



import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A__						:						Optional[int]          =      logging.get_logger(__name__)


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	List[str]=False  ,__UpperCamelCase							:	List[str]=False				):
  lowerCAmelCase_    :  Any             =				'''backbone.''' if is_semantic else ''''''

  lowerCAmelCase_    :  Any             =				[]
  for i in range(config.num_hidden_layers				):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")				)
    rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")				)
    rename_keys.append(
        (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")				)
    rename_keys.append(
        (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")				)
    rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")				)
    rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")				)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")				)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")				)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")				)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")				)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (f"""{prefix}cls_token""", '''beit.embeddings.cls_token'''),
          (f"""{prefix}patch_embed.proj.weight""", '''beit.embeddings.patch_embeddings.projection.weight'''),
          (f"""{prefix}patch_embed.proj.bias""", '''beit.embeddings.patch_embeddings.projection.bias'''),
      ]				)

  if has_lm_head:
    # mask token + shared relative position bias + layernorm
    rename_keys.extend(
        [
            ('''mask_token''', '''beit.embeddings.mask_token'''),
            (
                '''rel_pos_bias.relative_position_bias_table''',
                '''beit.encoder.relative_position_bias.relative_position_bias_table''',
            ),
            (
                '''rel_pos_bias.relative_position_index''',
                '''beit.encoder.relative_position_bias.relative_position_index''',
            ),
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
        ]				)
  elif is_semantic:
    # semantic segmentation classification heads
    rename_keys.extend(
        [
            ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
            ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
            ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
            ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
        ]				)
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
            ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
            ('''head.weight''', '''classifier.weight'''),
            ('''head.bias''', '''classifier.bias'''),
        ]				)

  return rename_keys


def     UpperCamelCase(						__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Optional[int]=False  ,__UpperCamelCase							:	Dict=False				):







  for i in range(config.num_hidden_layers				):
    lowerCAmelCase_    :  Optional[int]             =				'''backbone.''' if is_semantic else ''''''
    # queries, keys and values
    lowerCAmelCase_    :  Optional[Any]             =				state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""				)
    lowerCAmelCase_    :  List[str]             =				state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""				)
    lowerCAmelCase_    :  List[str]             =				state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""				)

    lowerCAmelCase_    :  List[str]             =				in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase_    :  str             =				q_bias
    lowerCAmelCase_    :  List[str]             =				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase_    :  str             =				in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase_    :  Any             =				v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCAmelCase_    :  Tuple             =				state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""				)
    lowerCAmelCase_    :  Optional[Any]             =				state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""				)

    lowerCAmelCase_    :  Union[str, Any]             =				gamma_a
    lowerCAmelCase_    :  Optional[Any]             =				gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      lowerCAmelCase_    :  Optional[int]             =				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""				)
      lowerCAmelCase_    :  Optional[Any]             =				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""				)

      lowerCAmelCase_    :  Union[str, Any]             =				table
      lowerCAmelCase_    :  int             =				index


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]  ,__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	Any				):
  lowerCAmelCase_    :  Optional[Any]             =				dct.pop(__UpperCamelCase				)
  lowerCAmelCase_    :  str             =				val


def     UpperCamelCase(						):
  lowerCAmelCase_    :  Union[str, Any]             =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowerCAmelCase_    :  List[Any]             =				Image.open(requests.get(__UpperCamelCase  ,stream=__UpperCamelCase				).raw				)
  return im


@torch.no_grad()
def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	Any				):
  lowerCAmelCase_    :  Optional[int]             =				BeitConfig()
  lowerCAmelCase_    :  List[str]             =				False
  lowerCAmelCase_    :  List[Any]             =				False
  lowerCAmelCase_    :  int             =				'''huggingface/label-files'''
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    lowerCAmelCase_    :  Optional[Any]             =				True
    lowerCAmelCase_    :  Optional[Any]             =				True
    lowerCAmelCase_    :  Union[str, Any]             =				True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    lowerCAmelCase_    :  Dict             =				True
    lowerCAmelCase_    :  Union[str, Any]             =				21841
    lowerCAmelCase_    :  Optional[int]             =				'''imagenet-22k-id2label.json'''
    lowerCAmelCase_    :  Any             =				json.load(open(hf_hub_download(__UpperCamelCase  ,__UpperCamelCase  ,repo_type='''dataset'''				)  ,'''r'''				)				)
    lowerCAmelCase_    :  List[str]             =				{int(__UpperCamelCase				): v for k, v in idalabel.items()}
    # this dataset contains 21843 labels but the model only has 21841
    # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
    del idalabel[9205]
    del idalabel[15027]
    lowerCAmelCase_    :  int             =				idalabel
    lowerCAmelCase_    :  Dict             =				{v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    lowerCAmelCase_    :  Union[str, Any]             =				True
    lowerCAmelCase_    :  Optional[Any]             =				1000
    lowerCAmelCase_    :  Union[str, Any]             =				'''imagenet-1k-id2label.json'''
    lowerCAmelCase_    :  Optional[Any]             =				json.load(open(hf_hub_download(__UpperCamelCase  ,__UpperCamelCase  ,repo_type='''dataset'''				)  ,'''r'''				)				)
    lowerCAmelCase_    :  int             =				{int(__UpperCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase_    :  Tuple             =				idalabel
    lowerCAmelCase_    :  Optional[int]             =				{v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      lowerCAmelCase_    :  Dict             =				384
    if "512" in checkpoint_url:
      lowerCAmelCase_    :  int             =				512
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    lowerCAmelCase_    :  List[Any]             =				True
    lowerCAmelCase_    :  Optional[Any]             =				150
    lowerCAmelCase_    :  int             =				'''ade20k-id2label.json'''
    lowerCAmelCase_    :  Optional[Any]             =				json.load(open(hf_hub_download(__UpperCamelCase  ,__UpperCamelCase  ,repo_type='''dataset'''				)  ,'''r'''				)				)
    lowerCAmelCase_    :  List[Any]             =				{int(__UpperCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase_    :  int             =				idalabel
    lowerCAmelCase_    :  Any             =				{v: k for k, v in idalabel.items()}
    lowerCAmelCase_    :  Tuple             =				640
    lowerCAmelCase_    :  Optional[Any]             =				True
  else:
    raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''				)

  # size of the architecture
  if "base" in checkpoint_url:
    pass
  elif "large" in checkpoint_url:
    lowerCAmelCase_    :  List[str]             =				1024
    lowerCAmelCase_    :  Dict             =				4096
    lowerCAmelCase_    :  Any             =				24
    lowerCAmelCase_    :  Tuple             =				16
    if "ade20k" in checkpoint_url:
      lowerCAmelCase_    :  Union[str, Any]             =				640
      lowerCAmelCase_    :  Optional[Any]             =				[7, 11, 15, 23]
  else:
    raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''				)

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase_    :  Optional[int]             =				torch.hub.load_state_dict_from_url(__UpperCamelCase  ,map_location='''cpu'''  ,check_hash=__UpperCamelCase				)
  lowerCAmelCase_    :  Dict             =				state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

  lowerCAmelCase_    :  Optional[int]             =				create_rename_keys(__UpperCamelCase  ,has_lm_head=__UpperCamelCase  ,is_semantic=__UpperCamelCase				)
  for src, dest in rename_keys:
    rename_key(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)
  read_in_q_k_v(__UpperCamelCase  ,__UpperCamelCase  ,has_lm_head=__UpperCamelCase  ,is_semantic=__UpperCamelCase				)
  if is_semantic:
    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
      lowerCAmelCase_    :  Any             =				state_dict.pop(__UpperCamelCase				)
      if key.startswith('''backbone.fpn'''				):
        lowerCAmelCase_    :  Dict             =				key.replace('''backbone.fpn'''  ,'''fpn'''				)
      lowerCAmelCase_    :  Optional[Any]             =				val

    # load HuggingFace model
  if checkpoint_url[-9:-4] == "pt22k":
    lowerCAmelCase_    :  Optional[Any]             =				BeitForMaskedImageModeling(__UpperCamelCase				)
  elif "ade20k" in checkpoint_url:
    lowerCAmelCase_    :  int             =				BeitForSemanticSegmentation(__UpperCamelCase				)
  else:
    lowerCAmelCase_    :  Dict             =				BeitForImageClassification(__UpperCamelCase				)
  model.eval()
  model.load_state_dict(__UpperCamelCase				)

  # Check outputs on an image
  if is_semantic:
    lowerCAmelCase_    :  Any             =				BeitImageProcessor(size=config.image_size  ,do_center_crop=__UpperCamelCase				)
    lowerCAmelCase_    :  int             =				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,split='''test'''				)
    lowerCAmelCase_    :  Dict             =				Image.open(ds[0]['''file''']				)
  else:
    lowerCAmelCase_    :  Optional[int]             =				BeitImageProcessor(
        size=config.image_size  ,resample=PILImageResampling.BILINEAR  ,do_center_crop=__UpperCamelCase				)
    lowerCAmelCase_    :  Union[str, Any]             =				prepare_img()

  lowerCAmelCase_    :  int             =				image_processor(images=__UpperCamelCase  ,return_tensors='''pt'''				)
  lowerCAmelCase_    :  int             =				encoding['''pixel_values''']

  lowerCAmelCase_    :  List[Any]             =				model(__UpperCamelCase				)
  lowerCAmelCase_    :  Optional[int]             =				outputs.logits

  # verify logits
  lowerCAmelCase_    :  Tuple             =				torch.Size([1, 1000]				)
  if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''				):
    lowerCAmelCase_    :  Optional[int]             =				torch.Size([1, 196, 8192]				)
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''				):
    lowerCAmelCase_    :  Tuple             =				torch.Size([1, 196, 8192]				)
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''				):
    lowerCAmelCase_    :  str             =				torch.Size([1, 21841]				)
    lowerCAmelCase_    :  Optional[Any]             =				torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]				)
    lowerCAmelCase_    :  Dict             =				2397
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''				):
    lowerCAmelCase_    :  Optional[int]             =				torch.Size([1, 21841]				)
    lowerCAmelCase_    :  Any             =				torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]				)
    lowerCAmelCase_    :  Any             =				2396
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''				):
    lowerCAmelCase_    :  int             =				torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]				)
    lowerCAmelCase_    :  Union[str, Any]             =				285
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''				):
    lowerCAmelCase_    :  Optional[Any]             =				torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]				)
    lowerCAmelCase_    :  int             =				281
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''				):
    lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]				)
    lowerCAmelCase_    :  Any             =				761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''				):
    lowerCAmelCase_    :  str             =				torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]				)
    lowerCAmelCase_    :  Union[str, Any]             =				761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''				):
    lowerCAmelCase_    :  Optional[Any]             =				torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]				)
    lowerCAmelCase_    :  Optional[int]             =				761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''				):
    lowerCAmelCase_    :  List[Any]             =				torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]				)
    lowerCAmelCase_    :  str             =				761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''				):
    lowerCAmelCase_    :  Dict             =				torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]				)
    lowerCAmelCase_    :  str             =				761
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''				):
    lowerCAmelCase_    :  Optional[Any]             =				(1, 150, 160, 160)
    lowerCAmelCase_    :  Dict             =				torch.tensor(
        [
            [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
            [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
            [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
        ]				)
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''				):
    lowerCAmelCase_    :  Dict             =				(1, 150, 160, 160)
    lowerCAmelCase_    :  Tuple             =				torch.tensor(
        [
            [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
            [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
            [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
        ]				)
  else:
    raise ValueError('''Can\'t verify logits as model is not supported'''				)

  assert logits.shape == expected_shape, "Shape of logits not as expected"
  if not has_lm_head:
    if is_semantic:
      assert torch.allclose(
          logits[0, :3, :3, :3]  ,__UpperCamelCase  ,atol=1e-3				), "First elements of logits not as expected"
    else:
      print('''Predicted class idx:'''  ,logits.argmax(-1				).item()				)
      assert torch.allclose(
          logits[0, :3]  ,__UpperCamelCase  ,atol=1e-3				), "First elements of logits not as expected"
      assert logits.argmax(-1				).item() == expected_class_idx, "Predicted class index not as expected"

  Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
  print(f"""Saving model to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(__UpperCamelCase				)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
      A__						:						Optional[int]          =      argparse.ArgumentParser()

      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
          type=str,
          help='''URL to the original PyTorch checkpoint (.pth file).''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
      )
      A__						:						Dict          =      parser.parse_args()
      convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










from collections import OrderedDict
from typing import Mapping

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


A__						:						Dict          =      logging.get_logger(__name__)

A__						:						List[str]          =      {
    '''google/electra-small-generator''': '''https://huggingface.co/google/electra-small-generator/resolve/main/config.json''',
    '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/config.json''',
    '''google/electra-large-generator''': '''https://huggingface.co/google/electra-large-generator/resolve/main/config.json''',
    '''google/electra-small-discriminator''': (
        '''https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-base-discriminator''': (
        '''https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-large-discriminator''': (
        '''https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'''
    ),
}



class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''electra'''




							def __init__( self  : Dict       ,       A_  : Dict=3_0_5_2_2       ,       A_  : List[Any]=1_2_8       ,       A_  : Dict=2_5_6       ,       A_  : Any=1_2       ,       A_  : Optional[Any]=4       ,       A_  : Optional[int]=1_0_2_4       ,       A_  : Optional[int]="gelu"       ,       A_  : str=0.1       ,       A_  : Optional[int]=0.1       ,       A_  : str=5_1_2       ,       A_  : Optional[int]=2       ,       A_  : Dict=0.02       ,       A_  : Union[str, Any]=1e-12       ,       A_  : List[Any]="first"       ,       A_  : List[str]=True       ,       A_  : List[str]="gelu"       ,       A_  : Any=0.1       ,       A_  : int=0       ,       A_  : Optional[Any]="absolute"       ,       A_  : Optional[int]=True       ,       A_  : str=None       ,       **A_  : List[Any]       ,       ):
									super().__init__(pad_token_id=A_       ,       **A_)

									lowerCAmelCase_    :  str             =				vocab_size
									lowerCAmelCase_    :  Union[str, Any]             =				embedding_size
									lowerCAmelCase_    :  Optional[int]             =				hidden_size
									lowerCAmelCase_    :  List[Any]             =				num_hidden_layers
									lowerCAmelCase_    :  List[str]             =				num_attention_heads
									lowerCAmelCase_    :  str             =				intermediate_size
									lowerCAmelCase_    :  Union[str, Any]             =				hidden_act
									lowerCAmelCase_    :  Union[str, Any]             =				hidden_dropout_prob
									lowerCAmelCase_    :  Tuple             =				attention_probs_dropout_prob
									lowerCAmelCase_    :  Optional[Any]             =				max_position_embeddings
									lowerCAmelCase_    :  Dict             =				type_vocab_size
									lowerCAmelCase_    :  Any             =				initializer_range
									lowerCAmelCase_    :  int             =				layer_norm_eps

									lowerCAmelCase_    :  Union[str, Any]             =				summary_type
									lowerCAmelCase_    :  str             =				summary_use_proj
									lowerCAmelCase_    :  str             =				summary_activation
									lowerCAmelCase_    :  List[Any]             =				summary_last_dropout
									lowerCAmelCase_    :  Optional[Any]             =				position_embedding_type
									lowerCAmelCase_    :  List[Any]             =				use_cache
									lowerCAmelCase_    :  Any             =				classifier_dropout





class        __snake_case		(  UpperCamelCase_		):




							@property
							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									if self.task == "multiple-choice":
											lowerCAmelCase_    :  Any             =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_    :  Union[str, Any]             =				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''token_type_ids''', dynamic_axis),
									    ])





