
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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        SCREAMING_SNAKE_CASE     (__a    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = KandinskyVaaControlnetImgaImgPipeline
   _UpperCamelCase					:	Any				      = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
   _UpperCamelCase					:	Any				      = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
   _UpperCamelCase					:	Union[str, Any]				      = [
       'generator',
       'height',
       'width',
       'strength',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   _UpperCamelCase					:	Any				      = False


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Any:





     """simple docstring"""







     return 32


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[int]:





     """simple docstring"""







     return 32


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Union[str, Any]:





     """simple docstring"""







     return self.time_input_dim


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[Any]:





     """simple docstring"""







     return self.time_input_dim * 4


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     List[str]:





     """simple docstring"""







     return 100


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     int:





     """simple docstring"""







     torch.manual_seed(0 )

     lowercase__			    =						{
         'in_channels': 8,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 8,
         'addition_embed_type': 'image_hint',
         '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': 'image_proj',
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': None,
     }

     lowercase__			    =						UNetaDConditionModel(**A__ )
     return model


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Union[str, Any]:





     """simple docstring"""







     return {
         "block_out_channels": [32, 32, 64, 64],
         "down_block_types": [
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
         "vq_embed_dim": 4,
     }


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Union[str, Any]:





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						VQModel(**self.dummy_movq_kwargs )
     return model


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Any:





     """simple docstring"""







     lowercase__			    =						self.dummy_unet
     lowercase__			    =						self.dummy_movq

     lowercase__			    =						{
         'num_train_timesteps': 1_000,
         'beta_schedule': 'linear',
         'beta_start': 0.00085,
         'beta_end': 0.012,
         'clip_sample': False,
         'set_alpha_to_one': False,
         'steps_offset': 0,
         'prediction_type': 'epsilon',
         'thresholding': False,
     }

     lowercase__			    =						DDIMScheduler(**A__ )

     lowercase__			    =						{
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }

     return components


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       int    ,  a    :       Any=0 )->     Tuple:





     """simple docstring"""







     lowercase__			    =						floats_tensor((1, self.text_embedder_hidden_size)    ,  rng=random.Random(A__ ) ).to(A__ )
     lowercase__			    =						floats_tensor((1, self.text_embedder_hidden_size)    ,  rng=random.Random(seed + 1 ) ).to(
         A__ )
     # create init_image
     lowercase__			    =						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(A__ ) ).to(A__ )
     lowercase__			    =						image.cpu().permute(0    ,  2    ,  3    ,  1 )[0]
     lowercase__			    =						Image.fromarray(np.uinta(A__ ) ).convert('RGB' ).resize((256, 256) )
     # create hint
     lowercase__			    =						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(A__ ) ).to(A__ )

     if str(A__ ).startswith('mps' ):
       lowercase__			    =						torch.manual_seed(A__ )
     else:
       lowercase__			    =						torch.Generator(device=A__ ).manual_seed(A__ )
     lowercase__			    =						{
         'image': init_image,
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'hint': hint,
         'generator': generator,
         'height': 64,
         'width': 64,
         'num_inference_steps': 10,
         'guidance_scale': 7.0,
         'strength': 0.2,
         'output_type': 'np',
     }
     return inputs


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





     """simple docstring"""







     lowercase__			    =						'cpu'

     lowercase__			    =						self.get_dummy_components()

     lowercase__			    =						self.pipeline_class(**A__ )
     lowercase__			    =						pipe.to(A__ )

     pipe.set_progress_bar_config(disable=A__ )

     lowercase__			    =						pipe(**self.get_dummy_inputs(A__ ) )
     lowercase__			    =						output.images

     lowercase__			    =						pipe(
         **self.get_dummy_inputs(A__ )    ,  return_dict=A__    ,  )[0]

     lowercase__			    =						image[0, -3:, -3:, -1]
     lowercase__			    =						image_from_tuple[0, -3:, -3:, -1]

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

     lowercase__			    =						np.array(
         [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
     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        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy' )

     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
     lowercase__			    =						init_image.resize((512, 512) )

     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/hint_image_cat.png' )
     lowercase__			    =						torch.from_numpy(np.array(A__ ) ).float() / 255.0
     lowercase__			    =						hint.permute(2    ,  0    ,  1 ).unsqueeze(0 )

     lowercase__			    =						'A robot, 4k photo'

     lowercase__			    =						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-prior'    ,  torch_dtype=torch.floataa )
     pipe_prior.to(A__ )

     lowercase__			    =						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,  torch_dtype=torch.floataa )
     lowercase__			    =						pipeline.to(A__ )

     pipeline.set_progress_bar_config(disable=A__ )

     lowercase__			    =						torch.Generator(device='cpu' ).manual_seed(0 )

     lowercase__    ,					lowercase__			    =						pipe_prior(
         A__    ,  image=A__    ,  strength=0.85    ,  generator=A__    ,  negative_prompt=''    ,  ).to_tuple()

     lowercase__			    =						pipeline(
         image=A__    ,  image_embeds=A__    ,  negative_image_embeds=A__    ,  hint=A__    ,  generator=A__    ,  num_inference_steps=100    ,  height=512    ,  width=512    ,  strength=0.5    ,  output_type='np'    ,  )

     lowercase__			    =						output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(A__    ,  A__ )









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







     return self.tokenizer.decode(*a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


