

'''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


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

if TYPE_CHECKING:
    import pyspark


@dataclass
class    a__  (							datasets.BuilderConfig  ):
   _SCREAMING_SNAKE_CASE    :     Optional[datasets.Features]	       =							None







def 					_A		(	snake_case		,							snake_case		,							)			->	Union[str, Any]:
   import pyspark

   def generate_fn():
      _lowercase			:      int			     =   df.select("*"		,							pyspark.sql.functions.spark_partition_id().alias("part_id"     )     )
      for partition_id in partition_order:
         _lowercase			:      Optional[int]			     =   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    a__  (							_BaseExamplesIterable  ):


   def __init__( self			,				_UpperCamelCase			,				_UpperCamelCase=None			,				):





      """simple docstring"""





      _lowercase			:      int			     =   df
      _lowercase			:      Tuple			     =   partition_order or range(self.df.rdd.getNumPartitions()  )
      _lowercase			:      Dict			     =   _generate_iterable_examples(self.df			,				self.partition_order  )


   def __iter__( self  ):





      """simple docstring"""





      yield from self.generate_examples_fn()


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Dict			     =   list(range(self.df.rdd.getNumPartitions()  )  )
      generator.shuffle(lowerCAmelCase__  )
      return SparkExamplesIterable(self.df			,				partition_order=lowerCAmelCase__  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[Any]			     =   self.split_shard_indices_by_worker(lowerCAmelCase__			,				lowerCAmelCase__  )
      return SparkExamplesIterable(self.df			,				partition_order=lowerCAmelCase__  )






   @property
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      return len(self.partition_order  )




class    a__  (							datasets.DatasetBuilder  ):
   _SCREAMING_SNAKE_CASE    :     Dict	       =							SparkConfig


   def __init__( self			,				_UpperCamelCase			,				_UpperCamelCase = None			,				_UpperCamelCase = None			,				**_UpperCamelCase			,				):





      """simple docstring"""





      import pyspark

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

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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      def create_cache_and_write_probe(_UpperCamelCase  ):
         # 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=lowerCAmelCase__  )
         _lowercase			:      Dict			     =   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(lowerCAmelCase__			,				"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			:      Union[str, Any]			     =   (
             self._spark.sparkContext.parallelize(range(1  )			,				1  ).mapPartitions(lowerCAmelCase__  ).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  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      import pyspark

      def get_arrow_batch_size(_UpperCamelCase  ):
         for batch in it:
            yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}  )

      _lowercase			:      Dict			     =   self.df.count()
      _lowercase			:      str			     =   df_num_rows if df_num_rows <= 100 else 100
      # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
      _lowercase			:      Optional[Any]			     =   (
          self.df.limit(lowerCAmelCase__  )
          .repartition(1  )
          .mapInArrow(lowerCAmelCase__			,				"batch_bytes: long"  )
          .agg(pyspark.sql.functions.sum("batch_bytes"  ).alias("sample_bytes"  )  )
          .collect()[0]
          .sample_bytes
          / sample_num_rows
      )
      _lowercase			:      Any			     =   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			:      Any			     =   min(lowerCAmelCase__			,				int(approx_total_size / max_shard_size  )  )
         _lowercase			:      Union[str, Any]			     =   self.df.repartition(lowerCAmelCase__  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				):





      """simple docstring"""





      import pyspark

      _lowercase			:      List[Any]			     =   ParquetWriter if file_format == "parquet" else ArrowWriter
      _lowercase			:      str			     =   os.path.join(self._working_dir			,				os.path.basename(lowerCAmelCase__  )  ) if self._working_dir else fpath
      _lowercase			:      List[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			:      Optional[Any]			     =   self.config.features
      _lowercase			:      Tuple			     =   self._writer_batch_size
      _lowercase			:      Union[str, Any]			     =   self._fs.storage_options

      def write_arrow(_UpperCamelCase  ):
         # Within the same SparkContext, no two task attempts will share the same attempt ID.
         _lowercase			:      Optional[int]			     =   pyspark.TaskContext().taskAttemptId()
         _lowercase			:      Dict			     =   next(lowerCAmelCase__			,				lowerCAmelCase__  )
         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[str]			     =   0
         _lowercase			:      Optional[int]			     =   writer_class(
             features=lowerCAmelCase__			,				path=working_fpath.replace("SSSSS"			,				f'''{shard_id:05d}'''  ).replace("TTTTT"			,				f'''{task_id:05d}'''  )			,				writer_batch_size=lowerCAmelCase__			,				storage_options=lowerCAmelCase__			,				embed_local_files=lowerCAmelCase__			,				)
         _lowercase			:      int			     =   pa.Table.from_batches([first_batch]  )
         writer.write_table(lowerCAmelCase__  )
         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=lowerCAmelCase__			,				storage_options=lowerCAmelCase__			,				embed_local_files=lowerCAmelCase__			,				)
            _lowercase			:      List[str]			     =   pa.Table.from_batches([batch]  )
            writer.write_table(lowerCAmelCase__  )

         if writer._num_bytes > 0:
            _lowercase			:      Any			     =   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(lowerCAmelCase__  )  ):
               _lowercase			:      Union[str, Any]			     =   os.path.join(os.path.dirname(lowerCAmelCase__  )			,				os.path.basename(lowerCAmelCase__  )  )
               shutil.move(lowerCAmelCase__			,				lowerCAmelCase__  )

      _lowercase			:      Any			     =   (
          self.df.mapInArrow(lowerCAmelCase__			,				"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			,				_UpperCamelCase			,				_UpperCamelCase = "arrow"			,				_UpperCamelCase = None			,				_UpperCamelCase = None			,				**_UpperCamelCase			,				):





      """simple docstring"""





      self._validate_cache_dir()

      _lowercase			:      List[str]			     =   convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE  )
      self._repartition_df_if_needed(lowerCAmelCase__  )
      _lowercase			:      Optional[int]			     =   not is_remote_filesystem(self._fs  )
      _lowercase			:      List[str]			     =   os.path.join if is_local else posixpath.join

      _lowercase			:      Optional[Any]			     =   "-TTTTT-SSSSS-of-NNNNN"
      _lowercase			:      Optional[Any]			     =   f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
      _lowercase			:      int			     =   path_join(self._output_dir			,				lowerCAmelCase__  )

      _lowercase			:      List[Any]			     =   0
      _lowercase			:      Optional[Any]			     =   0
      _lowercase			:      List[Any]			     =   0
      _lowercase			:      int			     =   []
      _lowercase			:      List[Any]			     =   []

      for task_id, content in self._prepare_split_single(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  ):
         (
             _lowercase       
         )			:      Optional[int]			     =   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(lowerCAmelCase__  )

      _lowercase			:      Tuple			     =   total_num_examples
      _lowercase			:      List[Any]			     =   total_num_bytes

      # should rename everything at the end
      logger.debug(f'''Renaming {total_shards} shards.'''  )
      if total_shards > 1:
         _lowercase			:      List[str]			     =   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			:      List[Any]			     =   self._fs

         # use the -SSSSS-of-NNNNN pattern
         def _rename_shard(
             _UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				):
            rename(
                lowerCAmelCase__			,				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			:      Dict			     =   []
         _lowercase			:      List[Any]			     =   0
         for i in range(len(lowerCAmelCase__  )  ):
            _lowercase			:      Any			     =   task_id_and_num_shards[i]
            for shard_id in range(lowerCAmelCase__  ):
               args.append([task_id, shard_id, global_shard_id]  )
               global_shard_id += 1
         self._spark.sparkContext.parallelize(lowerCAmelCase__			,				len(lowerCAmelCase__  )  ).map(lambda _UpperCamelCase  : _rename_shard(*lowerCAmelCase__  )  ).collect()
      else:
         # don't use any pattern
         _lowercase			:      Optional[Any]			     =   0
         _lowercase			:      Optional[Any]			     =   task_id_and_num_shards[0][0]
         self._rename(
             fpath.replace("SSSSS"			,				f'''{shard_id:05d}'''  ).replace("TTTTT"			,				f'''{task_id:05d}'''  )			,				fpath.replace(lowerCAmelCase__			,				""  )			,				)






   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				):





      """simple docstring"""





      return SparkExamplesIterable(self.df  )













"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					)	-> int:
								super().__init__()
								self.register_modules(transformer=lowerCAmelCase__      ,					vae=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )

								# create a imagenet -> id dictionary for easier use
								a      :		str	=				{}
								if idalabel is not None:
									for key, value in idalabel.items():
										for label in value.split(","      ):
											a      :		int	=				int(lowerCAmelCase__      )
									a      :		Any	=				dict(sorted(self.labels.items()      )      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[int]:

								if not isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[Any]	=				list(lowerCAmelCase__      )

								for l in label:
									if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

								return [self.labels[l] for l in label]






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = 4.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 50      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[ImagePipelineOutput, Tuple]:
								a      :		Dict	=				len(lowerCAmelCase__      )
								a      :		Tuple	=				self.transformer.config.sample_size
								a      :		Tuple	=				self.transformer.config.in_channels

								a      :		Optional[int]	=				randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=self.transformer.dtype      ,					)
								a      :		Tuple	=				torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

								a      :		List[str]	=				torch.tensor(lowerCAmelCase__      ,					device=self.device      ).reshape(-1      )
								a      :		str	=				torch.tensor([1000] * batch_size      ,					device=self.device      )
								a      :		Dict	=				torch.cat([class_labels, class_null]      ,					0      ) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(lowerCAmelCase__      )

								for t in self.progress_bar(self.scheduler.timesteps      ):
									if guidance_scale > 1:
										a      :		Any	=				latent_model_input[: len(lowerCAmelCase__      ) // 2]
										a      :		Tuple	=				torch.cat([half, half]      ,					dim=0      )
									a      :		List[str]	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )

									a      :		Dict	=				t
									if not torch.is_tensor(lowerCAmelCase__      ):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										a      :		Optional[int]	=				latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
											a      :		Union[str, Any]	=				torch.floataa if is_mps else torch.floataa
										else:
											a      :		str	=				torch.intaa if is_mps else torch.intaa
										a      :		List[str]	=				torch.tensor([timesteps]      ,					dtype=lowerCAmelCase__      ,					device=latent_model_input.device      )
									elif len(timesteps.shape      ) == 0:
										a      :		List[Any]	=				timesteps[None].to(latent_model_input.device      )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									a      :		Dict	=				timesteps.expand(latent_model_input.shape[0]      )
									# predict noise model_output
									a      :		Union[str, Any]	=				self.transformer(
									    lowerCAmelCase__      ,					timestep=lowerCAmelCase__      ,					class_labels=lowerCAmelCase__      ).sample

									# perform guidance
									if guidance_scale > 1:
										a,       a      :		Any	=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										a,       a      :		Union[str, Any]	=				torch.split(lowerCAmelCase__      ,					len(lowerCAmelCase__      ) // 2      ,					dim=0      )

										a      :		Dict	=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										a      :		Optional[int]	=				torch.cat([half_eps, half_eps]      ,					dim=0      )

										a      :		Optional[int]	=				torch.cat([eps, rest]      ,					dim=1      )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
										a,       a      :		str	=				torch.split(lowerCAmelCase__      ,					lowerCAmelCase__      ,					dim=1      )
									else:
										a      :		Any	=				noise_pred

									# compute previous image: x_t -> x_t-1
									a      :		Optional[int]	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								if guidance_scale > 1:
									a,       a      :		Tuple	=				latent_model_input.chunk(2      ,					dim=0      )
								else:
									a      :		Tuple	=				latent_model_input

								a      :		Optional[Any]	=				1 / self.vae.config.scaling_factor * latents
								a      :		Any	=				self.vae.decode(lowerCAmelCase__      ).sample

								a      :		Tuple	=				(samples / 2 + 0.5).clamp(0      ,					1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a      :		Optional[Any]	=				samples.cpu().permute(0      ,					2      ,					3      ,					1      ).float().numpy()

								if output_type == "pil":
									a      :		int	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (samples,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


