





'''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 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    42







class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase  ):


					@register_to_config
					def __init__(					self	:  List[Any]       ,     __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 = 0.18_215       ,     __snake_case	:  str = "group"       ,     ):





											'''simple docstring'''





											super().__init__()

											# pass init params to Encoder
											_snake_case:   Optional[Any]  					=		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       ,     )

											_snake_case:   int  					=		vq_embed_dim if vq_embed_dim is not None else latent_channels

											_snake_case:   Any  					=		nn.Convad(__snake_case       ,     __snake_case       ,     1       )
											_snake_case:   List[str]  					=		VectorQuantizer(__snake_case       ,     __snake_case       ,     beta=0.25       ,     remap=__snake_case       ,     sane_index_shape=__snake_case       )
											_snake_case:   Tuple  					=		nn.Convad(__snake_case       ,     __snake_case       ,     1       )

											# pass init params to Decoder
											_snake_case:   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    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  bool = True       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.encoder(__snake_case       )
											_snake_case:   Optional[Any]  					=		self.quant_conv(__snake_case       )

											if not return_dict:
																	return (h,)

											return VQEncoderOutput(latents=__snake_case       )


					@apply_forward_hook
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  bool = False       ,     __snake_case	:  bool = True       ):





											'''simple docstring'''





											if not force_not_quantize:
																	_snake_case					,						_snake_case					,						_snake_case:   List[Any]  					=		self.quantize(__snake_case       )
											else:
																	_snake_case:   int  					=		h
											_snake_case:   List[str]  					=		self.post_quant_conv(__snake_case       )
											_snake_case:   List[Any]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  bool = True       ):





											'''simple docstring'''





											_snake_case:   Any  					=		sample
											_snake_case:   Union[str, Any]  					=		self.encode(__snake_case       ).latents
											_snake_case:   str  					=		self.decode(__snake_case       ).sample

											if not return_dict:
																	return (dec,)

											return DecoderOutput(sample=__snake_case       )












'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		A   : Union[str, Any]             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





