import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


_lowerCamelCase								=logging.get_logger(__name__)

_lowerCamelCase								={
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}



class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				'musicgen_decoder'
      __UpperCAmelCase      		=				['past_key_values']
      def __init__(				self					:							Dict      ,snake_case					:							Any=2048      ,snake_case					:							Optional[Any]=2048      ,snake_case					:							Optional[Any]=24      ,snake_case					:							Optional[Any]=4096      ,snake_case					:							List[Any]=16      ,snake_case					:							Optional[Any]=0.0      ,snake_case					:							int=True      ,snake_case					:							List[str]="gelu"      ,snake_case					:							int=1024      ,snake_case					:							Optional[Any]=0.1      ,snake_case					:							Union[str, Any]=0.0      ,snake_case					:							Union[str, Any]=0.0      ,snake_case					:							str=0.02      ,snake_case					:							Dict=False      ,snake_case					:							int=4      ,snake_case					:							List[str]=2048      ,snake_case					:							List[str]=2048      ,snake_case					:							str=None      ,snake_case					:							Tuple=False      ,**snake_case					:							List[str]      ,):
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=ffn_dim
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=dropout
           SCREAMING_SNAKE_CASE										=attention_dropout
           SCREAMING_SNAKE_CASE										=activation_dropout
           SCREAMING_SNAKE_CASE										=activation_function
           SCREAMING_SNAKE_CASE										=initializer_factor
           SCREAMING_SNAKE_CASE										=layerdrop
           SCREAMING_SNAKE_CASE										=use_cache
           SCREAMING_SNAKE_CASE										=scale_embedding  # scale factor will be sqrt(d_model) if True
           SCREAMING_SNAKE_CASE										=num_codebooks
           super().__init__(
               pad_token_id=snake_case      ,bos_token_id=snake_case      ,eos_token_id=snake_case      ,tie_word_embeddings=snake_case      ,**snake_case      ,)







class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				'musicgen'
      __UpperCAmelCase      		=				True
      def __init__(				self					:							Optional[int]      ,**snake_case					:							Dict    ):
           super().__init__(**snake_case    )
           if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
                raise ValueError('Config has to be initialized with text_encoder, audio_encoder and decoder config'    )

           SCREAMING_SNAKE_CASE										=kwargs.pop('text_encoder'    )
           SCREAMING_SNAKE_CASE										=text_encoder_config.pop('model_type'    )

           SCREAMING_SNAKE_CASE										=kwargs.pop('audio_encoder'    )
           SCREAMING_SNAKE_CASE										=audio_encoder_config.pop('model_type'    )

           SCREAMING_SNAKE_CASE										=kwargs.pop('decoder'    )

           SCREAMING_SNAKE_CASE										=AutoConfig.for_model(snake_case      ,**snake_case    )
           SCREAMING_SNAKE_CASE										=AutoConfig.for_model(snake_case      ,**snake_case    )
           SCREAMING_SNAKE_CASE										=MusicgenDecoderConfig(**snake_case    )
           SCREAMING_SNAKE_CASE										=True
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							int      ,snake_case					:							PretrainedConfig      ,snake_case					:							PretrainedConfig      ,snake_case					:							MusicgenDecoderConfig      ,**snake_case					:							Dict      ,):
           return cls(
               text_encoder=text_encoder_config.to_dict()      ,audio_encoder=audio_encoder_config.to_dict()      ,decoder=decoder_config.to_dict()      ,**snake_case      ,)


      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=copy.deepcopy(self.__dict__    )
           SCREAMING_SNAKE_CASE										=self.text_encoder.to_dict()
           SCREAMING_SNAKE_CASE										=self.audio_encoder.to_dict()
           SCREAMING_SNAKE_CASE										=self.decoder.to_dict()
           SCREAMING_SNAKE_CASE										=self.__class__.model_type
           return output




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               'image': image,
               'mask_image': mask,
               'generator': generator,
               'num_inference_steps': 5,
               'eta': 0.0,
               'jump_length': 2,
               'jump_n_sample': 2,
               'output_type': 'numpy',
           }
           return inputs
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

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

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

