
"""simple docstring"""


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=1 )       ->				Tuple:
	if n_shave_prefix_segments >= 0:
		return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
	else:
		return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=0 )       ->				int:
	_lowerCAmelCase			=[]
	for old_item in old_list:
		_lowerCAmelCase			=old_item
		_lowerCAmelCase			=new_item.replace("""block."""					,				"""resnets.""" )
		_lowerCAmelCase			=new_item.replace("""conv_shorcut"""					,				"""conv1""" )
		_lowerCAmelCase			=new_item.replace("""in_shortcut"""					,				"""conv_shortcut""" )
		_lowerCAmelCase			=new_item.replace("""temb_proj"""					,				"""time_emb_proj""" )

		_lowerCAmelCase			=shave_segments(__UpperCamelCase					,				n_shave_prefix_segments=__UpperCamelCase )

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

	return mapping



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=0					,				__UpperCamelCase=False )       ->				Tuple:
	_lowerCAmelCase			=[]
	for old_item in old_list:
		_lowerCAmelCase			=old_item

		# In `model.mid`, the layer is called `attn`.
		if not in_mid:
			_lowerCAmelCase			=new_item.replace("""attn"""					,				"""attentions""" )
		_lowerCAmelCase			=new_item.replace(""".k."""					,				""".key.""" )
		_lowerCAmelCase			=new_item.replace(""".v."""					,				""".value.""" )
		_lowerCAmelCase			=new_item.replace(""".q."""					,				""".query.""" )

		_lowerCAmelCase			=new_item.replace("""proj_out"""					,				"""proj_attn""" )
		_lowerCAmelCase			=new_item.replace("""norm"""					,				"""group_norm""" )

		_lowerCAmelCase			=shave_segments(__UpperCamelCase					,				n_shave_prefix_segments=__UpperCamelCase )
		mapping.append({"""old""": old_item, """new""": new_item} )

	return mapping



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase=None					,				__UpperCamelCase=None					,				__UpperCamelCase=None )       ->				Dict:
	assert isinstance(__UpperCamelCase					,				__UpperCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."

	if attention_paths_to_split is not None:
		if config is None:
			raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'.""" )

		for path, path_map in attention_paths_to_split.items():
			_lowerCAmelCase			=old_checkpoint[path]
			_lowerCAmelCase			=old_tensor.shape[0] // 3

			_lowerCAmelCase			=(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

			_lowerCAmelCase			=old_tensor.shape[0] // config.get("""num_head_channels"""					,				1 ) // 3

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

			_lowerCAmelCase			=query.reshape(__UpperCamelCase ).squeeze()
			_lowerCAmelCase			=key.reshape(__UpperCamelCase ).squeeze()
			_lowerCAmelCase			=value.reshape(__UpperCamelCase ).squeeze()

	for path in paths:
		_lowerCAmelCase			=path["""new"""]

		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		_lowerCAmelCase			=new_path.replace("""down."""					,				"""down_blocks.""" )
		_lowerCAmelCase			=new_path.replace("""up."""					,				"""up_blocks.""" )

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

		if "attentions" in new_path:
			_lowerCAmelCase			=old_checkpoint[path["""old"""]].squeeze()
		else:
			_lowerCAmelCase			=old_checkpoint[path["""old"""]]



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Optional[int]:
	_lowerCAmelCase			={}

	_lowerCAmelCase			=checkpoint["""temb.dense.0.weight"""]
	_lowerCAmelCase			=checkpoint["""temb.dense.0.bias"""]
	_lowerCAmelCase			=checkpoint["""temb.dense.1.weight"""]
	_lowerCAmelCase			=checkpoint["""temb.dense.1.bias"""]

	_lowerCAmelCase			=checkpoint["""norm_out.weight"""]
	_lowerCAmelCase			=checkpoint["""norm_out.bias"""]

	_lowerCAmelCase			=checkpoint["""conv_in.weight"""]
	_lowerCAmelCase			=checkpoint["""conv_in.bias"""]
	_lowerCAmelCase			=checkpoint["""conv_out.weight"""]
	_lowerCAmelCase			=checkpoint["""conv_out.bias"""]

	_lowerCAmelCase			=len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """down""" in layer} )
	_lowerCAmelCase			={
	    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )
	}

	_lowerCAmelCase			=len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """up""" in layer} )
	_lowerCAmelCase			={layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )}

	for i in range(__UpperCamelCase ):
		_lowerCAmelCase			=(i - 1) // (config["""layers_per_block"""] + 1)

		if any("""downsample""" in layer for layer in down_blocks[i] ):
			_lowerCAmelCase			=checkpoint[
			    F'''down.{i}.downsample.op.weight'''
			]
			_lowerCAmelCase			=checkpoint[F'''down.{i}.downsample.op.bias''']
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

		if any("""block""" in layer for layer in down_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				2 ).split(""".""" )[:2] ) for layer in down_blocks[i] if """block""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
			    for layer_id in range(__UpperCamelCase )
			}

			if num_blocks > 0:
				for j in range(config["""layers_per_block"""] ):
					_lowerCAmelCase			=renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )

		if any("""attn""" in layer for layer in down_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				2 ).split(""".""" )[:2] ) for layer in down_blocks[i] if """attn""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
			    for layer_id in range(__UpperCamelCase )
			}

			if num_attn > 0:
				for j in range(config["""layers_per_block"""] ):
					_lowerCAmelCase			=renew_attention_paths(attns[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				config=__UpperCamelCase )

	_lowerCAmelCase			=[key for key in checkpoint if """mid.block_1""" in key]
	_lowerCAmelCase			=[key for key in checkpoint if """mid.block_2""" in key]
	_lowerCAmelCase			=[key for key in checkpoint if """mid.attn_1""" in key]

	# Mid new 2
	_lowerCAmelCase			=renew_resnet_paths(__UpperCamelCase )
	assign_to_checkpoint(
	    __UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]					,				)

	_lowerCAmelCase			=renew_resnet_paths(__UpperCamelCase )
	assign_to_checkpoint(
	    __UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]					,				)

	_lowerCAmelCase			=renew_attention_paths(__UpperCamelCase					,				in_mid=__UpperCamelCase )
	assign_to_checkpoint(
	    __UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]					,				)

	for i in range(__UpperCamelCase ):
		_lowerCAmelCase			=num_up_blocks - 1 - i

		if any("""upsample""" in layer for layer in up_blocks[i] ):
			_lowerCAmelCase			=checkpoint[
			    F'''up.{i}.upsample.conv.weight'''
			]
			_lowerCAmelCase			=checkpoint[F'''up.{i}.upsample.conv.bias''']

		if any("""block""" in layer for layer in up_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				2 ).split(""".""" )[:2] ) for layer in up_blocks[i] if """block""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )
			}

			if num_blocks > 0:
				for j in range(config["""layers_per_block"""] + 1 ):
					_lowerCAmelCase			={"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
					_lowerCAmelCase			=renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[replace_indices] )

		if any("""attn""" in layer for layer in up_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				2 ).split(""".""" )[:2] ) for layer in up_blocks[i] if """attn""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )
			}

			if num_attn > 0:
				for j in range(config["""layers_per_block"""] + 1 ):
					_lowerCAmelCase			={"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
					_lowerCAmelCase			=renew_attention_paths(attns[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[replace_indices] )

	_lowerCAmelCase			={k.replace("""mid_new_2"""					,				"""mid_block""" ): v for k, v in new_checkpoint.items()}
	return new_checkpoint



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				int:
	_lowerCAmelCase			={}

	_lowerCAmelCase			=checkpoint["""encoder.norm_out.weight"""]
	_lowerCAmelCase			=checkpoint["""encoder.norm_out.bias"""]

	_lowerCAmelCase			=checkpoint["""encoder.conv_in.weight"""]
	_lowerCAmelCase			=checkpoint["""encoder.conv_in.bias"""]
	_lowerCAmelCase			=checkpoint["""encoder.conv_out.weight"""]
	_lowerCAmelCase			=checkpoint["""encoder.conv_out.bias"""]

	_lowerCAmelCase			=checkpoint["""decoder.norm_out.weight"""]
	_lowerCAmelCase			=checkpoint["""decoder.norm_out.bias"""]

	_lowerCAmelCase			=checkpoint["""decoder.conv_in.weight"""]
	_lowerCAmelCase			=checkpoint["""decoder.conv_in.bias"""]
	_lowerCAmelCase			=checkpoint["""decoder.conv_out.weight"""]
	_lowerCAmelCase			=checkpoint["""decoder.conv_out.bias"""]

	_lowerCAmelCase			=len({""".""".join(layer.split(""".""" )[:3] ) for layer in checkpoint if """down""" in layer} )
	_lowerCAmelCase			={
	    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )
	}

	_lowerCAmelCase			=len({""".""".join(layer.split(""".""" )[:3] ) for layer in checkpoint if """up""" in layer} )
	_lowerCAmelCase			={layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )}

	for i in range(__UpperCamelCase ):
		_lowerCAmelCase			=(i - 1) // (config["""layers_per_block"""] + 1)

		if any("""downsample""" in layer for layer in down_blocks[i] ):
			_lowerCAmelCase			=checkpoint[
			    F'''encoder.down.{i}.downsample.conv.weight'''
			]
			_lowerCAmelCase			=checkpoint[
			    F'''encoder.down.{i}.downsample.conv.bias'''
			]

		if any("""block""" in layer for layer in down_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				3 ).split(""".""" )[:3] ) for layer in down_blocks[i] if """block""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
			    for layer_id in range(__UpperCamelCase )
			}

			if num_blocks > 0:
				for j in range(config["""layers_per_block"""] ):
					_lowerCAmelCase			=renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )

		if any("""attn""" in layer for layer in down_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				3 ).split(""".""" )[:3] ) for layer in down_blocks[i] if """attn""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
			    for layer_id in range(__UpperCamelCase )
			}

			if num_attn > 0:
				for j in range(config["""layers_per_block"""] ):
					_lowerCAmelCase			=renew_attention_paths(attns[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				config=__UpperCamelCase )

	_lowerCAmelCase			=[key for key in checkpoint if """mid.block_1""" in key]
	_lowerCAmelCase			=[key for key in checkpoint if """mid.block_2""" in key]
	_lowerCAmelCase			=[key for key in checkpoint if """mid.attn_1""" in key]

	# Mid new 2
	_lowerCAmelCase			=renew_resnet_paths(__UpperCamelCase )
	assign_to_checkpoint(
	    __UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]					,				)

	_lowerCAmelCase			=renew_resnet_paths(__UpperCamelCase )
	assign_to_checkpoint(
	    __UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]					,				)

	_lowerCAmelCase			=renew_attention_paths(__UpperCamelCase					,				in_mid=__UpperCamelCase )
	assign_to_checkpoint(
	    __UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]					,				)

	for i in range(__UpperCamelCase ):
		_lowerCAmelCase			=num_up_blocks - 1 - i

		if any("""upsample""" in layer for layer in up_blocks[i] ):
			_lowerCAmelCase			=checkpoint[
			    F'''decoder.up.{i}.upsample.conv.weight'''
			]
			_lowerCAmelCase			=checkpoint[
			    F'''decoder.up.{i}.upsample.conv.bias'''
			]

		if any("""block""" in layer for layer in up_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				3 ).split(""".""" )[:3] ) for layer in up_blocks[i] if """block""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )
			}

			if num_blocks > 0:
				for j in range(config["""layers_per_block"""] + 1 ):
					_lowerCAmelCase			={"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
					_lowerCAmelCase			=renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[replace_indices] )

		if any("""attn""" in layer for layer in up_blocks[i] ):
			_lowerCAmelCase			=len(
			    {""".""".join(shave_segments(__UpperCamelCase					,				3 ).split(""".""" )[:3] ) for layer in up_blocks[i] if """attn""" in layer} )
			_lowerCAmelCase			={
			    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(__UpperCamelCase )
			}

			if num_attn > 0:
				for j in range(config["""layers_per_block"""] + 1 ):
					_lowerCAmelCase			={"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
					_lowerCAmelCase			=renew_attention_paths(attns[j] )
					assign_to_checkpoint(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				additional_replacements=[replace_indices] )

	_lowerCAmelCase			={k.replace("""mid_new_2"""					,				"""mid_block""" ): v for k, v in new_checkpoint.items()}
	_lowerCAmelCase			=checkpoint["""quant_conv.weight"""]
	_lowerCAmelCase			=checkpoint["""quant_conv.bias"""]
	if "quantize.embedding.weight" in checkpoint:
		_lowerCAmelCase			=checkpoint["""quantize.embedding.weight"""]
	_lowerCAmelCase			=checkpoint["""post_quant_conv.weight"""]
	_lowerCAmelCase			=checkpoint["""post_quant_conv.bias"""]

	return new_checkpoint


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

			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
			)

			parser.add_argument(
			    '--config_file',
			    default=None,
			    type=str,
			    required=True,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

			__A 		= parser.parse_args()
			__A 		= torch.load(args.checkpoint_path)

			with open(args.config_file) as f:
						__A 		= json.loads(f.read())

			# unet case
			__A 		= {key.split('.')[0] for key in checkpoint.keys()}
			if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
						__A 		= convert_vq_autoenc_checkpoint(checkpoint, config)
			else:
						__A 		= convert_ddpm_checkpoint(checkpoint, config)

			if "ddpm" in config:
						del config["ddpm"]

			if config["_class_name"] == "VQModel":
						__A 		= VQModel(**config)
						model.load_state_dict(converted_checkpoint)
						model.save_pretrained(args.dump_path)
			elif config["_class_name"] == "AutoencoderKL":
						__A 		= AutoencoderKL(**config)
						model.load_state_dict(converted_checkpoint)
						model.save_pretrained(args.dump_path)
			else:
						__A 		= UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

						__A 		= DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

						__A 		= DDPMPipeline(unet=model, scheduler=scheduler)
						pipe.save_pretrained(args.dump_path)





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


__A 		= logging.get_logger(__name__)

__A 		= {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''ernie'''



			def __init__(							self  ,  __UpperCAmelCase=3_05_22  ,  __UpperCAmelCase=7_68  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=30_72  ,  __UpperCAmelCase="gelu"  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=5_12  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=0.0_2  ,  __UpperCAmelCase=1e-12  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase="absolute"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase  ,  )				->  str:
				super().__init__(pad_token_id=__UpperCAmelCase  ,  **__UpperCAmelCase						)

				_lowerCAmelCase			=vocab_size
				_lowerCAmelCase			=hidden_size
				_lowerCAmelCase			=num_hidden_layers
				_lowerCAmelCase			=num_attention_heads
				_lowerCAmelCase			=hidden_act
				_lowerCAmelCase			=intermediate_size
				_lowerCAmelCase			=hidden_dropout_prob
				_lowerCAmelCase			=attention_probs_dropout_prob
				_lowerCAmelCase			=max_position_embeddings
				_lowerCAmelCase			=type_vocab_size
				_lowerCAmelCase			=task_type_vocab_size
				_lowerCAmelCase			=use_task_id
				_lowerCAmelCase			=initializer_range
				_lowerCAmelCase			=layer_norm_eps
				_lowerCAmelCase			=position_embedding_type
				_lowerCAmelCase			=use_cache
				_lowerCAmelCase			=classifier_dropout







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							self						)				->  Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					_lowerCAmelCase			={0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
					_lowerCAmelCase			={0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				        ("""token_type_ids""", dynamic_axis),
				        ("""task_type_ids""", dynamic_axis),
				    ]						)



