






'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


UpperCamelCase__ =			datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark


@dataclass
class        _UpperCAmelCase						(		datasets.BuilderConfig			):
    __lowerCamelCase:   Optional[datasets.Features]		       =    None




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		):


   """simple docstring"""




   import pyspark

   def generate_fn():
      lowercase_	:    Dict           =     df.select("*"						,		pyspark.sql.functions.spark_partition_id().alias("part_id"							)							)
      for partition_id in partition_order:
         lowercase_	:    Dict           =     df_with_partition_id.select("*"							).where(F"""part_id = {partition_id}"""							).drop("part_id"							)
         lowercase_	:    Dict           =     partition_df.collect()
         lowercase_	:    Dict           =     0
         for row in rows:
            yield F"""{partition_id}_{row_id}""", row.asDict()
            row_id += 1

   return generate_fn


class        _UpperCAmelCase						(		_BaseExamplesIterable			):




    def __init__(			self  :       Tuple  ,     a  :       Optional[int]  ,     a  :       Any=None  ,     )     ->   Union[str, Any]:






       '''simple docstring'''






       lowercase_	:    Dict           =     df
       lowercase_	:    Optional[Any]           =     partition_order or range(self.df.rdd.getNumPartitions()			)
       lowercase_	:    int           =     _generate_iterable_examples(self.df  ,     self.partition_order			)




    def __iter__(			self  :       int			)     ->   Tuple:






       '''simple docstring'''






       yield from self.generate_examples_fn()




    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       int			)     ->   Union[str, Any]:






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     list(range(self.df.rdd.getNumPartitions()			)			)
       generator.shuffle(a_			)
       return SparkExamplesIterable(self.df  ,     partition_order=a_			)




    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       List[str]  ,     a  :       Tuple			)     ->   int:






       '''simple docstring'''






       lowercase_	:    Dict           =     self.split_shard_indices_by_worker(a_  ,     a_			)
       return SparkExamplesIterable(self.df  ,     partition_order=a_			)





    @property
    def 				lowerCAmelCase__				(			self  :       List[str]			)     ->   List[str]:






       '''simple docstring'''






       return len(self.partition_order			)


class        _UpperCAmelCase						(		datasets.DatasetBuilder			):
    __lowerCamelCase:   Any		       =    SparkConfig




    def __init__(			self  :       List[str]  ,     a  :       List[Any]  ,     a  :       Dict = None  ,     a  :       Dict = None  ,     **a  :       Union[str, Any]  ,     )     ->   Dict:






       '''simple docstring'''






       import pyspark

       lowercase_	:    str           =     pyspark.sql.SparkSession.builder.getOrCreate()
       lowercase_	:    Optional[Any]           =     df
       lowercase_	:    List[Any]           =     working_dir

       super().__init__(
           cache_dir=a_  ,     config_name=str(self.df.semanticHash()			)  ,     **a_  ,     )




    def 				lowerCAmelCase__				(			self  :       Optional[int]			)     ->   str:






       '''simple docstring'''






       def create_cache_and_write_probe(a  :       Any			):
          # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
          # already exist.
          os.makedirs(self._cache_dir  ,     exist_ok=a_			)
          lowercase_	:    Optional[Any]           =     os.path.join(self._cache_dir  ,     "fs_test" + uuid.uuida().hex			)
          # Opening the file in append mode will create a new file unless it already exists, in which case it will not
          # change the file contents.
          open(a_  ,     "a"			)
          return [probe_file]

       if self._spark.conf.get("spark.master"  ,     ""			).startswith("local"			):
          return

       # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
       # accessible to the driver.
       # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
       if self._cache_dir:
          lowercase_	:    List[str]           =     (
              self._spark.sparkContext.parallelize(range(1			)  ,     1			).mapPartitions(a_			).collect()
          )
          if os.path.isfile(probe[0]			):
             return

       raise ValueError(
           "When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"			)




    def 				lowerCAmelCase__				(			self  :       int			)     ->   Optional[int]:






       '''simple docstring'''






       return datasets.DatasetInfo(features=self.config.features			)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Any			)     ->   List[Any]:






       '''simple docstring'''






       return [datasets.SplitGenerator(name=datasets.Split.TRAIN			)]




    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       List[Any]			)     ->   Optional[int]:






       '''simple docstring'''






       import pyspark

       def get_arrow_batch_size(a  :       Union[str, Any]			):
          for batch in it:
             yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}			)

       lowercase_	:    str           =     self.df.count()
       lowercase_	:    List[Any]           =     df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
       # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
       lowercase_	:    Any           =     (
           self.df.limit(a_			)
           .repartition(1			)
           .mapInArrow(a_  ,     "batch_bytes: long"			)
           .agg(pyspark.sql.functions.sum("batch_bytes"			).alias("sample_bytes"			)			)
           .collect()[0]
           .sample_bytes
           / sample_num_rows
       )
       lowercase_	:    int           =     approx_bytes_per_row * df_num_rows
       if approx_total_size > max_shard_size:
          # Make sure there is at least one row per partition.
          lowercase_	:    Union[str, Any]           =     min(a_  ,     int(approx_total_size / max_shard_size			)			)
          lowercase_	:    int           =     self.df.repartition(a_			)




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Optional[Any]  ,     a  :       str  ,     a  :       List[str]  ,     )     ->   Optional[int]:






       '''simple docstring'''






       import pyspark

       lowercase_	:    str           =     ParquetWriter if file_format == "parquet" else ArrowWriter
       lowercase_	:    int           =     os.path.join(self._working_dir  ,     os.path.basename(a_			)			) if self._working_dir else fpath
       lowercase_	:    Optional[Any]           =     file_format == "parquet"

       # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
       # pickling the SparkContext.
       lowercase_	:    int           =     self.config.features
       lowercase_	:    Any           =     self._writer_batch_size
       lowercase_	:    Tuple           =     self._fs.storage_options

       def write_arrow(a  :       str			):
          # Within the same SparkContext, no two task attempts will share the same attempt ID.
          lowercase_	:    List[Any]           =     pyspark.TaskContext().taskAttemptId()
          lowercase_	:    Optional[int]           =     next(a_  ,     a_			)
          if first_batch is None:
             # Some partitions might not receive any data.
             return pa.RecordBatch.from_arrays(
                 [[task_id], [0], [0]]  ,     names=["task_id", "num_examples", "num_bytes"]  ,     )
          lowercase_	:    List[Any]           =     0
          lowercase_	:    Optional[int]           =     writer_class(
              features=a_  ,     path=working_fpath.replace("SSSSS"  ,     f"""{shard_id:05d}"""			).replace("TTTTT"  ,     f"""{task_id:05d}"""			)  ,     writer_batch_size=a_  ,     storage_options=a_  ,     embed_local_files=a_  ,     )
          lowercase_	:    Optional[Any]           =     pa.Table.from_batches([first_batch]			)
          writer.write_table(a_			)
          for batch in it:
             if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                lowercase_	:    List[str]           =     writer.finalize()
                writer.close()
                yield pa.RecordBatch.from_arrays(
                    [[task_id], [num_examples], [num_bytes]]  ,     names=["task_id", "num_examples", "num_bytes"]  ,     )
                shard_id += 1
                lowercase_	:    List[str]           =     writer_class(
                    features=writer._features  ,     path=working_fpath.replace("SSSSS"  ,     f"""{shard_id:05d}"""			).replace("TTTTT"  ,     f"""{task_id:05d}"""			)  ,     writer_batch_size=a_  ,     storage_options=a_  ,     embed_local_files=a_  ,     )
             lowercase_	:    Optional[int]           =     pa.Table.from_batches([batch]			)
             writer.write_table(a_			)

          if writer._num_bytes > 0:
             lowercase_	:    Dict           =     writer.finalize()
             writer.close()
             yield pa.RecordBatch.from_arrays(
                 [[task_id], [num_examples], [num_bytes]]  ,     names=["task_id", "num_examples", "num_bytes"]  ,     )

          if working_fpath != fpath:
             for file in os.listdir(os.path.dirname(a_			)			):
                lowercase_	:    str           =     os.path.join(os.path.dirname(a_			)  ,     os.path.basename(a_			)			)
                shutil.move(a_  ,     a_			)

       lowercase_	:    int           =     (
           self.df.mapInArrow(a_  ,     "task_id: long, num_examples: long, num_bytes: long"			)
           .groupBy("task_id"			)
           .agg(
               pyspark.sql.functions.sum("num_examples"			).alias("total_num_examples"			)  ,     pyspark.sql.functions.sum("num_bytes"			).alias("total_num_bytes"			)  ,     pyspark.sql.functions.count("num_bytes"			).alias("num_shards"			)  ,     pyspark.sql.functions.collect_list("num_examples"			).alias("shard_lengths"			)  ,     )
           .collect()
       )
       for row in stats:
          yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       int  ,     a  :       Dict = "arrow"  ,     a  :       List[Any] = None  ,     a  :       Dict = None  ,     **a  :       Optional[Any]  ,     )     ->   List[Any]:






       '''simple docstring'''






       self._validate_cache_dir()

       lowercase_	:    Union[str, Any]           =     convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE			)
       self._repartition_df_if_needed(a_			)
       lowercase_	:    Dict           =     not is_remote_filesystem(self._fs			)
       lowercase_	:    List[str]           =     os.path.join if is_local else posixpath.join

       lowercase_	:    Any           =     "-TTTTT-SSSSS-of-NNNNN"
       lowercase_	:    List[Any]           =     f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
       lowercase_	:    int           =     path_join(self._output_dir  ,     a_			)

       lowercase_	:    int           =     0
       lowercase_	:    Optional[Any]           =     0
       lowercase_	:    int           =     0
       lowercase_	:    Dict           =     []
       lowercase_	:    Any           =     []

       for task_id, content in self._prepare_split_single(a_  ,     a_  ,     a_			):
          (
              lowercase_
          )	:    Tuple           =     content
          if num_bytes > 0:
             total_num_examples += num_examples
             total_num_bytes += num_bytes
             total_shards += num_shards
             task_id_and_num_shards.append((task_id, num_shards)			)
             all_shard_lengths.extend(a_			)

       lowercase_	:    Dict           =     total_num_examples
       lowercase_	:    Any           =     total_num_bytes

       # should rename everything at the end
       logger.debug(f"""Renaming {total_shards} shards."""			)
       if total_shards > 1:
          lowercase_	:    List[Any]           =     all_shard_lengths

          # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
          # pickling error due to pickling the SparkContext.
          lowercase_	:    Any           =     self._fs

          # use the -SSSSS-of-NNNNN pattern
          def _rename_shard(
              a  :       int  ,     a  :       Any  ,     a  :       Union[str, Any]  ,     ):
             rename(
                 a_  ,     fpath.replace("SSSSS"  ,     f"""{shard_id:05d}"""			).replace("TTTTT"  ,     f"""{task_id:05d}"""			)  ,     fpath.replace("TTTTT-SSSSS"  ,     f"""{global_shard_id:05d}"""			).replace("NNNNN"  ,     f"""{total_shards:05d}"""			)  ,     )

          lowercase_	:    Optional[int]           =     []
          lowercase_	:    Dict           =     0
          for i in range(len(a_			)			):
             lowercase_	:    Tuple           =     task_id_and_num_shards[i]
             for shard_id in range(a_			):
                args.append([task_id, shard_id, global_shard_id]			)
                global_shard_id += 1
          self._spark.sparkContext.parallelize(a_  ,     len(a_			)			).map(lambda a			: _rename_shard(*a_			)			).collect()
       else:
          # don't use any pattern
          lowercase_	:    int           =     0
          lowercase_	:    Optional[int]           =     task_id_and_num_shards[0][0]
          self._rename(
              fpath.replace("SSSSS"  ,     f"""{shard_id:05d}"""			).replace("TTTTT"  ,     f"""{task_id:05d}"""			)  ,     fpath.replace(a_  ,     ""			)  ,     )





    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       str  ,     )     ->   Any:






       '''simple docstring'''






       return SparkExamplesIterable(self.df			)












'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

UpperCamelCase__ =			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=8							):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   lowercase_	:    Union[str, Any]           =     width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=512						,		_UpperCamelCase=512							):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     pil_image.resize((w, h)						,		resample=Image.BICUBIC						,		reducing_gap=1							)
   lowercase_	:    str           =     np.array(pil_image.convert("RGB"							)							)
   lowercase_	:    Optional[int]           =     arr.astype(np.floataa							) / 127.5 - 1
   lowercase_	:    int           =     np.transpose(_UpperCamelCase						,		[2, 0, 1]							)
   lowercase_	:    str           =     torch.from_numpy(_UpperCamelCase							).unsqueeze(0							)
   return image





class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       List[Any]  ,     a  :       UNetaDConditionModel  ,     a  :       DDPMScheduler  ,     a  :       VQModel  ,     ):






       '''simple docstring'''






       super().__init__()

       self.register_modules(
           unet=a  ,     scheduler=a  ,     movq=a  ,     )
       lowercase_	:    Dict           =     2 ** (len(self.movq.config.block_out_channels			) - 1)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Tuple  ,     a  :       List[str]  ,     a  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Dict           =     min(int(num_inference_steps * strength			)  ,     a			)

       lowercase_	:    str           =     max(num_inference_steps - init_timestep  ,     0			)
       lowercase_	:    Tuple           =     self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       int  ,     a  :       List[Any]  ,     a  :       Tuple  ,     a  :       Union[str, Any]  ,     a  :       int  ,     a  :       Tuple  ,     a  :       Optional[Any]=None			):






       '''simple docstring'''






       if not isinstance(a  ,     (torch.Tensor, PIL.Image.Image, list)			):
          raise ValueError(
              f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a			)}"""			)

       lowercase_	:    str           =     image.to(device=a  ,     dtype=a			)

       lowercase_	:    Any           =     batch_size * num_images_per_prompt

       if image.shape[1] == 4:
          lowercase_	:    str           =     image

       else:
          if isinstance(a  ,     a			) and len(a			) != batch_size:
             raise ValueError(
                 f"""You have passed a list of generators of length {len(a			)}, but requested an effective batch"""
                 f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""			)

          elif isinstance(a  ,     a			):
             lowercase_	:    str           =     [
                 self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(a			)
             ]
             lowercase_	:    List[Any]           =     torch.cat(a  ,     dim=0			)
          else:
             lowercase_	:    Tuple           =     self.movq.encode(a			).latent_dist.sample(a			)

          lowercase_	:    Union[str, Any]           =     self.movq.config.scaling_factor * init_latents

       lowercase_	:    Tuple           =     torch.cat([init_latents]  ,     dim=0			)

       lowercase_	:    List[Any]           =     init_latents.shape
       lowercase_	:    Union[str, Any]           =     randn_tensor(a  ,     generator=a  ,     device=a  ,     dtype=a			)

       # get latents
       lowercase_	:    Dict           =     self.scheduler.add_noise(a  ,     a  ,     a			)

       lowercase_	:    Tuple           =     init_latents

       return latents




    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       str=0			):






       '''simple docstring'''






       if is_accelerate_available():
          from accelerate import cpu_offload
       else:
          raise ImportError("Please install accelerate via `pip install accelerate`"			)

       lowercase_	:    Optional[Any]           =     torch.device(f"""cuda:{gpu_id}"""			)

       lowercase_	:    Optional[Any]           =     [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
          if cpu_offloaded_model is not None:
             cpu_offload(a  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       Union[str, Any]=0			):






       '''simple docstring'''






       if is_accelerate_available() and is_accelerate_version(">="  ,     "0.17.0.dev0"			):
          from accelerate import cpu_offload_with_hook
       else:
          raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."			)

       lowercase_	:    Any           =     torch.device(f"""cuda:{gpu_id}"""			)

       if self.device.type != "cpu":
          self.to("cpu"  ,     silence_dtype_warnings=a			)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       lowercase_	:    Optional[int]           =     None
       for cpu_offloaded_model in [self.unet, self.movq]:
          lowercase_    ,		lowercase_	:    Union[str, Any]           =     cpu_offload_with_hook(a  ,     a  ,     prev_module_hook=a			)

       # We'll offload the last model manually.
       lowercase_	:    List[Any]           =     hook




    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       if not hasattr(self.unet  ,     "_hf_hook"			):
          return self.device
       for module in self.unet.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





    @torch.no_grad()
    @replace_example_docstring(a			)
    def __call__(			self  :       Optional[int]  ,     a  :       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,     a  :       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]  ,     a  :       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,     a  :       int = 5_1_2  ,     a  :       int = 5_1_2  ,     a  :       int = 1_0_0  ,     a  :       float = 4.0  ,     a  :       float = 0.3  ,     a  :       int = 1  ,     a  :       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a  :       Optional[str] = "pil"  ,     a  :       bool = True  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self._execution_device

       lowercase_	:    Dict           =     guidance_scale > 1.0

       if isinstance(a  ,     a			):
          lowercase_	:    Dict           =     torch.cat(a  ,     dim=0			)
       lowercase_	:    Dict           =     image_embeds.shape[0]
       if isinstance(a  ,     a			):
          lowercase_	:    str           =     torch.cat(a  ,     dim=0			)

       if do_classifier_free_guidance:
          lowercase_	:    Optional[Any]           =     image_embeds.repeat_interleave(a  ,     dim=0			)
          lowercase_	:    int           =     negative_image_embeds.repeat_interleave(a  ,     dim=0			)

       lowercase_	:    int           =     torch.cat([negative_image_embeds, image_embeds]  ,     dim=0			).to(dtype=self.unet.dtype  ,     device=a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [image]
       if not all(isinstance(a  ,     (PIL.Image.Image, torch.Tensor)			) for i in image			):
          raise ValueError(
              f"""Input is in incorrect format: {[type(a			) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""			)

       lowercase_	:    List[Any]           =     torch.cat([prepare_image(a  ,     a  ,     a			) for i in image]  ,     dim=0			)
       lowercase_	:    List[Any]           =     image.to(dtype=image_embeds.dtype  ,     device=a			)

       lowercase_	:    Optional[int]           =     self.movq.encode(a			)["latents"]
       lowercase_	:    Dict           =     latents.repeat_interleave(a  ,     dim=0			)
       self.scheduler.set_timesteps(a  ,     device=a			)
       lowercase_    ,		lowercase_	:    List[Any]           =     self.get_timesteps(a  ,     a  ,     a			)
       lowercase_	:    Tuple           =     timesteps[:1].repeat(batch_size * num_images_per_prompt			)
       lowercase_    ,		lowercase_	:    Optional[Any]           =     downscale_height_and_width(a  ,     a  ,     self.movq_scale_factor			)
       lowercase_	:    Tuple           =     self.prepare_latents(
           a  ,     a  ,     a  ,     a  ,     image_embeds.dtype  ,     a  ,     a			)
       for i, t in enumerate(self.progress_bar(a			)			):
          # expand the latents if we are doing classifier free guidance
          lowercase_	:    Tuple           =     torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

          lowercase_	:    int           =     {"image_embeds": image_embeds}
          lowercase_	:    Optional[int]           =     self.unet(
              sample=a  ,     timestep=a  ,     encoder_hidden_states=a  ,     added_cond_kwargs=a  ,     return_dict=a  ,     )[0]

          if do_classifier_free_guidance:
             lowercase_    ,		lowercase_	:    List[Any]           =     noise_pred.split(latents.shape[1]  ,     dim=1			)
             lowercase_    ,		lowercase_	:    int           =     noise_pred.chunk(2			)
             lowercase_    ,		lowercase_	:    Any           =     variance_pred.chunk(2			)
             lowercase_	:    Dict           =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             lowercase_	:    Optional[Any]           =     torch.cat([noise_pred, variance_pred_text]  ,     dim=1			)

          if not (
              hasattr(self.scheduler.config  ,     "variance_type"			)
              and self.scheduler.config.variance_type in ["learned", "learned_range"]
          ):
             lowercase_    ,		lowercase_	:    List[Any]           =     noise_pred.split(latents.shape[1]  ,     dim=1			)

          # compute the previous noisy sample x_t -> x_t-1
          lowercase_	:    Dict           =     self.scheduler.step(
              a  ,     a  ,     a  ,     generator=a  ,     )[0]

       # post-processing
       lowercase_	:    Optional[Any]           =     self.movq.decode(a  ,     force_not_quantize=a			)["sample"]

       if output_type not in ["pt", "np", "pil"]:
          raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""			)

       if output_type in ["np", "pil"]:
          lowercase_	:    Tuple           =     image * 0.5 + 0.5
          lowercase_	:    Any           =     image.clamp(0  ,     1			)
          lowercase_	:    Any           =     image.cpu().permute(0  ,     2  ,     3  ,     1			).float().numpy()

       if output_type == "pil":
          lowercase_	:    Tuple           =     self.numpy_to_pil(a			)

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=a			)




