

"""simple docstring"""




from __future__ import annotations

from typing import Any





class 				lowerCamelCase  :

			def __init__(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Any    )				->      None:  # noqa: A002
						SCREAMING_SNAKE_CASE__									= item
						SCREAMING_SNAKE_CASE__									= next





class 				lowerCamelCase  :

			def __init__(   self			:  Optional[int]    )				->      None:
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= 0

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  Any    )				->      None:
						SCREAMING_SNAKE_CASE__									= Node(__UpperCAmelCase      ,			self.head    )
						self.size += 1

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Any:
						# Switched 'self.is_empty()' to 'self.head is None'
						# because mypy was considering the possibility that 'self.head'
						# can be None in below else part and giving error
						if self.head is None:
									return None
						else:
									SCREAMING_SNAKE_CASE__									= self.head.item
									SCREAMING_SNAKE_CASE__									= self.head.next
									self.size -= 1
									return item

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      bool:
						return self.head is None

			def __str__(   self			:  Union[str, Any]    )				->      str:
						if self.is_empty():
									return ""
						else:
									SCREAMING_SNAKE_CASE__									= self.head
									SCREAMING_SNAKE_CASE__									= """"""
									SCREAMING_SNAKE_CASE__									= []
									while iterate:
												item_list.append(str(iterate.item    )    )
												SCREAMING_SNAKE_CASE__									= iterate.next

									SCREAMING_SNAKE_CASE__									= """ --> """.join(__UpperCAmelCase    )

									return item_str





			def __len__(   self			:  str    )				->      int:
						return self.size





"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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     ):
			lowerCamelCase__				:						Optional[Any]   						=							KandinskyPipeline
			lowerCamelCase__				:						Union[str, Any]   						=							[
			    'prompt',
			    'image_embeds',
			    'negative_image_embeds',
			]
			lowerCamelCase__				:						Optional[Any]   						=							['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
			lowerCamelCase__				:						Union[str, Any]   						=							[
			    'generator',
			    'height',
			    'width',
			    'latents',
			    'guidance_scale',
			    'negative_prompt',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			lowerCamelCase__				:						Optional[Any]   						=							False

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      List[str]:
						return 3_2

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						return 3_2

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Any:
						return self.time_input_dim

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Any:
						return self.time_input_dim * 4

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Optional[Any]:
						return 1_0_0

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""    )
						return tokenizer

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Dict:
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= MCLIPConfig(
						    numDims=self.cross_attention_dim      ,			transformerDimensions=self.text_embedder_hidden_size      ,			hidden_size=self.text_embedder_hidden_size      ,			intermediate_size=3_7      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			vocab_size=1_0_0_5      ,			)

						SCREAMING_SNAKE_CASE__									= MultilingualCLIP(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= text_encoder.eval()

						return text_encoder

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						torch.manual_seed(0    )

						SCREAMING_SNAKE_CASE__									= {
						    """in_channels""": 4,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """text_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""": """text_image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						SCREAMING_SNAKE_CASE__									= UNetaDConditionModel(**__UpperCAmelCase    )
						return model

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Union[str, Any]:
						return {
						    "block_out_channels": [3_2, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      List[str]:
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= VQModel(**self.dummy_movq_kwargs    )
						return model

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.dummy_text_encoder
						SCREAMING_SNAKE_CASE__									= self.dummy_tokenizer
						SCREAMING_SNAKE_CASE__									= self.dummy_unet
						SCREAMING_SNAKE_CASE__									= self.dummy_movq

						SCREAMING_SNAKE_CASE__									= DDIMScheduler(
						    num_train_timesteps=1_0_0_0      ,			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      ,			)

						SCREAMING_SNAKE_CASE__									= {
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}
						return components

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Optional[Any]=0    )				->      str:
						SCREAMING_SNAKE_CASE__									= floats_tensor((1, self.cross_attention_dim)      ,			rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= floats_tensor((1, self.cross_attention_dim)      ,			rng=random.Random(seed + 1    )    ).to(__UpperCAmelCase    )
						if str(__UpperCAmelCase    ).startswith("""mps"""    ):
									SCREAMING_SNAKE_CASE__									= torch.manual_seed(__UpperCAmelCase    )
						else:
									SCREAMING_SNAKE_CASE__									= torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= {
						    """prompt""": """horse""",
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """guidance_scale""": 4.0,
						    """num_inference_steps""": 2,
						    """output_type""": """np""",
						}
						return inputs





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      int:
						SCREAMING_SNAKE_CASE__									= """cpu"""

						SCREAMING_SNAKE_CASE__									= self.get_dummy_components()

						SCREAMING_SNAKE_CASE__									= self.pipeline_class(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= pipe.to(__UpperCAmelCase    )

						pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
						SCREAMING_SNAKE_CASE__									= output.images

						SCREAMING_SNAKE_CASE__									= pipe(
						    **self.get_dummy_inputs(__UpperCAmelCase    )      ,			return_dict=__UpperCAmelCase      ,			)[0]

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

						assert image.shape == (1, 6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__									= np.array(
						    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]    )

						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    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      List[str]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""    )

						SCREAMING_SNAKE_CASE__									= KandinskyPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-prior"""      ,			torch_dtype=torch.floataa    )
						pipe_prior.to(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""      ,			torch_dtype=torch.floataa    )
						SCREAMING_SNAKE_CASE__									= pipeline.to(__UpperCAmelCase    )
						pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= """red cat, 4k photo"""

						SCREAMING_SNAKE_CASE__									= torch.Generator(device="""cuda"""    ).manual_seed(0    )
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= pipe_prior(
						    __UpperCAmelCase      ,			generator=__UpperCAmelCase      ,			num_inference_steps=5      ,			negative_prompt=""""""      ,			).to_tuple()

						SCREAMING_SNAKE_CASE__									= torch.Generator(device="""cuda"""    ).manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= pipeline(
						    __UpperCAmelCase      ,			image_embeds=__UpperCAmelCase      ,			negative_image_embeds=__UpperCAmelCase      ,			generator=__UpperCAmelCase      ,			num_inference_steps=1_0_0      ,			output_type="""np"""      ,			)

						SCREAMING_SNAKE_CASE__									= output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)

						assert_mean_pixel_difference(__UpperCAmelCase      ,			__UpperCAmelCase    )


