
'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :str   =  StableUnCLIPImgaImgPipeline
	snake_case__   :Union[str, Any]   =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	snake_case__   :Union[str, Any]   =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	snake_case__   :List[str]   =  frozenset(
	    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	snake_case__   :Optional[int]   =  frozenset([]     )





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				32
				lowerCAmelCase__										=				embedder_hidden_size

				# image encoding components

				lowerCAmelCase__										=				CLIPImageProcessor(crop_size=32   ,				size=32				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				CLIPVisionModelWithProjection(
				    CLIPVisionConfig(
				        hidden_size=__magic_name__   ,				projection_dim=__magic_name__   ,				num_hidden_layers=5   ,				num_attention_heads=4   ,				image_size=32   ,				intermediate_size=37   ,				patch_size=1   ,				)				)

				# regular denoising components

				torch.manual_seed(0				)
				lowerCAmelCase__										=				StableUnCLIPImageNormalizer(embedding_dim=__magic_name__				)
				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=__magic_name__   ,				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				)
				lowerCAmelCase__										=				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=__magic_name__   ,				layers_per_block=1   ,				upcast_attention=__magic_name__   ,				use_linear_projection=__magic_name__   ,				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				DDIMScheduler(
				    beta_schedule="scaled_linear"   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				prediction_type="v_prediction"   ,				set_alpha_to_one=__magic_name__   ,				steps_offset=1   ,				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				AutoencoderKL()

				lowerCAmelCase__										=				{
				    # image encoding components
				    "feature_extractor": feature_extractor,
				    "image_encoder": image_encoder.eval(),
				    # image noising components
				    "image_normalizer": image_normalizer.eval(),
				    "image_noising_scheduler": image_noising_scheduler,
				    # regular denoising components
				    "tokenizer": tokenizer,
				    "text_encoder": text_encoder.eval(),
				    "unet": unet.eval(),
				    "scheduler": scheduler,
				    "vae": vae.eval(),
				}

				return components





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Optional[int]=0   ,				__magic_name__      :							int=True				):


				"""simple docstring"""

				if str(__magic_name__				).startswith("mps"				):
							lowerCAmelCase__										=				torch.manual_seed(__magic_name__				)
				else:
							lowerCAmelCase__										=				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)

				lowerCAmelCase__										=				floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__magic_name__				)				).to(__magic_name__				)

				if pil_image:
							lowerCAmelCase__										=				input_image * 0.5 + 0.5
							lowerCAmelCase__										=				input_image.clamp(0   ,				1				)
							lowerCAmelCase__										=				input_image.cpu().permute(0   ,				2   ,				3   ,				1				).float().numpy()
							lowerCAmelCase__										=				DiffusionPipeline.numpy_to_pil(__magic_name__				)[0]

				return {
				    "prompt": "An anime racoon running a marathon",
				    "image": input_image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "output_type": "np",
				}





	@skip_mps
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				"cpu"  # ensure determinism for the device-dependent torch.Generator
				lowerCAmelCase__										=				self.get_dummy_components()
				lowerCAmelCase__										=				StableUnCLIPImgaImgPipeline(**__magic_name__				)
				lowerCAmelCase__										=				sd_pipe.to(__magic_name__				)
				sd_pipe.set_progress_bar_config(disable=__magic_name__				)

				lowerCAmelCase__										=				self.get_dummy_inputs(__magic_name__				)
				inputs.update({"image_embeds": None}				)
				lowerCAmelCase__										=				sd_pipe(**__magic_name__				).images
				lowerCAmelCase__										=				image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				lowerCAmelCase__										=				np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]				)

				assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

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

				self._test_attention_slicing_forward_pass(test_max_difference=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

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

				self._test_inference_batch_single_identical(test_max_difference=__magic_name__				)







	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()   ,				reason="XFormers attention is only available with CUDA and `xformers` installed"   ,				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__magic_name__				)






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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"				)

				lowerCAmelCase__										=				load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"				)

				lowerCAmelCase__										=				StableUnCLIPImgaImgPipeline.from_pretrained(
				    "fusing/stable-unclip-2-1-l-img2img"   ,				torch_dtype=torch.floataa				)
				pipe.to(__magic_name__				)
				pipe.set_progress_bar_config(disable=__magic_name__				)
				# 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(__magic_name__   ,				"anime turle"   ,				generator=__magic_name__   ,				output_type="np"				)

				lowerCAmelCase__										=				output.images[0]

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

				assert_mean_pixel_difference(__magic_name__   ,				__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				lowerCAmelCase__										=				load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"				)

				lowerCAmelCase__										=				load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"				)

				lowerCAmelCase__										=				StableUnCLIPImgaImgPipeline.from_pretrained(
				    "fusing/stable-unclip-2-1-h-img2img"   ,				torch_dtype=torch.floataa				)
				pipe.to(__magic_name__				)
				pipe.set_progress_bar_config(disable=__magic_name__				)
				# 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(__magic_name__   ,				"anime turle"   ,				generator=__magic_name__   ,				output_type="np"				)

				lowerCAmelCase__										=				output.images[0]

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

				assert_mean_pixel_difference(__magic_name__   ,				__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"				)

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

				lowerCAmelCase__										=				StableUnCLIPImgaImgPipeline.from_pretrained(
				    "fusing/stable-unclip-2-1-h-img2img"   ,				torch_dtype=torch.floataa				)
				lowerCAmelCase__										=				pipe.to(__magic_name__				)
				pipe.set_progress_bar_config(disable=__magic_name__				)
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				lowerCAmelCase__										=				pipe(
				    __magic_name__   ,				"anime turtle"   ,				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 * 10**9









import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


