






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 :      int										=				logging.get_logger(__name__)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]       ):
	snake_case__			:				List[Any]       =  SwinConfig.from_pretrained(
	    "microsoft/swin-tiny-patch4-window7-224"					,  out_features=["stage1", "stage2", "stage3", "stage4"]       )
	snake_case__			:				Union[str, Any]       =  MaskFormerConfig(backbone_config=_lowerCAmelCase       )

	snake_case__			:				Union[str, Any]       =  """huggingface/label-files"""
	if "ade20k-full" in model_name:
		# this should be ok
		snake_case__			:				Optional[int]       =  847
		snake_case__			:				List[Any]       =  """maskformer-ade20k-full-id2label.json"""
	elif "ade" in model_name:
		# this should be ok
		snake_case__			:				str       =  150
		snake_case__			:				Dict       =  """ade20k-id2label.json"""
	elif "coco-stuff" in model_name:
		# this should be ok
		snake_case__			:				Optional[Any]       =  171
		snake_case__			:				List[str]       =  """maskformer-coco-stuff-id2label.json"""
	elif "coco" in model_name:
		# TODO
		snake_case__			:				Optional[int]       =  133
		snake_case__			:				Union[str, Any]       =  """coco-panoptic-id2label.json"""
	elif "cityscapes" in model_name:
		# this should be ok
		snake_case__			:				Dict       =  19
		snake_case__			:				List[str]       =  """cityscapes-id2label.json"""
	elif "vistas" in model_name:
		# this should be ok
		snake_case__			:				str       =  65
		snake_case__			:				Optional[Any]       =  """mapillary-vistas-id2label.json"""

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

	return config







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]       ):
	snake_case__			:				Tuple       =  []
	# 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    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Any					,  snake_case_ :							int       ):
	snake_case__			:				Any       =  dct.pop(_lowerCAmelCase       )
	snake_case__			:				List[Any]       =  val







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Any       ):
	snake_case__			:				List[str]       =  [int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
	for i in range(len(backbone_config.depths       )       ):
		snake_case__			:				List[Any]       =  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)
			snake_case__			:				List[Any]       =  state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''       )
			snake_case__			:				Optional[Any]       =  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
			snake_case__			:				Tuple       =  in_proj_weight[:dim, :]
			snake_case__			:				Optional[Any]       =  in_proj_bias[: dim]
			snake_case__			:				Optional[Any]       =  in_proj_weight[
			    dim : dim * 2, :
			]
			snake_case__			:				List[str]       =  in_proj_bias[
			    dim : dim * 2
			]
			snake_case__			:				int       =  in_proj_weight[
			    -dim :, :
			]
			snake_case__			:				List[str]       =  in_proj_bias[-dim :]
			# fmt: on







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict					,  snake_case_ :							List[Any]       ):
	# fmt: off
	snake_case__			:				Any       =  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)
		snake_case__			:				int       =  state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''       )
		snake_case__			:				Union[str, Any]       =  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
		snake_case__			:				Any       =  in_proj_weight[: hidden_size, :]
		snake_case__			:				Optional[Any]       =  in_proj_bias[:config.hidden_size]
		snake_case__			:				str       =  in_proj_weight[hidden_size : hidden_size * 2, :]
		snake_case__			:				Dict       =  in_proj_bias[hidden_size : hidden_size * 2]
		snake_case__			:				Union[str, Any]       =  in_proj_weight[-hidden_size :, :]
		snake_case__			:				str       =  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)
		snake_case__			:				Tuple       =  state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''       )
		snake_case__			:				Union[str, Any]       =  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
		snake_case__			:				Optional[int]       =  in_proj_weight[: hidden_size, :]
		snake_case__			:				str       =  in_proj_bias[:config.hidden_size]
		snake_case__			:				Tuple       =  in_proj_weight[hidden_size : hidden_size * 2, :]
		snake_case__			:				Optional[Any]       =  in_proj_bias[hidden_size : hidden_size * 2]
		snake_case__			:				Any       =  in_proj_weight[-hidden_size :, :]
		snake_case__			:				Tuple       =  in_proj_bias[-hidden_size :]
	# fmt: on







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Dict       =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
	snake_case__			:				Tuple       =  Image.open(requests.get(_lowerCAmelCase					,  stream=_lowerCAmelCase       ).raw       )
	return im







@torch.no_grad()
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Tuple					,  snake_case_ :							Optional[int]					,  snake_case_ :							Dict = False       ):
	snake_case__			:				Dict       =  get_maskformer_config(_lowerCAmelCase       )

	# load original state_dict
	with open(_lowerCAmelCase					,  "rb"       ) as f:
		snake_case__			:				Tuple       =  pickle.load(_lowerCAmelCase       )
	snake_case__			:				Any       =  data["""model"""]

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

	# rename keys
	snake_case__			:				List[str]       =  create_rename_keys(_lowerCAmelCase       )
	for src, dest in rename_keys:
		rename_key(_lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase       )
	read_in_swin_q_k_v(_lowerCAmelCase					,  config.backbone_config       )
	read_in_decoder_q_k_v(_lowerCAmelCase					,  _lowerCAmelCase       )

	# update to torch tensors
	for key, value in state_dict.items():
		snake_case__			:				Any       =  torch.from_numpy(_lowerCAmelCase       )

	# load 🤗 model
	snake_case__			:				str       =  MaskFormerForInstanceSegmentation(_lowerCAmelCase       )
	model.eval()

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

	snake_case__			:				Dict       =  model.load_state_dict(_lowerCAmelCase					,  strict=_lowerCAmelCase       )
	assert missing_keys == [
	    "model.pixel_level_module.encoder.model.layernorm.weight",
	    "model.pixel_level_module.encoder.model.layernorm.bias",
	]
	assert len(_lowerCAmelCase       ) == 0, F'''Unexpected keys: {unexpected_keys}'''

	# verify results
	snake_case__			:				str       =  prepare_img()
	if "vistas" in model_name:
		snake_case__			:				Optional[Any]       =  65
	elif "cityscapes" in model_name:
		snake_case__			:				Optional[int]       =  65535
	else:
		snake_case__			:				Any       =  255
	snake_case__			:				str       =  True if """ade""" in model_name else False
	snake_case__			:				str       =  MaskFormerImageProcessor(ignore_index=_lowerCAmelCase					,  reduce_labels=_lowerCAmelCase       )

	snake_case__			:				List[Any]       =  image_processor(_lowerCAmelCase					,  return_tensors="pt"       )

	snake_case__			:				Tuple       =  model(**_lowerCAmelCase       )

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

	if model_name == "maskformer-swin-tiny-ade":
		snake_case__			:				Optional[int]       =  torch.tensor(
		    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]       )
	assert torch.allclose(outputs.class_queries_logits[0, :3, :3]					,  _lowerCAmelCase					,  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(_lowerCAmelCase       ).mkdir(exist_ok=_lowerCAmelCase       )
		model.save_pretrained(_lowerCAmelCase       )
		image_processor.save_pretrained(_lowerCAmelCase       )

	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 :      Optional[Any]										=				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 :      Optional[Any]										=				parser.parse_args()
				convert_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)











from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)

__lowerCamelCase :      List[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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xmod"



					def __init__( self					:  List[str]							,		__A					:  List[str]=3_0_5_2_2							,		__A					:  Tuple=7_6_8							,		__A					:  str=1_2							,		__A					:  List[Any]=1_2							,		__A					:  List[str]=3_0_7_2							,		__A					:  List[str]="gelu"							,		__A					:  List[Any]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=5_1_2							,		__A					:  Union[str, Any]=2							,		__A					:  List[Any]=0.0_2							,		__A					:  List[str]=1e-1_2							,		__A					:  Tuple=1							,		__A					:  List[Any]=0							,		__A					:  Optional[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		__A					:  Dict=None							,		__A					:  Optional[int]=False							,		__A					:  Dict=2							,		__A					:  List[str]=False							,		__A					:  Dict=True							,		__A					:  Union[str, Any]=True							,		__A					:  Tuple=("en_XX",)							,		__A					:  Optional[Any]=None							,		**__A					:  Tuple							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Union[str, Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				str       =  num_attention_heads
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				List[str]       =  hidden_dropout_prob
						snake_case__			:				Optional[int]       =  attention_probs_dropout_prob
						snake_case__			:				Union[str, Any]       =  max_position_embeddings
						snake_case__			:				str       =  type_vocab_size
						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				Dict       =  layer_norm_eps
						snake_case__			:				str       =  position_embedding_type
						snake_case__			:				List[str]       =  use_cache
						snake_case__			:				Tuple       =  classifier_dropout
						snake_case__			:				Any       =  pre_norm
						snake_case__			:				List[str]       =  adapter_reduction_factor
						snake_case__			:				List[Any]       =  adapter_layer_norm
						snake_case__			:				str       =  adapter_reuse_layer_norm
						snake_case__			:				Union[str, Any]       =  ln_before_adapter
						snake_case__			:				Tuple       =  list(__A	)
						snake_case__			:				int       =  default_language



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  int	):
						if self.task == "multiple-choice":
							snake_case__			:				Any       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Any       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



