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



def  _lowerCAmelCase				(				self			,      __a			,      __a			,      __a			,      __a      )  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =image.to(device=__a      )
     _UpperCamelCase  :Union[str, Any]       =image  # take image as image_embeddings
     _UpperCamelCase  :Dict       =image_embeddings.unsqueeze(1      )

     # duplicate image embeddings for each generation per prompt, using mps friendly method
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :str       =image_embeddings.shape
     _UpperCamelCase  :int       =image_embeddings.repeat(1			,      __a			,      1      )
     _UpperCamelCase  :int       =image_embeddings.view(bs_embed * num_images_per_prompt			,      __a			,      -1      )

     if do_classifier_free_guidance:
          _UpperCamelCase  :str       =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  :List[Any]       =torch.cat([uncond_embeddings, image_embeddings]      )

     return image_embeddings




class    lowerCamelCase__  (   __snake_case     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      )				->     Union[str, Any]:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :Any       ={"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        _UpperCamelCase  :List[Any]       =decoder_pipe_kwargs.get("""torch_dtype"""					,      lowerCAmelCase__     ) or prior.dtype

        _UpperCamelCase  :Union[str, Any]       =StableDiffusionImageVariationPipeline.from_pretrained(
            """lambdalabs/sd-image-variations-diffusers"""					,      **lowerCAmelCase__     )

        # replace `_encode_image` method
        _UpperCamelCase  :Tuple       =types.MethodType(_encode_image					,      self.decoder_pipe     )

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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      )				->     str:
        """simple docstring"""





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

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  _UpperCamelCase  :Any       =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  :Tuple       =text_input_ids[:, : self.tokenizer.model_max_length]

             _UpperCamelCase  :str       =self.text_encoder(text_input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :str       =text_encoder_output.text_embeds
             _UpperCamelCase  :Any       =text_encoder_output.last_hidden_state

        else:
             _UpperCamelCase  :Dict       =text_model_output[0].shape[0]
             _UpperCamelCase       ,      _UpperCamelCase  :Dict       =text_model_output[0], text_model_output[1]
             _UpperCamelCase  :Tuple       =text_attention_mask

        _UpperCamelCase  :Optional[Any]       =text_embeddings.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :Optional[int]       =text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :List[Any]       =text_mask.repeat_interleave(lowerCAmelCase__					,      dim=0     )

        if do_classifier_free_guidance:
             _UpperCamelCase  :Optional[int]       =[""""""] * batch_size

             _UpperCamelCase  :Dict       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )
             _UpperCamelCase  :Any       =uncond_input.attention_mask.bool().to(lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :Optional[int]       =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  :List[Any]       =uncond_embeddings.shape[1]
             _UpperCamelCase  :Dict       =uncond_embeddings.repeat(1					,      lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =uncond_embeddings.view(batch_size * num_images_per_prompt					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.shape[1]
             _UpperCamelCase  :List[Any]       =uncond_text_encoder_hidden_states.repeat(1					,      lowerCAmelCase__					,      1     )
             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt					,      lowerCAmelCase__					,      -1     )
             _UpperCamelCase  :Tuple       =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  :Any       =torch.cat([uncond_embeddings, text_embeddings]     )
             _UpperCamelCase  :Any       =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

             _UpperCamelCase  :Dict       =torch.cat([uncond_text_mask, text_mask]     )

        return text_embeddings, text_encoder_hidden_states, text_mask



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        if latents is None:
             _UpperCamelCase  :Optional[int]       =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  :List[str]       =latents.to(lowerCAmelCase__     )

        _UpperCamelCase  :Any       =latents * scheduler.init_noise_sigma
        return latents



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__ = None     )				->     int:
        """simple docstring"""





        self.decoder_pipe.to(lowerCAmelCase__     )
        super().to(lowerCAmelCase__     )




   @torch.no_grad()
   def __call__(    self					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 25					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 4.0					,      lowerCAmelCase__ = 8.0					,      lowerCAmelCase__ = 50					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 0.0					,      lowerCAmelCase__ = "pil"					,      lowerCAmelCase__ = True					,      )				->     Dict:
        """simple docstring"""





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

        _UpperCamelCase  :List[str]       =self._execution_device

        _UpperCamelCase  :List[str]       =batch_size * num_images_per_prompt

        _UpperCamelCase  :Optional[Any]       =prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self._encode_prompt(
            lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase__					,      device=lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =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					,      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  :Optional[int]       =torch.cat([prior_latents] * 2     ) if do_classifier_free_guidance else prior_latents

             _UpperCamelCase  :Dict       =self.prior(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      proj_embedding=lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__					,      attention_mask=lowerCAmelCase__					,      ).predicted_image_embedding

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

             if i + 1 == prior_timesteps_tensor.shape[0]:
                  _UpperCamelCase  :Tuple       =None
             else:
                  _UpperCamelCase  :Optional[int]       =prior_timesteps_tensor[i + 1]

             _UpperCamelCase  :Tuple       =self.prior_scheduler.step(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      sample=lowerCAmelCase__					,      generator=lowerCAmelCase__					,      prev_timestep=lowerCAmelCase__					,      ).prev_sample

        _UpperCamelCase  :Any       =self.prior.post_process_latents(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =prior_latents

        _UpperCamelCase  :Tuple       =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






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					A__								=     42

					# setable values
					A__								=     42
					A__								=     42
					A__								=     42
					A__								=     None

					# running values
					A__								=     None





					@classmethod
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										return cls(common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						)

@dataclass
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					A__								=     42





					@property
					def        __magic_name__			(			self						):
										return True





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = jnp.floataa						,	):
										lowerCamelCase__      =							dtype





					def        __magic_name__			(			self						,	_lowerCAmelCase = None						):
										if common is None:
															lowerCamelCase__      =							CommonSchedulerState.create(self						)

										lowerCamelCase__      =							jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]
										lowerCamelCase__      =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										lowerCamelCase__      =							sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										((lowerCamelCase__)     ,		)      =							jnp.where(state.timesteps == timestep						,	size=1						)
										lowerCamelCase__      =							step_index[0]

										lowerCamelCase__      =							state.sigmas[step_index]
										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def lms_derivative(_lowerCAmelCase						):
															lowerCamelCase__      =							1.0
															for k in range(_lowerCAmelCase						):
																				if current_order == k:
																									continue
																				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

										lowerCamelCase__      =							integrate.quad(_lowerCAmelCase						,	state.sigmas[t]						,	state.sigmas[t + 1]						,	epsrel=1E-4						)[0]

										return integrated_coeff





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = ()						):
										lowerCamelCase__      =							jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	_lowerCAmelCase						,	dtype=self.dtype						)

										lowerCamelCase__      =							jnp.floor(_lowerCAmelCase						).astype(jnp.intaa						)
										lowerCamelCase__      =							jnp.ceil(_lowerCAmelCase						).astype(jnp.intaa						)

										lowerCamelCase__      =							jnp.mod(_lowerCAmelCase						,	1.0						)

										lowerCamelCase__      =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCamelCase__      =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										lowerCamelCase__      =							jnp.concatenate([sigmas, jnp.array([0.0]						,	dtype=self.dtype						)]						)

										lowerCamelCase__      =							timesteps.astype(jnp.intaa						)

										# initial running values
										lowerCamelCase__      =							jnp.zeros((0,) + shape						,	dtype=self.dtype						)

										return state.replace(
										    timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	num_inference_steps=_lowerCAmelCase						,	derivatives=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 4						,	_lowerCAmelCase = True						,	):
										if state.num_inference_steps is None:
															raise ValueError(
															    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"						)

										lowerCamelCase__      =							state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

										# 2. Convert to an ODE derivative
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma
										lowerCamelCase__      =							state.replace(derivatives=jnp.append(state.derivatives						,	_lowerCAmelCase						)						)
										if len(state.derivatives						) > order:
															lowerCamelCase__      =							state.replace(derivatives=jnp.delete(state.derivatives						,	0						)						)

										# 3. Compute linear multistep coefficients
										lowerCamelCase__      =							min(timestep + 1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[self.get_lms_coefficient(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

										# 4. Compute previous sample based on the derivatives path
										lowerCamelCase__      =							sample + sum(
										    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase						,	reversed(state.derivatives						)						)						)

										if not return_dict:
															return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase						,	state=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							state.sigmas[timesteps].flatten()
										lowerCamelCase__      =							broadcast_to_shape_from_left(_lowerCAmelCase						,	noise.shape						)

										lowerCamelCase__      =							original_samples + noise * sigma

										return noisy_samples






					def __len__(			self						):
										return self.config.num_train_timesteps

