
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        lowercase      (	__lowerCamelCase     ):
		__lowercase : Dict			  =       42






class        lowercase      (	__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_8215       ,   A_ = "group"       ,   )						->      str:
				"""simple docstring"""
				super().__init__()

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

				UpperCamelCase					=  vq_embed_dim if vq_embed_dim is not None else latent_channels

				UpperCamelCase					=  nn.Convad(UpperCAmelCase_       ,   UpperCAmelCase_       ,   1 )
				UpperCamelCase					=  VectorQuantizer(UpperCAmelCase_       ,   UpperCAmelCase_       ,   beta=0.25       ,   remap=UpperCAmelCase_       ,   sane_index_shape=UpperCAmelCase_ )
				UpperCamelCase					=  nn.Convad(UpperCAmelCase_       ,   UpperCAmelCase_       ,   1 )

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



		@apply_forward_hook
		def __UpperCamelCase				(				self       ,   A_       ,   A_ = True )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase					=  self.encoder(UpperCAmelCase_ )
				UpperCamelCase					=  self.quant_conv(UpperCAmelCase_ )

				if not return_dict:
						return (h,)

				return VQEncoderOutput(latents=UpperCAmelCase_ )



		@apply_forward_hook
		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   A_ = True )						->      List[Any]:
				"""simple docstring"""
				# also go through quantization layer
				if not force_not_quantize:
						UpperCamelCase					=  self.quantize(UpperCAmelCase_ )
				else:
						UpperCamelCase					=  h
				UpperCamelCase					=  self.post_quant_conv(UpperCAmelCase_ )
				UpperCamelCase					=  self.decoder(UpperCAmelCase_       ,   quant if self.config.norm_type == 'spatial' else None )

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=UpperCAmelCase_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = True )						->      Any:
				"""simple docstring"""
				UpperCamelCase					=  sample
				UpperCamelCase					=  self.encode(UpperCAmelCase_ ).latents
				UpperCamelCase					=  self.decode(UpperCAmelCase_ ).sample

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=UpperCAmelCase_ )


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=2       ,   A_=0.02       ,   A_=1e-12       ,   A_=1       ,   A_=0       ,   A_=2       ,   A_="absolute"       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  layer_norm_eps
				UpperCamelCase						=  position_embedding_type
				UpperCamelCase						=  use_cache
				UpperCamelCase						=  classifier_dropout






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "multiple-choice":
						UpperCamelCase						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						UpperCamelCase						=  {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ] )
