






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   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       KandinskyVaaControlnetPipeline
							_UpperCAmelCase     =       ["image_embeds", "negative_image_embeds", "hint"]
							_UpperCAmelCase     =       ["image_embeds", "negative_image_embeds", "hint"]
							_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:      Optional[Any]						)		->		Union[str, Any]:
											return 32
							@property
							def lowerCAmelCase_ (     self:      List[str]						)		->		Dict:
											return 32
							@property
							def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[int]:
											return self.time_input_dim
							@property
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											return self.time_input_dim * 4
							@property
							def lowerCAmelCase_ (     self:      List[str]						)		->		List[str]:
											return 1_00
							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											torch.manual_seed(0						)

											snake_case__									=     {
											    '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,
											}

											snake_case__									=     UNetaDConditionModel(**UpperCamelCase						)
											return model
							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		List[str]:
											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 lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											torch.manual_seed(0						)
											snake_case__									=     VQModel(**self.dummy_movq_kwargs						)
											return model
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     self.dummy_unet
											snake_case__									=     self.dummy_movq

											snake_case__									=     DDIMScheduler(
											    num_train_timesteps=10_00       ,	beta_schedule='linear'       ,	beta_start=0.00_085       ,	beta_end=0.012       ,	clip_sample=UpperCamelCase       ,	set_alpha_to_one=UpperCamelCase       ,	steps_offset=1       ,	prediction_type='epsilon'       ,	thresholding=UpperCamelCase       ,	)

											snake_case__									=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}
											return components
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Dict=0						)		->		int:
											snake_case__									=     floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)
											snake_case__									=     floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(seed + 1						)						).to(
											    UpperCamelCase						)

											# create hint
											snake_case__									=     floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)

											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    '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 lowerCAmelCase_ (     self:      List[Any]						)		->		List[str]:
											snake_case__									=     'cpu'

											snake_case__									=     self.get_dummy_components()

											snake_case__									=     self.pipeline_class(**UpperCamelCase						)
											snake_case__									=     pipe.to(UpperCamelCase						)

											pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     pipe(**self.get_dummy_inputs(UpperCamelCase						)						)
											snake_case__									=     output.images

											snake_case__									=     pipe(
											    **self.get_dummy_inputs(UpperCamelCase						)       ,	return_dict=UpperCamelCase       ,	)[0]

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

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

											snake_case__									=     np.array(
											    [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]						)

											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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Optional[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'						)

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

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

											snake_case__									=     KandinskyVaaControlnetPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-2-controlnet-depth'       ,	torch_dtype=torch.floataa						)
											snake_case__									=     pipeline.to(UpperCamelCase						)
											pipeline.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     'A robot, 4k photo'

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

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

											snake_case__									=     output.images[0]

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

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)












from typing import TYPE_CHECKING

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


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




