




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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     SwinConfig.from_pretrained(
	    "microsoft/swin-tiny-patch4-window7-224"     ,	out_features=["stage1", "stage2", "stage3", "stage4"]							)
	snake_case_		    =     MaskFormerConfig(backbone_config=UpperCamelCase_							)

	snake_case_		    =     "huggingface/label-files"
	if "ade20k-full" in model_name:
		# this should be ok
		snake_case_		    =     847
		snake_case_		    =     "maskformer-ade20k-full-id2label.json"
	elif "ade" in model_name:
		# this should be ok
		snake_case_		    =     150
		snake_case_		    =     "ade20k-id2label.json"
	elif "coco-stuff" in model_name:
		# this should be ok
		snake_case_		    =     171
		snake_case_		    =     "maskformer-coco-stuff-id2label.json"
	elif "coco" in model_name:
		# TODO
		snake_case_		    =     133
		snake_case_		    =     "coco-panoptic-id2label.json"
	elif "cityscapes" in model_name:
		# this should be ok
		snake_case_		    =     19
		snake_case_		    =     "cityscapes-id2label.json"
	elif "vistas" in model_name:
		# this should be ok
		snake_case_		    =     65
		snake_case_		    =     "mapillary-vistas-id2label.json"

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

	return config


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	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 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     dct.pop(UpperCamelCase_							)
	snake_case_		    =     val


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	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							)							):
		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)
			snake_case_		    =     state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"							)
			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
			snake_case_		    =     in_proj_weight[:dim, :]
			snake_case_		    =     in_proj_bias[: dim]
			snake_case_		    =     in_proj_weight[
			    dim : dim * 2, :
			]
			snake_case_		    =     in_proj_bias[
			    dim : dim * 2
			]
			snake_case_		    =     in_proj_weight[
			    -dim :, :
			]
			snake_case_		    =     in_proj_bias[-dim :]
			# fmt: on


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	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)
		snake_case_		    =     state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"							)
		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
		snake_case_		    =     in_proj_weight[: hidden_size, :]
		snake_case_		    =     in_proj_bias[:config.hidden_size]
		snake_case_		    =     in_proj_weight[hidden_size : hidden_size * 2, :]
		snake_case_		    =     in_proj_bias[hidden_size : hidden_size * 2]
		snake_case_		    =     in_proj_weight[-hidden_size :, :]
		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)
		snake_case_		    =     state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"							)
		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
		snake_case_		    =     in_proj_weight[: hidden_size, :]
		snake_case_		    =     in_proj_bias[:config.hidden_size]
		snake_case_		    =     in_proj_weight[hidden_size : hidden_size * 2, :]
		snake_case_		    =     in_proj_bias[hidden_size : hidden_size * 2]
		snake_case_		    =     in_proj_weight[-hidden_size :, :]
		snake_case_		    =     in_proj_bias[-hidden_size :]
	# fmt: on


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_		    =     Image.open(requests.get(UpperCamelCase_     ,	stream=UpperCamelCase_							).raw							)
	return im


@torch.no_grad()
def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A = False							):







	'''simple docstring'''





	snake_case_		    =     get_maskformer_config(UpperCamelCase_							)

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

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

	# rename keys
	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():
		snake_case_		    =     torch.from_numpy(UpperCamelCase_							)

	# load 🤗 model
	snake_case_		    =     MaskFormerForInstanceSegmentation(UpperCamelCase_							)
	model.eval()

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

	snake_case_    ,      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
	snake_case_		    =     prepare_img()
	if "vistas" in model_name:
		snake_case_		    =     65
	elif "cityscapes" in model_name:
		snake_case_		    =     65535
	else:
		snake_case_		    =     255
	snake_case_		    =     True if "ade" in model_name else False
	snake_case_		    =     MaskFormerImageProcessor(ignore_index=UpperCamelCase_     ,	reduce_labels=UpperCamelCase_							)

	snake_case_		    =     image_processor(UpperCamelCase_     ,	return_tensors="pt"							)

	snake_case_		    =     model(**UpperCamelCase_							)

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

	if model_name == "maskformer-swin-tiny-ade":
		snake_case_		    =     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]     ,	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__":
		lowercase__				:     Optional[int] 		=						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."
		)

		lowercase__				:     Union[str, 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


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """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 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""xmod"""
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]=30522       ,   UpperCAmelCase_							:   Union[str, Any]=768       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=3072       ,   UpperCAmelCase_							:   Dict="gelu"       ,   UpperCAmelCase_							:   str=0.1       ,   UpperCAmelCase_							:   Dict=0.1       ,   UpperCAmelCase_							:   Optional[Any]=512       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   Optional[Any]=1e-1_2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   Optional[int]=2       ,   UpperCAmelCase_							:   Tuple="absolute"       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   UpperCAmelCase_							:   int=2       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Optional[int]=True       ,   UpperCAmelCase_							:   List[Any]=True       ,   UpperCAmelCase_							:   Union[str, Any]=("en_XX",)       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   str       ,   ):
						super().__init__(pad_token_id=UpperCAmelCase_       ,   bos_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= intermediate_size
						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__													= layer_norm_eps
						SCREAMING_SNAKE_CASE__													= position_embedding_type
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= classifier_dropout
						SCREAMING_SNAKE_CASE__													= pre_norm
						SCREAMING_SNAKE_CASE__													= adapter_reduction_factor
						SCREAMING_SNAKE_CASE__													= adapter_layer_norm
						SCREAMING_SNAKE_CASE__													= adapter_reuse_layer_norm
						SCREAMING_SNAKE_CASE__													= ln_before_adapter
						SCREAMING_SNAKE_CASE__													= list(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= default_language





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	def      A_				(			self							:   List[Any]							):
						if self.task == "multiple-choice":
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





