




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


snake_case_					:					List[str]       						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def   A		(self  :       str  ,							__A  :       str  ,							__A  :       Union[str, Any]  ,							__A  :       Union[str, Any]  ,							__A  :       Dict			)  -> Dict:







     """simple docstring"""


     UpperCAmelCase_		=				image.to(device=__A			)
     UpperCAmelCase_		=				image  # take image as image_embeddings
     UpperCAmelCase_		=				image_embeddings.unsqueeze(1			)

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

     if do_classifier_free_guidance:
          UpperCAmelCase_		=				torch.zeros_like(__A			)

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

     return image_embeddings







class __snake_case     (     SCREAMING_SNAKE_CASE__						):







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

        """simple docstring"""


        super().__init__()

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

        UpperCAmelCase_		=				decoder_pipe_kwargs.get('''torch_dtype'''   ,		lowerCAmelCase_) or prior.dtype

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

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

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







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		Optional[int]   ,		_snake_case						:		Optional[Any]   ,		_snake_case						:		str   ,		_snake_case						:		Optional[int]   ,		_snake_case						:		Optional[Union[CLIPTextModelOutput, Tuple]] = None   ,		_snake_case						:		Optional[torch.Tensor] = None   ,		):

        """simple docstring"""


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

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  UpperCAmelCase_		=				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_		=				text_input_ids[:, : self.tokenizer.model_max_length]

             UpperCAmelCase_		=				self.text_encoder(text_input_ids.to(lowerCAmelCase_))

             UpperCAmelCase_		=				text_encoder_output.text_embeds
             UpperCAmelCase_		=				text_encoder_output.last_hidden_state

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

        UpperCAmelCase_		=				text_embeddings.repeat_interleave(lowerCAmelCase_   ,		dim=0)
        UpperCAmelCase_		=				text_encoder_hidden_states.repeat_interleave(lowerCAmelCase_   ,		dim=0)
        UpperCAmelCase_		=				text_mask.repeat_interleave(lowerCAmelCase_   ,		dim=0)

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

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

             UpperCAmelCase_		=				uncond_embeddings_text_encoder_output.text_embeds
             UpperCAmelCase_		=				uncond_embeddings_text_encoder_output.last_hidden_state

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

             UpperCAmelCase_		=				uncond_embeddings.shape[1]
             UpperCAmelCase_		=				uncond_embeddings.repeat(1   ,		lowerCAmelCase_)
             UpperCAmelCase_		=				uncond_embeddings.view(batch_size * num_images_per_prompt   ,		lowerCAmelCase_)

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

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

        return text_embeddings, text_encoder_hidden_states, text_mask







   @property
   def lowerCamelCase		( self						:		List[Any]):

        """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(lowerCAmelCase_   ,		'''_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 lowerCamelCase		( self						:		str   ,		_snake_case						:		List[str]   ,		_snake_case						:		List[str]   ,		_snake_case						:		List[Any]   ,		_snake_case						:		Any   ,		_snake_case						:		Any   ,		_snake_case						:		str):

        """simple docstring"""


        if latents is None:
             UpperCAmelCase_		=				randn_tensor(lowerCAmelCase_   ,		generator=lowerCAmelCase_   ,		device=lowerCAmelCase_   ,		dtype=lowerCAmelCase_)
        else:
             if latents.shape != shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
             UpperCAmelCase_		=				latents.to(lowerCAmelCase_)

        UpperCAmelCase_		=				latents * scheduler.init_noise_sigma
        return latents







   def lowerCamelCase		( self						:		List[Any]   ,		_snake_case						:		Optional[Union[str, torch.device]] = None):

        """simple docstring"""


        self.decoder_pipe.to(lowerCAmelCase_)
        super().to(lowerCAmelCase_)







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

        """simple docstring"""


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

        UpperCAmelCase_		=				self._execution_device

        UpperCAmelCase_		=				batch_size * num_images_per_prompt

        UpperCAmelCase_		=				prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        UpperCAmelCase_		=				self._encode_prompt(
            lowerCAmelCase_   ,		lowerCAmelCase_   ,		lowerCAmelCase_   ,		lowerCAmelCase_   ,		lowerCAmelCase_   ,		lowerCAmelCase_)

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase_   ,		device=lowerCAmelCase_)
        UpperCAmelCase_		=				self.prior_scheduler.timesteps

        UpperCAmelCase_		=				self.prior.config.embedding_dim

        UpperCAmelCase_		=				self.prepare_latents(
            (batch_size, embedding_dim)   ,		text_embeddings.dtype   ,		lowerCAmelCase_   ,		lowerCAmelCase_   ,		lowerCAmelCase_   ,		self.prior_scheduler   ,		)

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

             UpperCAmelCase_		=				self.prior(
                 lowerCAmelCase_   ,		timestep=lowerCAmelCase_   ,		proj_embedding=lowerCAmelCase_   ,		encoder_hidden_states=lowerCAmelCase_   ,		attention_mask=lowerCAmelCase_   ,		).predicted_image_embedding

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

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

             UpperCAmelCase_		=				self.prior_scheduler.step(
                 lowerCAmelCase_   ,		timestep=lowerCAmelCase_   ,		sample=lowerCAmelCase_   ,		generator=lowerCAmelCase_   ,		prev_timestep=lowerCAmelCase_   ,		).prev_sample

        UpperCAmelCase_		=				self.prior.post_process_latents(lowerCAmelCase_)

        UpperCAmelCase_		=				prior_latents

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








import math as m






def     lowerCamelCase__				(   a						,						a						,						a=0.001 ) ->  Optional[int]:
   return (f(a + h ) - f(a - h )) / (2 * h)






def     lowerCamelCase__				(   a						,						a=0						,						a=1_00						,						a=0.0001						,						a=1E-6						,						a=False ) ->  List[Any]:
   _A:    Optional[Any]						     =			xa  # set the initial guess
   _A:    Dict						     =			[a]
   _A:    Tuple						     =			abs(f(a ) )
   _A:    Tuple						     =			lambda a : calc_derivative(a						,						a						,						h=a )  # noqa: E731  Derivative of f(x)
   for _ in range(a ):
      if fa(a ) == 0:
         raise ValueError('''No converging solution found''' )
      _A:    Any						     =			a - f(a ) / fa(a )  # Calculate the next estimate
      if logsteps:
         steps.append(a )
      if error < maxerror:
         break
   else:
      raise ValueError('''Iteration limit reached, no converging solution found''' )
   if logsteps:
      # If logstep is true, then log intermediate steps
      return a, error, steps
   return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   UpperCAmelCase__						:	Optional[Any]	     = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__						:	str	     = newton_raphson(
       f, xa=10, maxiter=1000, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")



