



import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


__A       =logging.get_logger(__name__)  # pylint: disable=invalid-name

def 			a		( self				:   Tuple					,  _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Optional[int]		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      image.to(device=_UpperCAmelCase		)
		__UpperCAmelCase       :	List[str]						=      image  # take image as image_embeddings
		__UpperCAmelCase       :	List[Any]						=      image_embeddings.unsqueeze(1		)

		# duplicate image embeddings for each generation per prompt, using mps friendly method
		__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase       :	int						=      image_embeddings.shape
		__UpperCAmelCase       :	List[str]						=      image_embeddings.repeat(1					,  _UpperCAmelCase					,  1		)
		__UpperCAmelCase       :	Tuple						=      image_embeddings.view(bs_embed * num_images_per_prompt					,  _UpperCAmelCase					,  -1		)

		if do_classifier_free_guidance:
				__UpperCAmelCase       :	Dict						=      torch.zeros_like(_UpperCAmelCase		)

				# For classifier free guidance, we need to do two forward passes.
				# Here we concatenate the unconditional and text embeddings into a single batch
				# to avoid doing two forward passes
				__UpperCAmelCase       :	Union[str, Any]						=      torch.cat([uncond_embeddings, image_embeddings]		)

		return image_embeddings

class 						UpperCAmelCase__     (     __UpperCamelCase  ):




			'''simple docstring'''






			def __init__(  self				:					Union[str, Any]			,			a_				:					PriorTransformer			,			a_				:					CLIPTokenizer			,			a_				:					CLIPTextModelWithProjection			,			a_				:					UnCLIPScheduler			,			a_				:					Optional[dict] = None			,			):
					'''simple docstring'''





					super().__init__()

					__UpperCAmelCase       :	Dict						=      {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

					__UpperCAmelCase       :	List[str]						=      decoder_pipe_kwargs.get('''torch_dtype'''			,			a_					) or prior.dtype

					__UpperCAmelCase       :	List[Any]						=      StableDiffusionImageVariationPipeline.from_pretrained(
					    '''lambdalabs/sd-image-variations-diffusers'''			,			**a_					)

					# replace `_encode_image` method
					__UpperCAmelCase       :	Optional[Any]						=      types.MethodType(_encode_image			,			self.decoder_pipe					)

					self.register_modules(
					    prior=a_			,			tokenizer=a_			,			text_encoder=a_			,			prior_scheduler=a_			,			)




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					Optional[int]			,			a_				:					Union[str, Any]			,			a_				:					int			,			a_				:					List[Any]			,			a_				:					Optional[Union[CLIPTextModelOutput, Tuple]] = None			,			a_				:					Optional[torch.Tensor] = None			,			):
					'''simple docstring'''





					if text_model_output is None:
							__UpperCAmelCase       :	Optional[int]						=      len(a_					) if isinstance(a_			,			a_					) else 1
							# get prompt text embeddings
							__UpperCAmelCase       :	Optional[int]						=      self.tokenizer(
							    a_			,			padding='''max_length'''			,			max_length=self.tokenizer.model_max_length			,			return_tensors='''pt'''			,			)
							__UpperCAmelCase       :	str						=      text_inputs.input_ids
							__UpperCAmelCase       :	Tuple						=      text_inputs.attention_mask.bool().to(a_					)

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									__UpperCAmelCase       :	Tuple						=      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
									logger.warning(
									    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
									    F' {self.tokenizer.model_max_length} tokens: {removed_text}'					)
									__UpperCAmelCase       :	Union[str, Any]						=      text_input_ids[:, : self.tokenizer.model_max_length]

							__UpperCAmelCase       :	Any						=      self.text_encoder(text_input_ids.to(a_					)					)

							__UpperCAmelCase       :	List[str]						=      text_encoder_output.text_embeds
							__UpperCAmelCase       :	Union[str, Any]						=      text_encoder_output.last_hidden_state

					else:
							__UpperCAmelCase       :	Any						=      text_model_output[0].shape[0]
							__UpperCAmelCase				,   __UpperCAmelCase       :	Optional[int]						=      text_model_output[0], text_model_output[1]
							__UpperCAmelCase       :	Optional[Any]						=      text_attention_mask

					__UpperCAmelCase       :	List[Any]						=      text_embeddings.repeat_interleave(a_			,			dim=0					)
					__UpperCAmelCase       :	Optional[Any]						=      text_encoder_hidden_states.repeat_interleave(a_			,			dim=0					)
					__UpperCAmelCase       :	Union[str, Any]						=      text_mask.repeat_interleave(a_			,			dim=0					)

					if do_classifier_free_guidance:
							__UpperCAmelCase       :	int						=      [''''''] * batch_size

							__UpperCAmelCase       :	Optional[int]						=      self.tokenizer(
							    a_			,			padding='''max_length'''			,			max_length=self.tokenizer.model_max_length			,			truncation=a_			,			return_tensors='''pt'''			,			)
							__UpperCAmelCase       :	List[Any]						=      uncond_input.attention_mask.bool().to(a_					)
							__UpperCAmelCase       :	int						=      self.text_encoder(uncond_input.input_ids.to(a_					)					)

							__UpperCAmelCase       :	str						=      uncond_embeddings_text_encoder_output.text_embeds
							__UpperCAmelCase       :	Optional[Any]						=      uncond_embeddings_text_encoder_output.last_hidden_state

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

							__UpperCAmelCase       :	Union[str, Any]						=      uncond_embeddings.shape[1]
							__UpperCAmelCase       :	Dict						=      uncond_embeddings.repeat(1			,			a_					)
							__UpperCAmelCase       :	Any						=      uncond_embeddings.view(batch_size * num_images_per_prompt			,			a_					)

							__UpperCAmelCase       :	Any						=      uncond_text_encoder_hidden_states.shape[1]
							__UpperCAmelCase       :	int						=      uncond_text_encoder_hidden_states.repeat(1			,			a_			,			1					)
							__UpperCAmelCase       :	Union[str, Any]						=      uncond_text_encoder_hidden_states.view(
							    batch_size * num_images_per_prompt			,			a_			,			-1					)
							__UpperCAmelCase       :	Union[str, Any]						=      uncond_text_mask.repeat_interleave(a_			,			dim=0					)

							# done duplicates

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							__UpperCAmelCase       :	str						=      torch.cat([uncond_embeddings, text_embeddings]					)
							__UpperCAmelCase       :	Tuple						=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

							__UpperCAmelCase       :	str						=      torch.cat([uncond_text_mask, text_mask]					)

					return text_embeddings, text_encoder_hidden_states, text_mask




			@property
			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					if self.device != torch.device('''meta'''					) or not hasattr(self.prior			,			'''_hf_hook'''					):
							return self.device
					for module in self.prior.modules():
							if (
							    hasattr(a_			,			'''_hf_hook'''					)
							    and hasattr(module._hf_hook			,			'''execution_device'''					)
							    and module._hf_hook.execution_device is not None
							):
									return torch.device(module._hf_hook.execution_device					)
					return self.device




			def 					snake_case__	(  self				:					Tuple			,			a_				:					str			,			a_				:					Union[str, Any]			,			a_				:					Dict			,			a_				:					Optional[int]			,			a_				:					Optional[int]			,			a_				:					Optional[Any]					):
					'''simple docstring'''





					if latents is None:
							__UpperCAmelCase       :	Tuple						=      randn_tensor(a_			,			generator=a_			,			device=a_			,			dtype=a_					)
					else:
							if latents.shape != shape:
									raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'					)
							__UpperCAmelCase       :	Dict						=      latents.to(a_					)

					__UpperCAmelCase       :	List[str]						=      latents * scheduler.init_noise_sigma
					return latents




			def 					snake_case__	(  self				:					int			,			a_				:					Optional[Union[str, torch.device]] = None					):
					'''simple docstring'''





					self.decoder_pipe.to(a_					)
					super().to(a_					)




			@torch.no_grad()
			def __call__(  self				:					Optional[Any]			,			a_				:					Optional[Union[str, List[str]]] = None			,			a_				:					Optional[int] = None			,			a_				:					Optional[int] = None			,			a_				:					int = 1			,			a_				:					int = 25			,			a_				:					Optional[torch.Generator] = None			,			a_				:					Optional[torch.FloatTensor] = None			,			a_				:					Optional[Union[CLIPTextModelOutput, Tuple]] = None			,			a_				:					Optional[torch.Tensor] = None			,			a_				:					float = 4.0			,			a_				:					float = 8.0			,			a_				:					int = 50			,			a_				:					Optional[int] = 1			,			a_				:					float = 0.0			,			a_				:					Optional[str] = "pil"			,			a_				:					bool = True			,			):
					'''simple docstring'''





					if prompt is not None:
							if isinstance(a_			,			a_					):
									__UpperCAmelCase       :	Optional[Any]						=      1
							elif isinstance(a_			,			a_					):
									__UpperCAmelCase       :	Optional[int]						=      len(a_					)
							else:
									raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(a_					)}'					)
					else:
							__UpperCAmelCase       :	Union[str, Any]						=      text_model_output[0].shape[0]

					__UpperCAmelCase       :	List[str]						=      self._execution_device

					__UpperCAmelCase       :	Optional[Any]						=      batch_size * num_images_per_prompt

					__UpperCAmelCase       :	Dict						=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

					__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase       :	int						=      self._encode_prompt(
					    a_			,			a_			,			a_			,			a_			,			a_			,			a_					)

					# prior

					self.prior_scheduler.set_timesteps(a_			,			device=a_					)
					__UpperCAmelCase       :	List[str]						=      self.prior_scheduler.timesteps

					__UpperCAmelCase       :	int						=      self.prior.config.embedding_dim

					__UpperCAmelCase       :	Union[str, Any]						=      self.prepare_latents(
					    (batch_size, embedding_dim)			,			text_embeddings.dtype			,			a_			,			a_			,			a_			,			self.prior_scheduler			,			)

					for i, t in enumerate(self.progress_bar(a_					)					):
							# expand the latents if we are doing classifier free guidance
							__UpperCAmelCase       :	Tuple						=      torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents

							__UpperCAmelCase       :	Tuple						=      self.prior(
							    a_			,			timestep=a_			,			proj_embedding=a_			,			encoder_hidden_states=a_			,			attention_mask=a_			,			).predicted_image_embedding

							if do_classifier_free_guidance:
									__UpperCAmelCase				,   __UpperCAmelCase       :	List[str]						=      predicted_image_embedding.chunk(2					)
									__UpperCAmelCase       :	str						=      predicted_image_embedding_uncond + prior_guidance_scale * (
									    predicted_image_embedding_text - predicted_image_embedding_uncond
									)

							if i + 1 == prior_timesteps_tensor.shape[0]:
									__UpperCAmelCase       :	str						=      None
							else:
									__UpperCAmelCase       :	Union[str, Any]						=      prior_timesteps_tensor[i + 1]

							__UpperCAmelCase       :	Tuple						=      self.prior_scheduler.step(
							    a_			,			timestep=a_			,			sample=a_			,			generator=a_			,			prev_timestep=a_			,			).prev_sample

					__UpperCAmelCase       :	Optional[Any]						=      self.prior.post_process_latents(a_					)

					__UpperCAmelCase       :	int						=      prior_latents

					__UpperCAmelCase       :	str						=      self.decoder_pipe(
					    image=a_			,			height=a_			,			width=a_			,			num_inference_steps=a_			,			guidance_scale=a_			,			generator=a_			,			output_type=a_			,			return_dict=a_			,			num_images_per_prompt=a_			,			eta=a_			,			)
					return output










import numpy as np

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   List[Any]="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	Union[str, Any]						=      bin(int.from_bytes(text.encode(_UpperCAmelCase					,  _UpperCAmelCase		)					,  '''big'''		)		)[2:]
		return bits.zfill(8 * ((len(_UpperCAmelCase		) + 7) // 8)		)

def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   str="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      int(_UpperCAmelCase					,  2		)
		return n.to_bytes((n.bit_length() + 7) // 8					,  '''big'''		).decode(_UpperCAmelCase					,  _UpperCAmelCase		) or "\0"

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





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

		__UpperCAmelCase       :	str						=      []
		__UpperCAmelCase       :	Any						=      []
		__UpperCAmelCase       :	Any						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		# sorted information data for the size of the output data
		__UpperCAmelCase       :	Optional[Any]						=      []
		# data position template + parity
		__UpperCAmelCase       :	Any						=      []
		# parity bit counter
		__UpperCAmelCase       :	Tuple						=      0
		# counter position of data bits
		__UpperCAmelCase       :	Optional[int]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
								data_out_gab.append('''P'''		)
								__UpperCAmelCase       :	Union[str, Any]						=      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(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a given parity
				__UpperCAmelCase       :	List[str]						=      0
				# counter to control the loop reading
				__UpperCAmelCase       :	str						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	Tuple						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	List[str]						=      '''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       :	int						=      0  # parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				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		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	str						=      []
		# Parity bit counter
		__UpperCAmelCase       :	List[str]						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	int						=      0
		# list of parity received
		__UpperCAmelCase       :	int						=      []
		__UpperCAmelCase       :	List[Any]						=      []

		for x in range(1					,  len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      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       :	Optional[int]						=      []
		__UpperCAmelCase       :	Dict						=      []
		__UpperCAmelCase       :	Union[str, Any]						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		#  sorted information data for the size of the output data
		__UpperCAmelCase       :	str						=      []
		# Data position feedback + parity
		__UpperCAmelCase       :	Any						=      []
		# Parity bit counter
		__UpperCAmelCase       :	int						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	List[Any]						=      0

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

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a certain parity
				__UpperCAmelCase       :	Union[str, Any]						=      0
				# Counter to control loop reading
				__UpperCAmelCase       :	Optional[Any]						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	int						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	int						=      '''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       :	Any						=      0  # Parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		__UpperCAmelCase       :	Optional[Any]						=      parity_received == parity
		return data_output, ack





