




'''simple docstring'''



import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_			=						logging.get_logger(__name__)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224"	,					out_features=["stage1", "stage2", "stage3", "stage4"]  )
   _SCREAMING_SNAKE_CASE	=				MaskFormerConfig(backbone_config=_UpperCAmelCase  )

   _SCREAMING_SNAKE_CASE	=				"huggingface/label-files"
   if "ade20k-full" in model_name:
      # this should be ok
      _SCREAMING_SNAKE_CASE	=				8_47
      _SCREAMING_SNAKE_CASE	=				"maskformer-ade20k-full-id2label.json"
   elif "ade" in model_name:
      # this should be ok
      _SCREAMING_SNAKE_CASE	=				1_50
      _SCREAMING_SNAKE_CASE	=				"ade20k-id2label.json"
   elif "coco-stuff" in model_name:
      # this should be ok
      _SCREAMING_SNAKE_CASE	=				1_71
      _SCREAMING_SNAKE_CASE	=				"maskformer-coco-stuff-id2label.json"
   elif "coco" in model_name:
      # TODO
      _SCREAMING_SNAKE_CASE	=				1_33
      _SCREAMING_SNAKE_CASE	=				"coco-panoptic-id2label.json"
   elif "cityscapes" in model_name:
      # this should be ok
      _SCREAMING_SNAKE_CASE	=				19
      _SCREAMING_SNAKE_CASE	=				"cityscapes-id2label.json"
   elif "vistas" in model_name:
      # this should be ok
      _SCREAMING_SNAKE_CASE	=				65
      _SCREAMING_SNAKE_CASE	=				"mapillary-vistas-id2label.json"

   _SCREAMING_SNAKE_CASE	=				json.load(open(hf_hub_download(_UpperCAmelCase	,					_UpperCAmelCase	,					repo_type="dataset"  )	,					"r"  )  )
   _SCREAMING_SNAKE_CASE	=				{int(_UpperCAmelCase  ): v for k, v in idalabel.items()}

   return config
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				[]
   # stem
   # fmt: off
   rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")  )
   rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")  )
   rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")  )
   rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")  )
   # stages
   for i in range(len(config.backbone_config.depths  )  ):
      for j in range(config.backbone_config.depths[i]  ):
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")  )
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")  )

      if i < 3:
         rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")  )
         rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")  )
         rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")  )
      rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")  )
      rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")  )

   # FPN
   rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")  )
   rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")  )
   rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")  )
   for source_index, target_index in zip(range(3	,					0	,					-1  )	,					range(0	,					3  )  ):
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")  )
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")  )
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")  )
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")  )
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")  )
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")  )
   rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")  )
   rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")  )

   # Transformer decoder
   for idx in range(config.decoder_config.decoder_layers  ):
      # self-attention out projection
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")  )
      # cross-attention out projection
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")  )
      # MLP 1
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")  )
      # MLP 2
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")  )
      # layernorm 1 (self-attention layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")  )
      # layernorm 2 (cross-attention layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")  )
      # layernorm 3 (final layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")  )

   rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")  )
   rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")  )

   # heads on top
   rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")  )

   rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")  )
   rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")  )

   rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")  )
   rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")  )

   for i in range(3  ):
      rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")  )
      rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")  )
   # fmt: on

   return rename_keys
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			int	,					__A    :			Union[str, Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				dct.pop(_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE	=				val
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Any  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				[int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
   for i in range(len(backbone_config.depths  )  ):
      _SCREAMING_SNAKE_CASE	=				num_features[i]
      for j in range(backbone_config.depths[i]  ):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         _SCREAMING_SNAKE_CASE	=				state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""  )
         _SCREAMING_SNAKE_CASE	=				state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""  )
         # next, add query, keys and values (in that order) to the state dict
         _SCREAMING_SNAKE_CASE	=				in_proj_weight[:dim, :]
         _SCREAMING_SNAKE_CASE	=				in_proj_bias[: dim]
         _SCREAMING_SNAKE_CASE	=				in_proj_weight[
             dim : dim * 2, :
         ]
         _SCREAMING_SNAKE_CASE	=				in_proj_bias[
             dim : dim * 2
         ]
         _SCREAMING_SNAKE_CASE	=				in_proj_weight[
             -dim :, :
         ]
         _SCREAMING_SNAKE_CASE	=				in_proj_bias[-dim :]
         # fmt: on
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			Tuple  )			->			List[str]:
   # fmt: off
   _SCREAMING_SNAKE_CASE	=				config.decoder_config.hidden_size
   for idx in range(config.decoder_config.decoder_layers  ):
      # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
      _SCREAMING_SNAKE_CASE	=				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""  )
      _SCREAMING_SNAKE_CASE	=				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""  )
      # next, add query, keys and values (in that order) to the state dict
      _SCREAMING_SNAKE_CASE	=				in_proj_weight[: hidden_size, :]
      _SCREAMING_SNAKE_CASE	=				in_proj_bias[:config.hidden_size]
      _SCREAMING_SNAKE_CASE	=				in_proj_weight[hidden_size : hidden_size * 2, :]
      _SCREAMING_SNAKE_CASE	=				in_proj_bias[hidden_size : hidden_size * 2]
      _SCREAMING_SNAKE_CASE	=				in_proj_weight[-hidden_size :, :]
      _SCREAMING_SNAKE_CASE	=				in_proj_bias[-hidden_size :]
      # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
      _SCREAMING_SNAKE_CASE	=				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""  )
      _SCREAMING_SNAKE_CASE	=				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""  )
      # next, add query, keys and values (in that order) to the state dict
      _SCREAMING_SNAKE_CASE	=				in_proj_weight[: hidden_size, :]
      _SCREAMING_SNAKE_CASE	=				in_proj_bias[:config.hidden_size]
      _SCREAMING_SNAKE_CASE	=				in_proj_weight[hidden_size : hidden_size * 2, :]
      _SCREAMING_SNAKE_CASE	=				in_proj_bias[hidden_size : hidden_size * 2]
      _SCREAMING_SNAKE_CASE	=				in_proj_weight[-hidden_size :, :]
      _SCREAMING_SNAKE_CASE	=				in_proj_bias[-hidden_size :]
   # fmt: on
def 			SCREAMING_SNAKE_CASE_			(							)			->			str:
   _SCREAMING_SNAKE_CASE	=				"http://images.cocodataset.org/val2017/000000039769.jpg"
   _SCREAMING_SNAKE_CASE	=				Image.open(requests.get(_UpperCAmelCase	,					stream=_UpperCAmelCase  ).raw  )
   return im
@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]	,					__A    :			List[Any]	,					__A    :			Any	,					__A    :			Any = False  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				get_maskformer_config(_UpperCAmelCase  )

   # load original state_dict
   with open(_UpperCAmelCase	,					"rb"  ) as f:
      _SCREAMING_SNAKE_CASE	=				pickle.load(_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE	=				data["model"]

   # for name, param in state_dict.items():
   #     print(name, param.shape)

   # rename keys
   _SCREAMING_SNAKE_CASE	=				create_rename_keys(_UpperCAmelCase  )
   for src, dest in rename_keys:
      rename_key(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase  )
   read_in_swin_q_k_v(_UpperCAmelCase	,					config.backbone_config  )
   read_in_decoder_q_k_v(_UpperCAmelCase	,					_UpperCAmelCase  )

   # update to torch tensors
   for key, value in state_dict.items():
      _SCREAMING_SNAKE_CASE	=				torch.from_numpy(_UpperCAmelCase  )

   # load 🤗 model
   _SCREAMING_SNAKE_CASE	=				MaskFormerForInstanceSegmentation(_UpperCAmelCase  )
   model.eval()

   for name, param in model.named_parameters():
      print(_UpperCAmelCase	,					param.shape  )

   _SCREAMING_SNAKE_CASE	=				model.load_state_dict(_UpperCAmelCase	,					strict=_UpperCAmelCase  )
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(_UpperCAmelCase  ) == 0, f"""Unexpected keys: {unexpected_keys}"""

   # verify results
   _SCREAMING_SNAKE_CASE	=				prepare_img()
   if "vistas" in model_name:
      _SCREAMING_SNAKE_CASE	=				65
   elif "cityscapes" in model_name:
      _SCREAMING_SNAKE_CASE	=				6_55_35
   else:
      _SCREAMING_SNAKE_CASE	=				2_55
   _SCREAMING_SNAKE_CASE	=				True if "ade" in model_name else False
   _SCREAMING_SNAKE_CASE	=				MaskFormerImageProcessor(ignore_index=_UpperCAmelCase	,					reduce_labels=_UpperCAmelCase  )

   _SCREAMING_SNAKE_CASE	=				image_processor(_UpperCAmelCase	,					return_tensors="pt"  )

   _SCREAMING_SNAKE_CASE	=				model(**_UpperCAmelCase  )

   print("Logits:"	,					outputs.class_queries_logits[0, :3, :3]  )

   if model_name == "maskformer-swin-tiny-ade":
      _SCREAMING_SNAKE_CASE	=				torch.tensor(
          [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]  )
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]	,					_UpperCAmelCase	,					atol=1e-4  )
   print("Looks ok!"  )

   if pytorch_dump_folder_path is not None:
      print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""  )
      Path(_UpperCAmelCase  ).mkdir(exist_ok=_UpperCAmelCase  )
      model.save_pretrained(_UpperCAmelCase  )
      image_processor.save_pretrained(_UpperCAmelCase  )

   if push_to_hub:
      print("Pushing model and image processor to the hub..."  )
      model.push_to_hub(f"""nielsr/{model_name}"""  )
      image_processor.push_to_hub(f"""nielsr/{model_name}"""  )


if __name__ == "__main__":
      lowerCamelCase_			=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='maskformer-swin-tiny-ade',
          type=str,
          help=('Name of the MaskFormer model you\'d like to convert',),
      )
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
          type=str,
          help='Path to the original state dict (.pth file).',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )

      lowerCamelCase_			=						parser.parse_args()
      convert_maskformer_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )







'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]=1_2_8_1_0_0  ,				__lowerCamelCase :     Optional[int]=1_5_3_6  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Tuple=6_1_4_4  ,				__lowerCamelCase :     List[str]="gelu"  ,				__lowerCamelCase :     int=0.1  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     Union[str, Any]=5_1_2  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     int=1e-7  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     Any=-1  ,				__lowerCamelCase :     Tuple=0  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Optional[int]=0  ,				__lowerCamelCase :     Any="gelu"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				relative_attention
      _SCREAMING_SNAKE_CASE	=				max_relative_positions
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				position_biased_input

      # Backwards compatibility
      if type(__lowerCamelCase   ) == str:
         _SCREAMING_SNAKE_CASE	=				[x.strip() for x in pos_att_type.lower().split("|"   )]

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

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

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "sequence"}
      if self._config.type_vocab_size > 0:
         return OrderedDict(
             [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]   )
      else:
         return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]   )



   @property
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return 1_2



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     Optional["TensorType"] = None  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().generate_dummy_inputs(preprocessor=__lowerCamelCase  ,				framework=__lowerCamelCase   )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs

