
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     lowercase		( unittest.TestCase		):




					def 			A__				(    self):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




					@property
					def 			A__				(    self):
										torch.manual_seed(0)
										lowercase						     =						UNetaDModel(
										    sample_size=(3_2, 6_4)       ,in_channels=1       ,out_channels=1       ,layers_per_block=2       ,block_out_channels=(1_2_8, 1_2_8)       ,down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')       ,up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')       ,)
										return model




					@property
					def 			A__				(    self):
										torch.manual_seed(0)
										lowercase						     =						UNetaDConditionModel(
										    sample_size=(6_4, 3_2)       ,in_channels=1       ,out_channels=1       ,layers_per_block=2       ,block_out_channels=(1_2_8, 1_2_8)       ,down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')       ,up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')       ,cross_attention_dim=1_0       ,)
										return model




					@property
					def 			A__				(    self):
										torch.manual_seed(0)
										lowercase						     =						AutoencoderKL(
										    sample_size=(1_2_8, 6_4)       ,in_channels=1       ,out_channels=1       ,latent_channels=1       ,layers_per_block=2       ,block_out_channels=(1_2_8, 1_2_8)       ,down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')       ,up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')       ,)
										lowercase						     =						UNetaDModel(
										    sample_size=(6_4, 3_2)       ,in_channels=1       ,out_channels=1       ,layers_per_block=2       ,block_out_channels=(1_2_8, 1_2_8)       ,down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')       ,up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')       ,)
										return vqvae, unet





					@slow
					def 			A__				(    self):
										lowercase						     =						"cpu"  # ensure determinism for the device-dependent torch.Generator
										lowercase						     =						Mel(
										    x_res=self.dummy_unet.config.sample_size[1]       ,y_res=self.dummy_unet.config.sample_size[0]       ,)

										lowercase						     =						DDPMScheduler()
										lowercase						     =						AudioDiffusionPipeline(vqvae=_a       ,unet=self.dummy_unet       ,mel=_a       ,scheduler=_a)
										lowercase						     =						pipe.to(_a)
										pipe.set_progress_bar_config(disable=_a)

										lowercase						     =						torch.Generator(device=_a).manual_seed(4_2)
										lowercase						     =						pipe(generator=_a       ,steps=4)
										lowercase						     =						output.audios[0]
										lowercase						     =						output.images[0]

										lowercase						     =						torch.Generator(device=_a).manual_seed(4_2)
										lowercase						     =						pipe(generator=_a       ,steps=4       ,return_dict=_a)
										lowercase						     =						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]
										)
										lowercase						     =						np.frombuffer(image.tobytes()       ,dtype='''uint8''')[:1_0]
										lowercase						     =						np.frombuffer(image_from_tuple.tobytes()       ,dtype='''uint8''')[:1_0]
										lowercase						     =						np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7])

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

										lowercase						     =						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]       ,)

										lowercase						     =						DDIMScheduler()
										lowercase						     =						self.dummy_vqvae_and_unet
										lowercase						     =						AudioDiffusionPipeline(
										    vqvae=self.dummy_vqvae_and_unet[0]       ,unet=dummy_vqvae_and_unet[1]       ,mel=_a       ,scheduler=_a)
										lowercase						     =						pipe.to(_a)
										pipe.set_progress_bar_config(disable=_a)

										np.random.seed(0)
										lowercase						     =						np.random.uniform(-1       ,1       ,((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,))
										lowercase						     =						torch.Generator(device=_a).manual_seed(4_2)
										lowercase						     =						pipe(raw_audio=_a       ,generator=_a       ,start_step=5       ,steps=1_0)
										lowercase						     =						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]
										)
										lowercase						     =						np.frombuffer(image.tobytes()       ,dtype='''uint8''')[:1_0]
										lowercase						     =						np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1])

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

										lowercase						     =						self.dummy_unet_condition
										lowercase						     =						AudioDiffusionPipeline(
										    vqvae=self.dummy_vqvae_and_unet[0]       ,unet=_a       ,mel=_a       ,scheduler=_a)
										lowercase						     =						pipe.to(_a)
										pipe.set_progress_bar_config(disable=_a)

										np.random.seed(0)
										lowercase						     =						torch.rand((1, 1, 1_0))
										lowercase						     =						pipe(generator=_a       ,encoding=_a)
										lowercase						     =						output.images[0]
										lowercase						     =						np.frombuffer(image.tobytes()       ,dtype='''uint8''')[:1_0]
										lowercase						     =						np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1])

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







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




					def 			A__				(    self):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





					def 			A__				(    self):
										lowercase						     =						torch_device

										lowercase						     =						DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256''')
										lowercase						     =						pipe.to(_a)
										pipe.set_progress_bar_config(disable=_a)

										lowercase						     =						torch.Generator(device=_a).manual_seed(4_2)
										lowercase						     =						pipe(generator=_a)
										lowercase						     =						output.audios[0]
										lowercase						     =						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]
										lowercase						     =						np.frombuffer(image.tobytes()       ,dtype='''uint8''')[:1_0]
										lowercase						     =						np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6])

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__		:Any			  =   {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Tuple			  =   ["DistilBertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Union[str, Any]			  =   [
							    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DistilBertForMaskedLM",
							    "DistilBertForMultipleChoice",
							    "DistilBertForQuestionAnswering",
							    "DistilBertForSequenceClassification",
							    "DistilBertForTokenClassification",
							    "DistilBertModel",
							    "DistilBertPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDistilBertForMaskedLM",
							    "TFDistilBertForMultipleChoice",
							    "TFDistilBertForQuestionAnswering",
							    "TFDistilBertForSequenceClassification",
							    "TFDistilBertForTokenClassification",
							    "TFDistilBertMainLayer",
							    "TFDistilBertModel",
							    "TFDistilBertPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Union[str, Any]			  =   [
							    "FlaxDistilBertForMaskedLM",
							    "FlaxDistilBertForMultipleChoice",
							    "FlaxDistilBertForQuestionAnswering",
							    "FlaxDistilBertForSequenceClassification",
							    "FlaxDistilBertForTokenClassification",
							    "FlaxDistilBertModel",
							    "FlaxDistilBertPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

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




