

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 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Union[str, Any]								=			KandinskyVaaPriorEmbaEmbPipeline
     UpperCamelCase_  :Any								=			["""prompt""", """image"""]
     UpperCamelCase_  :Tuple								=			["""prompt""", """image"""]
     UpperCamelCase_  :Any								=			[
         """num_images_per_prompt""",
         """strength""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_  :str								=			False






     @property
     def   UpperCAmelCase_							(							self  )->    int:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    Dict:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         return self.time_input_dim






     @property
     def   UpperCAmelCase_							(							self  )->    Any:
         return self.time_input_dim * 4






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         return 100






     @property
     def   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )
         return tokenizer






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         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=1_000		,	)
         return CLIPTextModelWithProjection(_lowercase  )






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         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(**_lowercase  )
         # 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   UpperCAmelCase_							(							self  )->    Tuple:
         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(_lowercase  )
         return model






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         UpperCamelCase_      =	CLIPImageProcessor(
             crop_size=224		,	do_center_crop=_lowercase		,	do_normalize=_lowercase		,	do_resize=_lowercase		,	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   UpperCAmelCase_							(							self  )->    Dict:
         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=1_000		,	clip_sample=_lowercase		,	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   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=0  )->    int:
         if str(_lowercase  ).startswith("mps"  ):
             UpperCamelCase_      =	torch.manual_seed(_lowercase  )
         else:
             UpperCamelCase_      =	torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )

         UpperCamelCase_      =	floats_tensor((1, 3, 64, 64)		,	rng=random.Random(_lowercase  )  ).to(_lowercase  )
         UpperCamelCase_      =	image.cpu().permute(0		,	2		,	3		,	1  )[0]
         UpperCamelCase_      =	Image.fromarray(np.uinta(_lowercase  )  ).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   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	"cpu"

         UpperCamelCase_      =	self.get_dummy_components()

         UpperCamelCase_      =	self.pipeline_class(**_lowercase  )
         UpperCamelCase_      =	pipe.to(_lowercase  )

         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	pipe(**self.get_dummy_inputs(_lowercase  )  )
         UpperCamelCase_      =	output.image_embeds

         UpperCamelCase_      =	pipe(
             **self.get_dummy_inputs(_lowercase  )		,	return_dict=_lowercase		,	)[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   UpperCAmelCase_							(							self  )->    List[str]:
         UpperCamelCase_      =	torch_device == "cpu"
         UpperCamelCase_      =	True
         UpperCamelCase_      =	False

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






     @skip_mps
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         UpperCamelCase_      =	torch_device == "cpu"
         UpperCamelCase_      =	False

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



import unittest

from transformers.image_processing_utils import get_size_dict






class 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    int:
         # Test a dict with the wrong keys raises an error
         UpperCamelCase_      =	{"wrong_key": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"height": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"width": 224, "shortest_edge": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         # Test a dict with the correct keys is returned as is
         UpperCamelCase_      =	{"height": 224, "width": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	_lowercase  )

         UpperCamelCase_      =	{"shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         UpperCamelCase_      =	{"longest_edge": 224, "shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"longest_edge": 224, "shortest_edge": 224}  )

         # Test a single int value which  represents (size, size)
         UpperCamelCase_      =	get_size_dict(224  )
         self.assertEqual(_lowercase		,	{"height": 224, "width": 224}  )

         # Test a single int value which represents the shortest edge
         UpperCamelCase_      =	get_size_dict(224		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         # Test a tuple of ints which represents (height, width)
         UpperCamelCase_      =	get_size_dict((150, 200)  )
         self.assertEqual(_lowercase		,	{"height": 150, "width": 200}  )

         # Test a tuple of ints which represents (width, height)
         UpperCamelCase_      =	get_size_dict((150, 200)		,	height_width_order=_lowercase  )
         self.assertEqual(_lowercase		,	{"height": 200, "width": 150}  )

         # Test an int representing the shortest edge and max_size which represents the longest edge
         UpperCamelCase_      =	get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224, "longest_edge": 256}  )

         # Test int with default_to_square=True and max_size fails
         with self.assertRaises(_lowercase  ):
             get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
