





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(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			42







class      lowerCamelCase(						__snake_case			, __snake_case	):
     '''simple docstring'''


     @register_to_config
     def __init__(    self				,     snake_case_ = 3				,     snake_case_ = 3				,     snake_case_ = ("DownEncoderBlock2D",)				,     snake_case_ = ("UpDecoderBlock2D",)				,     snake_case_ = (64,)				,     snake_case_ = 1				,     snake_case_ = "silu"				,     snake_case_ = 3				,     snake_case_ = 32				,     snake_case_ = 256				,     snake_case_ = 32				,     snake_case_ = None				,     snake_case_ = 0.1_8215				,     snake_case_ = "group"				,     ):
         super().__init__()

         # pass init params to Encoder
         _A     =							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_				,     )

         _A     =							vq_embed_dim if vq_embed_dim is not None else latent_channels

         _A     =							nn.Convad(snake_case_				,     snake_case_				,     1							)
         _A     =							VectorQuantizer(snake_case_				,     snake_case_				,     beta=0.25				,     remap=snake_case_				,     sane_index_shape=snake_case_							)
         _A     =							nn.Convad(snake_case_				,     snake_case_				,     1							)

         # pass init params to Decoder
         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = True							):
         _A     =							self.encoder(snake_case_							)
         _A     =							self.quant_conv(snake_case_							)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=snake_case_							)




     @apply_forward_hook
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = False				,     snake_case_ = True							):
         # also go through quantization layer
         if not force_not_quantize:
             _A,			_A,			_A     =							self.quantize(snake_case_							)
         else:
             _A     =							h
         _A     =							self.post_quant_conv(snake_case_							)
         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = True							):
         _A     =							sample
         _A     =							self.encode(snake_case_							).latents
         _A     =							self.decode(snake_case_							).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=snake_case_							)









from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__    :Any       =				logging.get_logger(__name__)

lowerCAmelCase__    :Union[str, Any]       =				{
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Any           =      'xmod'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=30522     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=3072     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE="absolute"     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=("en_XX",)     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									_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
									_UpperCAmelCase											=     pre_norm
									_UpperCAmelCase											=     adapter_reduction_factor
									_UpperCAmelCase											=     adapter_layer_norm
									_UpperCAmelCase											=     adapter_reuse_layer_norm
									_UpperCAmelCase											=     ln_before_adapter
									_UpperCAmelCase											=     list(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     default_language



class 						__a (							UpperCAmelCase     ):
		@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),
									    ]		)
