




'''simple docstring'''







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


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






def 						_lowercase						(      self		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			image.to(device=lowercase_    )
		_snake_case      			=			image  # take image as image_embeddings
		_snake_case      			=			image_embeddings.unsqueeze(1    )

		# duplicate image embeddings for each generation per prompt, using mps friendly method
		_snake_case			,       _snake_case			,       _snake_case      			=			image_embeddings.shape
		_snake_case      			=			image_embeddings.repeat(1		,				lowercase_		,				1    )
		_snake_case      			=			image_embeddings.view(bs_embed * num_images_per_prompt		,				lowercase_		,				-1    )

		if do_classifier_free_guidance:
				_snake_case      			=			torch.zeros_like(lowercase_    )

				# 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
				_snake_case      			=			torch.cat([uncond_embeddings, image_embeddings]    )

		return image_embeddings





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def __init__(       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = None,    ):
							super().__init__()

							_snake_case      			=			{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

							_snake_case      			=			decoder_pipe_kwargs.get("torch_dtype",    __UpperCamelCase					) or prior.dtype

							_snake_case      			=			StableDiffusionImageVariationPipeline.from_pretrained(
							    "lambdalabs/sd-image-variations-diffusers",    **__UpperCamelCase					)

							# replace `_encode_image` method
							_snake_case      			=			types.MethodType(_encode_image,    self.decoder_pipe					)

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






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = None,    ):
							if text_model_output is None:
									_snake_case      			=			len(__UpperCamelCase					) if isinstance(__UpperCamelCase,    __UpperCamelCase					) else 1
									# get prompt text embeddings
									_snake_case      			=			self.tokenizer(
									    __UpperCamelCase,    padding="max_length",    max_length=self.tokenizer.model_max_length,    return_tensors="pt",    )
									_snake_case      			=			text_inputs.input_ids
									_snake_case      			=			text_inputs.attention_mask.bool().to(__UpperCamelCase					)

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
											_snake_case      			=			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}'''					)
											_snake_case      			=			text_input_ids[:, : self.tokenizer.model_max_length]

									_snake_case      			=			self.text_encoder(text_input_ids.to(__UpperCamelCase					)					)

									_snake_case      			=			text_encoder_output.text_embeds
									_snake_case      			=			text_encoder_output.last_hidden_state

							else:
									_snake_case      			=			text_model_output[0].shape[0]
									_snake_case			,       _snake_case      			=			text_model_output[0], text_model_output[1]
									_snake_case      			=			text_attention_mask

							_snake_case      			=			text_embeddings.repeat_interleave(__UpperCamelCase,    dim=0					)
							_snake_case      			=			text_encoder_hidden_states.repeat_interleave(__UpperCamelCase,    dim=0					)
							_snake_case      			=			text_mask.repeat_interleave(__UpperCamelCase,    dim=0					)

							if do_classifier_free_guidance:
									_snake_case      			=			[""] * batch_size

									_snake_case      			=			self.tokenizer(
									    __UpperCamelCase,    padding="max_length",    max_length=self.tokenizer.model_max_length,    truncation=__UpperCamelCase,    return_tensors="pt",    )
									_snake_case      			=			uncond_input.attention_mask.bool().to(__UpperCamelCase					)
									_snake_case      			=			self.text_encoder(uncond_input.input_ids.to(__UpperCamelCase					)					)

									_snake_case      			=			uncond_embeddings_text_encoder_output.text_embeds
									_snake_case      			=			uncond_embeddings_text_encoder_output.last_hidden_state

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

									_snake_case      			=			uncond_embeddings.shape[1]
									_snake_case      			=			uncond_embeddings.repeat(1,    __UpperCamelCase					)
									_snake_case      			=			uncond_embeddings.view(batch_size * num_images_per_prompt,    __UpperCamelCase					)

									_snake_case      			=			uncond_text_encoder_hidden_states.shape[1]
									_snake_case      			=			uncond_text_encoder_hidden_states.repeat(1,    __UpperCamelCase,    1					)
									_snake_case      			=			uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt,    __UpperCamelCase,    -1					)
									_snake_case      			=			uncond_text_mask.repeat_interleave(__UpperCamelCase,    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
									_snake_case      			=			torch.cat([uncond_embeddings, text_embeddings]					)
									_snake_case      			=			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

									_snake_case      			=			torch.cat([uncond_text_mask, text_mask]					)

							return text_embeddings, text_encoder_hidden_states, text_mask






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							if self.device != torch.device("meta"					) or not hasattr(self.prior,    "_hf_hook"					):
									return self.device
							for module in self.prior.modules():
									if (
									    hasattr(__UpperCamelCase,    "_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 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					):
							if latents is None:
									_snake_case      			=			randn_tensor(__UpperCamelCase,    generator=__UpperCamelCase,    device=__UpperCamelCase,    dtype=__UpperCamelCase					)
							else:
									if latents.shape != shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
									_snake_case      			=			latents.to(__UpperCamelCase					)

							_snake_case      			=			latents * scheduler.init_noise_sigma
							return latents






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase = None					):
							self.decoder_pipe.to(__UpperCamelCase					)
							super().to(__UpperCamelCase					)






					@torch.no_grad()
					def __call__(       self,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = 1,    __UpperCamelCase = 25,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = 4.0,    __UpperCamelCase = 8.0,    __UpperCamelCase = 50,    __UpperCamelCase = 1,    __UpperCamelCase = 0.0,    __UpperCamelCase = "pil",    __UpperCamelCase = True,    ):
							if prompt is not None:
									if isinstance(__UpperCamelCase,    __UpperCamelCase					):
											_snake_case      			=			1
									elif isinstance(__UpperCamelCase,    __UpperCamelCase					):
											_snake_case      			=			len(__UpperCamelCase					)
									else:
											raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase					)}'''					)
							else:
									_snake_case      			=			text_model_output[0].shape[0]

							_snake_case      			=			self._execution_device

							_snake_case      			=			batch_size * num_images_per_prompt

							_snake_case      			=			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

							_snake_case			,       _snake_case			,       _snake_case      			=			self._encode_prompt(
							    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)

							# prior

							self.prior_scheduler.set_timesteps(__UpperCamelCase,    device=__UpperCamelCase					)
							_snake_case      			=			self.prior_scheduler.timesteps

							_snake_case      			=			self.prior.config.embedding_dim

							_snake_case      			=			self.prepare_latents(
							    (batch_size, embedding_dim),    text_embeddings.dtype,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    self.prior_scheduler,    )

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

									_snake_case      			=			self.prior(
									    __UpperCamelCase,    timestep=__UpperCamelCase,    proj_embedding=__UpperCamelCase,    encoder_hidden_states=__UpperCamelCase,    attention_mask=__UpperCamelCase,    ).predicted_image_embedding

									if do_classifier_free_guidance:
											_snake_case			,       _snake_case      			=			predicted_image_embedding.chunk(2					)
											_snake_case      			=			predicted_image_embedding_uncond + prior_guidance_scale * (
											    predicted_image_embedding_text - predicted_image_embedding_uncond
											)

									if i + 1 == prior_timesteps_tensor.shape[0]:
											_snake_case      			=			None
									else:
											_snake_case      			=			prior_timesteps_tensor[i + 1]

									_snake_case      			=			self.prior_scheduler.step(
									    __UpperCamelCase,    timestep=__UpperCamelCase,    sample=__UpperCamelCase,    generator=__UpperCamelCase,    prev_timestep=__UpperCamelCase,    ).prev_sample

							_snake_case      			=			self.prior.post_process_latents(__UpperCamelCase					)

							_snake_case      			=			prior_latents

							_snake_case      			=			self.decoder_pipe(
							    image=__UpperCamelCase,    height=__UpperCamelCase,    width=__UpperCamelCase,    num_inference_steps=__UpperCamelCase,    guidance_scale=__UpperCamelCase,    generator=__UpperCamelCase,    output_type=__UpperCamelCase,    return_dict=__UpperCamelCase,    num_images_per_prompt=__UpperCamelCase,    eta=__UpperCamelCase,    )
							return output











'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_		,				lowercase_="utf-8"		,				lowercase_="surrogatepass"    ):



		'''simple docstring'''







		_snake_case      			=			bin(int.from_bytes(text.encode(lowercase_		,				lowercase_    )		,				"big"    )    )[2:]
		return bits.zfill(8 * ((len(lowercase_    ) + 7) // 8)    )






def 						_lowercase						(      lowercase_		,				lowercase_="utf-8"		,				lowercase_="surrogatepass"    ):



		'''simple docstring'''







		_snake_case      			=			int(lowercase_		,				2    )
		return n.to_bytes((n.bit_length() + 7) // 8		,				"big"    ).decode(lowercase_		,				lowercase_    ) or "\0"






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







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

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

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

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

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

				qtd_bp += 1

		# Mount the message
		_snake_case      			=			0  # parity bit counter
		for x in range(0		,				size_par + len(lowercase_    )    ):
				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 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			[]
		# Parity bit counter
		_snake_case      			=			0
		# Counter p data bit reading
		_snake_case      			=			0
		# list of parity received
		_snake_case      			=			[]
		_snake_case      			=			[]

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

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

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

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

		_snake_case      			=			parity_received == parity
		return data_output, ack





