






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	@property
	def       __magic_name__    (    self			)							->				int:
							torch.manual_seed(0			)
							__magic_name__      :       Dict       						= UNetaDModel(
							    sample_size=(32, 64)      ,       in_channels=1      ,       out_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")      ,       )
							return model
	@property
	def       __magic_name__    (    self			)							->				Dict:
							torch.manual_seed(0			)
							__magic_name__      :       Optional[int]       						= UNetaDConditionModel(
							    sample_size=(64, 32)      ,       in_channels=1      ,       out_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       cross_attention_dim=10      ,       )
							return model
	@property
	def       __magic_name__    (    self			)							->				Tuple:
							torch.manual_seed(0			)
							__magic_name__      :       List[str]       						= AutoencoderKL(
							    sample_size=(1_28, 64)      ,       in_channels=1      ,       out_channels=1      ,       latent_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")      ,       up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")      ,       )
							__magic_name__      :       Union[str, Any]       						= UNetaDModel(
							    sample_size=(64, 32)      ,       in_channels=1      ,       out_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")      ,       )
							return vqvae, unet







	@slow
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       int       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
							__magic_name__      :       Union[str, Any]       						= Mel(
							    x_res=self.dummy_unet.config.sample_size[1]      ,       y_res=self.dummy_unet.config.sample_size[0]      ,       )

							__magic_name__      :       Optional[int]       						= DDPMScheduler()
							__magic_name__      :       Any       						= AudioDiffusionPipeline(vqvae=lowerCAmelCase__      ,       unet=self.dummy_unet      ,       mel=lowerCAmelCase__      ,       scheduler=lowerCAmelCase__			)
							__magic_name__      :       str       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(42			)
							__magic_name__      :       Union[str, Any]       						= pipe(generator=lowerCAmelCase__      ,       steps=4			)
							__magic_name__      :       int       						= output.audios[0]
							__magic_name__      :       List[str]       						= output.images[0]

							__magic_name__      :       str       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(42			)
							__magic_name__      :       str       						= pipe(generator=lowerCAmelCase__      ,       steps=4      ,       return_dict=lowerCAmelCase__			)
							__magic_name__      :       Optional[Any]       						= output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							__magic_name__      :       List[str]       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       str       						= np.frombuffer(image_from_tuple.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       Tuple       						= np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() == 0

							__magic_name__      :       List[str]       						= Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]      ,       y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]      ,       )

							__magic_name__      :       Dict       						= DDIMScheduler()
							__magic_name__      :       Any       						= self.dummy_vqvae_and_unet
							__magic_name__      :       List[Any]       						= AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]      ,       unet=dummy_vqvae_and_unet[1]      ,       mel=lowerCAmelCase__      ,       scheduler=lowerCAmelCase__			)
							__magic_name__      :       int       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							np.random.seed(0			)
							__magic_name__      :       List[Any]       						= np.random.uniform(-1      ,       1      ,       ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)			)
							__magic_name__      :       str       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(42			)
							__magic_name__      :       int       						= pipe(raw_audio=lowerCAmelCase__      ,       generator=lowerCAmelCase__      ,       start_step=5      ,       steps=10			)
							__magic_name__      :       int       						= output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							__magic_name__      :       str       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       List[Any]       						= np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0

							__magic_name__      :       List[str]       						= self.dummy_unet_condition
							__magic_name__      :       List[str]       						= AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]      ,       unet=lowerCAmelCase__      ,       mel=lowerCAmelCase__      ,       scheduler=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							np.random.seed(0			)
							__magic_name__      :       Optional[Any]       						= torch.rand((1, 1, 10)			)
							__magic_name__      :       List[Any]       						= pipe(generator=lowerCAmelCase__      ,       encoding=lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= output.images[0]
							__magic_name__      :       List[Any]       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       int       						= np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0





@slow
@require_torch_gpu
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Optional[int]       						= torch_device

							__magic_name__      :       Dict       						= DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""			)
							__magic_name__      :       List[str]       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(42			)
							__magic_name__      :       Any       						= pipe(generator=lowerCAmelCase__			)
							__magic_name__      :       int       						= output.audios[0]
							__magic_name__      :       List[str]       						= output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							__magic_name__      :       List[str]       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       Optional[int]       						= np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
