
'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase							:      Tuple		 =   logging.get_logger(__name__)




def lowercase_		(      _lowercase )   ->							List[str]:
					'''simple docstring'''

					if "resnet-50" in model_name:
										lowerCamelCase_    :					int   =						ResNetConfig.from_pretrained('''microsoft/resnet-50''' )
					elif "resnet-101" in model_name:
										lowerCamelCase_    :					str   =						ResNetConfig.from_pretrained('''microsoft/resnet-101''' )
					else:
										raise ValueError('''Model name should include either resnet50 or resnet101''' )

					lowerCamelCase_    :					Optional[int]   =						DetrConfig(use_timm_backbone=lowerCAmelCase_	,				backbone_config=lowerCAmelCase_ )

					# set label attributes
					lowerCamelCase_    :					int   =						'''panoptic''' in model_name
					if is_panoptic:
										lowerCamelCase_    :					Dict   =						250
					else:
										lowerCamelCase_    :					str   =						91
										lowerCamelCase_    :					Dict   =						'''huggingface/label-files'''
										lowerCamelCase_    :					int   =						'''coco-detection-id2label.json'''
										lowerCamelCase_    :					Dict   =						json.load(open(hf_hub_download(lowerCAmelCase_	,				lowerCAmelCase_	,				repo_type='''dataset''' )	,				'''r''' ) )
										lowerCamelCase_    :					Dict   =						{int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
										lowerCamelCase_    :					Optional[Any]   =						idalabel
										lowerCamelCase_    :					Union[str, Any]   =						{v: k for k, v in idalabel.items()}

					return config, is_panoptic




def lowercase_		(      _lowercase )   ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_    :					Tuple   =						[]

					# stem
					# fmt: off
					rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''') )
					rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''') )
					rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''') )
					rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''') )
					rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''') )
					# stages
					for stage_idx in range(len(config.backbone_config.depths ) ):
										for layer_idx in range(config.backbone_config.depths[stage_idx] ):
															# shortcut
															if layer_idx == 0:
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																				    ) )
															# 3 convs
															for i in range(3 ):
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																				    ) )
																				rename_keys.append(
																				    (
																				        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																				        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																				    ) )
    # fmt: on

					for i in range(config.encoder_layers ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append(
										    (
										        F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
										        F"""encoder.layers.{i}.self_attn.out_proj.weight""",
										    ) )
										rename_keys.append(
										    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""") )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""") )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""") )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""") )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""") )
										rename_keys.append(
										    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
										rename_keys.append(
										    (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
										rename_keys.append(
										    (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""") )
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""") )
										# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
										rename_keys.append(
										    (
										        F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
										        F"""decoder.layers.{i}.self_attn.out_proj.weight""",
										    ) )
										rename_keys.append(
										    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""") )
										rename_keys.append(
										    (
										        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
										        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
										    ) )
										rename_keys.append(
										    (
										        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
										        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
										    ) )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""") )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""") )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""") )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""") )
										rename_keys.append(
										    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
										rename_keys.append(
										    (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
										rename_keys.append(
										    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
										rename_keys.append(
										    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
										rename_keys.append(
										    (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""") )
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""") )

					# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
					rename_keys.extend(
					    [
					        ('''input_proj.weight''', '''input_projection.weight'''),
					        ('''input_proj.bias''', '''input_projection.bias'''),
					        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
					        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
					        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
					        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
					        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
					        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
					        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
					        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
					        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
					        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
					        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
					    ] )

					return rename_keys




def lowercase_		(      _lowercase	,				_lowercase	,				_lowercase )   ->							int:
					'''simple docstring'''

					lowerCamelCase_    :					Dict   =						state_dict.pop(lowerCAmelCase_ )
					lowerCamelCase_    :					List[Any]   =						val




def lowercase_		(      _lowercase	,				_lowercase=False )   ->							Any:
					'''simple docstring'''

					lowerCamelCase_    :					List[str]   =						''''''
					if is_panoptic:
										lowerCamelCase_    :					Union[str, Any]   =						'''detr.'''

					# first: transformer encoder
					for i in range(6 ):
										# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
										lowerCamelCase_    :					str   =						state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
										lowerCamelCase_    :					int   =						state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
										# next, add query, keys and values (in that order) to the state dict
										lowerCamelCase_    :					Any   =						in_proj_weight[:256, :]
										lowerCamelCase_    :					int   =						in_proj_bias[:256]
										lowerCamelCase_    :					Union[str, Any]   =						in_proj_weight[256:512, :]
										lowerCamelCase_    :					List[str]   =						in_proj_bias[256:512]
										lowerCamelCase_    :					Optional[int]   =						in_proj_weight[-256:, :]
										lowerCamelCase_    :					Dict   =						in_proj_bias[-256:]
					# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
					for i in range(6 ):
										# read in weights + bias of input projection layer of self-attention
										lowerCamelCase_    :					Optional[int]   =						state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
										lowerCamelCase_    :					Union[str, Any]   =						state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
										# next, add query, keys and values (in that order) to the state dict
										lowerCamelCase_    :					Tuple   =						in_proj_weight[:256, :]
										lowerCamelCase_    :					Optional[int]   =						in_proj_bias[:256]
										lowerCamelCase_    :					Any   =						in_proj_weight[256:512, :]
										lowerCamelCase_    :					int   =						in_proj_bias[256:512]
										lowerCamelCase_    :					Dict   =						in_proj_weight[-256:, :]
										lowerCamelCase_    :					Any   =						in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										lowerCamelCase_    :					Optional[int]   =						state_dict.pop(
										    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
										lowerCamelCase_    :					Tuple   =						state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
										# next, add query, keys and values (in that order) of cross-attention to the state dict
										lowerCamelCase_    :					Any   =						in_proj_weight_cross_attn[:256, :]
										lowerCamelCase_    :					str   =						in_proj_bias_cross_attn[:256]
										lowerCamelCase_    :					str   =						in_proj_weight_cross_attn[256:512, :]
										lowerCamelCase_    :					Tuple   =						in_proj_bias_cross_attn[256:512]
										lowerCamelCase_    :					Optional[Any]   =						in_proj_weight_cross_attn[-256:, :]
										lowerCamelCase_    :					List[Any]   =						in_proj_bias_cross_attn[-256:]




def lowercase_		(      )   ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_    :					Union[str, Any]   =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCamelCase_    :					Union[str, Any]   =						Image.open(requests.get(lowerCAmelCase_	,				stream=lowerCAmelCase_ ).raw )

					return im




@torch.no_grad()
def lowercase_		(      _lowercase	,				_lowercase=None	,				_lowercase=False )   ->							List[str]:
					'''simple docstring'''

					lowerCamelCase_,					lowerCamelCase_    :					Optional[int]   =						get_detr_config(lowerCAmelCase_ )

					# load original model from torch hub
					lowerCamelCase_    :					Any   =						{
					    '''detr-resnet-50''': '''detr_resnet50''',
					    '''detr-resnet-101''': '''detr_resnet101''',
					}
					logger.info(F"""Converting model {model_name}...""" )
					lowerCamelCase_    :					Optional[int]   =						torch.hub.load('''facebookresearch/detr'''	,				model_name_to_original_name[model_name]	,				pretrained=lowerCAmelCase_ ).eval()
					lowerCamelCase_    :					Tuple   =						detr.state_dict()
					# rename keys
					for src, dest in create_rename_keys(lowerCAmelCase_ ):
										if is_panoptic:
															lowerCamelCase_    :					List[str]   =						'''detr.''' + src
										rename_key(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_ )
					# query, key and value matrices need special treatment
					read_in_q_k_v(lowerCAmelCase_	,				is_panoptic=lowerCAmelCase_ )
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					lowerCamelCase_    :					int   =						'''detr.model.''' if is_panoptic else '''model.'''
					for key in state_dict.copy().keys():
										if is_panoptic:
															if (
															    key.startswith('''detr''' )
															    and not key.startswith('''class_labels_classifier''' )
															    and not key.startswith('''bbox_predictor''' )
															):
																				lowerCamelCase_    :					List[Any]   =						state_dict.pop(lowerCAmelCase_ )
																				lowerCamelCase_    :					Optional[Any]   =						val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				lowerCamelCase_    :					int   =						state_dict.pop(lowerCAmelCase_ )
																				lowerCamelCase_    :					str   =						val
															elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
																				continue
															else:
																				lowerCamelCase_    :					List[Any]   =						state_dict.pop(lowerCAmelCase_ )
																				lowerCamelCase_    :					Optional[int]   =						val
										else:
															if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
																				lowerCamelCase_    :					Any   =						state_dict.pop(lowerCAmelCase_ )
																				lowerCamelCase_    :					List[str]   =						val

    # finally, create HuggingFace model and load state dict
					lowerCamelCase_    :					str   =						DetrForSegmentation(lowerCAmelCase_ ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_ )
					model.load_state_dict(lowerCAmelCase_ )
					model.eval()

					# verify our conversion on an image
					lowerCamelCase_    :					Any   =						'''coco_panoptic''' if is_panoptic else '''coco_detection'''
					lowerCamelCase_    :					Dict   =						DetrImageProcessor(format=lowerCAmelCase_ )

					lowerCamelCase_    :					Optional[Any]   =						processor(images=prepare_img()	,				return_tensors='''pt''' )
					lowerCamelCase_    :					Union[str, Any]   =						encoding['''pixel_values''']

					lowerCamelCase_    :					Union[str, Any]   =						detr(lowerCAmelCase_ )
					lowerCamelCase_    :					Optional[Any]   =						model(lowerCAmelCase_ )

					assert torch.allclose(outputs.logits	,				original_outputs['''pred_logits''']	,				atol=1e-3 )
					assert torch.allclose(outputs.pred_boxes	,				original_outputs['''pred_boxes''']	,				atol=1e-3 )
					if is_panoptic:
										assert torch.allclose(outputs.pred_masks	,				original_outputs['''pred_masks''']	,				atol=1e-4 )
					print('''Looks ok!''' )

					if pytorch_dump_folder_path is not None:
										# Save model and image processor
										logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
										Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
										model.save_pretrained(lowerCAmelCase_ )
										processor.save_pretrained(lowerCAmelCase_ )

					if push_to_hub:
										# Upload model and image processor to the hub
										logger.info('''Uploading PyTorch model and image processor to the hub...''' )
										model.push_to_hub(F"""nielsr/{model_name}""" )
										processor.push_to_hub(F"""nielsr/{model_name}""" )


if __name__ == "__main__":
					__lowercase							:      List[str]		 =   argparse.ArgumentParser()

					parser.add_argument(
					    '''--model_name''',
					    default='''detr-resnet-50''',
					    type=str,
					    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
					    help='''Name of the DETR model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
					)
					parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
					__lowercase							:      Any		 =   parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    Any						    =  {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """pegasus"""
  a_        = ["""past_key_values"""]
  a_        = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]=5_0_2_6_5					,  lowerCAmelCase_ :		Union[str, Any]=1_0_2_4					,  lowerCAmelCase_ :		Union[str, Any]=1_2					,  lowerCAmelCase_ :		Dict=4_0_9_6					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		List[Any]=1_2					,  lowerCAmelCase_ :		Union[str, Any]=4_0_9_6					,  lowerCAmelCase_ :		Union[str, Any]=1_6					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Optional[int]=0.0					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Any=True					,  lowerCAmelCase_ :		Dict="gelu"					,  lowerCAmelCase_ :		Optional[int]=1_0_2_4					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		str=0.0					,  lowerCAmelCase_ :		Union[str, Any]=0.0					,  lowerCAmelCase_ :		Optional[int]=0.02					,  lowerCAmelCase_ :		Tuple=0					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		Union[str, Any]=0					,  lowerCAmelCase_ :		int=1					,  lowerCAmelCase_ :		Tuple=1					,  **lowerCAmelCase_ :		Tuple					,  )							->						List[str]:
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	d_model
     __lowerCAmelCase								=	encoder_ffn_dim
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	encoder_attention_heads
     __lowerCAmelCase								=	decoder_ffn_dim
     __lowerCAmelCase								=	decoder_layers
     __lowerCAmelCase								=	decoder_attention_heads
     __lowerCAmelCase								=	dropout
     __lowerCAmelCase								=	attention_dropout
     __lowerCAmelCase								=	activation_dropout
     __lowerCAmelCase								=	activation_function
     __lowerCAmelCase								=	init_std
     __lowerCAmelCase								=	encoder_layerdrop
     __lowerCAmelCase								=	decoder_layerdrop
     __lowerCAmelCase								=	use_cache
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowerCAmelCase_					,  eos_token_id=lowerCAmelCase_					,  is_encoder_decoder=lowerCAmelCase_					,  decoder_start_token_id=lowerCAmelCase_					,  forced_eos_token_id=lowerCAmelCase_					,  **lowerCAmelCase_					,  )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						int:
     return self.encoder_attention_heads






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						int:
     return self.d_model

