




from typing import Mapping

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


_A							    =	logging.get_logger(__name__)

_A							    =	{
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}


class 							_lowerCAmelCase		(     UpperCamelCase__	):
				lowerCamelCase__									=    """longt5"""
				lowerCamelCase__									=    ["""past_key_values"""]
				lowerCamelCase__									=    {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




				def __init__( self		,		snake_case_=32_128		,		snake_case_=512		,		snake_case_=64		,		snake_case_=2_048		,		snake_case_=6		,		snake_case_=None		,		snake_case_=8		,		snake_case_=127		,		snake_case_=16		,		snake_case_=32		,		snake_case_=128		,		snake_case_=0.1		,		snake_case_=1E-6		,		snake_case_=1.0		,		snake_case_="relu"		,		snake_case_=True		,		snake_case_="local"		,		snake_case_=True		,		snake_case_=0		,		snake_case_=1		,		**snake_case_		,		)    ->      Optional[Any]:
											SCREAMING_SNAKE_CASE			: Dict	     =vocab_size
											SCREAMING_SNAKE_CASE			: Tuple	     =d_model
											SCREAMING_SNAKE_CASE			: Any	     =d_kv
											SCREAMING_SNAKE_CASE			: Tuple	     =d_ff
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =num_layers
											# default = symmetry
											SCREAMING_SNAKE_CASE			: List[str]	     =num_decoder_layers if num_decoder_layers is not None else self.num_layers
											SCREAMING_SNAKE_CASE			: List[str]	     =num_heads
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =local_radius
											SCREAMING_SNAKE_CASE			: List[Any]	     =global_block_size
											SCREAMING_SNAKE_CASE			: Dict	     =relative_attention_num_buckets
											SCREAMING_SNAKE_CASE			: int	     =relative_attention_max_distance
											SCREAMING_SNAKE_CASE			: Tuple	     =dropout_rate
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =layer_norm_epsilon
											SCREAMING_SNAKE_CASE			: int	     =initializer_factor
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =feed_forward_proj
											SCREAMING_SNAKE_CASE			: int	     =encoder_attention_type
											SCREAMING_SNAKE_CASE			: Any	     =use_cache

											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.feed_forward_proj.split('''-'''							)
											SCREAMING_SNAKE_CASE			: str	     =act_info[-1]
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =act_info[0] == "gated"

											if len(lowerCAmelCase__							) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__							) > 2:
																		raise ValueError(
																		    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
																		    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																		    '''\'gated-gelu\' or \'relu\''''							)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																		SCREAMING_SNAKE_CASE			: Optional[int]	     ="gelu_new"

											super().__init__(
											    pad_token_id=lowerCAmelCase__		,		eos_token_id=lowerCAmelCase__		,		is_encoder_decoder=lowerCAmelCase__		,		**lowerCAmelCase__		,		)







class 							_lowerCAmelCase		(     UpperCamelCase__	):




				@property
				def     __a		( self							)    ->      Mapping[str, Mapping[int, str]]:
											SCREAMING_SNAKE_CASE			: List[Any]	     ={
											    "input_ids": {0: "batch", 1: "encoder_sequence"},
											    "attention_mask": {0: "batch", 1: "encoder_sequence"},
											}
											if self.use_past:
																		SCREAMING_SNAKE_CASE			: List[Any]	     ="past_encoder_sequence + sequence"
																		SCREAMING_SNAKE_CASE			: str	     ={0: "batch"}
																		SCREAMING_SNAKE_CASE			: List[str]	     ={0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={0: "batch", 1: "decoder_sequence"}
																		SCREAMING_SNAKE_CASE			: Optional[int]	     ={0: "batch", 1: "decoder_sequence"}

											if self.use_past:
																		self.fill_with_past_key_values_(lowerCAmelCase__		,		direction='''inputs'''							)

											return common_inputs







				@property
				def     __a		( self							)    ->      int:
											return 13












import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A							    =	logging.get_logger(__name__)
def 						lowerCAmelCase_       (	)       ->			Any:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Dict	     =DetaConfig(
							    num_queries=900	,	encoder_ffn_dim=2048	,	decoder_ffn_dim=2048	,	num_feature_levels=5	,	assign_first_stage=__a	,	with_box_refine=__a	,	two_stage=__a	,	)

							# set labels
							SCREAMING_SNAKE_CASE			: Dict	     =91
							SCREAMING_SNAKE_CASE			: Dict	     ='''huggingface/label-files'''
							SCREAMING_SNAKE_CASE			: List[str]	     ='''coco-detection-id2label.json'''
							SCREAMING_SNAKE_CASE			: Tuple	     =json.load(open(cached_download(hf_hub_url(__a	,	__a	,	repo_type='''dataset''' ) )	,	'''r''' ) )
							SCREAMING_SNAKE_CASE			: int	     ={int(__a ): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =idalabel
							SCREAMING_SNAKE_CASE			: Any	     ={v: k for k, v in idalabel.items()}

							return config
def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: int	     =[]

							# stem
							# fmt: off
							rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''') )
							rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''') )
							rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''') )
							rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''') )
							rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																												    ) )
    # transformer encoder
							for i in range(config.encoder_layers ):
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias') )

							# transformer decoder
							for i in range(config.decoder_layers ):
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias') )

							# fmt: on

							return rename_keys
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Optional[Any]	     =dct.pop(__a )
							SCREAMING_SNAKE_CASE			: List[Any]	     =val
def 						lowerCAmelCase_       (	__a	,	__a )       ->			Dict:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Tuple	     =config.d_model
							for i in range(config.decoder_layers ):
														# read in weights + bias of input projection layer of self-attention
														SCREAMING_SNAKE_CASE			: List[str]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
														SCREAMING_SNAKE_CASE			: Optional[Any]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_weight[:hidden_size, :]
														SCREAMING_SNAKE_CASE			: List[Any]	     =in_proj_bias[:hidden_size]
														SCREAMING_SNAKE_CASE			: int	     =in_proj_weight[
														    hidden_size : hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE			: Any	     =in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE			: List[str]	     =in_proj_weight[-hidden_size:, :]
														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_bias[-hidden_size:]
def 						lowerCAmelCase_       (	)       ->			int:





							"""simple docstring"""





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

							return im
@torch.no_grad()
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: str	     =get_deta_config()

							# load original state dict
							if model_name == "deta-resnet-50":
														SCREAMING_SNAKE_CASE			: int	     ='''adet_checkpoint0011.pth'''
							elif model_name == "deta-resnet-50-24-epochs":
														SCREAMING_SNAKE_CASE			: str	     ='''adet_2x_checkpoint0023.pth'''
							else:
														raise ValueError(f'Model name {model_name} not supported' )
							SCREAMING_SNAKE_CASE			: Optional[int]	     =hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,	filename=__a )
							SCREAMING_SNAKE_CASE			: str	     =torch.load(__a	,	map_location='''cpu''' )['''model''']

							# rename keys
							SCREAMING_SNAKE_CASE			: int	     =create_rename_keys(__a )
							for src, dest in rename_keys:
														rename_key(__a	,	__a	,	__a )
							read_in_decoder_q_k_v(__a	,	__a )

							# fix some prefixes
							for key in state_dict.copy().keys():
														if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																					SCREAMING_SNAKE_CASE			: str	     =state_dict.pop(__a )
																					SCREAMING_SNAKE_CASE			: List[str]	     =val
														if "input_proj" in key:
																					SCREAMING_SNAKE_CASE			: Tuple	     =state_dict.pop(__a )
																					SCREAMING_SNAKE_CASE			: Tuple	     =val
														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																					SCREAMING_SNAKE_CASE			: Dict	     =state_dict.pop(__a )
																					SCREAMING_SNAKE_CASE			: List[str]	     =val

    # finally, create HuggingFace model and load state dict
							SCREAMING_SNAKE_CASE			: str	     =DetaForObjectDetection(__a )
							model.load_state_dict(__a )
							model.eval()

							SCREAMING_SNAKE_CASE			: int	     ='''cuda''' if torch.cuda.is_available() else '''cpu'''
							model.to(__a )

							# load image processor
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =DetaImageProcessor(format='''coco_detection''' )

							# verify our conversion on image
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =prepare_img()
							SCREAMING_SNAKE_CASE			: Any	     =processor(images=__a	,	return_tensors='''pt''' )
							SCREAMING_SNAKE_CASE			: List[Any]	     =encoding['''pixel_values''']
							SCREAMING_SNAKE_CASE			: Dict	     =model(pixel_values.to(__a ) )

							# verify logits
							if model_name == "deta-resnet-50":
														SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.tensor(
														    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
														SCREAMING_SNAKE_CASE			: Tuple	     =torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]] )
							elif model_name == "deta-resnet-50-24-epochs":
														SCREAMING_SNAKE_CASE			: Any	     =torch.tensor(
														    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
														SCREAMING_SNAKE_CASE			: List[Any]	     =torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]] )

							assert torch.allclose(outputs.logits[0, :3, :3]	,	expected_logits.to(__a )	,	atol=1E-4 )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,	expected_boxes.to(__a )	,	atol=1E-4 )
							print('''Everything ok!''' )

							if pytorch_dump_folder_path:
														# Save model and processor
														logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
														Path(__a ).mkdir(exist_ok=__a )
														model.save_pretrained(__a )
														processor.save_pretrained(__a )

							# Push to hub
							if push_to_hub:
														print('''Pushing model and processor to hub...''' )
														model.push_to_hub(f'jozhang97/{model_name}' )
														processor.push_to_hub(f'jozhang97/{model_name}' )


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

							parser.add_argument(
							    """--model_name""",
							    type=str,
							    default="""deta-resnet-50""",
							    choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
							    help="""Name of the 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 or not to push the converted model to the 🤗 hub."""
							)
							_A							    =	parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






