
'''simple docstring'''





# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {}
SCREAMING_SNAKE_CASE     =       {}
SCREAMING_SNAKE_CASE     =       {}







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__ = None   ,       ):
  UpperCAmelCase__      :					Any				       =							aliases if aliases is not None else []
  if format_type in _FORMAT_TYPES:
    logger.warning(
        F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""		)
  UpperCAmelCase__      :					int				       =							formatter_cls
  for alias in set(aliases + [format_type]		):
    if alias in _FORMAT_TYPES_ALIASES:
      logger.warning(
          F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""		)
    UpperCAmelCase__      :					Optional[int]				       =							format_type







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__ = None		):
  UpperCAmelCase__      :					Dict				       =							aliases if aliases is not None else []
  for alias in set(aliases + [format_type]		):
    UpperCAmelCase__      :					Dict				       =							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
      from .torch_formatter import TorchFormatter

      _register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
      SCREAMING_SNAKE_CASE     =       ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
      _register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
      from .tf_formatter import TFFormatter

      _register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
      SCREAMING_SNAKE_CASE     =       ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
      _register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
      from .jax_formatter import JaxFormatter

      _register_formatter(JaxFormatter, """jax""", aliases=[])
else:
      SCREAMING_SNAKE_CASE     =       ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
      _register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def snake_case_     (					lowercase__		):
  if format_type in _FORMAT_TYPES_ALIASES:
    return _FORMAT_TYPES_ALIASES[format_type]
  else:
    return format_type







def snake_case_     (					lowercase__   ,       **lowercase__		):
  UpperCAmelCase__      :					List[Any]				       =							get_format_type_from_alias(lowercase__		)
  if format_type in _FORMAT_TYPES:
    return _FORMAT_TYPES[format_type](**lowercase__		)
  if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
  else:
    raise ValueError(
        F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"""		)





'''simple docstring'''





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 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    torch.FloatTensor






class 							UpperCAmelCase_							(    A					,				A				):

 '''simple docstring'''







 @register_to_config
 def __init__(					self		:  List[str]			,				snake_case__		:  int = 3			,				snake_case__		:  int = 3			,				snake_case__		:  Tuple[str] = ("DownEncoderBlock2D",)			,				snake_case__		:  Tuple[str] = ("UpDecoderBlock2D",)			,				snake_case__		:  Tuple[int] = (64,)			,				snake_case__		:  int = 1			,				snake_case__		:  str = "silu"			,				snake_case__		:  int = 3			,				snake_case__		:  int = 32			,				snake_case__		:  int = 2_56			,				snake_case__		:  int = 32			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  float = 0.18215			,				snake_case__		:  str = "group"			,				):



   '''simple docstring'''




   super().__init__()

   # pass init params to Encoder
   UpperCAmelCase__      :					Tuple				       =							Encoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				down_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				double_z=snake_case__			,				)

   UpperCAmelCase__      :					str				       =							vq_embed_dim if vq_embed_dim is not None else latent_channels

   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )
   UpperCAmelCase__      :					int				       =							VectorQuantizer(snake_case__			,				snake_case__			,				beta=0.25			,				remap=snake_case__			,				sane_index_shape=snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )

   # pass init params to Decoder
   UpperCAmelCase__      :					Union[str, Any]				       =							Decoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				up_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				norm_type=snake_case__			,				)







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							self.encoder(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							self.quant_conv(snake_case__ )

   if not return_dict:
     return (h,)

   return VQEncoderOutput(latents=snake_case__ )







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = False			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   if not force_not_quantize:
     UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							self.quantize(snake_case__ )
   else:
     UpperCAmelCase__      :					Tuple				       =							h
   UpperCAmelCase__      :					int				       =							self.post_quant_conv(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							self.decoder(snake_case__			,				quant if self.config.norm_type == "spatial" else None )

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							sample
   UpperCAmelCase__      :					int				       =							self.encode(snake_case__ ).latents
   UpperCAmelCase__      :					Any				       =							self.decode(snake_case__ ).sample

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )



