

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 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						StableUnCLIPPipeline
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_PARAMS
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_BATCH_PARAMS
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_IMAGE_PARAMS
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_IMAGE_PARAMS

			# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
			SCREAMING_SNAKE_CASE							   =						False





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




						'''simple docstring'''
						__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=__snake_case					,  projection_dim=__snake_case					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  )					)

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

						torch.manual_seed(0					)
						__a        =DDPMScheduler(
						    variance_type='fixed_small_log'					,  prediction_type='sample'					,  num_train_timesteps=1000					,  clip_sample=__snake_case					,  clip_sample_range=5.0					,  beta_schedule='squaredcos_cap_v2'					,  )

						# regular denoising components

						torch.manual_seed(0					)
						__a        =StableUnCLIPImageNormalizer(embedding_dim=__snake_case					)
						__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=__snake_case					,  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=1000					,  )					)

						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=__snake_case					,  layers_per_block=1					,  upcast_attention=__snake_case					,  use_linear_projection=__snake_case					,  )

						torch.manual_seed(0					)
						__a        =DDIMScheduler(
						    beta_schedule='scaled_linear'					,  beta_start=0.0_0085					,  beta_end=0.012					,  prediction_type='v_prediction'					,  set_alpha_to_one=__snake_case					,  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 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					)   -> Optional[int]:




						'''simple docstring'''
						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
						__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 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =torch_device == 'cpu'

						self._test_attention_slicing_forward_pass(test_max_difference=__snake_case					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =torch_device in ['cpu', 'mps']

						self._test_inference_batch_single_identical(test_max_difference=__snake_case					)






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





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__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(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)
						# 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=__snake_case					,  output_type='np'					)

						__a        =output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(__snake_case					,  __snake_case					)





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




						'''simple docstring'''
						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(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)
						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









import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> None:




						'''simple docstring'''
						warnings.warn(
						    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use CLIPImageProcessor instead.'					,  __snake_case					,  )
						super().__init__(*__snake_case					,  **__snake_case					)






