





import argparse

import torch

from diffusers import UNetaDConditionModel


def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str			,							lowerCAmelCase:		Optional[Any]=None			,							lowerCAmelCase:		List[Any]=None			,							lowerCAmelCase:		Optional[int]=None )->						int:
  assert isinstance(lowerCAmelCase			,							lowerCAmelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      _snake_case      : Any														=		old_checkpoint[path]
      _snake_case      : Dict														=		old_tensor.shape[0] // 3

      _snake_case      : Dict														=		(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

      _snake_case      : Union[str, Any]														=		old_tensor.shape[0] // config['num_head_channels'] // 3

      _snake_case      : Dict														=		old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
      _snake_case							,    _snake_case							,    _snake_case      : Any														=		old_tensor.split(channels // num_heads			,							dim=1 )

      _snake_case      : List[Any]														=		query.reshape(lowerCAmelCase )
      _snake_case      : int														=		key.reshape(lowerCAmelCase )
      _snake_case      : Union[str, Any]														=		value.reshape(lowerCAmelCase )

  for path in paths:
    _snake_case      : str														=		path['new']

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _snake_case      : Dict														=		new_path.replace(replacement['old']			,							replacement['new'] )

        # proj_attn.weight has to be converted from conv 1D to linear
    _snake_case      : str														=		old_checkpoint[path['old']]
    _snake_case      : List[str]														=		['proj_attn.weight']
    _snake_case      : Dict														=		['proj_out.weight', 'proj_in.weight']
    if any(k in new_path for k in names ):
      _snake_case      : int														=		weight[:, :, 0]
    elif any(k in new_path for k in names_a ) and len(weight.shape ) > 2 and ".attentions." not in new_path:
      _snake_case      : List[Any]														=		weight[:, :, 0]
    else:
      _snake_case      : Dict														=		weight


def 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		List[Any]=0 )->						Optional[int]:
  _snake_case      : Any														=		[]
  for old_item in old_list:
    _snake_case      : str														=		old_item

    #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
    #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

    #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
    #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

    #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

    mapping.append({'old': old_item, 'new': new_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Tuple=1 )->						Union[str, Any]:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('.' )[n_shave_prefix_segments:] )
  else:
    return ".".join(path.split('.' )[:n_shave_prefix_segments] )


def 			lowerCamelCase_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Optional[Any]=0 )->						List[Any]:
  _snake_case      : Optional[Any]														=		[]
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str=0 )->						str:
  _snake_case      : int														=		[]
  for old_item in old_list:
    _snake_case      : List[Any]														=		old_item.replace('in_layers.0'			,							'norm1' )
    _snake_case      : Any														=		new_item.replace('in_layers.2'			,							'conv1' )

    _snake_case      : List[Any]														=		new_item.replace('out_layers.0'			,							'norm2' )
    _snake_case      : List[str]														=		new_item.replace('out_layers.3'			,							'conv2' )

    _snake_case      : int														=		new_item.replace('emb_layers.1'			,							'time_emb_proj' )
    _snake_case      : Dict														=		new_item.replace('skip_connection'			,							'conv_shortcut' )

    _snake_case      : int														=		shave_segments(lowerCAmelCase			,							n_shave_prefix_segments=lowerCAmelCase )

    if "temopral_conv" not in old_item:
      mapping.append({'old': old_item, 'new': new_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[int]			,							lowerCAmelCase:		Optional[int]=None			,							lowerCAmelCase:		Dict=False )->						Optional[Any]:
  _snake_case      : Union[str, Any]														=		{}
  _snake_case      : Union[str, Any]														=		list(checkpoint.keys() )

  _snake_case      : Optional[int]														=		'model.diffusion_model.'

  # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  if sum(k.startswith('model_ema' ) for k in keys ) > 1_00 and extract_ema:
    print(F"""Checkpoint {path} has both EMA and non-EMA weights.""" )
    print(
        'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
        ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.' )
    for key in keys:
      if key.startswith('model.diffusion_model' ):
        _snake_case      : Optional[int]														=		'model_ema.' + ''.join(key.split('.' )[1:] )
        _snake_case      : Any														=		checkpoint.pop(lowerCAmelCase )
  else:
    if sum(k.startswith('model_ema' ) for k in keys ) > 1_00:
      print(
          'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
          ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.' )

    for key in keys:
      _snake_case      : str														=		checkpoint.pop(lowerCAmelCase )

  _snake_case      : Union[str, Any]														=		{}

  _snake_case      : Any														=		unet_state_dict['time_embed.0.weight']
  _snake_case      : Optional[Any]														=		unet_state_dict['time_embed.0.bias']
  _snake_case      : str														=		unet_state_dict['time_embed.2.weight']
  _snake_case      : Dict														=		unet_state_dict['time_embed.2.bias']

  if config["class_embed_type"] is None:
    # No parameters to port
    ...
  elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
    _snake_case      : str														=		unet_state_dict['label_emb.0.0.weight']
    _snake_case      : Tuple														=		unet_state_dict['label_emb.0.0.bias']
    _snake_case      : str														=		unet_state_dict['label_emb.0.2.weight']
    _snake_case      : Optional[Any]														=		unet_state_dict['label_emb.0.2.bias']
  else:
    raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}""" )

  _snake_case      : List[str]														=		unet_state_dict['input_blocks.0.0.weight']
  _snake_case      : Tuple														=		unet_state_dict['input_blocks.0.0.bias']

  _snake_case      : List[Any]														=		[v for v in unet_state_dict if v.startswith('input_blocks.0.1' )]
  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : str														=		{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Optional[int]														=		unet_state_dict['out.0.weight']
  _snake_case      : Union[str, Any]														=		unet_state_dict['out.0.bias']
  _snake_case      : Any														=		unet_state_dict['out.2.weight']
  _snake_case      : int														=		unet_state_dict['out.2.bias']

  # Retrieves the keys for the input blocks only
  _snake_case      : Union[str, Any]														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'input_blocks' in layer} )
  _snake_case      : List[str]														=		{
      layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

  # Retrieves the keys for the middle blocks only
  _snake_case      : Any														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'middle_block' in layer} )
  _snake_case      : Dict														=		{
      layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

  # Retrieves the keys for the output blocks only
  _snake_case      : Optional[int]														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'output_blocks' in layer} )
  _snake_case      : Tuple														=		{
      layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

  for i in range(1			,							lowerCAmelCase ):
    _snake_case      : Dict														=		(i - 1) // (config['layers_per_block'] + 1)
    _snake_case      : Union[str, Any]														=		(i - 1) % (config['layers_per_block'] + 1)

    _snake_case      : str														=		[
        key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
    ]
    _snake_case      : Optional[int]														=		[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
    _snake_case      : Union[str, Any]														=		[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

    if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
      _snake_case      : str														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.weight""" )
      _snake_case      : Optional[int]														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.bias""" )

    _snake_case      : Optional[Any]														=		renew_resnet_paths(lowerCAmelCase )
    _snake_case      : Union[str, Any]														=		{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    assign_to_checkpoint(
        lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    _snake_case      : List[Any]														=		[key for key in resnets if 'temopral_conv' in key]
    _snake_case      : Union[str, Any]														=		renew_temp_conv_paths(lowerCAmelCase )
    _snake_case      : List[str]														=		{
        'old': F"""input_blocks.{i}.0.temopral_conv""",
        'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
    }
    assign_to_checkpoint(
        lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    if len(lowerCAmelCase ):
      _snake_case      : List[Any]														=		renew_attention_paths(lowerCAmelCase )
      _snake_case      : str														=		{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    if len(lowerCAmelCase ):
      _snake_case      : Tuple														=		renew_attention_paths(lowerCAmelCase )
      _snake_case      : Optional[int]														=		{
          'old': F"""input_blocks.{i}.2""",
          'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Tuple														=		middle_blocks[0]
  _snake_case      : Union[str, Any]														=		[key for key in resnet_a if 'temopral_conv' in key]
  _snake_case      : Optional[Any]														=		middle_blocks[1]
  _snake_case      : List[str]														=		middle_blocks[2]
  _snake_case      : List[str]														=		middle_blocks[3]
  _snake_case      : Tuple														=		[key for key in resnet_a if 'temopral_conv' in key]

  _snake_case      : str														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : Dict														=		{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : Tuple														=		renew_temp_conv_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : List[Any]														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : List[str]														=		{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : Any														=		renew_temp_conv_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : List[Any]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : int														=		{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  for i in range(lowerCAmelCase ):
    _snake_case      : Union[str, Any]														=		i // (config['layers_per_block'] + 1)
    _snake_case      : Optional[int]														=		i % (config['layers_per_block'] + 1)
    _snake_case      : Dict														=		[shave_segments(lowerCAmelCase			,							2 ) for name in output_blocks[i]]
    _snake_case      : Optional[Any]														=		{}

    for layer in output_block_layers:
      _snake_case							,    _snake_case      : str														=		layer.split('.' )[0], shave_segments(lowerCAmelCase			,							1 )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase )
      else:
        _snake_case      : Union[str, Any]														=		[layer_name]

    if len(lowerCAmelCase ) > 1:
      _snake_case      : Union[str, Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
      _snake_case      : Optional[Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
      _snake_case      : int														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

      _snake_case      : Tuple														=		renew_resnet_paths(lowerCAmelCase )
      _snake_case      : int														=		renew_resnet_paths(lowerCAmelCase )

      _snake_case      : Optional[int]														=		{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      _snake_case      : Union[str, Any]														=		[key for key in resnets if 'temopral_conv' in key]
      _snake_case      : Dict														=		renew_temp_conv_paths(lowerCAmelCase )
      _snake_case      : int														=		{
          'old': F"""output_blocks.{i}.0.temopral_conv""",
          'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      _snake_case      : Dict														=		{k: sorted(lowerCAmelCase ) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        _snake_case      : List[Any]														=		list(output_block_list.values() ).index(['conv.bias', 'conv.weight'] )
        _snake_case      : Union[str, Any]														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.weight"""
        ]
        _snake_case      : Dict														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase ) == 2:
          _snake_case      : Optional[int]														=		[]

      if len(lowerCAmelCase ):
        _snake_case      : Tuple														=		renew_attention_paths(lowerCAmelCase )
        _snake_case      : Tuple														=		{
            'old': F"""output_blocks.{i}.1""",
            'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      if len(lowerCAmelCase ):
        _snake_case      : List[str]														=		renew_attention_paths(lowerCAmelCase )
        _snake_case      : str														=		{
            'old': F"""output_blocks.{i}.2""",
            'new': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )
    else:
      _snake_case      : List[str]														=		renew_resnet_paths(lowerCAmelCase			,							n_shave_prefix_segments=1 )
      for path in resnet_0_paths:
        _snake_case      : Any														=		'.'.join(['output_blocks', str(lowerCAmelCase ), path['old']] )
        _snake_case      : Optional[int]														=		'.'.join(['up_blocks', str(lowerCAmelCase ), 'resnets', str(lowerCAmelCase ), path['new']] )
        _snake_case      : Any														=		unet_state_dict[old_path]

      _snake_case      : Optional[int]														=		[l for l in output_block_layers if 'temopral_conv' in l]
      for path in temopral_conv_paths:
        _snake_case      : Optional[Any]														=		path.split('temopral_conv.' )[-1]
        _snake_case      : str														=		'.'.join(['output_blocks', str(lowerCAmelCase ), str(lowerCAmelCase ), 'temopral_conv', pruned_path] )
        _snake_case      : Optional[int]														=		'.'.join(['up_blocks', str(lowerCAmelCase ), 'temp_convs', str(lowerCAmelCase ), pruned_path] )
        _snake_case      : Tuple														=		unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
      )
      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      lowerCAmelCase_        =			parser.parse_args()

      lowerCAmelCase_        =			torch.load(args.checkpoint_path, map_location="""cpu""")
      lowerCAmelCase_        =			UNetaDConditionModel()

      lowerCAmelCase_        =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

      lowerCAmelCase_        =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
      lowerCAmelCase_        =			set(converted_ckpt.keys()) - set(unet.state_dict().keys())

      assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

      # load state_dict
      unet.load_state_dict(converted_ckpt)

      unet.save_pretrained(args.dump_path)

      # -- finish converting the unet --











from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_							:				Union[str, Any]					    ="""mobilebert"""







  def __init__(					self					:    int					, UpperCamelCase					:    int=3_05_22					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    Any=24					, UpperCamelCase					:    List[str]=4					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    str="relu"					, UpperCamelCase					:    Dict=0.0					, UpperCamelCase					:    int=0.1					, UpperCamelCase					:    Optional[Any]=5_12					, UpperCamelCase					:    str=2					, UpperCamelCase					:    Tuple=0.02					, UpperCamelCase					:    Union[str, Any]=1e-1_2					, UpperCamelCase					:    Optional[Any]=0					, UpperCamelCase					:    Union[str, Any]=1_28					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    int=True					, UpperCamelCase					:    List[Any]=1_28					, UpperCamelCase					:    Dict=False					, UpperCamelCase					:    List[str]=True					, UpperCamelCase					:    Optional[Any]=4					, UpperCamelCase					:    int="no_norm"					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    str=None					, **UpperCamelCase					:    str					, ):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase					, **UpperCamelCase							)

    _snake_case      : Union[str, Any]														=		vocab_size
    _snake_case      : Any														=		hidden_size
    _snake_case      : Tuple														=		num_hidden_layers
    _snake_case      : Any														=		num_attention_heads
    _snake_case      : Optional[Any]														=		hidden_act
    _snake_case      : List[Any]														=		intermediate_size
    _snake_case      : List[Any]														=		hidden_dropout_prob
    _snake_case      : Optional[int]														=		attention_probs_dropout_prob
    _snake_case      : int														=		max_position_embeddings
    _snake_case      : Optional[int]														=		type_vocab_size
    _snake_case      : Any														=		initializer_range
    _snake_case      : Optional[int]														=		layer_norm_eps
    _snake_case      : Tuple														=		embedding_size
    _snake_case      : int														=		trigram_input
    _snake_case      : Optional[int]														=		use_bottleneck
    _snake_case      : List[Any]														=		intra_bottleneck_size
    _snake_case      : Any														=		use_bottleneck_attention
    _snake_case      : str														=		key_query_shared_bottleneck
    _snake_case      : List[str]														=		num_feedforward_networks
    _snake_case      : int														=		normalization_type
    _snake_case      : str														=		classifier_activation

    if self.use_bottleneck:
      _snake_case      : Any														=		intra_bottleneck_size
    else:
      _snake_case      : Union[str, Any]														=		hidden_size

    _snake_case      : Dict														=		classifier_dropout




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case      : str														=		{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case      : Dict														=		{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]							)




