

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	KandinskyVaaControlnetImgaImgPipeline
			__A    	=	["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
			__A    	=	["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
			__A    	=	[
			    """generator""",
			    """height""",
			    """width""",
			    """strength""",
			    """guidance_scale""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			__A    	=	False


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim * 4


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 1_00


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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				):

				"""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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.dummy_unet
				snake_case_   		=			self.dummy_movq

				snake_case_   		=			{
				    'num_train_timesteps': 10_00,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				snake_case_   		=			DDIMScheduler(**__UpperCamelCase				)

				snake_case_   		=			{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				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 init_image
				snake_case_   		=			floats_tensor((1, 3, 64, 64)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				)[0]
				snake_case_   		=			Image.fromarray(np.uinta(__UpperCamelCase				)				).convert('RGB'				).resize((2_56, 2_56)				)
				# 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': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'hint': hint,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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(
				    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]				)
				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				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'				)

				snake_case_   		=			load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
				snake_case_   		=			init_image.resize((5_12, 5_12)				)

				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_   		=			'A robot, 4k photo'

				snake_case_   		=			KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'			,					torch_dtype=torch.floataa				)
				pipe_prior.to(__UpperCamelCase				)

				snake_case_   		=			KandinskyVaaControlnetImgaImgPipeline.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_   		=			torch.Generator(device='cpu'				).manual_seed(0				)

				snake_case_		,							snake_case_   		=			pipe_prior(
				    __UpperCamelCase			,					image=__UpperCamelCase			,					strength=0.85			,					generator=__UpperCamelCase			,					negative_prompt=''			,					).to_tuple()

				snake_case_   		=			pipeline(
				    image=__UpperCamelCase			,					image_embeds=__UpperCamelCase			,					negative_image_embeds=__UpperCamelCase			,					hint=__UpperCamelCase			,					generator=__UpperCamelCase			,					num_inference_steps=1_00			,					height=5_12			,					width=5_12			,					strength=0.5			,					output_type='np'			,					)

				snake_case_   		=			output.images[0]

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

				assert_mean_pixel_difference(__UpperCamelCase			,					__UpperCamelCase				)



from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


A									=  logging.get_logger(__name__)

A									=  'T5Config'


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig
