self.size += 1

		def   lowercase__    (      self				:      Tuple )				->						Any:


					"""simple docstring"""

					if self.head is None:
								return None
					else:
								__snake_case				:			Union[str, Any]       			=    self.head.item
								__snake_case				:			Union[str, Any]       			=    self.head.next
								self.size -= 1
								return item

		def   lowercase__    (      self				:      str )				->						bool:


					"""simple docstring"""

					return self.head is None

		def __str__(      self				:      str )				->						str:


					"""simple docstring"""

					if self.is_empty():
								return ""
					else:
								__snake_case				:			Dict       			=    self.head
								__snake_case				:			Optional[int]       			=    """"""
								__snake_case				:			list[str]       			=    []
								while iterate:
											item_list.append(str(iterate.item ) )
											__snake_case				:			int       			=    iterate.next

								__snake_case				:			Union[str, Any]       			=    """ --> """.join(__magic_name__ )

								return item_str

		def __len__(      self				:      Optional[int] )				->						int:


					"""simple docstring"""

					return self.size


