'''simple docstring'''




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  logging.get_logger(__name__)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      List[Any]=False    )    ->    str:


       """simple docstring"""







       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
                  " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
                  " instructions."    )
              raise

       if not is_sharded:
              _UpperCAmelCase     :  Optional[int]							      =    os.path.abspath(_UpperCAmelCase    )
              logger.info(F"""Loading PyTorch weights from {pt_path}"""    )

              _UpperCAmelCase     :  Tuple							      =    torch.load(_UpperCAmelCase			,	map_location="cpu"    )
              logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters."""    )

              _UpperCAmelCase     :  Tuple							      =    convert_pytorch_state_dict_to_flax(_UpperCAmelCase			,	_UpperCAmelCase    )
       else:
              # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
              _UpperCAmelCase     :  Optional[Any]							      =    convert_pytorch_sharded_state_dict_to_flax(_UpperCAmelCase			,	_UpperCAmelCase    )
       return flax_state_dict

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Tuple[str]			,	_UpperCAmelCase  :      np.ndarray			,	_UpperCAmelCase  :      Dict[str, jnp.ndarray]			,	_UpperCAmelCase  :      str			,	)    ->    (Tuple[str], np.ndarray):


       """simple docstring"""







       def is_key_or_prefix_key_in_dict(_UpperCAmelCase  :      Tuple[str]    ) -> bool:
              return len(set(_UpperCAmelCase    ) & {key, (model_prefix,) + key}    ) > 0

       # layer norm
       _UpperCAmelCase     :  int							      =    pt_tuple_key[:-1] + ("scale",)
       if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer mean
       _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-1] + ("mean",)
       if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer var
       _UpperCAmelCase     :  List[str]							      =    pt_tuple_key[:-1] + ("var",)
       if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # embedding
       _UpperCAmelCase     :  Optional[Any]							      =    pt_tuple_key[:-1] + ("embedding",)
       if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              return renamed_pt_tuple_key, pt_tensor

       # conv layer
       _UpperCAmelCase     :  int							      =    pt_tuple_key[:-1] + ("kernel",)
       if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              _UpperCAmelCase     :  int							      =    pt_tensor.transpose(2			,	3			,	1			,	0    )
              return renamed_pt_tuple_key, pt_tensor

       # linear layer
       _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-1] + ("kernel",)
       if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
              _UpperCAmelCase     :  Tuple							      =    pt_tensor.T
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm weight
       _UpperCAmelCase     :  Any							      =    pt_tuple_key[:-1] + ("weight",)
       if pt_tuple_key[-1] == "gamma":
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm bias
       _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-1] + ("bias",)
       if pt_tuple_key[-1] == "beta":
              return renamed_pt_tuple_key, pt_tensor

       # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
       _UpperCAmelCase     :  List[str]							      =    None
       if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
              _UpperCAmelCase     :  List[str]							      =    pt_tuple_key[-2] + "_g"
       elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
              _UpperCAmelCase     :  int							      =    pt_tuple_key[-2] + "_v"
       if name is not None:
              _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[:-3] + (name,)
              return renamed_pt_tuple_key, pt_tensor

       return pt_tuple_key, pt_tensor

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      List[Any]    )    ->    Tuple:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    {k: v.numpy() for k, v in pt_state_dict.items()}

       _UpperCAmelCase     :  Optional[Any]							      =    flax_model.base_model_prefix

       # use params dict if the model contains batch norm layers
       if "params" in flax_model.params:
              _UpperCAmelCase     :  str							      =    flax_model.params["params"]
       else:
              _UpperCAmelCase     :  Union[str, Any]							      =    flax_model.params
       _UpperCAmelCase     :  List[Any]							      =    flatten_dict(_UpperCAmelCase    )

       # add batch_stats keys,values to dict
       if "batch_stats" in flax_model.params:
              _UpperCAmelCase     :  Dict							      =    flatten_dict(flax_model.params["batch_stats"]    )
              random_flax_state_dict.update(_UpperCAmelCase    )

       _UpperCAmelCase     :  str							      =    {}

       _UpperCAmelCase     :  List[Any]							      =    (model_prefix not in flax_model_params) and (
           model_prefix in {k.split("."    )[0] for k in pt_state_dict.keys()}
       )
       _UpperCAmelCase     :  List[str]							      =    (model_prefix in flax_model_params) and (
           model_prefix not in {k.split("."    )[0] for k in pt_state_dict.keys()}
       )

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              _UpperCAmelCase     :  int							      =    tuple(pt_key.split("."    )    )

              # remove base model prefix if necessary
              _UpperCAmelCase     :  Any							      =    pt_tuple_key[0] == model_prefix
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _UpperCAmelCase     :  Tuple							      =    pt_tuple_key[1:]

              # Correctly rename weight parameters
              _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    rename_key_and_reshape_tensor(
                  _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

              # add model prefix if necessary
              _UpperCAmelCase     :  Optional[int]							      =    (model_prefix,) + flax_key in random_flax_state_dict
              if load_base_model_into_model_with_head and require_base_model_prefix:
                     _UpperCAmelCase     :  Union[str, Any]							      =    (model_prefix,) + flax_key

              if flax_key in random_flax_state_dict:
                     if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                            raise ValueError(
                                F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                                F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )

        # add batch stats if the model contains batchnorm layers
              if "batch_stats" in flax_model.params:
                     if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                            _UpperCAmelCase     :  List[str]							      =    jnp.asarray(_UpperCAmelCase    )
                            continue
                     # remove num_batches_tracked key
                     if "num_batches_tracked" in flax_key[-1]:
                            flax_state_dict.pop(_UpperCAmelCase			,	_UpperCAmelCase    )
                            continue

                     # also add unexpected weight so that warning is thrown
                     _UpperCAmelCase     :  Union[str, Any]							      =    jnp.asarray(_UpperCAmelCase    )

              else:
                     # also add unexpected weight so that warning is thrown
                     _UpperCAmelCase     :  Optional[int]							      =    jnp.asarray(_UpperCAmelCase    )

       return unflatten_dict(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Tuple			,	_UpperCAmelCase  :      Optional[Any]    )    ->    Optional[int]:


       """simple docstring"""







       import torch

       # Load the index
       _UpperCAmelCase     :  Union[str, Any]							      =    {}
       for shard_file in shard_filenames:
              # load using msgpack utils
              _UpperCAmelCase     :  List[str]							      =    torch.load(_UpperCAmelCase    )
              _UpperCAmelCase     :  int							      =    {k: v.numpy() for k, v in pt_state_dict.items()}

              _UpperCAmelCase     :  List[Any]							      =    flax_model.base_model_prefix

              # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
              if "batch_stats" in flax_model.params:
                     _UpperCAmelCase     :  Optional[Any]							      =    flax_model.params["params"]

                     _UpperCAmelCase     :  List[Any]							      =    flatten_dict(_UpperCAmelCase    )
                     random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]    )    )
              else:
                     _UpperCAmelCase     :  List[Any]							      =    flax_model.params
                     _UpperCAmelCase     :  List[str]							      =    flatten_dict(_UpperCAmelCase    )

              _UpperCAmelCase     :  Optional[int]							      =    (model_prefix not in flax_model_params) and (
                  model_prefix in {k.split("."    )[0] for k in pt_state_dict.keys()}
              )
              _UpperCAmelCase     :  int							      =    (model_prefix in flax_model_params) and (
                  model_prefix not in {k.split("."    )[0] for k in pt_state_dict.keys()}
              )
              # Need to change some parameters name to match Flax names
              for pt_key, pt_tensor in pt_state_dict.items():
                     _UpperCAmelCase     :  int							      =    tuple(pt_key.split("."    )    )

                     # remove base model prefix if necessary
                     _UpperCAmelCase     :  List[Any]							      =    pt_tuple_key[0] == model_prefix
                     if load_model_with_head_into_base_model and has_base_model_prefix:
                            _UpperCAmelCase     :  str							      =    pt_tuple_key[1:]

                     # Correctly rename weight parameters
                     _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    rename_key_and_reshape_tensor(
                         _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )
                     # add model prefix if necessary
                     _UpperCAmelCase     :  Tuple							      =    (model_prefix,) + flax_key in random_flax_state_dict
                     if load_base_model_into_model_with_head and require_base_model_prefix:
                            _UpperCAmelCase     :  Tuple							      =    (model_prefix,) + flax_key

                     if flax_key in random_flax_state_dict:
                            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                                   raise ValueError(
                                       F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                                       F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )

            # add batch stats if the model contains batchnorm layers
                     if "batch_stats" in flax_model.params:
                            if "mean" in flax_key[-1]:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    jnp.asarray(_UpperCAmelCase    )
                                   continue
                            if "var" in flax_key[-1]:
                                   _UpperCAmelCase     :  str							      =    jnp.asarray(_UpperCAmelCase    )
                                   continue
                            # remove num_batches_tracked key
                            if "num_batches_tracked" in flax_key[-1]:
                                   flax_state_dict.pop(_UpperCAmelCase			,	_UpperCAmelCase    )
                                   continue

                            # also add unexpected weight so that warning is thrown
                            _UpperCAmelCase     :  List[str]							      =    jnp.asarray(_UpperCAmelCase    )

                     else:
                            # also add unexpected weight so that warning is thrown
                            _UpperCAmelCase     :  Any							      =    jnp.asarray(_UpperCAmelCase    )
       return unflatten_dict(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[int]			,	_UpperCAmelCase  :      Any    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    os.path.abspath(_UpperCAmelCase    )
       logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""    )

       # import correct flax class
       _UpperCAmelCase     :  List[str]							      =    getattr(_UpperCAmelCase			,	"Flax" + model.__class__.__name__    )

       # load flax weight dict
       with open(_UpperCAmelCase			,	"rb"    ) as state_f:
              try:
                     _UpperCAmelCase     :  Dict							      =    from_bytes(_UpperCAmelCase			,	state_f.read()    )
              except UnpicklingError:
                     raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """    )

       return load_flax_weights_in_pytorch_model(_UpperCAmelCase			,	_UpperCAmelCase    )


def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
                  " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
                  " instructions."    )
              raise

       # check if we have bf16 weights
       _UpperCAmelCase     :  List[str]							      =    flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase    : x.dtype == jnp.bfloataa			,	_UpperCAmelCase    )    ).values()
       if any(_UpperCAmelCase    ):
              # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
              # and bf16 is not fully supported in PT yet.
              logger.warning(
                  "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
                  "before loading those in PyTorch model."    )
              _UpperCAmelCase     :  Optional[int]							      =    jax.tree_util.tree_map(
                  lambda _UpperCAmelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params			,	_UpperCAmelCase    )

       _UpperCAmelCase     :  Tuple							      =    flatten_dict(_UpperCAmelCase    )
       _UpperCAmelCase     :  List[str]							      =    pt_model.state_dict()

       _UpperCAmelCase     :  Tuple							      =    (pt_model.base_model_prefix in flax_state) and (
           pt_model.base_model_prefix not in {k.split("."    )[0] for k in pt_model_dict.keys()}
       )
       _UpperCAmelCase     :  Union[str, Any]							      =    (pt_model.base_model_prefix not in flax_state) and (
           pt_model.base_model_prefix in {k.split("."    )[0] for k in pt_model_dict.keys()}
       )

       # keep track of unexpected & missing keys
       _UpperCAmelCase     :  List[Any]							      =    []
       _UpperCAmelCase     :  str							      =    set(pt_model_dict.keys()    )

       for flax_key_tuple, flax_tensor in flax_state_dict.items():
              _UpperCAmelCase     :  Tuple							      =    flax_key_tuple[0] == pt_model.base_model_prefix
              _UpperCAmelCase     :  Optional[Any]							      =    ".".join((pt_model.base_model_prefix,) + flax_key_tuple    ) in pt_model_dict

              # adapt flax_key to prepare for loading from/to base model only
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _UpperCAmelCase     :  str							      =    flax_key_tuple[1:]
              elif load_base_model_into_model_with_head and require_base_model_prefix:
                     _UpperCAmelCase     :  int							      =    (pt_model.base_model_prefix,) + flax_key_tuple

              # rename flax weights to PyTorch format
              if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_UpperCAmelCase    ) not in pt_model_dict:
                     # conv layer
                     _UpperCAmelCase     :  int							      =    flax_key_tuple[:-1] + ("weight",)
                     _UpperCAmelCase     :  Optional[Any]							      =    jnp.transpose(_UpperCAmelCase			,	(3, 2, 0, 1)    )
              elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase    ) not in pt_model_dict:
                     # linear layer
                     _UpperCAmelCase     :  List[Any]							      =    flax_key_tuple[:-1] + ("weight",)
                     _UpperCAmelCase     :  List[Any]							      =    flax_tensor.T
              elif flax_key_tuple[-1] in ["scale", "embedding"]:
                     _UpperCAmelCase     :  int							      =    flax_key_tuple[:-1] + ("weight",)

              # adding batch stats from flax batch norm to pt
              elif "mean" in flax_key_tuple[-1]:
                     _UpperCAmelCase     :  Optional[Any]							      =    flax_key_tuple[:-1] + ("running_mean",)
              elif "var" in flax_key_tuple[-1]:
                     _UpperCAmelCase     :  List[str]							      =    flax_key_tuple[:-1] + ("running_var",)

              if "batch_stats" in flax_state:
                     _UpperCAmelCase     :  Union[str, Any]							      =    ".".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
              else:
                     _UpperCAmelCase     :  Optional[Any]							      =    ".".join(_UpperCAmelCase    )

              # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
              _UpperCAmelCase     :  Optional[Any]							      =    {}
              # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
              for key in pt_model_dict:
                     _UpperCAmelCase     :  Optional[int]							      =    key.split("."    )
                     _UpperCAmelCase     :  Tuple							      =    None
                     if key_components[-3::2] == ["parametrizations", "original0"]:
                            _UpperCAmelCase     :  Union[str, Any]							      =    key_components[-2] + "_g"
                     elif key_components[-3::2] == ["parametrizations", "original1"]:
                            _UpperCAmelCase     :  int							      =    key_components[-2] + "_v"
                     if name is not None:
                            _UpperCAmelCase     :  List[str]							      =    key_components[:-3] + [name]
                            _UpperCAmelCase     :  Optional[Any]							      =    ".".join(_UpperCAmelCase    )
                            _UpperCAmelCase     :  Any							      =    key

              if flax_key in special_pt_names:
                     _UpperCAmelCase     :  Union[str, Any]							      =    special_pt_names[flax_key]

              if flax_key in pt_model_dict:
                     if flax_tensor.shape != pt_model_dict[flax_key].shape:
                            raise ValueError(
                                F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                                F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )
                     else:
                            # add weight to pytorch dict
                            _UpperCAmelCase     :  Optional[Any]							      =    np.asarray(_UpperCAmelCase    ) if not isinstance(_UpperCAmelCase			,	np.ndarray    ) else flax_tensor
                            _UpperCAmelCase     :  int							      =    torch.from_numpy(_UpperCAmelCase    )
                            # remove from missing keys
                            missing_keys.remove(_UpperCAmelCase    )
              else:
                     # weight is not expected by PyTorch model
                     unexpected_keys.append(_UpperCAmelCase    )

       pt_model.load_state_dict(_UpperCAmelCase    )

       # re-transform missing_keys to list
       _UpperCAmelCase     :  Optional[int]							      =    list(_UpperCAmelCase    )

       if len(_UpperCAmelCase    ) > 0:
              logger.warning(
                  "Some weights of the Flax model were not used when initializing the PyTorch model"
                  F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
                  F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
                  " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
                  F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
                  " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
                  " FlaxBertForSequenceClassification model)."    )
       else:
              logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""    )
       if len(_UpperCAmelCase    ) > 0:
              logger.warning(
                  F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
                  F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
                  " use it for predictions and inference."    )
       else:
              logger.warning(
                  F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
                  "If your task is similar to the task the model of the checkpoint was trained on, "
                  F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""    )

       return pt_model




"""simple docstring"""





from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_a       =							'CompVis/stable-diffusion-v1-1'
_a       =							'CompVis/stable-diffusion-v1-2'
_a       =							'CompVis/stable-diffusion-v1-3'
_a       =							'CompVis/stable-diffusion-v1-4'




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 def __init__(			self							:			Dict,					UpperCAmelCase__							:			AutoencoderKL,					UpperCAmelCase__							:			CLIPTextModel,					UpperCAmelCase__							:			CLIPTokenizer,					UpperCAmelCase__							:			UNetaDConditionModel,					UpperCAmelCase__							:			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],					UpperCAmelCase__							:			StableDiffusionSafetyChecker,					UpperCAmelCase__							:			CLIPImageProcessor,					UpperCAmelCase__							:			bool = True,					):
    super()._init_()

    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline(
        vae=UpperCAmelCase__,					text_encoder=UpperCAmelCase__,					tokenizer=UpperCAmelCase__,					unet=UpperCAmelCase__,					scheduler=UpperCAmelCase__,					safety_checker=UpperCAmelCase__,					feature_extractor=UpperCAmelCase__,					requires_safety_checker=UpperCAmelCase__,					)

    self.register_modules(pipelinea=self.pipea,					pipelinea=self.pipea,					pipelinea=self.pipea,					pipelinea=self.pipea	)


 @property
 def 					_lowercase (			self							:			List[str]	):
    return {k: getattr(self,					UpperCAmelCase__	) for k in self.config.keys() if not k.startswith("_"	)}


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Union[str, int]] = "auto"	):
    if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       __lowercase								=						self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[str]	):
    self.enable_attention_slicing(UpperCAmelCase__	)


 @torch.no_grad()
 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Tuple,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			str,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Any,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Optional[int],					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)



 @torch.no_grad()
 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			str,					):
    __lowercase								=						"cuda" if torch.cuda.is_available() else "cpu"
    self.to(UpperCAmelCase__	)

    # Checks if the height and width are divisible by 8 or not
    if height % 8 != 0 or width % 8 != 0:
       raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}."""	)

    # Get first result from Stable Diffusion Checkpoint v1.1
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get first result from Stable Diffusion Checkpoint v1.2
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get first result from Stable Diffusion Checkpoint v1.3
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get first result from Stable Diffusion Checkpoint v1.4
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
    return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]	)




