

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


UpperCamelCase__  :      Optional[int]			=    4
UpperCamelCase__  :      int			=    3



class       __snake_case  (    lowerCAmelCase__       ):
  pass






def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str]       ):





    """simple docstring"""


    for shard in shards:
        for i in range(_SCREAMING_SNAKE_CASE       ):
            yield {"i": i, "shard": shard}


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						int(os.environ['RANK']       )
    SCREAMING_SNAKE_CASE_									=						int(os.environ['WORLD_SIZE']       )

    SCREAMING_SNAKE_CASE_									=						ArgumentParser()
    parser.add_argument('--streaming' ,   type=_SCREAMING_SNAKE_CASE       )
    parser.add_argument('--local_rank' ,   type=_SCREAMING_SNAKE_CASE       )
    parser.add_argument('--num_workers' ,   type=_SCREAMING_SNAKE_CASE ,   default=0       )
    SCREAMING_SNAKE_CASE_									=						parser.parse_args()
    SCREAMING_SNAKE_CASE_									=						args.streaming
    SCREAMING_SNAKE_CASE_									=						args.num_workers

    SCREAMING_SNAKE_CASE_									=						{'shards': [f"""shard_{shard_idx}""" for shard_idx in range(_SCREAMING_SNAKE_CASE       )]}
    SCREAMING_SNAKE_CASE_									=						IterableDataset.from_generator(_SCREAMING_SNAKE_CASE ,   gen_kwargs=_SCREAMING_SNAKE_CASE       )
    if not streaming:
        SCREAMING_SNAKE_CASE_									=						Dataset.from_list(list(_SCREAMING_SNAKE_CASE       )       )

    SCREAMING_SNAKE_CASE_									=						split_dataset_by_node(_SCREAMING_SNAKE_CASE ,   rank=_SCREAMING_SNAKE_CASE ,   world_size=_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						torch.utils.data.DataLoader(_SCREAMING_SNAKE_CASE ,   num_workers=_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						NUM_SHARDS * NUM_ITEMS_PER_SHARD
    SCREAMING_SNAKE_CASE_									=						full_size // world_size
    expected_local_size += int(rank < (full_size % world_size)       )

    SCREAMING_SNAKE_CASE_									=						sum(1 for _ in dataloader       )
    if local_size != expected_local_size:
        raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""       )


if __name__ == "__main__":
       main()





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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

      # pass init params to Encoder
      SCREAMING_SNAKE_CASE_									=						Encoder(
          in_channels=_A						,		out_channels=_A						,		down_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		double_z=_A						,		)

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

      # pass init params to Decoder
      SCREAMING_SNAKE_CASE_									=						Decoder(
          in_channels=_A						,		out_channels=_A						,		up_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		norm_type=_A						,		)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


