
import argparse
import json

import torch

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Tuple=1      )					->						int:
				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('.'      )[n_shave_prefix_segments:]      )
				else:
								return ".".join(path.split('.'      )[:n_shave_prefix_segments]      )




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Union[str, Any]   ,							SCREAMING_SNAKE_CASE	:				Any=0      )					->						Dict:
				__lowercase								= []
				for old_item in old_list:
								__lowercase								= old_item
								__lowercase								= new_item.replace('block.'   ,							'resnets.'      )
								__lowercase								= new_item.replace('conv_shorcut'   ,							'conv1'      )
								__lowercase								= new_item.replace('in_shortcut'   ,							'conv_shortcut'      )
								__lowercase								= new_item.replace('temb_proj'   ,							'time_emb_proj'      )

								__lowercase								= shave_segments(SCREAMING_SNAKE_CASE   ,							n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

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

				return mapping




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Union[str, Any]   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]=0   ,							SCREAMING_SNAKE_CASE	:				int=False      )					->						str:
				__lowercase								= []
				for old_item in old_list:
								__lowercase								= old_item

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

								__lowercase								= new_item.replace('proj_out'   ,							'proj_attn'      )
								__lowercase								= new_item.replace('norm'   ,							'group_norm'      )

								__lowercase								= shave_segments(SCREAMING_SNAKE_CASE   ,							n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )
								mapping.append({'old': old_item, 'new': new_item}      )

				return mapping




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				List[Any]=None   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]=None   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]=None      )					->						Dict:
				assert isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ), "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():
												__lowercase								= old_checkpoint[path]
												__lowercase								= old_tensor.shape[0] // 3

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

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

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

												__lowercase								= query.reshape(SCREAMING_SNAKE_CASE      ).squeeze()
												__lowercase								= key.reshape(SCREAMING_SNAKE_CASE      ).squeeze()
												__lowercase								= value.reshape(SCREAMING_SNAKE_CASE      ).squeeze()

				for path in paths:
								__lowercase								= path['new']

								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								__lowercase								= new_path.replace('down.'   ,							'down_blocks.'      )
								__lowercase								= new_path.replace('up.'   ,							'up_blocks.'      )

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

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						Any:
				__lowercase								= {}

				__lowercase								= checkpoint['temb.dense.0.weight']
				__lowercase								= checkpoint['temb.dense.0.bias']
				__lowercase								= checkpoint['temb.dense.1.weight']
				__lowercase								= checkpoint['temb.dense.1.bias']

				__lowercase								= checkpoint['norm_out.weight']
				__lowercase								= checkpoint['norm_out.bias']

				__lowercase								= checkpoint['conv_in.weight']
				__lowercase								= checkpoint['conv_in.bias']
				__lowercase								= checkpoint['conv_out.weight']
				__lowercase								= checkpoint['conv_out.bias']

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

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

				for i in range(SCREAMING_SNAKE_CASE      ):
								__lowercase								= (i - 1) // (config['layers_per_block'] + 1)

								if any('downsample' in layer for layer in down_blocks[i]      ):
												__lowercase								= checkpoint[
												    F"""down.{i}.downsample.op.weight"""
												]
												__lowercase								= 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]      ):
												__lowercase								= len(
												    {'.'.join(shave_segments(SCREAMING_SNAKE_CASE   ,							2      ).split('.'      )[:2]      ) for layer in down_blocks[i] if 'block' in layer}      )
												__lowercase								= {
												    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
												    for layer_id in range(SCREAMING_SNAKE_CASE      )
												}

												if num_blocks > 0:
																for j in range(config['layers_per_block']      ):
																				__lowercase								= renew_resnet_paths(blocks[j]      )
																				assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )

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

												if num_attn > 0:
																for j in range(config['layers_per_block']      ):
																				__lowercase								= renew_attention_paths(attns[j]      )
																				assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							config=SCREAMING_SNAKE_CASE      )

				__lowercase								= [key for key in checkpoint if 'mid.block_1' in key]
				__lowercase								= [key for key in checkpoint if 'mid.block_2' in key]
				__lowercase								= [key for key in checkpoint if 'mid.attn_1' in key]

				# Mid new 2
				__lowercase								= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]   ,							)

				__lowercase								= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]   ,							)

				__lowercase								= renew_attention_paths(SCREAMING_SNAKE_CASE   ,							in_mid=SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]   ,							)

				for i in range(SCREAMING_SNAKE_CASE      ):
								__lowercase								= num_up_blocks - 1 - i

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

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

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

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

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

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Union[str, Any]      )					->						int:
				__lowercase								= {}

				__lowercase								= checkpoint['encoder.norm_out.weight']
				__lowercase								= checkpoint['encoder.norm_out.bias']

				__lowercase								= checkpoint['encoder.conv_in.weight']
				__lowercase								= checkpoint['encoder.conv_in.bias']
				__lowercase								= checkpoint['encoder.conv_out.weight']
				__lowercase								= checkpoint['encoder.conv_out.bias']

				__lowercase								= checkpoint['decoder.norm_out.weight']
				__lowercase								= checkpoint['decoder.norm_out.bias']

				__lowercase								= checkpoint['decoder.conv_in.weight']
				__lowercase								= checkpoint['decoder.conv_in.bias']
				__lowercase								= checkpoint['decoder.conv_out.weight']
				__lowercase								= checkpoint['decoder.conv_out.bias']

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

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

				for i in range(SCREAMING_SNAKE_CASE      ):
								__lowercase								= (i - 1) // (config['layers_per_block'] + 1)

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

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

												if num_blocks > 0:
																for j in range(config['layers_per_block']      ):
																				__lowercase								= renew_resnet_paths(blocks[j]      )
																				assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )

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

												if num_attn > 0:
																for j in range(config['layers_per_block']      ):
																				__lowercase								= renew_attention_paths(attns[j]      )
																				assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							config=SCREAMING_SNAKE_CASE      )

				__lowercase								= [key for key in checkpoint if 'mid.block_1' in key]
				__lowercase								= [key for key in checkpoint if 'mid.block_2' in key]
				__lowercase								= [key for key in checkpoint if 'mid.attn_1' in key]

				# Mid new 2
				__lowercase								= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]   ,							)

				__lowercase								= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]   ,							)

				__lowercase								= renew_attention_paths(SCREAMING_SNAKE_CASE   ,							in_mid=SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]   ,							)

				for i in range(SCREAMING_SNAKE_CASE      ):
								__lowercase								= num_up_blocks - 1 - i

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

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

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

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

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

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

				return new_checkpoint


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__							=    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.""")

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

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

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

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

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

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

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



from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


											super().__init__(pad_token_id=_UpperCAmelCase					,			**_UpperCAmelCase    )

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





							@property
							def       a__			(			self  :       Any    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


											if self.task == "multiple-choice":
															__lowercase								= {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															__lowercase								= {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]    )





