


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 							__UpperCamelCase    (		A__		,				unittest.TestCase ):
	A_           =   KandinskyVaaControlnetPipeline
	A_           =   ["image_embeds", "negative_image_embeds", "hint"]
	A_           =   ["image_embeds", "negative_image_embeds", "hint"]
	A_           =   [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	A_           =   False




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 32




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 32




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.time_input_dim




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.time_input_dim * 4




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 100




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)

						__a      :	Tuple    =  {
						    'in_channels': 8,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 8,
						    'addition_embed_type': 'image_hint',
						    '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,
						}

						__a      :	Optional[int]    =  UNetaDConditionModel(**__snake_case							)
						return model




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return {
						    "block_out_channels": [32, 32, 64, 64],
						    "down_block_types": [
						        "DownEncoderBlock2D",
						        "DownEncoderBlock2D",
						        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	Optional[int]    =  VQModel(**self.dummy_movq_kwargs							)
						return model




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  self.dummy_unet
						__a      :	Optional[Any]    =  self.dummy_movq

						__a      :	Dict    =  DDIMScheduler(
						    num_train_timesteps=1000						,    beta_schedule='linear'						,    beta_start=0.00085						,    beta_end=0.012						,    clip_sample=__snake_case						,    set_alpha_to_one=__snake_case						,    steps_offset=1						,    prediction_type='epsilon'						,    thresholding=__snake_case						,    )

						__a      :	Optional[Any]    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}
						return components




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0							):

						'''simple docstring'''





						__a      :	int    =  floats_tensor((1, self.text_embedder_hidden_size)						,    rng=random.Random(__snake_case							)							).to(__snake_case							)
						__a      :	List[Any]    =  floats_tensor((1, self.text_embedder_hidden_size)						,    rng=random.Random(seed + 1							)							).to(
						    __snake_case							)

						# create hint
						__a      :	Tuple    =  floats_tensor((1, 3, 64, 64)						,    rng=random.Random(__snake_case							)							).to(__snake_case							)

						if str(__snake_case							).startswith('mps'							):
											__a      :	Union[str, Any]    =  torch.manual_seed(__snake_case							)
						else:
											__a      :	int    =  torch.Generator(device=__snake_case							).manual_seed(__snake_case							)
						__a      :	List[str]    =  {
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'hint': hint,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'guidance_scale': 4.0,
						    'num_inference_steps': 2,
						    'output_type': 'np',
						}
						return inputs





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  'cpu'

						__a      :	Any    =  self.get_dummy_components()

						__a      :	str    =  self.pipeline_class(**__snake_case							)
						__a      :	str    =  pipe.to(__snake_case							)

						pipe.set_progress_bar_config(disable=__snake_case							)

						__a      :	Optional[int]    =  pipe(**self.get_dummy_inputs(__snake_case							)							)
						__a      :	Optional[Any]    =  output.images

						__a      :	Any    =  pipe(
						    **self.get_dummy_inputs(__snake_case							)						,    return_dict=__snake_case						,    )[0]

						__a      :	int    =  image[0, -3:, -3:, -1]
						__a      :	List[str]    =  image_from_tuple[0, -3:, -3:, -1]

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

						__a      :	List[Any]    =  np.array(
						    [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]							)

						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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'							)

						__a      :	Dict    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/hint_image_cat.png'							)
						__a      :	Tuple    =  torch.from_numpy(np.array(__snake_case							)							).float() / 255.0
						__a      :	Any    =  hint.permute(2						,    0						,    1							).unsqueeze(0							)

						__a      :	List[Any]    =  KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'						,    torch_dtype=torch.floataa							)
						pipe_prior.to(__snake_case							)

						__a      :	Any    =  KandinskyVaaControlnetPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-controlnet-depth'						,    torch_dtype=torch.floataa							)
						__a      :	int    =  pipeline.to(__snake_case							)
						pipeline.set_progress_bar_config(disable=__snake_case							)

						__a      :	List[str]    =  'A robot, 4k photo'

						__a      :	Any    =  torch.Generator(device='cuda'							).manual_seed(0							)
						__a   ,    __a      :	Any    =  pipe_prior(
						    __snake_case						,    generator=__snake_case						,    num_inference_steps=5						,    negative_prompt=''						,    ).to_tuple()

						__a      :	List[str]    =  torch.Generator(device='cuda'							).manual_seed(0							)
						__a      :	Optional[Any]    =  pipeline(
						    image_embeds=__snake_case						,    negative_image_embeds=__snake_case						,    hint=__snake_case						,    generator=__snake_case						,    num_inference_steps=100						,    output_type='np'						,    )

						__a      :	Tuple    =  output.images[0]

						assert image.shape == (512, 512, 3)

						assert_mean_pixel_difference(__snake_case						,    __snake_case							)







'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



