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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    torch.FloatTensor




class  __magic_name__  (		__a      ,	__a    ):





   """simple docstring"""




   @register_to_config
   def __init__(	self					:  Optional[int]		,  _lowercase					:  int = 65_536		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  int = 2		,  _lowercase					:  int = 2		,  _lowercase					:  int = 0		,  _lowercase					:  str = "fourier"		,  _lowercase					:  bool = True		,  _lowercase					:  bool = False		,  _lowercase					:  float = 0.0		,  _lowercase					:  Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,  _lowercase					:  Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,  _lowercase					:  Tuple[str] = "UNetMidBlock1D"		,  _lowercase					:  str = None		,  _lowercase					:  Tuple[int] = (32, 32, 64)		,  _lowercase					:  str = None		,  _lowercase					:  int = 8		,  _lowercase					:  int = 1		,  _lowercase					:  bool = False		,  ):



    """simple docstring"""



    super().__init__()
    _UpperCamelCase:     Union[str, Any]							= sample_size

    # time
    if time_embedding_type == "fourier":
     _UpperCamelCase:     List[str]							= GaussianFourierProjection(
         embedding_size=8		,  set_W_to_weight=_lowercase		,  log=_lowercase		,  flip_sin_to_cos=_lowercase							)
     _UpperCamelCase:     List[str]							= 2 * block_out_channels[0]
    elif time_embedding_type == "positional":
     _UpperCamelCase:     List[str]							= Timesteps(
         block_out_channels[0]		,  flip_sin_to_cos=_lowercase		,  downscale_freq_shift=_lowercase							)
     _UpperCamelCase:     Tuple							= block_out_channels[0]

    if use_timestep_embedding:
     _UpperCamelCase:     Optional[Any]							= block_out_channels[0] * 4
     _UpperCamelCase:     Dict							= TimestepEmbedding(
         in_channels=_lowercase		,  time_embed_dim=_lowercase		,  act_fn=_lowercase		,  out_dim=block_out_channels[0]		,  )

    _UpperCamelCase:     List[str]							= nn.ModuleList([]							)
    _UpperCamelCase:     Optional[Any]							= None
    _UpperCamelCase:     int							= nn.ModuleList([]							)
    _UpperCamelCase:     Union[str, Any]							= None

    # down
    _UpperCamelCase:     Dict							= in_channels
    for i, down_block_type in enumerate(_lowercase							):
     _UpperCamelCase:     Dict							= output_channel
     _UpperCamelCase:     Tuple							= block_out_channels[i]

     if i == 0:
      input_channel += extra_in_channels

     _UpperCamelCase:     int							= i == len(_lowercase							) - 1

     _UpperCamelCase:     Union[str, Any]							= get_down_block(
         _lowercase		,  num_layers=_lowercase		,  in_channels=_lowercase		,  out_channels=_lowercase		,  temb_channels=block_out_channels[0]		,  add_downsample=not is_final_block or downsample_each_block		,  )
     self.down_blocks.append(_lowercase							)

    # mid
    _UpperCamelCase:     Tuple							= get_mid_block(
        _lowercase		,  in_channels=block_out_channels[-1]		,  mid_channels=block_out_channels[-1]		,  out_channels=block_out_channels[-1]		,  embed_dim=block_out_channels[0]		,  num_layers=_lowercase		,  add_downsample=_lowercase		,  )

    # up
    _UpperCamelCase:     str							= list(reversed(_lowercase							)							)
    _UpperCamelCase:     List[Any]							= reversed_block_out_channels[0]
    if out_block_type is None:
     _UpperCamelCase:     List[Any]							= out_channels
    else:
     _UpperCamelCase:     str							= block_out_channels[0]

    for i, up_block_type in enumerate(_lowercase							):
     _UpperCamelCase:     List[str]							= output_channel
     _UpperCamelCase:     List[Any]							= (
         reversed_block_out_channels[i + 1] if i < len(_lowercase							) - 1 else final_upsample_channels
     )

     _UpperCamelCase:     str							= i == len(_lowercase							) - 1

     _UpperCamelCase:     List[Any]							= get_up_block(
         _lowercase		,  num_layers=_lowercase		,  in_channels=_lowercase		,  out_channels=_lowercase		,  temb_channels=block_out_channels[0]		,  add_upsample=not is_final_block		,  )
     self.up_blocks.append(_lowercase							)
     _UpperCamelCase:     Union[str, Any]							= output_channel

    # out
    _UpperCamelCase:     Union[str, Any]							= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,  32							)
    _UpperCamelCase:     int							= get_out_block(
        out_block_type=_lowercase		,  num_groups_out=_lowercase		,  embed_dim=block_out_channels[0]		,  out_channels=_lowercase		,  act_fn=_lowercase		,  fc_dim=block_out_channels[-1] // 4		,  )



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  torch.FloatTensor		,  _lowercase					:  Union[torch.Tensor, float, int]		,  _lowercase					:  bool = True		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= timestep
    if not torch.is_tensor(_lowercase							):
     _UpperCamelCase:     List[str]							= torch.tensor([timesteps]		,  dtype=torch.long		,  device=sample.device							)
    elif torch.is_tensor(_lowercase							) and len(timesteps.shape							) == 0:
     _UpperCamelCase:     Tuple							= timesteps[None].to(sample.device							)

    _UpperCamelCase:     int							= self.time_proj(_lowercase							)
    if self.config.use_timestep_embedding:
     _UpperCamelCase:     int							= self.time_mlp(_lowercase							)
    else:
     _UpperCamelCase:     Dict							= timestep_embed[..., None]
     _UpperCamelCase:     Optional[Any]							= timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
     _UpperCamelCase:     Optional[Any]							= timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

    # 2. down
    _UpperCamelCase:     str							= ()
    for downsample_block in self.down_blocks:
     _UpperCamelCase	,							_UpperCamelCase:     str							= downsample_block(hidden_states=_lowercase		,  temb=_lowercase							)
     down_block_res_samples += res_samples

    # 3. mid
    if self.mid_block:
     _UpperCamelCase:     str							= self.mid_block(_lowercase		,  _lowercase							)

    # 4. up
    for i, upsample_block in enumerate(self.up_blocks							):
     _UpperCamelCase:     Optional[int]							= down_block_res_samples[-1:]
     _UpperCamelCase:     int							= down_block_res_samples[:-1]
     _UpperCamelCase:     Tuple							= upsample_block(_lowercase		,  res_hidden_states_tuple=_lowercase		,  temb=_lowercase							)

    # 5. post-process
    if self.out_block:
     _UpperCamelCase:     Tuple							= self.out_block(_lowercase		,  _lowercase							)

    if not return_dict:
     return (sample,)

    return UNetaDOutput(sample=_lowercase							)
from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)




def       lowerCAmelCase_     (		lowercase:							Optional[Any]  ,					lowercase:							Tuple		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= []
 for part_id in partition_order:
  _UpperCamelCase:     int							= df.where(F"""SPARK_PARTITION_ID() = {part_id}"""		).collect()
  for row_idx, row in enumerate(lowercase		):
   expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict())		)
 return expected_row_ids_and_row_dicts




@require_not_windows
@require_dill_gt_0_3_2
def       lowerCAmelCase_     (		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     int							= spark.range(100		).repartition(1		)
 _UpperCamelCase:     int							= Spark(lowercase		)
 # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
 # that each partition can hold 2 rows.
 spark_builder._repartition_df_if_needed(max_shard_size=16		)
 # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
 assert spark_builder.df.rdd.getNumPartitions() == 50




@require_not_windows
@require_dill_gt_0_3_2
def       lowerCAmelCase_     (		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Optional[Any]							= spark.range(10		).repartition(2		)
 _UpperCamelCase:     int							= [1, 0]
 _UpperCamelCase:     Any							= _generate_iterable_examples(lowercase  ,					lowercase		)  # Reverse the partitions.
 _UpperCamelCase:     List[str]							= _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase  ,					lowercase		)

 for i, (row_id, row_dict) in enumerate(generate_fn()		):
  _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= expected_row_ids_and_row_dicts[i]
  assert row_id == expected_row_id
  assert row_dict == expected_row_dict




@require_not_windows
@require_dill_gt_0_3_2
def       lowerCAmelCase_     (		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Any							= spark.range(10		).repartition(1		)
 _UpperCamelCase:     int							= SparkExamplesIterable(lowercase		)
 assert it.n_shards == 1
 for i, (row_id, row_dict) in enumerate(lowercase		):
  assert row_id == F"""0_{i}"""
  assert row_dict == {"id": i}




@require_not_windows
@require_dill_gt_0_3_2
def       lowerCAmelCase_     (		)    ->				Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:     Any							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     str							= spark.range(30		).repartition(3		)
 # Mock the generator so that shuffle reverses the partition indices.
 with patch('''numpy.random.Generator'''		) as generator_mock:
  _UpperCamelCase:     Union[str, Any]							= lambda lowercase		: x.reverse()
  _UpperCamelCase:     List[str]							= _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase  ,					[2, 1, 0]		)

  _UpperCamelCase:     Union[str, Any]							= SparkExamplesIterable(lowercase		).shuffle_data_sources(lowercase		)
  assert shuffled_it.n_shards == 3
  for i, (row_id, row_dict) in enumerate(lowercase		):
   _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= expected_row_ids_and_row_dicts[i]
   assert row_id == expected_row_id
   assert row_dict == expected_row_dict




@require_not_windows
@require_dill_gt_0_3_2
def       lowerCAmelCase_     (		)    ->				Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:     str							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Tuple							= spark.range(20		).repartition(4		)

 # Partitions 0 and 2
 _UpperCamelCase:     List[Any]							= SparkExamplesIterable(lowercase		).shard_data_sources(worker_id=0  ,					num_workers=2		)
 assert shard_it_a.n_shards == 2
 _UpperCamelCase:     Dict							= _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase  ,					[0, 2]		)
 for i, (row_id, row_dict) in enumerate(lowercase		):
  _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= expected_row_ids_and_row_dicts_a[i]
  assert row_id == expected_row_id
  assert row_dict == expected_row_dict

 # Partitions 1 and 3
 _UpperCamelCase:     str							= SparkExamplesIterable(lowercase		).shard_data_sources(worker_id=1  ,					num_workers=2		)
 assert shard_it_a.n_shards == 2
 _UpperCamelCase:     str							= _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase  ,					[1, 3]		)
 for i, (row_id, row_dict) in enumerate(lowercase		):
  _UpperCamelCase	,							_UpperCamelCase:     str							= expected_row_ids_and_row_dicts_a[i]
  assert row_id == expected_row_id
  assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def       lowerCAmelCase_     (		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     Any							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Union[str, Any]							= spark.range(100		).repartition(1		)
 _UpperCamelCase:     Optional[Any]							= Spark(lowercase		)
 # Choose a small max_shard_size for maximum partitioning.
 spark_builder._repartition_df_if_needed(max_shard_size=1		)
 # The new number of partitions should not be greater than the number of rows.
 assert spark_builder.df.rdd.getNumPartitions() == 100