


"""simple docstring"""


import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


a_											=     [nn.LayerNorm]

a_											=     logging.get_logger(__name__)

a_											=     version.parse(version.parse(torch.__version__).base_version)

a_											=     parsed_torch_version_base >= version.parse("2.0")
a_											=     parsed_torch_version_base >= version.parse("1.12")
a_											=     parsed_torch_version_base >= version.parse("1.11")
a_											=     parsed_torch_version_base < version.parse("1.11")






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      self )						->						Any:
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(__lowercase  ,      __lowercase  ,      parent.dim  ,      self )
				else:
								return _softmax_backward_data(__lowercase  ,      __lowercase  ,      parent.dim  ,      self.dtype )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase = 0 )						->						nn.Linear:
				_A       	=      index.to(layer.weight.device )
				_A       	=      layer.weight.index_select(__lowercase  ,      __lowercase ).clone().detach()
				if layer.bias is not None:
								if dim == 1:
												_A       	=      layer.bias.clone().detach()
								else:
												_A       	=      layer.bias[index].clone().detach()
				_A       	=      list(layer.weight.size() )
				_A       	=      len(__lowercase )
				_A       	=      nn.Linear(new_size[1]  ,      new_size[0]  ,      bias=layer.bias is not None ).to(layer.weight.device )
				_A       	=      False
				new_layer.weight.copy_(W.contiguous() )
				_A       	=      True
				if layer.bias is not None:
								_A       	=      False
								new_layer.bias.copy_(b.contiguous() )
								_A       	=      True
				return new_layer
class snake_case   (	nn.Module):







		def __init__(       self   :							Any     , a__   :							List[Any]     , a__   :							Dict		)							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()
						_A       	=      nf
						_A       	=      nn.Parameter(torch.empty(a__     , a__		)		)
						_A       	=      nn.Parameter(torch.zeros(a__		)		)
						nn.init.normal_(self.weight     , std=0.0_2		)







		def 			a_			(       self   :							int     , a__   :							Dict		)							->    Tuple:





						'''simple docstring'''







						_A       	=      x.size()[:-1] + (self.nf,)
						_A       	=      torch.addmm(self.bias     , x.view(-1     , x.size(-1		)		)     , self.weight		)
						_A       	=      x.view(a__		)
						return x






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase = 1 )						->						ConvaD:
				_A       	=      index.to(layer.weight.device )
				_A       	=      layer.weight.index_select(__lowercase  ,      __lowercase ).clone().detach()
				if dim == 0:
								_A       	=      layer.bias.clone().detach()
				else:
								_A       	=      layer.bias[index].clone().detach()
				_A       	=      list(layer.weight.size() )
				_A       	=      len(__lowercase )
				_A       	=      ConvaD(new_size[1]  ,      new_size[0] ).to(layer.weight.device )
				_A       	=      False
				new_layer.weight.copy_(W.contiguous() )
				_A       	=      True
				_A       	=      False
				new_layer.bias.copy_(b.contiguous() )
				_A       	=      True
				return new_layer






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase = None )						->						Union[nn.Linear, ConvaD]:
				if isinstance(__lowercase  ,      nn.Linear ):
								return prune_linear_layer(__lowercase  ,      __lowercase  ,      dim=0 if dim is None else dim )
				elif isinstance(__lowercase  ,      __lowercase ):
								return prune_convad_layer(__lowercase  ,      __lowercase  ,      dim=1 if dim is None else dim )
				else:
								raise ValueError(f"""Can't prune layer of class {layer.__class__}""" )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      *__lowercase )						->						torch.Tensor:
				assert len(__lowercase ) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

				# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
				_A       	=      len(inspect.signature(__lowercase ).parameters )
				if num_args_in_forward_chunk_fn != len(__lowercase ):
								raise ValueError(
								    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowercase )} input """
								    "tensors are given" )

				if chunk_size > 0:
								_A       	=      input_tensors[0].shape[chunk_dim]
								for input_tensor in input_tensors:
												if input_tensor.shape[chunk_dim] != tensor_shape:
																raise ValueError(
																    f"""All input tenors have to be of the same shape: {tensor_shape}, """
																    f"""found shape {input_tensor.shape[chunk_dim]}""" )

								if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
												raise ValueError(
												    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
												    f"""size {chunk_size}""" )

								_A       	=      input_tensors[0].shape[chunk_dim] // chunk_size

								# chunk input tensor into tuples
								_A       	=      tuple(input_tensor.chunk(__lowercase  ,      dim=__lowercase ) for input_tensor in input_tensors )
								# apply forward fn to every tuple
								_A       	=      tuple(forward_fn(*__lowercase ) for input_tensors_chunk in zip(*__lowercase ) )
								# concatenate output at same dimension
								return torch.cat(__lowercase  ,      dim=__lowercase )

				return forward_fn(*__lowercase )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple[Set[int], torch.LongTensor]:
				_A       	=      torch.ones(__lowercase  ,      __lowercase )
				_A       	=      set(__lowercase ) - already_pruned_heads  # Convert to set and remove already pruned heads
				for head in heads:
								# Compute how many pruned heads are before the head and move the index accordingly
								_A       	=      head - sum(1 if h < head else 0 for h in already_pruned_heads )
								_A       	=      0
				_A       	=      mask.view(-1 ).contiguous().eq(1 )
				_A       	=      torch.arange(len(__lowercase ) )[mask].long()
				return heads, index






def   a__			(					*__lowercase  ,      __lowercase = None )						->						Tuple[torch.Tensor, ...]:
				return torch.meshgrid(*__lowercase  ,      indexing=__lowercase )






def   a__			(					__lowercase )						->						Tuple[torch.device, int, int]:
				return tensor.device, storage_ptr(__lowercase ), storage_size(__lowercase )



"""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


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






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

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

				if do_classifier_free_guidance:
								_A       	=      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
								_A       	=      torch.cat([uncond_embeddings, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

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

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

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

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

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







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







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

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

										_A       	=      self.text_encoder(text_input_ids.to(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

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

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

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

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

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

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      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
										_A       	=      torch.cat([uncond_embeddings, text_embeddings]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

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

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''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 			a_			(       self   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      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}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[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     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

						_A	,		_A	,		_A       	=      self._encode_prompt(
						    a__     , a__     , a__     , a__     , a__     , a__		)

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      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
										_A       	=      torch.cat([prior_latents] * 2		) if do_classifier_free_guidance else prior_latents

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

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

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

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      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