import unittest

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

from diffusers import KandinskyVaaPriorPipeline, 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      (       SCREAMING_SNAKE_CASE_			,    unittest.TestCase       ):
							lowercase__  :    Any									=							KandinskyVaaPriorPipeline
							lowercase__  :    List[str]									=							["""prompt"""]
							lowercase__  :    int									=							["""prompt""", """negative_prompt"""]
							lowercase__  :    int									=							[
							    """num_images_per_prompt""",
							    """generator""",
							    """num_inference_steps""",
							    """latents""",
							    """negative_prompt""",
							    """guidance_scale""",
							    """output_type""",
							    """return_dict""",
							]
							lowercase__  :    Tuple									=							False




							@property
							def 		__snake_case(  self				:   Dict )   ->			List[str]:



												'''simple docstring'''

												return 32




							@property
							def 		__snake_case(  self				:   Any )   ->			str:



												'''simple docstring'''

												return 32




							@property
							def 		__snake_case(  self				:   str )   ->			Optional[int]:



												'''simple docstring'''

												return self.time_input_dim




							@property
							def 		__snake_case(  self				:   str )   ->			int:



												'''simple docstring'''

												return self.time_input_dim * 4




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			Union[str, Any]:



												'''simple docstring'''

												return 100




							@property
							def 		__snake_case(  self				:   Tuple )   ->			List[str]:



												'''simple docstring'''

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




							@property
							def 		__snake_case(  self				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

												torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    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-05		,							num_attention_heads=4		,							num_hidden_layers=5		,							pad_token_id=1		,							vocab_size=1_000		,							)
												return CLIPTextModelWithProjection(__a )




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			Any:



												'''simple docstring'''

												torch.manual_seed(0 )

												SCREAMING_SNAKE_CASE           =    {
												    'num_attention_heads': 2,
												    'attention_head_dim': 12,
												    'embedding_dim': self.text_embedder_hidden_size,
												    'num_layers': 1,
												}

												SCREAMING_SNAKE_CASE           =    PriorTransformer(**__a )
												# 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
												SCREAMING_SNAKE_CASE           =    nn.Parameter(torch.ones(model.clip_std.shape ) )
												return model




							@property
							def 		__snake_case(  self				:   List[str] )   ->			List[str]:



												'''simple docstring'''

												torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    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		,							)

												SCREAMING_SNAKE_CASE           =    CLIPVisionModelWithProjection(__a )
												return model




							@property
							def 		__snake_case(  self				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    CLIPImageProcessor(
												    crop_size=224		,							do_center_crop=__a		,							do_normalize=__a		,							do_resize=__a		,							image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]		,							image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]		,							resample=3		,							size=224		,							)

												return image_processor




							def 		__snake_case(  self				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    UnCLIPScheduler(
												    variance_type="fixed_small_log"		,							prediction_type="sample"		,							num_train_timesteps=1_000		,							clip_sample=__a		,							clip_sample_range=10.0		,							)

												SCREAMING_SNAKE_CASE           =    {
												    'prior': prior,
												    'image_encoder': image_encoder,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'scheduler': scheduler,
												    'image_processor': image_processor,
												}

												return components




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Tuple=0 )   ->			Any:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    'cpu'

												SCREAMING_SNAKE_CASE           =    self.get_dummy_components()

												SCREAMING_SNAKE_CASE           =    self.pipeline_class(**__a )
												SCREAMING_SNAKE_CASE           =    pipe.to(__a )

												pipe.set_progress_bar_config(disable=__a )

												SCREAMING_SNAKE_CASE           =    pipe(**self.get_dummy_inputs(__a ) )
												SCREAMING_SNAKE_CASE           =    output.image_embeds

												SCREAMING_SNAKE_CASE           =    pipe(
												    **self.get_dummy_inputs(__a )		,							return_dict=__a		,							)[0]

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

												assert image.shape == (1, 32)

												SCREAMING_SNAKE_CASE           =    np.array(
												    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )

												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 		__snake_case(  self				:   Tuple )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    torch_device == 'cpu'
												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    False

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





							@skip_mps
							def 		__snake_case(  self				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    torch_device == 'cpu'
												SCREAMING_SNAKE_CASE           =    False

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





from __future__ import annotations

from collections.abc import Iterator


class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   int )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    value
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None



class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   Node )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tree




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Node | None )   ->			int:



												'''simple docstring'''

												if node is None:
																	return 0
												return node.value + (
												    self.depth_first_search(node.left ) + self.depth_first_search(node.right )
												)





							def __iter__(  self				:   List[Any] )   ->			Iterator[int]:



												'''simple docstring'''

												yield self.depth_first_search(self.tree )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




