





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 SCREAMING_SNAKE_CASE_    (       snake_case_							,					unittest.TestCase       ):
						__magic_name__:      List[Any]			   =							KandinskyPriorPipeline
						__magic_name__:      Optional[Any]			   =							["prompt"]
						__magic_name__:      Any			   =							["prompt", "negative_prompt"]
						__magic_name__:      str			   =							[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						__magic_name__:      Union[str, Any]			   =							False






						@property
						def UpperCAmelCase_					(   self   :   int						)				->     List[Any]:







							"""simple docstring"""


							return 32






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







							"""simple docstring"""


							return 32






						@property
						def UpperCAmelCase_					(   self   :   int						)				->     Optional[Any]:







							"""simple docstring"""


							return self.time_input_dim






						@property
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     Tuple:







							"""simple docstring"""


							return self.time_input_dim * 4






						@property
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Dict:







							"""simple docstring"""


							return 100






						@property
						def UpperCAmelCase_					(   self   :   Optional[int]						)				->     Any:







							"""simple docstring"""


							snake_case_		:      Dict           =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
							return tokenizer






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







							"""simple docstring"""


							torch.manual_seed(0						)
							snake_case_		:      List[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 UpperCAmelCase_					(   self   :   List[Any]						)				->     Any:







							"""simple docstring"""


							torch.manual_seed(0						)

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

							snake_case_		:      Optional[Any]           =						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
							snake_case_		:      Union[str, Any]           =						nn.Parameter(torch.ones(model.clip_std.shape						)						)
							return model






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







							"""simple docstring"""


							torch.manual_seed(0						)
							snake_case_		:      Optional[Any]           =						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		, )

							snake_case_		:      Tuple           =						CLIPVisionModelWithProjection(_A						)
							return model






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







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						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 UpperCAmelCase_					(   self   :   Any						)				->     int:







							"""simple docstring"""


							snake_case_		:      Tuple           =						self.dummy_prior
							snake_case_		:      Optional[int]           =						self.dummy_image_encoder
							snake_case_		:      List[str]           =						self.dummy_text_encoder
							snake_case_		:      List[str]           =						self.dummy_tokenizer
							snake_case_		:      List[str]           =						self.dummy_image_processor

							snake_case_		:      int           =						UnCLIPScheduler(
							    variance_type='fixed_small_log'		, prediction_type='sample'		, num_train_timesteps=1000		, clip_sample=_A		, clip_sample_range=1_0.0		, )

							snake_case_		:      List[str]           =						{
							    'prior': prior,
							    'image_encoder': image_encoder,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'scheduler': scheduler,
							    'image_processor': image_processor,
							}

							return components






						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   Optional[int]		, _A   :   Optional[Any]=0						)				->     List[str]:







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     str:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						'cpu'

							snake_case_		:      Optional[Any]           =						self.get_dummy_components()

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

							pipe.set_progress_bar_config(disable=_A						)

							snake_case_		:      List[Any]           =						pipe(**self.get_dummy_inputs(_A						)						)
							snake_case_		:      List[str]           =						output.image_embeds

							snake_case_		:      Optional[int]           =						pipe(
							    **self.get_dummy_inputs(_A						)		, return_dict=_A		, )[0]

							snake_case_		:      Tuple           =						image[0, -10:]
							snake_case_		:      Union[str, Any]           =						image_from_tuple[0, -10:]

							assert image.shape == (1, 32)

							snake_case_		:      Optional[Any]           =						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 UpperCAmelCase_					(   self   :   List[str]						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						torch_device == 'cpu'
							snake_case_		:      Tuple           =						True
							snake_case_		:      str           =						False

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






						@skip_mps
						def UpperCAmelCase_					(   self   :   List[str]						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      List[str]           =						torch_device == 'cpu'
							snake_case_		:      Tuple           =						False

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


"""simple docstring"""






import numpy as np



def     a_		(   lowerCamelCase   ,     lowerCamelCase="utf-8"   ,     lowerCamelCase="surrogatepass"			):
				UpperCAmelCase__			 =				bin(int.from_bytes(text.encode(lowerCamelCase   ,     lowerCamelCase			)   ,     'big'			)			)[2:]
				return bits.zfill(8 * ((len(lowerCamelCase			) + 7) // 8)			)



def     a_		(   lowerCamelCase   ,     lowerCamelCase="utf-8"   ,     lowerCamelCase="surrogatepass"			):
				UpperCAmelCase__			 =				int(lowerCamelCase   ,     2			)
				return n.to_bytes((n.bit_length() + 7) // 8   ,     'big'			).decode(lowerCamelCase   ,     lowerCamelCase			) or "\0"



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				if size_par + len(lowerCamelCase			) <= 2**size_par - (len(lowerCamelCase			) - 1):
								raise ValueError('size of parity don\'t match with size of data'			)

				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[bin(lowerCamelCase			)[2:] for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			)]

				# sorted information data for the size of the output data
				UpperCAmelCase__			 =				[]
				# data position template + parity
				UpperCAmelCase__			 =				[]
				# parity bit counter
				UpperCAmelCase__			 =				0
				# counter position of data bits
				UpperCAmelCase__			 =				0

				for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			):
								# Performs a template of bit positions - who should be given,
								# and who should be parity
								if qtd_bp < size_par:
												if (np.log(lowerCamelCase			) / np.log(2			)).is_integer():
																data_out_gab.append('P'			)
																UpperCAmelCase__			 =				qtd_bp + 1
												else:
																data_out_gab.append('D'			)
								else:
												data_out_gab.append('D'			)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data[cont_data]			)
												cont_data += 1
								else:
												data_ord.append(lowerCamelCase			)

    # Calculates parity
				UpperCAmelCase__			 =				0  # parity bit counter
				for bp in range(1   ,     size_par + 1			):
								# Bit counter one for a given parity
								UpperCAmelCase__			 =				0
								# counter to control the loop reading
								UpperCAmelCase__			 =				0
								for x in data_ord:
												if x is not None:
																try:
																				UpperCAmelCase__			 =				(bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				UpperCAmelCase__			 =				'0'
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(cont_bo % 2			)

								qtd_bp += 1

				# Mount the message
				UpperCAmelCase__			 =				0  # parity bit counter
				for x in range(0   ,     size_par + len(lowerCamelCase			)			):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]			)			)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]			)

				return data_out



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				[]
				# Parity bit counter
				UpperCAmelCase__			 =				0
				# Counter p data bit reading
				UpperCAmelCase__			 =				0
				# list of parity received
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]

				for x in range(1   ,     len(lowerCamelCase			) + 1			):
								# Performs a template of bit positions - who should be given,
								#  and who should be parity
								if qtd_bp < size_par and (np.log(lowerCamelCase			) / np.log(2			)).is_integer():
												data_out_gab.append('P'			)
												UpperCAmelCase__			 =				qtd_bp + 1
								else:
												data_out_gab.append('D'			)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_output.append(data[cont_data]			)
								else:
												parity_received.append(data[cont_data]			)
								cont_data += 1

				# -----------calculates the parity with the data
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[bin(lowerCamelCase			)[2:] for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			)]

				#  sorted information data for the size of the output data
				UpperCAmelCase__			 =				[]
				# Data position feedback + parity
				UpperCAmelCase__			 =				[]
				# Parity bit counter
				UpperCAmelCase__			 =				0
				# Counter p data bit reading
				UpperCAmelCase__			 =				0

				for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			):
								# Performs a template position of bits - who should be given,
								# and who should be parity
								if qtd_bp < size_par and (np.log(lowerCamelCase			) / np.log(2			)).is_integer():
												data_out_gab.append('P'			)
												UpperCAmelCase__			 =				qtd_bp + 1
								else:
												data_out_gab.append('D'			)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data_output[cont_data]			)
												cont_data += 1
								else:
												data_ord.append(lowerCamelCase			)

    # Calculates parity
				UpperCAmelCase__			 =				0  # parity bit counter
				for bp in range(1   ,     size_par + 1			):
								# Bit counter one for a certain parity
								UpperCAmelCase__			 =				0
								# Counter to control loop reading
								UpperCAmelCase__			 =				0
								for x in data_ord:
												if x is not None:
																try:
																				UpperCAmelCase__			 =				(bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				UpperCAmelCase__			 =				'0'
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(str(cont_bo % 2			)			)

								qtd_bp += 1

				# Mount the message
				UpperCAmelCase__			 =				0  # Parity bit counter
				for x in range(0   ,     size_par + len(lowerCamelCase			)			):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]			)			)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]			)

				UpperCAmelCase__			 =				parity_received == parity
				return data_output, ack



