import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()



class  UpperCamelCase				( __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  StableDiffusionXLImgaImgPipeline
     A_										=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     A_										=  PipelineTesterMixin.required_optional_params - {'latents'}
     A_										=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     A_										=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     A_										=  IMAGE_TO_IMAGE_IMAGE_PARAMS



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           torch.manual_seed(0						)
           _lowerCamelCase			    =				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=A_				,				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				,				)
           _lowerCamelCase			    =				EulerDiscreteScheduler(
               beta_start=0.00085				,				beta_end=0.012				,				steps_offset=1				,				beta_schedule='''scaled_linear'''				,				timestep_spacing='''leading'''				,				)
           torch.manual_seed(0						)
           _lowerCamelCase			    =				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=1_28				,				)
           torch.manual_seed(0						)
           _lowerCamelCase			    =				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=10_00				,				hidden_act='''gelu'''				,				projection_dim=32				,				)
           _lowerCamelCase			    =				CLIPTextModel(A_						)
           _lowerCamelCase			    =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,				local_files_only=A_						)

           _lowerCamelCase			    =				CLIPTextModelWithProjection(A_						)
           _lowerCamelCase			    =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,				local_files_only=A_						)

           _lowerCamelCase			    =				{
               '''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				,				A_				,				A_=0						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				floats_tensor((1, 3, 32, 32)				,				rng=random.Random(A_						)						).to(A_						)
           _lowerCamelCase			    =				image / 2 + 0.5
           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''',
               '''image''': image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 5.0,
               '''output_type''': '''numpy''',
               '''strength''': 0.75,
           }
           return inputs



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           _lowerCamelCase			    =				self.get_dummy_components()
           _lowerCamelCase			    =				StableDiffusionXLImgaImgPipeline(**A_						)
           _lowerCamelCase			    =				sd_pipe.to(A_						)
           sd_pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				self.get_dummy_inputs(A_						)
           _lowerCamelCase			    =				sd_pipe(**A_						).images
           _lowerCamelCase			    =				image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)

           _lowerCamelCase			    =				np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]						)

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



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






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



     def 		UpperCamelCase_						(					self						) ->				str:






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



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           pass





     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_dummy_components()
           _lowerCamelCase			    =				StableDiffusionXLImgaImgPipeline(**A_						)
           _lowerCamelCase			    =				sd_pipe.to(A_						)
           _lowerCamelCase			    =				sd_pipe.to(A_						)
           sd_pipe.set_progress_bar_config(disable=A_						)

           # forward without prompt embeds
           _lowerCamelCase			    =				self.get_dummy_inputs(A_						)
           _lowerCamelCase			    =				3 * ['''this is a negative prompt''']
           _lowerCamelCase			    =				negative_prompt
           _lowerCamelCase			    =				3 * [inputs['''prompt''']]

           _lowerCamelCase			    =				sd_pipe(**A_						)
           _lowerCamelCase			    =				output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           _lowerCamelCase			    =				self.get_dummy_inputs(A_						)
           _lowerCamelCase			    =				3 * ['''this is a negative prompt''']
           _lowerCamelCase			    =				3 * [inputs.pop('''prompt'''						)]

           (
               (
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					
           )			    =				sd_pipe.encode_prompt(A_				,				negative_prompt=A_						)

           _lowerCamelCase			    =				sd_pipe(
               **A_				,				prompt_embeds=A_				,				negative_prompt_embeds=A_				,				pooled_prompt_embeds=A_				,				negative_pooled_prompt_embeds=A_				,				)
           _lowerCamelCase			    =				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





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

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				List[str]:






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



     def 		UpperCamelCase_						(					self				,				A_				,				A_="cpu"				,				A_=torch.floataa				,				A_=0						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				torch.Generator(device=A_						).manual_seed(A_						)
           _lowerCamelCase			    =				np.random.RandomState(A_						).standard_normal((1, 4, 64, 64)						)
           _lowerCamelCase			    =				torch.from_numpy(A_						).to(device=A_				,				dtype=A_						)
           _lowerCamelCase			    =				{
               '''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						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''						)
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				self.get_inputs(A_						)
           _lowerCamelCase			    =				pipe(**A_						).images
           _lowerCamelCase			    =				image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowerCamelCase			    =				np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]						)
           assert np.abs(image_slice - expected_slice						).max() < 7E-3
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['vqvae']



     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				) ->				Tuple:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return 50 if isinstance(self.scheduler				,				A_						) else 10_00



     @torch.no_grad()
     def __call__(					self				,				A_ = 1				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_=True				,				) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






           """simple docstring"""
           _lowerCamelCase			    =				steps or self.get_default_steps()
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size						) == int:
                 _lowerCamelCase			    =				(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                 _lowerCamelCase			    =				randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )				,				generator=A_				,				device=self.device				,				)
           _lowerCamelCase			    =				noise
           _lowerCamelCase			    =				None

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(A_				,				A_						)
                 _lowerCamelCase			    =				self.mel.audio_slice_to_image(A_						)
                 _lowerCamelCase			    =				np.frombuffer(input_image.tobytes()				,				dtype='''uint8'''						).reshape(
                     (input_image.height, input_image.width)						)
                 _lowerCamelCase			    =				(input_image / 2_55) * 2 - 1
                 _lowerCamelCase			    =				torch.tensor(input_image[np.newaxis, :, :]				,				dtype=torch.float						).to(self.device						)

                 if self.vqvae is not None:
                       _lowerCamelCase			    =				self.vqvae.encode(torch.unsqueeze(A_				,				0						)						).latent_dist.sample(
                           generator=A_						)[0]
                       _lowerCamelCase			    =				self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                       _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				self.scheduler.timesteps[start_step - 1]						)

                 _lowerCamelCase			    =				(
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 _lowerCamelCase			    =				int(mask_start_secs * pixels_per_second						)
                 _lowerCamelCase			    =				int(mask_end_secs * pixels_per_second						)
                 _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				torch.tensor(self.scheduler.timesteps[start_step:]						)						)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
                 if isinstance(self.unet				,				A_						):
                       _lowerCamelCase			    =				self.unet(A_				,				A_				,				A_						)['''sample''']
                 else:
                       _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']

                 if isinstance(self.scheduler				,				A_						):
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				eta=A_				,				generator=A_				,				)['''prev_sample''']
                 else:
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				generator=A_				,				)['''prev_sample''']

                 if mask is not None:
                       if mask_start > 0:
                             _lowerCamelCase			    =				mask[:, step, :, :mask_start]
                       if mask_end > 0:
                             _lowerCamelCase			    =				mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 _lowerCamelCase			    =				1 / self.vqvae.config.scaling_factor * images
                 _lowerCamelCase			    =				self.vqvae.decode(A_						)['''sample''']

           _lowerCamelCase			    =				(images / 2 + 0.5).clamp(0				,				1						)
           _lowerCamelCase			    =				images.cpu().permute(0				,				2				,				3				,				1						).numpy()
           _lowerCamelCase			    =				(images * 2_55).round().astype('''uint8'''						)
           _lowerCamelCase			    =				list(
               (Image.fromarray(_[:, :, 0]						) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(A_				,				mode='''RGB'''						).convert('''L'''						) for _ in images)						)

           _lowerCamelCase			    =				[self.mel.image_to_audio(A_						) for _ in images]
           if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(A_						)[:, np.newaxis, :]						)				,				**ImagePipelineOutput(A_						)						)



     @torch.no_grad()
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = 50						) ->				np.ndarray:






           """simple docstring"""
           assert isinstance(self.scheduler				,				A_						)
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				np.array(
               [np.frombuffer(image.tobytes()				,				dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
           _lowerCamelCase			    =				(sample / 2_55) * 2 - 1
           _lowerCamelCase			    =				torch.Tensor(A_						).to(self.device						)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,				(0,)						)						):
                 _lowerCamelCase			    =				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 _lowerCamelCase			    =				self.scheduler.alphas_cumprod[t]
                 _lowerCamelCase			    =				(
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 _lowerCamelCase			    =				1 - alpha_prod_t
                 _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']
                 _lowerCamelCase			    =				(1 - alpha_prod_t_prev) ** 0.5 * model_output
                 _lowerCamelCase			    =				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 _lowerCamelCase			    =				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample





     @staticmethod
     def 		UpperCamelCase_						(					A_				,				A_				,				A_						) ->				torch.Tensor:






           """simple docstring"""
           _lowerCamelCase			    =				acos(torch.dot(torch.flatten(A_						)				,				torch.flatten(A_						)						) / torch.norm(A_						) / torch.norm(A_						)						)
           return sin((1 - alpha) * theta						) * xa / sin(A_						) + sin(alpha * theta						) * xa / sin(A_						)