

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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: str      ,      a							: int = 6_5536      ,      a							: Optional[int] = None      ,      a							: int = 2      ,      a							: int = 2      ,      a							: int = 0      ,      a							: str = "fourier"      ,      a							: bool = True      ,      a							: bool = False      ,      a							: float = 0.0      ,      a							: Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,      a							: Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,      a							: Tuple[str] = "UNetMidBlock1D"      ,      a							: str = None      ,      a							: Tuple[int] = (32, 32, 64)      ,      a							: str = None      ,      a							: int = 8      ,      a							: int = 1      ,      a							: bool = False      ,      )							->       Optional[int]:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE   :      List[str]				     =       sample_size

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

				if use_timestep_embedding:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       block_out_channels[0] * 4
					SCREAMING_SNAKE_CASE   :      Dict				     =       TimestepEmbedding(
					    in_channels=a      ,      time_embed_dim=a      ,      act_fn=a      ,      out_dim=block_out_channels[0]      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.ModuleList([]	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       None
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       nn.ModuleList([]	)
				SCREAMING_SNAKE_CASE   :      str				     =       None

				# down
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       in_channels
				for i, down_block_type in enumerate(a	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       output_channel
					SCREAMING_SNAKE_CASE   :      str				     =       block_out_channels[i]

					if i == 0:
						input_channel += extra_in_channels

					SCREAMING_SNAKE_CASE   :      Tuple				     =       i == len(a	) - 1

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

				# mid
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       get_mid_block(
				    a      ,      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=a      ,      add_downsample=a      ,      )

				# up
				SCREAMING_SNAKE_CASE   :      str				     =       list(reversed(a	)	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       reversed_block_out_channels[0]
				if out_block_type is None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       out_channels
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       block_out_channels[0]

				for i, up_block_type in enumerate(a	):
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       output_channel
					SCREAMING_SNAKE_CASE   :      Tuple				     =       (
					    reversed_block_out_channels[i + 1] if i < len(a	) - 1 else final_upsample_channels
					)

					SCREAMING_SNAKE_CASE   :      str				     =       i == len(a	) - 1

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

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






			def 							__UpperCamelCase (			self							: Dict      ,      a							: torch.FloatTensor      ,      a							: Union[torch.Tensor, float, int]      ,      a							: bool = True      ,      )							->       Union[UNetaDOutput, Tuple]:

				"""simple docstring"""

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

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

				# 2. down
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       ()
				for downsample_block in self.down_blocks:
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       downsample_block(hidden_states=a      ,      temb=a	)
					down_block_res_samples += res_samples

				# 3. mid
				if self.mid_block:
					SCREAMING_SNAKE_CASE   :      str				     =       self.mid_block(a      ,      a	)

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

				# 5. post-process
				if self.out_block:
					SCREAMING_SNAKE_CASE   :      Any				     =       self.out_block(a      ,      a	)

				if not return_dict:
					return (sample,)

				return UNetaDOutput(sample=a	)


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


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






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""simple docstring"""

				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}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) 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(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       pa.Table:

				"""simple docstring"""

				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
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      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(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       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(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise