'''simple docstring'''




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        __magic_name__		(       SCREAMING_SNAKE_CASE__,	unittest.TestCase):
			UpperCamelCase__							=			KandinskyPriorPipeline
			UpperCamelCase__							=			['prompt']
			UpperCamelCase__							=			['prompt', 'negative_prompt']
			UpperCamelCase__							=			[
			    'num_images_per_prompt',
			    'generator',
			    'num_inference_steps',
			    'latents',
			    'negative_prompt',
			    'guidance_scale',
			    'output_type',
			    'return_dict',
			]
			UpperCamelCase__							=			False







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return 32







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										return 32







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										return self.time_input_dim







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										return self.time_input_dim * 4







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										return 100







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     Union[str, Any]									=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
										return tokenizer







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										torch.manual_seed(0       )
										lowercase_   :     Union[str, Any]									=			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=1000							,     )
										return CLIPTextModelWithProjection(a_       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										torch.manual_seed(0       )

										lowercase_   :     List[str]									=			{
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 12,
										    """embedding_dim""": self.text_embedder_hidden_size,
										    """num_layers""": 1,
										}

										lowercase_   :     str									=			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
										lowercase_   :     Any									=			nn.Parameter(torch.ones(model.clip_std.shape       )       )
										return model







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										torch.manual_seed(0       )
										lowercase_   :     Optional[int]									=			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							,     )

										lowercase_   :     Optional[Any]									=			CLIPVisionModelWithProjection(a_       )
										return model







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										lowercase_   :     Tuple									=			CLIPImageProcessor(
										    crop_size=224							,     do_center_crop=a_							,     do_normalize=a_							,     do_resize=a_							,     image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							,     image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							,     resample=3							,     size=224							,     )

										return image_processor







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     List[Any]									=			self.dummy_prior
										lowercase_   :     Any									=			self.dummy_image_encoder
										lowercase_   :     Union[str, Any]									=			self.dummy_text_encoder
										lowercase_   :     int									=			self.dummy_tokenizer
										lowercase_   :     Any									=			self.dummy_image_processor

										lowercase_   :     Union[str, Any]									=			UnCLIPScheduler(
										    variance_type="""fixed_small_log"""							,     prediction_type="""sample"""							,     num_train_timesteps=1000							,     clip_sample=a_							,     clip_sample_range=10.0							,     )

										lowercase_   :     List[Any]									=			{
										    """prior""": prior,
										    """image_encoder""": image_encoder,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """scheduler""": scheduler,
										    """image_processor""": image_processor,
										}

										return components







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    List[Any]							,     lowercase_							:    List[str]=0       ):



										if str(a_       ).startswith("""mps"""       ):
																	lowercase_   :     List[Any]									=			torch.manual_seed(a_       )
										else:
																	lowercase_   :     str									=			torch.Generator(device=a_       ).manual_seed(a_       )
										lowercase_   :     List[str]									=			{
										    """prompt""": """horse""",
										    """generator""": generator,
										    """guidance_scale""": 4.0,
										    """num_inference_steps""": 2,
										    """output_type""": """np""",
										}
										return inputs







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Dict									=			"""cpu"""

										lowercase_   :     Optional[int]									=			self.get_dummy_components()

										lowercase_   :     Union[str, Any]									=			self.pipeline_class(**a_       )
										lowercase_   :     Any									=			pipe.to(a_       )

										pipe.set_progress_bar_config(disable=a_       )

										lowercase_   :     Union[str, Any]									=			pipe(**self.get_dummy_inputs(a_       )       )
										lowercase_   :     Dict									=			output.image_embeds

										lowercase_   :     List[Any]									=			pipe(
										    **self.get_dummy_inputs(a_       )							,     return_dict=a_							,     )[0]

										lowercase_   :     Dict									=			image[0, -10:]
										lowercase_   :     Union[str, Any]									=			image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										lowercase_   :     Optional[Any]									=			np.array(
										    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]       )

										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       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_   :     Dict									=			torch_device == """cpu"""
										lowercase_   :     List[str]									=			True
										lowercase_   :     Any									=			False

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







			@skip_mps
			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										lowercase_   :     Optional[int]									=			torch_device == """cpu"""
										lowercase_   :     Tuple									=			False

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









'''simple docstring'''

import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[Any]            =       KandinskyVaaPriorEmbaEmbPipeline
 _lowerCamelCase			:       Tuple            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       [
     'num_images_per_prompt',
     'strength',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase			:       List[str]            =       False

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    return self.time_input_dim

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    return self.time_input_dim * 4

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return 100

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



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

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    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(a_       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    torch.manual_seed(0       )

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

    UpperCamelCase__			    =    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
    UpperCamelCase__			    =    nn.Parameter(torch.ones(model.clip_std.shape       )       )
    return model

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    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,       )

    UpperCamelCase__			    =    CLIPVisionModelWithProjection(a_       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPImageProcessor(
        crop_size=224,       do_center_crop=a_,       do_normalize=a_,       do_resize=a_,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

    return image_processor

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



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

    UpperCamelCase__			    =    UnCLIPScheduler(
        variance_type="fixed_small_log",       prediction_type="sample",       num_train_timesteps=1000,       clip_sample=a_,       clip_sample_range=10.0,       )

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

    return components

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						List[str],       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    if str(a_       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(a_       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )

    UpperCamelCase__			    =    floats_tensor((1, 3, 64, 64),       rng=random.Random(a_       )       ).to(a_       )
    UpperCamelCase__			    =    image.cpu().permute(0,       2,       3,       1       )[0]
    UpperCamelCase__			    =    Image.fromarray(np.uinta(a_       )       ).convert("RGB"       ).resize((256, 256)       )

    UpperCamelCase__			    =    {
        "prompt": "horse",
        "image": init_image,
        "strength": 0.5,
        "generator": generator,
        "guidance_scale": 4.0,
        "num_inference_steps": 2,
        "output_type": "np",
    }
    return inputs

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"

    UpperCamelCase__			    =    self.get_dummy_components()

    UpperCamelCase__			    =    self.pipeline_class(**a_       )
    UpperCamelCase__			    =    pipe.to(a_       )

    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    pipe(**self.get_dummy_inputs(a_       )       )
    UpperCamelCase__			    =    output.image_embeds

    UpperCamelCase__			    =    pipe(
        **self.get_dummy_inputs(a_       ),       return_dict=a_,       )[0]

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

    assert image.shape == (1, 32)

    UpperCamelCase__			    =    np.array(
        [
            0.1_071_284,
            1.3_330_271,
            0.61_260_223,
            -0.6_691_065,
            -0.3_846_852,
            -1.0_303_661,
            0.22_716_111,
            0.03_348_901,
            0.30_040_675,
            -0.24_805_029,
        ]       )

    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 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    True
    UpperCamelCase__			    =    False

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

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    False

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