




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 			a__    (					snake_case__ ,				snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    str					=    StableUnCLIPPipeline
						_a							:    Union[str, Any]					=    TEXT_TO_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_BATCH_PARAMS
						_a							:    Optional[int]					=    TEXT_TO_IMAGE_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						_a							:    Optional[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       3_2
											__lowerCAmelCase				   =       embedder_hidden_size

											# prior components

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

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModelWithProjection(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=_A		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       PriorTransformer(
											    num_attention_heads=2		,			attention_head_dim=1_2		,			embedding_dim=_A		,			num_layers=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDPMScheduler(
											    variance_type="fixed_small_log"		,			prediction_type="sample"		,			num_train_timesteps=1_0_0_0		,			clip_sample=_A		,			clip_sample_range=5.0		,			beta_schedule="squaredcos_cap_v2"		,			)

											# regular denoising components

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       StableUnCLIPImageNormalizer(embedding_dim=_A			)
											__lowerCAmelCase				   =       DDPMScheduler(beta_schedule="squaredcos_cap_v2"			)

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

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=3_2		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       UNetaDConditionModel(
											    sample_size=3_2		,			in_channels=4		,			out_channels=4		,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")		,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")		,			block_out_channels=(3_2, 6_4)		,			attention_head_dim=(2, 4)		,			class_embed_type="projection"		,			projection_class_embeddings_input_dim=embedder_projection_dim * 2		,			cross_attention_dim=_A		,			layers_per_block=1		,			upcast_attention=_A		,			use_linear_projection=_A		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDIMScheduler(
											    beta_schedule="scaled_linear"		,			beta_start=0.0_00_85		,			beta_end=0.0_12		,			prediction_type="v_prediction"		,			set_alpha_to_one=_A		,			steps_offset=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       AutoencoderKL()

											__lowerCAmelCase				   =       {
											    # 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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device == "cpu"

											self._test_attention_slicing_forward_pass(test_max_difference=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device in ["cpu", "mps"]

											self._test_inference_batch_single_identical(test_max_difference=_A			)






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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"			)

											__lowerCAmelCase				   =       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,			torch_dtype=torch.floataa			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)
											# 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()

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

											__lowerCAmelCase				   =       output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

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

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

											__lowerCAmelCase				   =       torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 1_0**9








'''simple docstring'''
import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          debug_launcher(test_script.main )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          debug_launcher(test_ops.main )
