


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     a		(lowerCamelCase_					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  KandinskyVaaControlnetImgaImgPipeline
							__UpperCAmelCase   :							Any					  =  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
							__UpperCAmelCase   :							Dict					  =  ['''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   :							str					  =  False

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

							@property
							def    __snake_case (							self	:  Optional[int]    )     ->   Tuple:
														return 32

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

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Dict:
														return self.time_input_dim * 4

							@property
							def    __snake_case (							self	:  List[str]    )     ->   Any:
														return 100

							@property
							def    __snake_case (							self	:  Dict    )     ->   Optional[Any]:
														torch.manual_seed(0    )

														__snake_case		:      List[str]										=       {
														    "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,
														}

														__snake_case		:      int										=       UNetaDConditionModel(**__snake_case    )
														return model

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[Any]:
														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    __snake_case (							self	:  int    )     ->   Optional[int]:
														torch.manual_seed(0    )
														__snake_case		:      Union[str, Any]										=       VQModel(**self.dummy_movq_kwargs    )
														return model

							def    __snake_case (							self	:  str    )     ->   Optional[int]:
														__snake_case		:      Union[str, Any]										=       self.dummy_unet
														__snake_case		:      int										=       self.dummy_movq

														__snake_case		:      int										=       {
														    "num_train_timesteps": 1000,
														    "beta_schedule": "linear",
														    "beta_start": 0.0_00_85,
														    "beta_end": 0.0_12,
														    "clip_sample": False,
														    "set_alpha_to_one": False,
														    "steps_offset": 0,
														    "prediction_type": "epsilon",
														    "thresholding": False,
														}

														__snake_case		:      str										=       DDIMScheduler(**__snake_case    )

														__snake_case		:      Optional[int]										=       {
														    "unet": unet,
														    "scheduler": scheduler,
														    "movq": movq,
														}

														return components

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  str=0    )     ->   Optional[Any]:
														__snake_case		:      Tuple										=       floats_tensor((1, self.text_embedder_hidden_size)   ,     rng=random.Random(__snake_case    )    ).to(__snake_case    )
														__snake_case		:      List[Any]										=       floats_tensor((1, self.text_embedder_hidden_size)   ,     rng=random.Random(seed + 1    )    ).to(
														    __snake_case    )
														# create init_image
														__snake_case		:      Union[str, Any]										=       floats_tensor((1, 3, 64, 64)   ,     rng=random.Random(__snake_case    )    ).to(__snake_case    )
														__snake_case		:      str										=       image.cpu().permute(0   ,     2   ,     3   ,     1    )[0]
														__snake_case		:      List[str]										=       Image.fromarray(np.uinta(__snake_case    )    ).convert("RGB"    ).resize((256, 256)    )
														# create hint
														__snake_case		:      List[Any]										=       floats_tensor((1, 3, 64, 64)   ,     rng=random.Random(__snake_case    )    ).to(__snake_case    )

														if str(__snake_case    ).startswith("mps"    ):
																					__snake_case		:      int										=       torch.manual_seed(__snake_case    )
														else:
																					__snake_case		:      str										=       torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
														__snake_case		:      Optional[Any]										=       {
														    "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    __snake_case (							self	:  Tuple    )     ->   Optional[int]:
														__snake_case		:      Dict										=       "cpu"

														__snake_case		:      int										=       self.get_dummy_components()

														__snake_case		:      List[str]										=       self.pipeline_class(**__snake_case    )
														__snake_case		:      List[str]										=       pipe.to(__snake_case    )

														pipe.set_progress_bar_config(disable=__snake_case    )

														__snake_case		:      str										=       pipe(**self.get_dummy_inputs(__snake_case    )    )
														__snake_case		:      Tuple										=       output.images

														__snake_case		:      Union[str, Any]										=       pipe(
														    **self.get_dummy_inputs(__snake_case    )   ,     return_dict=__snake_case   ,     )[0]

														__snake_case		:      str										=       image[0, -3:, -3:, -1]
														__snake_case		:      Dict										=       image_from_tuple[0, -3:, -3:, -1]

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

														__snake_case		:      Union[str, Any]										=       np.array(
														    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]    )
														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     a		(unittest.TestCase				):




							"""simple docstring"""


							def    __snake_case (							self	:  int    )     ->   Optional[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def    __snake_case (							self	:  int    )     ->   List[Any]:
														__snake_case		:      Tuple										=       load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"    )

														__snake_case		:      List[Any]										=       load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"    )
														__snake_case		:      Optional[Any]										=       init_image.resize((512, 512)    )

														__snake_case		:      Optional[Any]										=       load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/kandinskyv22/hint_image_cat.png"    )
														__snake_case		:      List[str]										=       torch.from_numpy(np.array(__snake_case    )    ).float() / 255.0
														__snake_case		:      Any										=       hint.permute(2   ,     0   ,     1    ).unsqueeze(0    )

														__snake_case		:      Union[str, Any]										=       "A robot, 4k photo"

														__snake_case		:      List[Any]										=       KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
														    "kandinsky-community/kandinsky-2-2-prior"   ,     torch_dtype=torch.floataa    )
														pipe_prior.to(__snake_case    )

														__snake_case		:      int										=       KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
														    "kandinsky-community/kandinsky-2-2-controlnet-depth"   ,     torch_dtype=torch.floataa    )
														__snake_case		:      Union[str, Any]										=       pipeline.to(__snake_case    )

														pipeline.set_progress_bar_config(disable=__snake_case    )

														__snake_case		:      Optional[Any]										=       torch.Generator(device="cpu"    ).manual_seed(0    )

														__snake_case			,      __snake_case		:      List[str]										=       pipe_prior(
														    __snake_case   ,     image=__snake_case   ,     strength=0.85   ,     generator=__snake_case   ,     negative_prompt=""   ,     ).to_tuple()

														__snake_case		:      Dict										=       pipeline(
														    image=__snake_case   ,     image_embeds=__snake_case   ,     negative_image_embeds=__snake_case   ,     hint=__snake_case   ,     generator=__snake_case   ,     num_inference_steps=100   ,     height=512   ,     width=512   ,     strength=0.5   ,     output_type="np"   ,     )

														__snake_case		:      Union[str, Any]										=       output.images[0]

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

														assert_mean_pixel_difference(__snake_case   ,     __snake_case    )





import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "encodec"

							def __init__(							self	:  Any   ,     lowerCamelCase	:  Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,     lowerCamelCase	:  List[str]=24000   ,     lowerCamelCase	:  int=1   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Optional[int]=128   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[str]=1   ,     lowerCamelCase	:  str=[8, 5, 4, 2]   ,     lowerCamelCase	:  List[str]="weight_norm"   ,     lowerCamelCase	:  Any=7   ,     lowerCamelCase	:  Tuple=7   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  List[Any]="reflect"   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  int=1.0   ,     lowerCamelCase	:  Optional[Any]=1024   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=True   ,     **lowerCamelCase	:  Dict   ,     )     ->   Any:
														__snake_case		:      Tuple										=       target_bandwidths
														__snake_case		:      Union[str, Any]										=       sampling_rate
														__snake_case		:      Union[str, Any]										=       audio_channels
														__snake_case		:      Dict										=       normalize
														__snake_case		:      List[Any]										=       chunk_length_s
														__snake_case		:      Tuple										=       overlap
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      List[Any]										=       num_filters
														__snake_case		:      Union[str, Any]										=       num_residual_layers
														__snake_case		:      Optional[int]										=       upsampling_ratios
														__snake_case		:      List[str]										=       norm_type
														__snake_case		:      Optional[int]										=       kernel_size
														__snake_case		:      Dict										=       last_kernel_size
														__snake_case		:      Tuple										=       residual_kernel_size
														__snake_case		:      List[Any]										=       dilation_growth_rate
														__snake_case		:      Optional[int]										=       use_causal_conv
														__snake_case		:      Tuple										=       pad_mode
														__snake_case		:      Union[str, Any]										=       compress
														__snake_case		:      Union[str, Any]										=       num_lstm_layers
														__snake_case		:      int										=       trim_right_ratio
														__snake_case		:      Tuple										=       codebook_size
														__snake_case		:      Optional[Any]										=       codebook_dim if codebook_dim is not None else hidden_size
														__snake_case		:      int										=       use_conv_shortcut

														if self.norm_type not in ["weight_norm", "time_group_norm"]:
																					raise ValueError(
																					    F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'    )

														super().__init__(**lowerCamelCase    )

							@property
							def    __snake_case (							self	:  int    )     ->   Optional[int]:
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate    )

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														if self.chunk_length_s is None or self.overlap is None:
																					return None
														else:
																					return max(1   ,     int((1.0 - self.overlap) * self.chunk_length    )    )

							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      Union[str, Any]										=       np.prod(self.upsampling_ratios    )
														return math.ceil(self.sampling_rate / hop_length    )






							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )

