




import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      StableUnCLIPPipeline
				SCREAMING_SNAKE_CASE_ :							int											=      TEXT_TO_IMAGE_PARAMS
				SCREAMING_SNAKE_CASE_ :							Any											=      TEXT_TO_IMAGE_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      TEXT_TO_IMAGE_IMAGE_PARAMS
				SCREAMING_SNAKE_CASE_ :							Tuple											=      TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				SCREAMING_SNAKE_CASE_ :							List[str]											=      False





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Optional[Any]:
							a					=    32
							a					=    embedder_hidden_size

							# prior components

							torch.manual_seed(0							)
							a					=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

							torch.manual_seed(0							)
							a					=    CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=__lowerCamelCase      ,   projection_dim=__lowerCamelCase      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=10_00      ,   )							)

							torch.manual_seed(0							)
							a					=    PriorTransformer(
							    num_attention_heads=2      ,   attention_head_dim=12      ,   embedding_dim=__lowerCamelCase      ,   num_layers=1      ,   )

							torch.manual_seed(0							)
							a					=    DDPMScheduler(
							    variance_type="fixed_small_log"      ,   prediction_type="sample"      ,   num_train_timesteps=10_00      ,   clip_sample=__lowerCamelCase      ,   clip_sample_range=5.0      ,   beta_schedule="squaredcos_cap_v2"      ,   )

							# regular denoising components

							torch.manual_seed(0							)
							a					=    StableUnCLIPImageNormalizer(embedding_dim=__lowerCamelCase							)
							a					=    DDPMScheduler(beta_schedule="squaredcos_cap_v2"							)

							torch.manual_seed(0							)
							a					=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

							torch.manual_seed(0							)
							a					=    CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=__lowerCamelCase      ,   projection_dim=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      ,   )							)

							torch.manual_seed(0							)
							a					=    UNetaDConditionModel(
							    sample_size=32      ,   in_channels=4      ,   out_channels=4      ,   down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")      ,   up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")      ,   block_out_channels=(32, 64)      ,   attention_head_dim=(2, 4)      ,   class_embed_type="projection"      ,   projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,   cross_attention_dim=__lowerCamelCase      ,   layers_per_block=1      ,   upcast_attention=__lowerCamelCase      ,   use_linear_projection=__lowerCamelCase      ,   )

							torch.manual_seed(0							)
							a					=    DDIMScheduler(
							    beta_schedule="scaled_linear"      ,   beta_start=0.00_085      ,   beta_end=0.012      ,   prediction_type="v_prediction"      ,   set_alpha_to_one=__lowerCamelCase      ,   steps_offset=1      ,   )

							torch.manual_seed(0							)
							a					=    AutoencoderKL()

							a					=    {
							    # prior components
							    "prior_tokenizer": prior_tokenizer,
							    "prior_text_encoder": prior_text_encoder,
							    "prior": prior,
							    "prior_scheduler": prior_scheduler,
							    # image noising components
							    "image_normalizer": image_normalizer,
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder,
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							}

							return components





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Optional[Any]=0							)      ->  Any:
							if str(__lowerCamelCase							).startswith("mps"							):
										a					=    torch.manual_seed(__lowerCamelCase							)
							else:
										a					=    torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)
							a					=    {
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "prior_num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs





				def 							__UpperCAmelCase							(   self       :  int							)      ->  int:
							a					=    torch_device == "cpu"

							self._test_attention_slicing_forward_pass(test_max_difference=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Union[str, Any]:
							a					=    torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=__lowerCamelCase							)




@slow
@require_torch_gpu
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



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





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  List[Any]:
							a					=    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"							)

							a					=    StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"      ,   torch_dtype=torch.floataa							)
							pipe.to(__lowerCamelCase							)
							pipe.set_progress_bar_config(disable=__lowerCamelCase							)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							a					=    torch.Generator(device="cpu"							).manual_seed(0							)
							a					=    pipe("anime turle"      ,   generator=__lowerCamelCase      ,   output_type="np"							)

							a					=    output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Dict:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							a					=    StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"      ,   torch_dtype=torch.floataa							)
							a					=    pipe.to(__lowerCamelCase							)
							pipe.set_progress_bar_config(disable=__lowerCamelCase							)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							a					=    pipe(
							    "anime turtle"      ,   prior_num_inference_steps=2      ,   num_inference_steps=2      ,   output_type="np"      ,   )

							a					=    torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9












from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase	:       List[str]     =	[8, 5, 9, 7]
__lowerCAmelCase	:       str     =	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase	:       Optional[Any]     =	[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  list[int]:
							return [
							    sum(p_item[i] for p_item in self.__allocated_resources_table							)
							    for i in range(len(self.__allocated_resources_table[0]							)							)
							]





				def 							__UpperCAmelCase							(   self       :  str							)      ->  list[int]:
							return np.array(self.__claim_vector							) - np.array(
							    self.__processes_resource_summation()							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  list[list[int]]:
							return [
							    list(np.array(self.__maximum_claim_table[i]							) - np.array(__lowerCamelCase							)							)
							    for i, allocated_resource in enumerate(self.__allocated_resources_table							)
							]





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  dict[int, list[int]]:
							return {self.__need().index(__lowerCamelCase							): i for i in self.__need()}





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   **__lowerCamelCase       :  Any							)      ->  None:
							a					=    self.__need()
							a					=    self.__allocated_resources_table
							a					=    self.__available_resources()
							a					=    self.__need_index_manager()
							for kw, val in kwargs.items():
										if kw and val is True:
													self.__pretty_data()
							print("_" * 50 + "\n"							)
							while need_list:
										a					=    False
										for each_need in need_list:
													a					=    True
													for index, need in enumerate(__lowerCamelCase							):
																if need > available_resources[index]:
																			a					=    False
																			break
													if execution:
																a					=    True
																# get the original index of the process from ind_ctrl db
																for original_need_index, need_clone in need_index_manager.items():
																			if each_need == need_clone:
																						a					=    original_need_index
																print(f"""Process {process_number + 1} is executing."""							)
																# remove the process run from stack
																need_list.remove(__lowerCamelCase							)
																# update available/freed resources stack
																a					=    np.array(__lowerCamelCase							) + np.array(
																    alloc_resources_table[process_number]							)
																print(
																    "Updated available resource stack for processes: "
																    + " ".join([str(__lowerCamelCase							) for x in available_resources]							)							)
																break
										if safe:
													print("The process is in a safe state.\n"							)
										else:
													print("System in unsafe state. Aborting...\n"							)
													break





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							print(" " * 9 + "Allocated Resource Table"							)
							for item in self.__allocated_resources_table:
										print(
										    f"""P{self.__allocated_resources_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(" " * 9 + "System Resource Table"							)
							for item in self.__maximum_claim_table:
										print(
										    f"""P{self.__maximum_claim_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(
							    "Current Usage by Active Processes: "
							    + " ".join(str(__lowerCamelCase							) for x in self.__claim_vector							)							)
							print(
							    "Initial Available Resources:       "
							    + " ".join(str(__lowerCamelCase							) for x in self.__available_resources()							)							)
							time.sleep(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






