



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MegaForCausalLM",
							    "MegaForMaskedLM",
							    "MegaForMultipleChoice",
							    "MegaForQuestionAnswering",
							    "MegaForSequenceClassification",
							    "MegaForTokenClassification",
							    "MegaModel",
							    "MegaPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mega import (
														    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MegaForCausalLM,
														    MegaForMaskedLM,
														    MegaForMultipleChoice,
														    MegaForQuestionAnswering,
														    MegaForSequenceClassification,
														    MegaForTokenClassification,
														    MegaModel,
														    MegaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					KandinskyVaaPipeline
			UpperCamelCase        =					[
			    '''image_embeds''',
			    '''negative_image_embeds''',
			]
			UpperCamelCase        =					['''image_embeds''', '''negative_image_embeds''']
			UpperCamelCase        =					[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase        =					False



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



						"""simple docstring"""






						return 32



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



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  Any)       ->   List[Any]:



						"""simple docstring"""






						return self.time_input_dim



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						return self.time_input_dim * 4



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



						"""simple docstring"""






						return 1_00



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   Dict:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'in_channels': 4,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 8,
						    'addition_embed_type': 'image',
						    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
						    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
						    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
						    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
						    'layers_per_block': 1,
						    'encoder_hid_dim': self.text_embedder_hidden_size,
						    'encoder_hid_dim_type': 'image_proj',
						    'cross_attention_dim': self.cross_attention_dim,
						    'attention_head_dim': 4,
						    'resnet_time_scale_shift': 'scale_shift',
						    'class_embed_type': None,
						}

						_UpperCAmelCase			=       UNetaDConditionModel(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   List[Any]:



						"""simple docstring"""






						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}



			@property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Optional[Any]:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       VQModel(**self.dummy_movq_kwargs)
						return model



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_unet
						_UpperCAmelCase			=       self.dummy_movq

						_UpperCAmelCase			=       DDIMScheduler(
						    num_train_timesteps=10_00			,		beta_schedule='linear'			,		beta_start=0.0_0_0_8_5			,		beta_end=0.0_1_2			,		clip_sample=A			,		set_alpha_to_one=A			,		steps_offset=1			,		prediction_type='epsilon'			,		thresholding=A			,		)

						_UpperCAmelCase			=       {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}
						return components



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  str			,		A       :  Tuple=0)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, self.text_embedder_hidden_size)			,		rng=random.Random(A)).to(A)
						_UpperCAmelCase			=       floats_tensor((1, self.text_embedder_hidden_size)			,		rng=random.Random(seed + 1)).to(
						    A)
						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)
						_UpperCAmelCase			=       {
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'guidance_scale': 4.0,
						    'num_inference_steps': 2,
						    'output_type': 'np',
						}
						return inputs



			def    _lowerCamelCase	(						self       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       pipe(**self.get_dummy_inputs(A))
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       pipe(
						    **self.get_dummy_inputs(A)			,		return_dict=A			,		)[0]

						_UpperCAmelCase			=       image[0, -3:, -3:, -1]
						_UpperCAmelCase			=       image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						_UpperCAmelCase			=       np.array(
						    [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6])

						assert (
						    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
						), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
						), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







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



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy')

						_UpperCAmelCase			=       KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'			,		torch_dtype=torch.floataa)
						pipe_prior.to(A)

						_UpperCAmelCase			=       KandinskyVaaPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-decoder'			,		torch_dtype=torch.floataa)
						_UpperCAmelCase			=       pipeline.to(A)
						pipeline.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       'red cat, 4k photo'

						_UpperCAmelCase			=       torch.Generator(device='cuda').manual_seed(0)
						_UpperCAmelCase	,					_UpperCAmelCase			=       pipe_prior(
						    A			,		generator=A			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

						_UpperCAmelCase			=       torch.Generator(device='cuda').manual_seed(0)
						_UpperCAmelCase			=       pipeline(
						    image_embeds=A			,		negative_image_embeds=A			,		generator=A			,		num_inference_steps=1_00			,		output_type='np'			,		)

						_UpperCAmelCase			=       output.images[0]

						assert image.shape == (5_12, 5_12, 3)

						assert_mean_pixel_difference(A			,		A)
