





"""simple docstring"""


import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


a__    :   Optional[int]    						= logging.get_logger(__name__)



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							Optional[Any] 							=   """semantic"""

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE=1_0000      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=768      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=0.7      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=1_0048      ,    __SCREAMING_SNAKE_CASE=12_9595      ,    __SCREAMING_SNAKE_CASE=12_9599      ,    __SCREAMING_SNAKE_CASE=1_0000      ,    __SCREAMING_SNAKE_CASE=256      ,    __SCREAMING_SNAKE_CASE=49.9      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE      ,    do_sample=__SCREAMING_SNAKE_CASE      ,    eos_token_id=__SCREAMING_SNAKE_CASE      ,    renormalize_logits=__SCREAMING_SNAKE_CASE      ,    max_new_tokens=__SCREAMING_SNAKE_CASE      ,    output_scores=__SCREAMING_SNAKE_CASE      ,    return_dict_in_generate=__SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE      ,    output_attentions=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE      ,    )

        snake_case__										= text_encoding_offset
        snake_case__										= text_pad_token
        snake_case__										= eos_token_id
        snake_case__										= semantic_infer_token
        snake_case__										= semantic_vocab_size
        snake_case__										= max_input_semantic_length
        snake_case__										= semantic_rate_hz



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							str 							=   """coarse_acoustics"""

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=0.7      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=1_2048      ,    __SCREAMING_SNAKE_CASE=75      ,    __SCREAMING_SNAKE_CASE=2      ,    __SCREAMING_SNAKE_CASE=1_2050      ,    __SCREAMING_SNAKE_CASE=256      ,    __SCREAMING_SNAKE_CASE = 630      ,    __SCREAMING_SNAKE_CASE = 60      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE      ,    do_sample=__SCREAMING_SNAKE_CASE      ,    renormalize_logits=__SCREAMING_SNAKE_CASE      ,    output_scores=__SCREAMING_SNAKE_CASE      ,    return_dict_in_generate=__SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE      ,    output_attentions=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE      ,    )

        snake_case__										= coarse_semantic_pad_token
        snake_case__										= coarse_rate_hz
        snake_case__										= n_coarse_codebooks
        snake_case__										= coarse_infer_token
        snake_case__										= max_coarse_input_length
        snake_case__										= max_coarse_history
        snake_case__										= sliding_window_len



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							List[str] 							=   """fine_acoustics"""

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE=0.5      ,    __SCREAMING_SNAKE_CASE=512      ,    __SCREAMING_SNAKE_CASE=1024      ,    __SCREAMING_SNAKE_CASE=8      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        super().__init__(temperature=__SCREAMING_SNAKE_CASE   )

        snake_case__										= max_fine_history_length
        snake_case__										= max_fine_input_length
        snake_case__										= n_fine_codebooks



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							str 							=   """bark"""
       snake_case__ :							Optional[int] 							=   True

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE=2_4000      ,    __SCREAMING_SNAKE_CASE=1024      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        if semantic_config is None:
         snake_case__										= {}
         logger.info("semantic_config is None. initializing the semantic model with default values."   )

        if coarse_acoustics_config is None:
         snake_case__										= {}
         logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."   )

        if fine_acoustics_config is None:
         snake_case__										= {}
         logger.info("fine_acoustics_config is None. initializing the fine model with default values."   )

        snake_case__										= BarkSemanticGenerationConfig(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE   )

        snake_case__										= sample_rate
        snake_case__										= codebook_size

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(	cls      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        return cls(
            semantic_config=semantic_config.to_dict()      ,    coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,    fine_acoustics_config=fine_acoustics_config.to_dict()      ,    **__SCREAMING_SNAKE_CASE      ,    )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= copy.deepcopy(self.__dict__   )

        snake_case__										= self.semantic_config.to_dict()
        snake_case__										= self.coarse_acoustics_config.to_dict()
        snake_case__										= self.fine_acoustics_config.to_dict()

        snake_case__										= self.__class__.model_type
        return output








"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class    lowercase_ (   __lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							List[str] 							=   PaintByExamplePipeline
       snake_case__ :							Any 							=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__ :							Optional[int] 							=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__ :							int 							=   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        torch.manual_seed(0   )
        snake_case__										= UNetaDConditionModel(
            block_out_channels=(32, 64)      ,    layers_per_block=2      ,    sample_size=32      ,    in_channels=9      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    cross_attention_dim=32      ,    )
        snake_case__										= PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
        torch.manual_seed(0   )
        snake_case__										= 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      ,    )
        torch.manual_seed(0   )
        snake_case__										= CLIPVisionConfig(
            hidden_size=32      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    image_size=32      ,    patch_size=4      ,    )
        snake_case__										= PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE      ,    proj_size=32   )
        snake_case__										= CLIPImageProcessor(crop_size=32      ,    size=32   )

        snake_case__										= {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "image_encoder": image_encoder,
            "safety_checker": None,
            "feature_extractor": feature_extractor,
        }
        return components

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= np.array(image.convert("RGB"   )   )
        snake_case__										= image[None].transpose(0      ,    3      ,    1      ,    2   )
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(dtype=torch.floataa   ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE="cpu"      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

        snake_case__										= floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((32, 32)   )

        if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
         snake_case__										= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
        snake_case__										= {
            "example_image": example_image,
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= pipe.to("cpu"   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.get_dummy_inputs()
        snake_case__										= pipe(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        snake_case__										= np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= "cpu"
        snake_case__										= self.get_dummy_inputs()
        inputs.pop("mask_image"   )
        snake_case__										= self.convert_to_pt(inputs.pop("image"   )   )
        snake_case__										= image.clamp(0      ,    1   ) / 2

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**self.get_dummy_components()   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= pipe(image=__SCREAMING_SNAKE_CASE      ,    mask_image=mask_image[:, 0]      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= mask_image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]

        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )

        snake_case__										= pipe(**self.get_dummy_inputs()   )
        snake_case__										= output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/dog_in_bucket.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/mask.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/panda.jpg"   )

        snake_case__										= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= torch.manual_seed(321   )
        snake_case__										= pipe(
            image=__SCREAMING_SNAKE_CASE      ,    mask_image=__SCREAMING_SNAKE_CASE      ,    example_image=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    guidance_scale=5.0      ,    num_inference_steps=50      ,    output_type="np"      ,    )

        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        snake_case__										= np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

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

