

from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCamelCase__  :      str			=    logging.get_logger(__name__)


UpperCamelCase__  :      Tuple			=    TypeVar("DatasetType", Dataset, IterableDataset)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[DatasetType] ,   _SCREAMING_SNAKE_CASE					:		Optional[List[float]] = None ,   _SCREAMING_SNAKE_CASE					:		Optional[int] = None ,   _SCREAMING_SNAKE_CASE					:		Optional[DatasetInfo] = None ,   _SCREAMING_SNAKE_CASE					:		Optional[NamedSplit] = None ,   _SCREAMING_SNAKE_CASE					:		Literal["first_exhausted", "all_exhausted"] = "first_exhausted" ,   ):





    """simple docstring"""


    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('Unable to interleave an empty list of datasets.'       )
    for i, dataset in enumerate(_SCREAMING_SNAKE_CASE       ):
        if not isinstance(_SCREAMING_SNAKE_CASE ,   (Dataset, IterableDataset)       ):
            if isinstance(_SCREAMING_SNAKE_CASE ,   (DatasetDict, IterableDatasetDict)       ):
                if not dataset:
                    raise ValueError(
                        f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                        'is an empty dataset dictionary.'       )
                raise ValueError(
                    f"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE       )}\n"""
                    f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE       )       )}']"""       )
            raise ValueError(
                f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE       ).__name__}."""       )
        if i == 0:
            SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						(
                (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            raise ValueError(
                f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""       )
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(f"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""       )
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   info=_SCREAMING_SNAKE_CASE ,   split=_SCREAMING_SNAKE_CASE ,   stopping_strategy=_SCREAMING_SNAKE_CASE       )
    else:
        return _interleave_iterable_datasets(
            _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   info=_SCREAMING_SNAKE_CASE ,   split=_SCREAMING_SNAKE_CASE ,   stopping_strategy=_SCREAMING_SNAKE_CASE       )





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[DatasetType] ,   _SCREAMING_SNAKE_CASE					:		Optional[DatasetInfo] = None ,   _SCREAMING_SNAKE_CASE					:		Optional[NamedSplit] = None ,   _SCREAMING_SNAKE_CASE					:		int = 0 ,   ):





    """simple docstring"""


    if not dsets:
        raise ValueError('Unable to concatenate an empty list of datasets.'       )
    for i, dataset in enumerate(_SCREAMING_SNAKE_CASE       ):
        if not isinstance(_SCREAMING_SNAKE_CASE ,   (Dataset, IterableDataset)       ):
            if isinstance(_SCREAMING_SNAKE_CASE ,   (DatasetDict, IterableDatasetDict)       ):
                if not dataset:
                    raise ValueError(
                        f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                        'is an empty dataset dictionary.'       )
                raise ValueError(
                    f"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE       )}\n"""
                    f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE       )       )}']"""       )
            raise ValueError(
                f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE       ).__name__}."""       )
        if i == 0:
            SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						(
                (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            raise ValueError(
                f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""       )
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_SCREAMING_SNAKE_CASE ,   info=_SCREAMING_SNAKE_CASE ,   split=_SCREAMING_SNAKE_CASE ,   axis=_SCREAMING_SNAKE_CASE       )
    else:
        return _concatenate_iterable_datasets(_SCREAMING_SNAKE_CASE ,   info=_SCREAMING_SNAKE_CASE ,   split=_SCREAMING_SNAKE_CASE ,   axis=_SCREAMING_SNAKE_CASE       )








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)


