


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     lowerCamelCase__		(							snake_case__   ,		unittest.TestCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Union[str, Any]					=						KandinskyVaaPriorEmbaEmbPipeline
			_UpperCamelCase						:   str					=						["prompt", "image"]
			_UpperCamelCase						:   Any					=						["prompt", "image"]
			_UpperCamelCase						:   Tuple					=						[
			    "num_images_per_prompt",
			    "strength",
			    "generator",
			    "num_inference_steps",
			    "latents",
			    "negative_prompt",
			    "guidance_scale",
			    "output_type",
			    "return_dict",
			]
			_UpperCamelCase						:   Any					=						False




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return 32




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return 32




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return self.time_input_dim




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return self.time_input_dim * 4




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return 100




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			@property
			def 			snake_case__			(							self       ):

						'''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(snake_case       )




			@property
			def 			snake_case__			(							self       ):

						'''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(**snake_case       )
						# 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 			snake_case__			(							self       ):

						'''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(snake_case       )
						return model




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    CLIPImageProcessor(
						    crop_size=224 ,  do_center_crop=snake_case ,  do_normalize=snake_case ,  do_resize=snake_case ,  image_mean=[0.48145466, 0.4578275, 0.40821073] ,  image_std=[0.26862954, 0.26130258, 0.27577711] ,  resample=3 ,  size=224 ,  )

						return image_processor




			def 			snake_case__			(							self       ):

						'''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=snake_case ,  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 			snake_case__			(							self ,  snake_case ,  snake_case=0       ):

						'''simple docstring'''


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

						UpperCamelCase__   =    floats_tensor((1, 3, 64, 64) ,  rng=random.Random(snake_case       )       ).to(snake_case       )
						UpperCamelCase__   =    image.cpu().permute(0 ,  2 ,  3 ,  1       )[0]
						UpperCamelCase__   =    Image.fromarray(np.uinta(snake_case       )       ).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 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    "cpu"

						UpperCamelCase__   =    self.get_dummy_components()

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

						pipe.set_progress_bar_config(disable=snake_case       )

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

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

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

						assert image.shape == (1, 32)

						UpperCamelCase__   =    np.array(
						    [
						        0.1071284,
						        1.3330271,
						        0.61260223,
						        -0.6691065,
						        -0.3846852,
						        -1.0303661,
						        0.22716111,
						        0.03348901,
						        0.30040675,
						        -0.24805029,
						    ]       )

						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       ):

						'''simple docstring'''


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

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







			@skip_mps
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    torch_device == "cpu"
						UpperCamelCase__   =    False

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






import numpy as np
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:Dict="utf-8"			,       _A				:Optional[Any]="surrogatepass"					)->						int:
			UpperCamelCase__   =    bin(int.from_bytes(text.encode(_A			,       _A					)			,       "big"					)					)[2:]
			return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:int="utf-8"			,       _A				:List[str]="surrogatepass"					)->						Optional[Any]:
			UpperCamelCase__   =    int(_A			,       2					)
			return n.to_bytes((n.bit_length() + 7) // 8			,       "big"					).decode(_A			,       _A					) or "\0"
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:Tuple					)->						Optional[int]:
			if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
						raise ValueError("size of parity don't match with size of data"					)

			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 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(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(_A					) / 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(_A					)

    # 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(_A					)					):
						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     UpperCamelCase_(			_A				:int			,       _A				:str					)->						Dict:
			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(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / 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(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 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(_A					) + 1					):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / 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(_A					)

    # 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(_A					)					):
						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


