
"""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   ):




		'''simple docstring'''
		SCREAMING_SNAKE_CASE__    :		Optional[Any]						  =	StableDiffusionXLPipeline
		SCREAMING_SNAKE_CASE__    :		List[str]						  =	TEXT_TO_IMAGE_PARAMS
		SCREAMING_SNAKE_CASE__    :		Tuple						  =	TEXT_TO_IMAGE_BATCH_PARAMS
		SCREAMING_SNAKE_CASE__    :		int						  =	TEXT_TO_IMAGE_IMAGE_PARAMS
		SCREAMING_SNAKE_CASE__    :		List[str]						  =	TEXT_TO_IMAGE_IMAGE_PARAMS







		def  UpperCamelCase__      (	self						):

					"""simple docstring"""
					torch.manual_seed(0						)
					UpperCAmelCase_      :      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=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       ,     )
					UpperCAmelCase_      :      Union[str, Any]      =					EulerDiscreteScheduler(
					    beta_start=0.0_00_85       ,     beta_end=0.0_12       ,     steps_offset=1       ,     beta_schedule="scaled_linear"       ,     timestep_spacing="leading"       ,     )
					torch.manual_seed(0						)
					UpperCAmelCase_      :      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						)
					UpperCAmelCase_      :      Dict      =					CLIPTextConfig(
					    bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     intermediate_size=37       ,     layer_norm_eps=1E-0_5       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=1000       ,     hidden_act="gelu"       ,     projection_dim=32       ,     )
					UpperCAmelCase_      :      Optional[int]      =					CLIPTextModel(lowercase_						)
					UpperCAmelCase_      :      Any      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,     local_files_only=lowercase_						)

					UpperCAmelCase_      :      Union[str, Any]      =					CLIPTextModelWithProjection(lowercase_						)
					UpperCAmelCase_      :      int      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,     local_files_only=lowercase_						)

					UpperCAmelCase_      :      List[str]      =					{
					    "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  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=0						):

					"""simple docstring"""
					if str(lowercase_						).startswith("mps"						):
								UpperCAmelCase_      :      Optional[Any]      =					torch.manual_seed(lowercase_						)
					else:
								UpperCAmelCase_      :      Optional[int]      =					torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
					UpperCAmelCase_      :      List[str]      =					{
					    "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  UpperCamelCase__      (	self						):

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

					UpperCAmelCase_      :      Dict      =					self.get_dummy_inputs(lowercase_						)
					UpperCAmelCase_      :      Tuple      =					sd_pipe(**lowercase_						).images
					UpperCAmelCase_      :      int      =					image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					UpperCAmelCase_      :      int      =					np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]						)

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







		def  UpperCamelCase__      (	self						):

					"""simple docstring"""
					UpperCAmelCase_      :      int      =					self.get_dummy_components()
					UpperCAmelCase_      :      Union[str, Any]      =					StableDiffusionXLPipeline(**lowercase_						)
					UpperCAmelCase_      :      Optional[Any]      =					sd_pipe.to(lowercase_						)
					UpperCAmelCase_      :      Any      =					sd_pipe.to(lowercase_						)
					sd_pipe.set_progress_bar_config(disable=lowercase_						)

					# forward without prompt embeds
					UpperCAmelCase_      :      List[Any]      =					self.get_dummy_inputs(lowercase_						)
					UpperCAmelCase_      :      int      =					3 * ["this is a negative prompt"]
					UpperCAmelCase_      :      str      =					negative_prompt
					UpperCAmelCase_      :      Union[str, Any]      =					3 * [inputs["prompt"]]

					UpperCAmelCase_      :      str      =					sd_pipe(**lowercase_						)
					UpperCAmelCase_      :      List[Any]      =					output.images[0, -3:, -3:, -1]

					# forward with prompt embeds
					UpperCAmelCase_      :      Dict      =					self.get_dummy_inputs(lowercase_						)
					UpperCAmelCase_      :      Union[str, Any]      =					3 * ["this is a negative prompt"]
					UpperCAmelCase_      :      Union[str, Any]      =					3 * [inputs.pop("prompt"						)]

					(
					    UpperCAmelCase_     
					)      :      Dict      =					sd_pipe.encode_prompt(lowercase_       ,     negative_prompt=lowercase_						)

					UpperCAmelCase_      :      str      =					sd_pipe(
					    **lowercase_       ,     prompt_embeds=lowercase_       ,     negative_prompt_embeds=lowercase_       ,     pooled_prompt_embeds=lowercase_       ,     negative_pooled_prompt_embeds=lowercase_       ,     )
					UpperCAmelCase_      :      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







		def  UpperCamelCase__      (	self						):

					"""simple docstring"""
					super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)







		def  UpperCamelCase__      (	self						):

					"""simple docstring"""
					super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




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




		'''simple docstring'''







		def  UpperCamelCase__      (	self						):

					"""simple docstring"""
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







		def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_="cpu"       ,     lowercase_=torch.floataa       ,     lowercase_=0						):

					"""simple docstring"""
					UpperCAmelCase_      :      Any      =					torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
					UpperCAmelCase_      :      Optional[Any]      =					np.random.RandomState(lowercase_						).standard_normal((1, 4, 64, 64)						)
					UpperCAmelCase_      :      int      =					torch.from_numpy(lowercase_						).to(device=lowercase_       ,     dtype=lowercase_						)
					UpperCAmelCase_      :      int      =					{
					    "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  UpperCamelCase__      (	self						):

					"""simple docstring"""
					UpperCAmelCase_      :      int      =					DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"						)
					pipe.to(lowercase_						)
					pipe.set_progress_bar_config(disable=lowercase_						)

					UpperCAmelCase_      :      Optional[int]      =					self.get_inputs(lowercase_						)
					UpperCAmelCase_      :      Optional[Any]      =					pipe(**lowercase_						).images
					UpperCAmelCase_      :      int      =					image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					UpperCAmelCase_      :      Optional[int]      =					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]						)
					assert np.abs(image_slice - expected_slice						).max() < 7E-3




"""simple docstring"""





from .hash_table import HashTable


class A_			(lowercase__   ):




  '''simple docstring'''







  def __init__(	self       ,     *lowercase_       ,     **lowercase_						):

     """simple docstring"""
     super().__init__(*lowercase_       ,     **lowercase_						)







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=None						):

     """simple docstring"""
     UpperCAmelCase_      :      Tuple      =					1
     UpperCAmelCase_      :      List[str]      =					self.hash_function(key + i * i						)

     while self.values[new_key] is not None and self.values[new_key] != key:
        i += 1
        UpperCAmelCase_      :      List[Any]      =					(
            self.hash_function(key + i * i						)
            if not self.balanced_factor() >= self.lim_charge
            else None
        )

        if new_key is None:
           break

     return new_key


