




'''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


a_      :		Any							      =       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
     import pyspark


@dataclass
class   __UpperCamelCase       ( datasets.BuilderConfig				):
       lowercase	:    Optional[datasets.Features]      						=None





def      a_							(      __snake_case							:			"pyspark.sql.DataFrame"   ,       __snake_case							:			List[int]   ,       )							->						Dict:



       """simple docstring"""
       import pyspark

       def generate_fn():
              lowerCamelCase_							=df.select('''*'''   ,       pyspark.sql.functions.spark_partition_id().alias('''part_id'''   )   )
              for partition_id in partition_order:
                     lowerCamelCase_							=df_with_partition_id.select('''*'''   ).where(F'''part_id = {partition_id}'''   ).drop('''part_id'''   )
                     lowerCamelCase_							=partition_df.collect()
                     lowerCamelCase_							=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,		lowerCAmelCase,		lowerCAmelCase=None,		):



              """simple docstring"""







              lowerCamelCase_							=df
              lowerCamelCase_							=partition_order or range(self.df.rdd.getNumPartitions()  )
              lowerCamelCase_							=_generate_iterable_examples(self.df,		self.partition_order  )






       def __iter__(							self  ):



              """simple docstring"""







              yield from self.generate_examples_fn()






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=list(range(self.df.rdd.getNumPartitions()  )  )
              generator.shuffle(_A  )
              return SparkExamplesIterable(self.df,		partition_order=_A  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=self.split_shard_indices_by_worker(_A,		_A  )
              return SparkExamplesIterable(self.df,		partition_order=_A  )






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return len(self.partition_order  )





class   __UpperCamelCase       ( datasets.DatasetBuilder				):
       lowercase	:    Optional[Any]      						=SparkConfig






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



              """simple docstring"""







              import pyspark

              lowerCamelCase_							=pyspark.sql.SparkSession.builder.getOrCreate()
              lowerCamelCase_							=df
              lowerCamelCase_							=working_dir

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






       def      lowercase__     (							self  ):



              """simple docstring"""







              def create_cache_and_write_probe(lowerCAmelCase  ):
                     # 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  )
                     lowerCamelCase_							=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:
                     lowerCamelCase_							=(
                         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      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              import pyspark

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

              lowerCamelCase_							=self.df.count()
              lowerCamelCase_							=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.
              lowerCamelCase_							=(
                  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
              )
              lowerCamelCase_							=approx_bytes_per_row * df_num_rows
              if approx_total_size > max_shard_size:
                     # Make sure there is at least one row per partition.
                     lowerCamelCase_							=min(_A,		int(approx_total_size / max_shard_size  )  )
                     lowerCamelCase_							=self.df.repartition(_A  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase,		):



              """simple docstring"""







              import pyspark

              lowerCamelCase_							=ParquetWriter if file_format == '''parquet''' else ArrowWriter
              lowerCamelCase_							=os.path.join(self._working_dir,		os.path.basename(_A  )  ) if self._working_dir else fpath
              lowerCamelCase_							=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.
              lowerCamelCase_							=self.config.features
              lowerCamelCase_							=self._writer_batch_size
              lowerCamelCase_							=self._fs.storage_options

              def write_arrow(lowerCAmelCase  ):
                     # Within the same SparkContext, no two task attempts will share the same attempt ID.
                     lowerCamelCase_							=pyspark.TaskContext().taskAttemptId()
                     lowerCamelCase_							=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'''],		)
                     lowerCamelCase_							=0
                     lowerCamelCase_							=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,		)
                     lowerCamelCase_							=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:
                                   lowerCamelCase_,      lowerCamelCase_							=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
                                   lowerCamelCase_							=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,		)
                            lowerCamelCase_							=pa.Table.from_batches([batch]  )
                            writer.write_table(_A  )

                     if writer._num_bytes > 0:
                            lowerCamelCase_,      lowerCamelCase_							=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  )  ):
                                   lowerCamelCase_							=os.path.join(os.path.dirname(_A  ),		os.path.basename(_A  )  )
                                   shutil.move(_A,		_A  )

              lowerCamelCase_							=(
                  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      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase = "arrow",		lowerCAmelCase = None,		lowerCAmelCase = None,		**lowerCAmelCase,		):



              """simple docstring"""







              self._validate_cache_dir()

              lowerCamelCase_							=convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE  )
              self._repartition_df_if_needed(_A  )
              lowerCamelCase_							=not is_remote_filesystem(self._fs  )
              lowerCamelCase_							=os.path.join if is_local else posixpath.join

              lowerCamelCase_							='''-TTTTT-SSSSS-of-NNNNN'''
              lowerCamelCase_							=f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
              lowerCamelCase_							=path_join(self._output_dir,		_A  )

              lowerCamelCase_							=0
              lowerCamelCase_							=0
              lowerCamelCase_							=0
              lowerCamelCase_							=[]
              lowerCamelCase_							=[]

              for task_id, content in self._prepare_split_single(_A,		_A,		_A  ):
                     (
                         (
                         lowerCamelCase_							
                     ),      (
                         lowerCamelCase_							
                     ),      (
                         lowerCamelCase_							
                     ),      (
                         lowerCamelCase_							
                     ),      							
                     )							=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  )

              lowerCamelCase_							=total_num_examples
              lowerCamelCase_							=total_num_bytes

              # should rename everything at the end
              logger.debug(f'''Renaming {total_shards} shards.'''  )
              if total_shards > 1:
                     lowerCamelCase_							=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.
                     lowerCamelCase_							=self._fs

                     # use the -SSSSS-of-NNNNN pattern
                     def _rename_shard(
                         lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase,		):
                            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}'''  ),		)

                     lowerCamelCase_							=[]
                     lowerCamelCase_							=0
                     for i in range(len(_A  )  ):
                            lowerCamelCase_,      lowerCamelCase_							=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 lowerCAmelCase  : _rename_shard(*_A  )  ).collect()
              else:
                     # don't use any pattern
                     lowerCamelCase_							=0
                     lowerCamelCase_							=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      lowercase__     (							self,		lowerCAmelCase,		):



              """simple docstring"""







              return SparkExamplesIterable(self.df  )










import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					if hor == 1_28:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 64, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					__lowerCAmelCase				   =       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""     )
					__lowerCAmelCase				   =       model.state_dict()
					__lowerCAmelCase				   =       {
					    "down_block_types": down_block_types,
					    "block_out_channels": block_out_channels,
					    "up_block_types": up_block_types,
					    "layers_per_block": 1,
					    "use_timestep_embedding": True,
					    "out_block_type": "OutConv1DBlock",
					    "norm_num_groups": 8,
					    "downsample_each_block": False,
					    "in_channels": 14,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "sample_size": 6_55_36,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )
					__lowerCAmelCase				   =       dict(zip(model.state_dict().keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					torch.save(hf_value_function.state_dict()      ,      F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""     )
					with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""      ,      "w"     ) as f:
										json.dump(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					__lowerCAmelCase				   =       {
					    "in_channels": 14,
					    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
					    "up_block_types": (),
					    "out_block_type": "ValueFunction",
					    "mid_block_type": "ValueFunctionMidBlock1D",
					    "block_out_channels": (32, 64, 1_28, 2_56),
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "sample_size": 6_55_36,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "use_timestep_embedding": True,
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "norm_num_groups": 8,
					    "act_fn": "mish",
					}

					__lowerCAmelCase				   =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"     )
					__lowerCAmelCase				   =       model
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )

					__lowerCAmelCase				   =       dict(zip(state_dict.keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )

					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					torch.save(hf_value_function.state_dict()      ,      "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"     )
					with open("hub/hopper-medium-v2/value_function/config.json"      ,      "w"     ) as f:
										json.dump(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
					unet(32)
					# unet(128)
					value_function()




