


'''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_		(					A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       str         =     KandinskyVaaPriorEmbaEmbPipeline
						UpperCAmelCase__	:       int         =     ['''prompt''', '''image''']
						UpperCAmelCase__	:       Optional[Any]         =     ['''prompt''', '''image''']
						UpperCAmelCase__	:       Union[str, Any]         =     [
						    '''num_images_per_prompt''',
						    '''strength''',
						    '''generator''',
						    '''num_inference_steps''',
						    '''latents''',
						    '''negative_prompt''',
						    '''guidance_scale''',
						    '''output_type''',
						    '''return_dict''',
						]
						UpperCAmelCase__	:       Optional[Any]         =     False
						@property
						def 					__A					( self				:					Any  )							->					Any:
										return 32
						@property
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										return 32
						@property
						def 					__A					( self				:					Optional[Any]  )							->					List[str]:
										return self.time_input_dim
						@property
						def 					__A					( self				:					Union[str, Any]  )							->					Optional[Any]:
										return self.time_input_dim * 4
						@property
						def 					__A					( self				:					Any  )							->					Optional[int]:
										return 1_00
						@property
						def 					__A					( self				:					Optional[Any]  )							->					Dict:
										__magic_name__	  = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )
										return tokenizer
						@property
						def 					__A					( self				:					List[str]  )							->					str:
										torch.manual_seed(0  )
										__magic_name__	  = 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=10_00	,				)
										return CLIPTextModelWithProjection(_lowerCamelCase  )
						@property
						def 					__A					( self				:					Union[str, Any]  )							->					Dict:
										torch.manual_seed(0  )

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

										__magic_name__	  = 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
										__magic_name__	  = nn.Parameter(torch.ones(model.clip_std.shape  )  )
										return model
						@property
						def 					__A					( self				:					Union[str, Any]  )							->					Optional[int]:
										torch.manual_seed(0  )
										__magic_name__	  = CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size	,				image_size=2_24	,				projection_dim=self.text_embedder_hidden_size	,				intermediate_size=37	,				num_attention_heads=4	,				num_channels=3	,				num_hidden_layers=5	,				patch_size=14	,				)

										__magic_name__	  = CLIPVisionModelWithProjection(_lowerCamelCase  )
										return model
						@property
						def 					__A					( self				:					Any  )							->					Optional[Any]:
										__magic_name__	  = CLIPImageProcessor(
										    crop_size=2_24	,				do_center_crop=_lowerCamelCase	,				do_normalize=_lowerCamelCase	,				do_resize=_lowerCamelCase	,				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=2_24	,				)

										return image_processor
						def 					__A					( self				:					Any  )							->					Optional[Any]:
										__magic_name__	  = self.dummy_prior
										__magic_name__	  = self.dummy_image_encoder
										__magic_name__	  = self.dummy_text_encoder
										__magic_name__	  = self.dummy_tokenizer
										__magic_name__	  = self.dummy_image_processor

										__magic_name__	  = UnCLIPScheduler(
										    variance_type="fixed_small_log"	,				prediction_type="sample"	,				num_train_timesteps=10_00	,				clip_sample=_lowerCamelCase	,				clip_sample_range=10.0	,				)

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

										return components
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Tuple=0  )							->					List[str]:
										if str(_lowerCamelCase  ).startswith("mps"  ):
														__magic_name__	  = torch.manual_seed(_lowerCamelCase  )
										else:
														__magic_name__	  = torch.Generator(device=_lowerCamelCase  ).manual_seed(_lowerCamelCase  )

										__magic_name__	  = floats_tensor((1, 3, 64, 64)	,				rng=random.Random(_lowerCamelCase  )  ).to(_lowerCamelCase  )
										__magic_name__	  = image.cpu().permute(0	,				2	,				3	,				1  )[0]
										__magic_name__	  = Image.fromarray(np.uinta(_lowerCamelCase  )  ).convert("RGB"  ).resize((2_56, 2_56)  )

										__magic_name__	  = {
										    "prompt": "horse",
										    "image": init_image,
										    "strength": 0.5,
										    "generator": generator,
										    "guidance_scale": 4.0,
										    "num_inference_steps": 2,
										    "output_type": "np",
										}
										return inputs
						def 					__A					( self				:					Union[str, Any]  )							->					Tuple:
										__magic_name__	  = "cpu"

										__magic_name__	  = self.get_dummy_components()

										__magic_name__	  = self.pipeline_class(**_lowerCamelCase  )
										__magic_name__	  = pipe.to(_lowerCamelCase  )

										pipe.set_progress_bar_config(disable=_lowerCamelCase  )

										__magic_name__	  = pipe(**self.get_dummy_inputs(_lowerCamelCase  )  )
										__magic_name__	  = output.image_embeds

										__magic_name__	  = pipe(
										    **self.get_dummy_inputs(_lowerCamelCase  )	,				return_dict=_lowerCamelCase	,				)[0]

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

										assert image.shape == (1, 32)

										__magic_name__	  = 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 					__A					( self				:					Optional[int]  )							->					int:
										__magic_name__	  = torch_device == "cpu"
										__magic_name__	  = True
										__magic_name__	  = False

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

						@skip_mps
						def 					__A					( self				:					List[Any]  )							->					str:
										__magic_name__	  = torch_device == "cpu"
										__magic_name__	  = False

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







'''simple docstring'''
import numpy as np

__magic_name__   :   str  							=[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]  )							->					None:
										__magic_name__	  = np.array(_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					str  )							->					np.ndarray:
										__magic_name__  ,      __magic_name__	  = np.where(letter == self.SQUARE  )
										__magic_name__	  = np.concatenate([indexa + 1, indexa + 1]  )
										return indexes
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					int  )							->					str:
										return self.SQUARE[indexa - 1, indexa - 1]
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.lower()
										__magic_name__	  = message.replace("j"	,				"i"  )

										__magic_name__	  = ""
										for letter_index in range(len(_lowerCamelCase  )  ):
														if message[letter_index] != " ":
																		__magic_name__	  = self.letter_to_numbers(message[letter_index]  )
																		__magic_name__	  = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
														elif message[letter_index] == " ":
																		__magic_name__	  = encoded_message + " "

										return encoded_message

						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.replace(" "	,				"  "  )
										__magic_name__	  = ""
										for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
														if message[numbers_index * 2] != " ":
																		__magic_name__	  = message[numbers_index * 2]
																		__magic_name__	  = message[numbers_index * 2 + 1]

																		__magic_name__	  = self.numbers_to_letter(int(_lowerCamelCase  )	,				int(_lowerCamelCase  )  )
																		__magic_name__	  = decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		__magic_name__	  = decoded_message + " "

										return decoded_message



