
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:
			lowerCAmelCase__      :      Optional[int]         =		[tensor.shape for tensor in tensor_list]
			return all(shape == shapes[0] for shape in shapes[1:]     )







class      A__							(     __magic_name__						,   __magic_name__						,   __magic_name__						,   unittest.TestCase	):
				lowercase			    =  StableDiffusionLatentUpscalePipeline
				lowercase			    =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    'height',
				    'width',
				    'cross_attention_kwargs',
				    'negative_prompt_embeds',
				    'prompt_embeds',
				}
				lowercase			    =  PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
				lowercase			    =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowercase			    =  frozenset(
				    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				lowercase			    =  frozenset([]	)

				lowercase			    =  True






				@property
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		1
							lowerCAmelCase__      :      Optional[Any]         =		4
							lowerCAmelCase__      :      List[Any]         =		(16, 16)

							lowerCAmelCase__      :      Tuple         =		floats_tensor((batch_size, num_channels) + sizes    ,     rng=random.Random(0   )   ).to(a   )
							return image






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      Any         =		UNetaDConditionModel(
							    act_fn='gelu'    ,     attention_head_dim=8    ,     norm_num_groups=a    ,     block_out_channels=[32, 32, 64, 64]    ,     time_cond_proj_dim=160    ,     conv_in_kernel=1    ,     conv_out_kernel=1    ,     cross_attention_dim=32    ,     down_block_types=(
							        'KDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							    )    ,     in_channels=8    ,     mid_block_type=a    ,     only_cross_attention=a    ,     out_channels=5    ,     resnet_time_scale_shift='scale_shift'    ,     time_embedding_type='fourier'    ,     timestep_post_act='gelu'    ,     up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')    ,     )
							lowerCAmelCase__      :      int         =		AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]    ,     in_channels=3    ,     out_channels=3    ,     down_block_types=[
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							    ]    ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']    ,     latent_channels=4    ,     )
							lowerCAmelCase__      :      Union[str, Any]         =		EulerDiscreteScheduler(prediction_type='sample'   )
							lowerCAmelCase__      :      Any         =		CLIPTextConfig(
							    bos_token_id=0    ,     eos_token_id=2    ,     hidden_size=32    ,     intermediate_size=37    ,     layer_norm_eps=1E-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=1_000    ,     hidden_act='quick_gelu'    ,     projection_dim=512    ,     )
							lowerCAmelCase__      :      Tuple         =		CLIPTextModel(a   )
							lowerCAmelCase__      :      List[str]         =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

							lowerCAmelCase__      :      str         =		{
							    'unet': model.eval(),
							    'vae': vae.eval(),
							    'scheduler': scheduler,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							}

							return components






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Optional[int]    ,     a					:      Optional[int]=0   ):






							'''simple docstring'''







							if str(a   ).startswith('mps'   ):
										lowerCAmelCase__      :      Tuple         =		torch.manual_seed(a   )
							else:
										lowerCAmelCase__      :      Optional[Any]         =		torch.Generator(device=a   ).manual_seed(a   )
							lowerCAmelCase__      :      str         =		{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': self.dummy_image.cpu(),
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		'cpu'

							lowerCAmelCase__      :      Optional[int]         =		self.get_dummy_components()
							lowerCAmelCase__      :      Optional[Any]         =		self.pipeline_class(**a   )
							pipe.to(a   )
							pipe.set_progress_bar_config(disable=a   )

							lowerCAmelCase__      :      Tuple         =		self.get_dummy_inputs(a   )
							lowerCAmelCase__      :      List[str]         =		pipe(**a   ).images
							lowerCAmelCase__      :      Optional[int]         =		image[0, -3:, -3:, -1]

							self.assertEqual(image.shape    ,     (1, 256, 256, 3)   )
							lowerCAmelCase__      :      int         =		np.array(
							    [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]   )
							lowerCAmelCase__      :      Optional[int]         =		np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(a    ,     1E-3   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							super().test_attention_slicing_forward_pass(expected_max_diff=7E-3   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().test_cpu_offload_forward_pass(expected_max_diff=3E-3   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							super().test_inference_batch_single_identical(expected_max_diff=7E-3   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							super().test_save_load_local(expected_max_difference=3E-3   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							super().test_save_load_optional_components(expected_max_difference=3E-3   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		[
							    'DDIMScheduler',
							    'DDPMScheduler',
							    'PNDMScheduler',
							    'HeunDiscreteScheduler',
							    'EulerAncestralDiscreteScheduler',
							    'KDPM2DiscreteScheduler',
							    'KDPM2AncestralDiscreteScheduler',
							    'DPMSolverSDEScheduler',
							]
							lowerCAmelCase__      :      Union[str, Any]         =		self.get_dummy_components()
							lowerCAmelCase__      :      str         =		self.pipeline_class(**a   )

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=a   )

							pipe.to(a   )
							pipe.set_progress_bar_config(disable=a   )
							lowerCAmelCase__      :      Union[str, Any]         =		self.get_dummy_inputs(a   )
							lowerCAmelCase__      :      Tuple         =		2

							lowerCAmelCase__      :      List[str]         =		[]
							for scheduler_enum in KarrasDiffusionSchedulers:
										if scheduler_enum.name in skip_schedulers:
													# no sigma schedulers are not supported
													# no schedulers
													continue

										lowerCAmelCase__      :      Optional[int]         =		getattr(a    ,     scheduler_enum.name   )
										lowerCAmelCase__      :      Optional[int]         =		scheduler_cls.from_config(pipe.scheduler.config   )
										lowerCAmelCase__      :      List[Any]         =		pipe(**a   )[0]
										outputs.append(a   )

							assert check_same_shape(a   )







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






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		torch.manual_seed(33   )

							lowerCAmelCase__      :      Union[str, Any]         =		StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    ,     torch_dtype=torch.floataa   )
							pipe.to('cuda'   )

							lowerCAmelCase__      :      Optional[int]         =		StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'    ,     torch_dtype=torch.floataa   )
							upscaler.to('cuda'   )

							lowerCAmelCase__      :      Union[str, Any]         =		'a photo of an astronaut high resolution, unreal engine, ultra realistic'

							lowerCAmelCase__      :      int         =		pipe(a    ,     generator=a    ,     output_type='latent'   ).images

							lowerCAmelCase__      :      str         =		upscaler(
							    prompt=a    ,     image=a    ,     num_inference_steps=20    ,     guidance_scale=0    ,     generator=a    ,     output_type='np'    ,     ).images[0]

							lowerCAmelCase__      :      int         =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'   )
							assert np.abs((expected_image - image).mean()   ) < 5E-2






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		torch.manual_seed(33   )

							lowerCAmelCase__      :      Tuple         =		StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'    ,     torch_dtype=torch.floataa   )
							upscaler.to('cuda'   )

							lowerCAmelCase__      :      Dict         =		'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

							lowerCAmelCase__      :      List[str]         =		load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'   )

							lowerCAmelCase__      :      int         =		upscaler(
							    prompt=a    ,     image=a    ,     num_inference_steps=20    ,     guidance_scale=0    ,     generator=a    ,     output_type='np'    ,     ).images[0]

							lowerCAmelCase__      :      Union[str, Any]         =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'   )
							assert np.abs((expected_image - image).max()   ) < 5E-2

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate







@dataclass(frozen=__magic_name__	)
class      A__							(     __magic_name__	):
				lowercase			    =  field(default='audio-classification'						,   metadata={'include_in_asdict_even_if_is_default': True}	)
				lowercase			    =  Features({'audio': Audio()}	)
				lowercase			    =  Features({'labels': ClassLabel}	)
				lowercase			    =  "audio"
				lowercase			    =  "labels"






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      Tuple   ):






							'''simple docstring'''







							if self.label_column not in features:
										raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
							if not isinstance(features[self.label_column]    ,     a   ):
										raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
							lowerCAmelCase__      :      Tuple         =		copy.deepcopy(self   )
							lowerCAmelCase__      :      List[Any]         =		self.label_schema.copy()
							lowerCAmelCase__      :      List[Any]         =		features[self.label_column]
							lowerCAmelCase__      :      Optional[int]         =		label_schema
							return task_template






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return {
							    self.audio_column: "audio",
							    self.label_column: "labels",
							}