



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       _lowercase				(  lowerCAmelCase, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				KandinskyPriorPipeline
	__A		      =				["prompt"]
	__A		      =				["prompt", "negative_prompt"]
	__A		      =				[
	    "num_images_per_prompt",
	    "generator",
	    "num_inference_steps",
	    "latents",
	    "negative_prompt",
	    "guidance_scale",
	    "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"""

						a   	=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
						return tokenizer




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						torch.manual_seed(0						)
						a   	=  CLIPTextConfig(
						    bos_token_id=0						,			eos_token_id=2						,			hidden_size=self.text_embedder_hidden_size						,			projection_dim=self.text_embedder_hidden_size						,			intermediate_size=37						,			layer_norm_eps=1E-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=1000						,			)
						return CLIPTextModelWithProjection(lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						torch.manual_seed(0						)

						a   	=  {
						    "num_attention_heads": 2,
						    "attention_head_dim": 12,
						    "embedding_dim": self.text_embedder_hidden_size,
						    "num_layers": 1,
						}

						a   	=  PriorTransformer(**lowerCamelCase_						)
						# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
						a   	=  nn.Parameter(torch.ones(model.clip_std.shape						)						)
						return model




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						torch.manual_seed(0						)
						a   	=  CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size						,			image_size=224						,			projection_dim=self.text_embedder_hidden_size						,			intermediate_size=37						,			num_attention_heads=4						,			num_channels=3						,			num_hidden_layers=5						,			patch_size=14						,			)

						a   	=  CLIPVisionModelWithProjection(lowerCamelCase_						)
						return model




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  CLIPImageProcessor(
						    crop_size=224						,			do_center_crop=lowerCamelCase_						,			do_normalize=lowerCamelCase_						,			do_resize=lowerCamelCase_						,			image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,			image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,			resample=3						,			size=224						,			)

						return image_processor




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.dummy_prior
						a   	=  self.dummy_image_encoder
						a   	=  self.dummy_text_encoder
						a   	=  self.dummy_tokenizer
						a   	=  self.dummy_image_processor

						a   	=  UnCLIPScheduler(
						    variance_type="fixed_small_log"						,			prediction_type="sample"						,			num_train_timesteps=1000						,			clip_sample=lowerCamelCase_						,			clip_sample_range=10.0						,			)

						a   	=  {
						    "prior": prior,
						    "image_encoder": image_encoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "scheduler": scheduler,
						    "image_processor": image_processor,
						}

						return components




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_=0						):
						"""simple docstring"""

						if str(lowerCamelCase_						).startswith("mps"						):
											a   	=  torch.manual_seed(lowerCamelCase_						)
						else:
											a   	=  torch.Generator(device=lowerCamelCase_						).manual_seed(lowerCamelCase_						)
						a   	=  {
						    "prompt": "horse",
						    "generator": generator,
						    "guidance_scale": 4.0,
						    "num_inference_steps": 2,
						    "output_type": "np",
						}
						return inputs




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  "cpu"

						a   	=  self.get_dummy_components()

						a   	=  self.pipeline_class(**lowerCamelCase_						)
						a   	=  pipe.to(lowerCamelCase_						)

						pipe.set_progress_bar_config(disable=lowerCamelCase_						)

						a   	=  pipe(**self.get_dummy_inputs(lowerCamelCase_						)						)
						a   	=  output.image_embeds

						a   	=  pipe(
						    **self.get_dummy_inputs(lowerCamelCase_						)						,			return_dict=lowerCamelCase_						,			)[0]

						a   	=  image[0, -10:]
						a   	=  image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						a   	=  np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2




	@skip_mps
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  torch_device == "cpu"
						a   	=  True
						a   	=  False

						self._test_inference_batch_single_identical(
						    test_max_difference=lowerCamelCase_						,			relax_max_difference=lowerCamelCase_						,			test_mean_pixel_difference=lowerCamelCase_						,			)




	@skip_mps
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  torch_device == "cpu"
						a   	=  False

						self._test_attention_slicing_forward_pass(
						    test_max_difference=lowerCamelCase_						,			test_mean_pixel_difference=lowerCamelCase_						,			)





from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline
