
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


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


def  A__ (       self,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      image.to(device=__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      image  # take image as image_embeddings
   SCREAMING_SNAKE_CASE_	=      image_embeddings.unsqueeze(1						)

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

   if do_classifier_free_guidance:
      SCREAMING_SNAKE_CASE_	=      torch.zeros_like(__lowerCamelCase						)

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

   return image_embeddings





class 				UpperCamelCase__		(	__A	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A ,     _A ,     _A ,     _A = None ,     )				->				int:
          super().__init__()

          SCREAMING_SNAKE_CASE_	=      {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

          SCREAMING_SNAKE_CASE_	=      decoder_pipe_kwargs.get('''torch_dtype''' ,     __lowercase	) or prior.dtype

          SCREAMING_SNAKE_CASE_	=      StableDiffusionImageVariationPipeline.from_pretrained(
              '''lambdalabs/sd-image-variations-diffusers''' ,     **__lowercase	)

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

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



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A = None ,     _A = None ,     )				->				Tuple:
          if text_model_output is None:
             SCREAMING_SNAKE_CASE_	=      len(__lowercase	) if isinstance(__lowercase ,     __lowercase	) else 1
             # get prompt text embeddings
             SCREAMING_SNAKE_CASE_	=      self.tokenizer(
                 __lowercase ,     padding='''max_length''' ,     max_length=self.tokenizer.model_max_length ,     return_tensors='''pt''' ,     )
             SCREAMING_SNAKE_CASE_	=      text_inputs.input_ids
             SCREAMING_SNAKE_CASE_	=      text_inputs.attention_mask.bool().to(__lowercase	)

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

             SCREAMING_SNAKE_CASE_	=      self.text_encoder(text_input_ids.to(__lowercase	)	)

             SCREAMING_SNAKE_CASE_	=      text_encoder_output.text_embeds
             SCREAMING_SNAKE_CASE_	=      text_encoder_output.last_hidden_state

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

          SCREAMING_SNAKE_CASE_	=      text_embeddings.repeat_interleave(__lowercase ,     dim=0	)
          SCREAMING_SNAKE_CASE_	=      text_encoder_hidden_states.repeat_interleave(__lowercase ,     dim=0	)
          SCREAMING_SNAKE_CASE_	=      text_mask.repeat_interleave(__lowercase ,     dim=0	)

          if do_classifier_free_guidance:
             SCREAMING_SNAKE_CASE_	=      [''''''] * batch_size

             SCREAMING_SNAKE_CASE_	=      self.tokenizer(
                 __lowercase ,     padding='''max_length''' ,     max_length=self.tokenizer.model_max_length ,     truncation=__lowercase ,     return_tensors='''pt''' ,     )
             SCREAMING_SNAKE_CASE_	=      uncond_input.attention_mask.bool().to(__lowercase	)
             SCREAMING_SNAKE_CASE_	=      self.text_encoder(uncond_input.input_ids.to(__lowercase	)	)

             SCREAMING_SNAKE_CASE_	=      uncond_embeddings_text_encoder_output.text_embeds
             SCREAMING_SNAKE_CASE_	=      uncond_embeddings_text_encoder_output.last_hidden_state

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

             SCREAMING_SNAKE_CASE_	=      uncond_embeddings.shape[1]
             SCREAMING_SNAKE_CASE_	=      uncond_embeddings.repeat(1 ,     __lowercase	)
             SCREAMING_SNAKE_CASE_	=      uncond_embeddings.view(batch_size * num_images_per_prompt ,     __lowercase	)

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

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

          return text_embeddings, text_encoder_hidden_states, text_mask



       @property
       def     _UpperCamelCase	(							self	)				->				List[str]:
          if self.device != torch.device('''meta'''	) or not hasattr(self.prior ,     '''_hf_hook'''	):
             return self.device
          for module in self.prior.modules():
             if (
                 hasattr(__lowercase ,     '''_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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A	)				->				str:
          if latents is None:
             SCREAMING_SNAKE_CASE_	=      randn_tensor(__lowercase ,     generator=__lowercase ,     device=__lowercase ,     dtype=__lowercase	)
          else:
             if latents.shape != shape:
                raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
             SCREAMING_SNAKE_CASE_	=      latents.to(__lowercase	)

          SCREAMING_SNAKE_CASE_	=      latents * scheduler.init_noise_sigma
          return latents



       def     _UpperCamelCase	(							self ,     _A = None	)				->				int:
          self.decoder_pipe.to(__lowercase	)
          super().to(__lowercase	)




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

          SCREAMING_SNAKE_CASE_	=      self._execution_device

          SCREAMING_SNAKE_CASE_	=      batch_size * num_images_per_prompt

          SCREAMING_SNAKE_CASE_	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self._encode_prompt(
              __lowercase ,     __lowercase ,     __lowercase ,     __lowercase ,     __lowercase ,     __lowercase	)

          # prior

          self.prior_scheduler.set_timesteps(__lowercase ,     device=__lowercase	)
          SCREAMING_SNAKE_CASE_	=      self.prior_scheduler.timesteps

          SCREAMING_SNAKE_CASE_	=      self.prior.config.embedding_dim

          SCREAMING_SNAKE_CASE_	=      self.prepare_latents(
              (batch_size, embedding_dim) ,     text_embeddings.dtype ,     __lowercase ,     __lowercase ,     __lowercase ,     self.prior_scheduler ,     )

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

             SCREAMING_SNAKE_CASE_	=      self.prior(
                 __lowercase ,     timestep=__lowercase ,     proj_embedding=__lowercase ,     encoder_hidden_states=__lowercase ,     attention_mask=__lowercase ,     ).predicted_image_embedding

             if do_classifier_free_guidance:
                SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      predicted_image_embedding.chunk(2	)
                SCREAMING_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]:
                SCREAMING_SNAKE_CASE_	=      None
             else:
                SCREAMING_SNAKE_CASE_	=      prior_timesteps_tensor[i + 1]

             SCREAMING_SNAKE_CASE_	=      self.prior_scheduler.step(
                 __lowercase ,     timestep=__lowercase ,     sample=__lowercase ,     generator=__lowercase ,     prev_timestep=__lowercase ,     ).prev_sample

          SCREAMING_SNAKE_CASE_	=      self.prior.post_process_latents(__lowercase	)

          SCREAMING_SNAKE_CASE_	=      prior_latents

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






'''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


_UpperCAmelCase					:			Optional[Any]				     =					[nn.LayerNorm]

_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			Tuple				     =					version.parse(version.parse(torch.__version__).base_version)

_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""2.0""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""1.12""")
_UpperCAmelCase					:			List[str]				     =					parsed_torch_version_base >= version.parse("""1.11""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base < version.parse("""1.11""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					self):
						from torch import _softmax_backward_data

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 0):
						__lowerCAmelCase											=					index.to(layer.weight.device)
						__lowerCAmelCase											=					layer.weight.index_select(lowerCamelCase,					lowerCamelCase).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		__lowerCAmelCase											=					layer.bias.clone().detach()
												else:
																		__lowerCAmelCase											=					layer.bias[index].clone().detach()
						__lowerCAmelCase											=					list(layer.weight.size())
						__lowerCAmelCase											=					len(lowerCamelCase)
						__lowerCAmelCase											=					nn.Linear(new_size[1],					new_size[0],					bias=layer.bias is not None).to(layer.weight.device)
						__lowerCAmelCase											=					False
						new_layer.weight.copy_(W.contiguous())
						__lowerCAmelCase											=					True
						if layer.bias is not None:
												__lowerCAmelCase											=					False
												new_layer.bias.copy_(b.contiguous())
												__lowerCAmelCase											=					True
						return new_layer


class    a__					(			nn.Module   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase		):
										super().__init__()
										__lowerCAmelCase											=					nf
										__lowerCAmelCase											=					nn.Parameter(torch.empty(__lowercase							,      __lowercase		)		)
										__lowerCAmelCase											=					nn.Parameter(torch.zeros(__lowercase		)		)
										nn.init.normal_(self.weight							,      std=0.0_2		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					x.size()[:-1] + (self.nf,)
										__lowerCAmelCase											=					torch.addmm(self.bias							,      x.view(-1							,      x.size(-1		)		)							,      self.weight		)
										__lowerCAmelCase											=					x.view(__lowercase		)
										return x

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = None):
						if isinstance(lowerCamelCase,					nn.Linear):
												return prune_linear_layer(lowerCamelCase,					lowerCamelCase,					dim=0 if dim is None else dim)
						elif isinstance(lowerCamelCase,					lowerCamelCase):
												return prune_convad_layer(lowerCamelCase,					lowerCamelCase,					dim=1 if dim is None else dim)
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					*lowerCamelCase):
						assert len(lowerCamelCase) > 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
						__lowerCAmelCase											=					len(inspect.signature(lowerCamelCase).parameters)
						if num_args_in_forward_chunk_fn != len(lowerCamelCase):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(lowerCamelCase)} input """
												    '''tensors are given''')

						if chunk_size > 0:
												__lowerCAmelCase											=					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}""")

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

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

						return forward_fn(*lowerCamelCase)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					torch.ones(lowerCamelCase,					lowerCamelCase)
						__lowerCAmelCase											=					set(lowerCamelCase) - 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
												__lowerCAmelCase											=					head - sum(1 if h < head else 0 for h in already_pruned_heads)
												__lowerCAmelCase											=					0
						__lowerCAmelCase											=					mask.view(-1).contiguous().eq(1)
						__lowerCAmelCase											=					torch.arange(len(lowerCamelCase))[mask].long()
						return heads, index

def 				__magic_name__(    *lowerCamelCase,					lowerCamelCase = None):
						return torch.meshgrid(*lowerCamelCase,					indexing=lowerCamelCase)




def 				__magic_name__(    lowerCamelCase):
						return tensor.device, storage_ptr(lowerCamelCase), storage_size(lowerCamelCase)

