





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   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						42


class   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase     ):
 @register_to_config
 def __init__( self    : Dict     ,	UpperCAmelCase    : int = 65536     ,	UpperCAmelCase    : Optional[int] = None     ,	UpperCAmelCase    : int = 2     ,	UpperCAmelCase    : int = 2     ,	UpperCAmelCase    : int = 0     ,	UpperCAmelCase    : str = "fourier"     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     ,	UpperCAmelCase    : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     ,	UpperCAmelCase    : Tuple[str] = "UNetMidBlock1D"     ,	UpperCAmelCase    : str = None     ,	UpperCAmelCase    : Tuple[int] = (32, 32, 64)     ,	UpperCAmelCase    : str = None     ,	UpperCAmelCase    : int = 8     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : bool = False     ,	)     ->      Any:
     super().__init__()
     lowerCamelCase__       : List[str]   					=						sample_size

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

     if use_timestep_embedding:
         lowerCamelCase__       : str   					=						block_out_channels[0] * 4
         lowerCamelCase__       : Tuple   					=						TimestepEmbedding(
             in_channels=UpperCAmelCase     ,	time_embed_dim=UpperCAmelCase     ,	act_fn=UpperCAmelCase     ,	out_dim=block_out_channels[0]     ,	)

     lowerCamelCase__       : Any   					=						nn.ModuleList([]      )
     lowerCamelCase__       : Tuple   					=						None
     lowerCamelCase__       : Dict   					=						nn.ModuleList([]      )
     lowerCamelCase__       : Optional[int]   					=						None

     # down
     lowerCamelCase__       : Optional[Any]   					=						in_channels
     for i, down_block_type in enumerate(UpperCAmelCase      ):
         lowerCamelCase__       : Dict   					=						output_channel
         lowerCamelCase__       : int   					=						block_out_channels[i]

         if i == 0:
             input_channel += extra_in_channels

         lowerCamelCase__       : Optional[Any]   					=						i == len(UpperCAmelCase      ) - 1

         lowerCamelCase__       : List[str]   					=						get_down_block(
             UpperCAmelCase     ,	num_layers=UpperCAmelCase     ,	in_channels=UpperCAmelCase     ,	out_channels=UpperCAmelCase     ,	temb_channels=block_out_channels[0]     ,	add_downsample=not is_final_block or downsample_each_block     ,	)
         self.down_blocks.append(UpperCAmelCase      )

     # mid
     lowerCamelCase__       : int   					=						get_mid_block(
         UpperCAmelCase     ,	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=UpperCAmelCase     ,	add_downsample=UpperCAmelCase     ,	)

     # up
     lowerCamelCase__       : str   					=						list(reversed(UpperCAmelCase      )      )
     lowerCamelCase__       : List[Any]   					=						reversed_block_out_channels[0]
     if out_block_type is None:
         lowerCamelCase__       : str   					=						out_channels
     else:
         lowerCamelCase__       : Optional[int]   					=						block_out_channels[0]

     for i, up_block_type in enumerate(UpperCAmelCase      ):
         lowerCamelCase__       : Dict   					=						output_channel
         lowerCamelCase__       : int   					=						(
             reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase      ) - 1 else final_upsample_channels
         )

         lowerCamelCase__       : List[Any]   					=						i == len(UpperCAmelCase      ) - 1

         lowerCamelCase__       : Optional[Any]   					=						get_up_block(
             UpperCAmelCase     ,	num_layers=UpperCAmelCase     ,	in_channels=UpperCAmelCase     ,	out_channels=UpperCAmelCase     ,	temb_channels=block_out_channels[0]     ,	add_upsample=not is_final_block     ,	)
         self.up_blocks.append(UpperCAmelCase      )
         lowerCamelCase__       : Any   					=						output_channel

     # out
     lowerCamelCase__       : List[Any]   					=						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,	32      )
     lowerCamelCase__       : List[Any]   					=						get_out_block(
         out_block_type=UpperCAmelCase     ,	num_groups_out=UpperCAmelCase     ,	embed_dim=block_out_channels[0]     ,	out_channels=UpperCAmelCase     ,	act_fn=UpperCAmelCase     ,	fc_dim=block_out_channels[-1] // 4     ,	)







 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : torch.FloatTensor     ,	UpperCAmelCase    : Union[torch.Tensor, float, int]     ,	UpperCAmelCase    : bool = True     ,	)     ->      Union[UNetaDOutput, Tuple]:
     lowerCamelCase__       : Optional[int]   					=						timestep
     if not torch.is_tensor(UpperCAmelCase      ):
         lowerCamelCase__       : Optional[Any]   					=						torch.tensor([timesteps]     ,	dtype=torch.long     ,	device=sample.device      )
     elif torch.is_tensor(UpperCAmelCase      ) and len(timesteps.shape      ) == 0:
         lowerCamelCase__       : Any   					=						timesteps[None].to(sample.device      )

     lowerCamelCase__       : Optional[int]   					=						self.time_proj(UpperCAmelCase      )
     if self.config.use_timestep_embedding:
         lowerCamelCase__       : Optional[int]   					=						self.time_mlp(UpperCAmelCase      )
     else:
         lowerCamelCase__       : Optional[int]   					=						timestep_embed[..., None]
         lowerCamelCase__       : str   					=						timestep_embed.repeat([1, 1, sample.shape[2]]      ).to(sample.dtype      )
         lowerCamelCase__       : List[str]   					=						timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])      )

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

     # 3. mid
     if self.mid_block:
         lowerCamelCase__       : List[Any]   					=						self.mid_block(UpperCAmelCase     ,	UpperCAmelCase      )

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks      ):
         lowerCamelCase__       : List[str]   					=						down_block_res_samples[-1:]
         lowerCamelCase__       : List[Any]   					=						down_block_res_samples[:-1]
         lowerCamelCase__       : Optional[Any]   					=						upsample_block(UpperCAmelCase     ,	res_hidden_states_tuple=UpperCAmelCase     ,	temb=UpperCAmelCase      )

     # 5. post-process
     if self.out_block:
         lowerCamelCase__       : Optional[int]   					=						self.out_block(UpperCAmelCase     ,	UpperCAmelCase      )

     if not return_dict:
         return (sample,)

     return UNetaDOutput(sample=UpperCAmelCase      )












import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_UpperCAmelCase							:			Optional[Any]        =							datasets.utils.logging.get_logger(__name__)

@dataclass
class   lowerCAmelCase				(   datasets.BuilderConfig     ):
 UpperCAmelCase__						=						1_00_00
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None


class   lowerCAmelCase				(   datasets.ArrowBasedBuilder     ):
 UpperCAmelCase__						=						ParquetConfig
 def 	A_      ( self    : int      )     ->      Optional[int]:
     return datasets.DatasetInfo(features=self.config.features      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : str      )     ->      Any:
     if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""      )
     lowerCamelCase__       : List[Any]   					=						dl_manager.download_and_extract(self.config.data_files      )
     if isinstance(UpperCAmelCase     ,	(str, list, tuple)      ):
         lowerCamelCase__       : int   					=						data_files
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : int   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : str   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,	gen_kwargs={'files': files}      )]
     lowerCamelCase__       : Optional[Any]   					=						[]
     for split_name, files in data_files.items():
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : Optional[Any]   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : Dict   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
             for file in itertools.chain.from_iterable(UpperCAmelCase      ):
                 with open(UpperCAmelCase     ,	'rb'      ) as f:
                     lowerCamelCase__       : Optional[Any]   					=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase      )      )
                 break
         splits.append(datasets.SplitGenerator(name=UpperCAmelCase     ,	gen_kwargs={'files': files}      )      )
     return splits
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : pa.Table      )     ->      pa.Table:
     if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowerCamelCase__       : Any   					=						table_cast(UpperCAmelCase     ,	self.info.features.arrow_schema      )
     return pa_table







 def 	A_      ( self    : Any     ,	UpperCAmelCase    : Tuple      )     ->      List[str]:
     lowerCamelCase__       : Optional[Any]   					=						self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema      ) != sorted(self.config.columns      ):
             raise ValueError(
                 F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""      )
     for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase      )      ):
         with open(UpperCAmelCase     ,	'rb'      ) as f:
             lowerCamelCase__       : List[Any]   					=						pq.ParquetFile(UpperCAmelCase      )
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size     ,	columns=self.config.columns      )      ):
                     lowerCamelCase__       : Tuple   					=						pa.Table.from_batches([record_batch]      )
                     # Uncomment for debugging (will print the Arrow table size and elements)
                     # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                     # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                     yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase      )
             except ValueError as e:
                 logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase      )}: {e}"""      )
                 raise





