"""simple docstring"""






import argparse
import json

import torch

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



def   a_						( lowerCamelCase ,      lowerCamelCase=1	):
				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('.'	)[n_shave_prefix_segments:]	)
				else:
								return ".".join(path.split('.'	)[:n_shave_prefix_segments]	)



def   a_						( lowerCamelCase ,      lowerCamelCase=0	):
				UpperCAmelCase__					=							[]
				for old_item in old_list:
								UpperCAmelCase__					=							old_item
								UpperCAmelCase__					=							new_item.replace('block.' ,      'resnets.'	)
								UpperCAmelCase__					=							new_item.replace('conv_shorcut' ,      'conv1'	)
								UpperCAmelCase__					=							new_item.replace('in_shortcut' ,      'conv_shortcut'	)
								UpperCAmelCase__					=							new_item.replace('temb_proj' ,      'time_emb_proj'	)

								UpperCAmelCase__					=							shave_segments(lowerCamelCase ,      n_shave_prefix_segments=lowerCamelCase	)

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

				return mapping



def   a_						( lowerCamelCase ,      lowerCamelCase=0 ,      lowerCamelCase=False	):
				UpperCAmelCase__					=							[]
				for old_item in old_list:
								UpperCAmelCase__					=							old_item

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

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

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

				return mapping



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase=None ,      lowerCamelCase=None ,      lowerCamelCase=None	):
				assert isinstance(lowerCamelCase ,      lowerCamelCase	), "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():
												UpperCAmelCase__					=							old_checkpoint[path]
												UpperCAmelCase__					=							old_tensor.shape[0] // 3

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

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

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

												UpperCAmelCase__					=							query.reshape(lowerCamelCase	).squeeze()
												UpperCAmelCase__					=							key.reshape(lowerCamelCase	).squeeze()
												UpperCAmelCase__					=							value.reshape(lowerCamelCase	).squeeze()

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

								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

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

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

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



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							{}

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

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

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

				UpperCAmelCase__					=							len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'down' in layer}	)
				UpperCAmelCase__					=							{
				    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)
				}

				UpperCAmelCase__					=							len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'up' in layer}	)
				UpperCAmelCase__					=							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)}

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

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

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

								if any('attn' in layer for layer in down_blocks[i]	):
												UpperCAmelCase__					=							len(
												    {'.'.join(shave_segments(lowerCamelCase ,      2	).split('.'	)[:2]	) for layer in down_blocks[i] if 'attn' in layer}	)
												UpperCAmelCase__					=							{
												    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
												    for layer_id in range(lowerCamelCase	)
												}

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

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

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

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

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

				for i in range(lowerCamelCase	):
								UpperCAmelCase__					=							num_up_blocks - 1 - i

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

								if any('block' in layer for layer in up_blocks[i]	):
												UpperCAmelCase__					=							len(
												    {'.'.join(shave_segments(lowerCamelCase ,      2	).split('.'	)[:2]	) for layer in up_blocks[i] if 'block' in layer}	)
												UpperCAmelCase__					=							{
												    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)
												}

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

								if any('attn' in layer for layer in up_blocks[i]	):
												UpperCAmelCase__					=							len(
												    {'.'.join(shave_segments(lowerCamelCase ,      2	).split('.'	)[:2]	) for layer in up_blocks[i] if 'attn' in layer}	)
												UpperCAmelCase__					=							{
												    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)
												}

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

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



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							{}

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

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

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

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

				UpperCAmelCase__					=							len({'.'.join(layer.split('.'	)[:3]	) for layer in checkpoint if 'down' in layer}	)
				UpperCAmelCase__					=							{
				    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)
				}

				UpperCAmelCase__					=							len({'.'.join(layer.split('.'	)[:3]	) for layer in checkpoint if 'up' in layer}	)
				UpperCAmelCase__					=							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)}

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

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

								if any('block' in layer for layer in down_blocks[i]	):
												UpperCAmelCase__					=							len(
												    {'.'.join(shave_segments(lowerCamelCase ,      3	).split('.'	)[:3]	) for layer in down_blocks[i] if 'block' in layer}	)
												UpperCAmelCase__					=							{
												    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
												    for layer_id in range(lowerCamelCase	)
												}

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

								if any('attn' in layer for layer in down_blocks[i]	):
												UpperCAmelCase__					=							len(
												    {'.'.join(shave_segments(lowerCamelCase ,      3	).split('.'	)[:3]	) for layer in down_blocks[i] if 'attn' in layer}	)
												UpperCAmelCase__					=							{
												    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
												    for layer_id in range(lowerCamelCase	)
												}

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

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

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

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

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

				for i in range(lowerCamelCase	):
								UpperCAmelCase__					=							num_up_blocks - 1 - i

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

								if any('block' in layer for layer in up_blocks[i]	):
												UpperCAmelCase__					=							len(
												    {'.'.join(shave_segments(lowerCamelCase ,      3	).split('.'	)[:3]	) for layer in up_blocks[i] if 'block' in layer}	)
												UpperCAmelCase__					=							{
												    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)
												}

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

								if any('attn' in layer for layer in up_blocks[i]	):
												UpperCAmelCase__					=							len(
												    {'.'.join(shave_segments(lowerCamelCase ,      3	).split('.'	)[:3]	) for layer in up_blocks[i] if 'attn' in layer}	)
												UpperCAmelCase__					=							{
												    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase	)
												}

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

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

				return new_checkpoint


if __name__ == "__main__":
						lowerCAmelCase__  :  Union[str, Any]											=     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.')

						lowerCAmelCase__  :  List[str]											=     parser.parse_args()
						lowerCAmelCase__  :  int											=     torch.load(args.checkpoint_path)

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

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

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

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

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

												lowerCAmelCase__  :  str											=     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 OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



