from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_SCREAMING_SNAKE_CASE      						=	logging.get_logger(__name__)  # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE_						(	_a						):







   def __init__(			self		:		int					,					lowerCamelCase_		:		str					,					lowerCamelCase_		:		Dict							):





    """simple docstring"""


    super().__init__()
    self.register_modules(unet=lowerCamelCase_					,					scheduler=lowerCamelCase_							)







   @torch.no_grad()
   def __call__(			self		:		Tuple					,					lowerCamelCase_		:		Optional[int] = 1					,					lowerCamelCase_		:		Optional[Any] = 100					,					lowerCamelCase_		:		Tuple = None					,					lowerCamelCase_		:		Any = None					,					lowerCamelCase_		:		Tuple = True					,					):





    """simple docstring"""


    if audio_length_in_s is None:
     UpperCamelCase          =	self.unet.config.sample_size / self.unet.config.sample_rate

    UpperCamelCase          =	audio_length_in_s * self.unet.config.sample_rate

    UpperCamelCase          =	2 ** len(self.unet.up_blocks							)
    if sample_size < 3 * down_scale_factor:
     raise ValueError(
         f"""{audio_length_in_s} is too small. Make sure it\'s bigger or equal to"""
         f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""							)

    UpperCamelCase          =	int(lowerCamelCase_							)
    if sample_size % down_scale_factor != 0:
     UpperCamelCase          =	(
         (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
     ) * down_scale_factor
     logger.info(
         f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
         f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
         """ process."""							)
    UpperCamelCase          =	int(lowerCamelCase_							)

    UpperCamelCase          =	next(iter(self.unet.parameters()							)							).dtype
    UpperCamelCase          =	(batch_size, self.unet.config.in_channels, sample_size)
    if isinstance(lowerCamelCase_					,					lowerCamelCase_							) and len(lowerCamelCase_							) != batch_size:
     raise ValueError(
         f"""You have passed a list of generators of length {len(lowerCamelCase_							)}, but requested an effective batch"""
         f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

    UpperCamelCase          =	randn_tensor(lowerCamelCase_					,					generator=lowerCamelCase_					,					device=self.device					,					dtype=lowerCamelCase_							)

    # set step values
    self.scheduler.set_timesteps(lowerCamelCase_					,					device=audio.device							)
    UpperCamelCase          =	self.scheduler.timesteps.to(lowerCamelCase_							)

    for t in self.progress_bar(self.scheduler.timesteps							):
     # 1. predict noise model_output
     UpperCamelCase          =	self.unet(lowerCamelCase_					,					lowerCamelCase_							).sample

     # 2. compute previous image: x_t -> t_t-1
     UpperCamelCase          =	self.scheduler.step(lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_							).prev_sample

    UpperCamelCase          =	audio.clamp(-1					,					1							).float().cpu().numpy()

    UpperCamelCase          =	audio[:, :, :original_sample_size]

    if not return_dict:
     return (audio,)

    return AudioPipelineOutput(audios=lowerCamelCase_							)




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	Any     =			KandinskyImgaImgPipeline
       lowercase_ :	Union[str, Any]     =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
       lowercase_ :	Any     =			[
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       lowercase_ :	List[Any]     =			[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_ :	Union[str, Any]     =			False
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           return self.time_input_dim
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return self.time_input_dim * 4
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           return 1_00
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							str      		=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
           return tokenizer
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		MCLIPConfig(
               numDims=self.cross_attention_dim,      transformerDimensions=self.text_embedder_hidden_size,      hidden_size=self.text_embedder_hidden_size,      intermediate_size=37,      num_attention_heads=4,      num_hidden_layers=5,      vocab_size=10_05,      )

           _lowercase       :							Optional[int]      		=		MultilingualCLIP(lowerCamelCase)
           _lowercase       :							List[str]      		=		text_encoder.eval()

           return text_encoder
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)

           _lowercase       :							Union[str, Any]      		=		{
               'in_channels': 4,
               # Out channels is double in channels because predicts mean and variance
               'out_channels': 8,
               'addition_embed_type': 'text_image',
               'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
               'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
               'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
               'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
               'layers_per_block': 1,
               'encoder_hid_dim': self.text_embedder_hidden_size,
               'encoder_hid_dim_type': 'text_image_proj',
               'cross_attention_dim': self.cross_attention_dim,
               'attention_head_dim': 4,
               'resnet_time_scale_shift': 'scale_shift',
               'class_embed_type': None,
           }

           _lowercase       :							Optional[Any]      		=		UNetaDConditionModel(**lowerCamelCase)
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		VQModel(**self.dummy_movq_kwargs)
           return model
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.dummy_text_encoder
           _lowercase       :							List[Any]      		=		self.dummy_tokenizer
           _lowercase       :							int      		=		self.dummy_unet
           _lowercase       :							int      		=		self.dummy_movq

           _lowercase       :							Optional[int]      		=		{
               'num_train_timesteps': 10_00,
               'beta_schedule': 'linear',
               'beta_start': 0.0_0_0_8_5,
               'beta_end': 0.0_1_2,
               'clip_sample': False,
               'set_alpha_to_one': False,
               'steps_offset': 0,
               'prediction_type': 'epsilon',
               'thresholding': False,
           }

           _lowercase       :							List[Any]      		=		DDIMScheduler(**lowerCamelCase)

           _lowercase       :							List[Any]      		=		{
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(seed + 1)).to(lowerCamelCase)
           # create init_image
           _lowercase       :							Tuple      		=		floats_tensor((1, 3, 64, 64),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		image.cpu().permute(0,      2,      3,      1)[0]
           _lowercase       :							Tuple      		=		Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB').resize((2_56, 2_56))

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							List[str]      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							Tuple      		=		{
               'prompt': 'horse',
               'image': init_image,
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'generator': generator,
               'height': 64,
               'width': 64,
               'num_inference_steps': 10,
               'guidance_scale': 7.0,
               'strength': 0.2,
               'output_type': 'np',
           }
           return inputs




       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Dict      		=		'cpu'

           _lowercase       :							Tuple      		=		self.get_dummy_components()

           _lowercase       :							str      		=		self.pipeline_class(**lowerCamelCase)
           _lowercase       :							str      		=		pipe.to(lowerCamelCase)

           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[str]      		=		pipe(**self.get_dummy_inputs(lowerCamelCase))
           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							List[Any]      		=		pipe(
               **self.get_dummy_inputs(lowerCamelCase),      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							List[str]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							List[Any]      		=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           _lowercase       :							Tuple      		=		np.array(
               [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3])
           assert (
               np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

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




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_img2img_frog.npy')

           _lowercase       :							str      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
           _lowercase       :							Optional[int]      		=		'A red cartoon frog, 4k'

           _lowercase       :							Union[str, Any]      		=		KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior',      torch_dtype=torch.floataa)
           pipe_prior.to(lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		KandinskyImgaImgPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1',      torch_dtype=torch.floataa)
           _lowercase       :							List[Any]      		=		pipeline.to(lowerCamelCase)

           pipeline.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							str      		=		torch.Generator(device='cpu').manual_seed(0)
           _lowercase		,				_lowercase       :							List[Any]      		=		pipe_prior(
               lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=5,      negative_prompt='',      ).to_tuple()

           _lowercase       :							Union[str, Any]      		=		pipeline(
               lowerCamelCase,      image=lowerCamelCase,      image_embeds=lowerCamelCase,      negative_image_embeds=lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=1_00,      height=7_68,      width=7_68,      strength=0.2,      output_type='np',      )

           _lowercase       :							Dict      		=		output.images[0]

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

           assert_mean_pixel_difference(lowerCamelCase,      lowerCamelCase)


