



from __future__ import annotations

__A   			=[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]

def        a						(      _UpperCAmelCase							:					list[list[int]] ,   _UpperCAmelCase							:					list[int] ,   _UpperCAmelCase							:					list[int] ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					list[list[int]] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[
		    [0 for col in range(len(grid[0]						)						)] for row in range(len(_UpperCAmelCase						)						)
		]  # the reference grid
		__UpperCAmelCase    :     Any													=	1
		__UpperCAmelCase    :     int													=	[
		    [0 for col in range(len(grid[0]						)						)] for row in range(len(_UpperCAmelCase						)						)
		]  # the action grid

		__UpperCAmelCase    :     Optional[Any]													=	init[0]
		__UpperCAmelCase    :     Tuple													=	init[1]
		__UpperCAmelCase    :     str													=	0
		__UpperCAmelCase    :     Union[str, Any]													=	g + heuristic[x][y]  # cost from starting cell to destination cell
		__UpperCAmelCase    :     str													=	[[f, g, x, y]]

		__UpperCAmelCase    :     int													=	False  # flag that is set when search is complete
		__UpperCAmelCase    :     List[str]													=	False  # flag set if we can't find expand

		while not found and not resign:
				if len(_UpperCAmelCase						) == 0:
						raise ValueError('''Algorithm is unable to find solution'''						)
				else:  # to choose the least costliest action so as to move closer to the goal
						cell.sort()
						cell.reverse()
						__UpperCAmelCase    :     Tuple													=	cell.pop()
						__UpperCAmelCase    :     Optional[int]													=	next_cell[2]
						__UpperCAmelCase    :     List[str]													=	next_cell[3]
						__UpperCAmelCase    :     int													=	next_cell[1]

						if x == goal[0] and y == goal[1]:
								__UpperCAmelCase    :     List[str]													=	True
						else:
								for i in range(len(_UpperCAmelCase						)						):  # to try out different valid actions
										__UpperCAmelCase    :     Optional[int]													=	x + DIRECTIONS[i][0]
										__UpperCAmelCase    :     Union[str, Any]													=	y + DIRECTIONS[i][1]
										if xa >= 0 and xa < len(_UpperCAmelCase						) and ya >= 0 and ya < len(grid[0]						):
												if closed[xa][ya] == 0 and grid[xa][ya] == 0:
														__UpperCAmelCase    :     Union[str, Any]													=	g + cost
														__UpperCAmelCase    :     Tuple													=	ga + heuristic[xa][ya]
														cell.append([fa, ga, xa, ya]						)
														__UpperCAmelCase    :     Any													=	1
														__UpperCAmelCase    :     Any													=	i
		__UpperCAmelCase    :     str													=	[]
		__UpperCAmelCase    :     Union[str, Any]													=	goal[0]
		__UpperCAmelCase    :     List[Any]													=	goal[1]
		invpath.append([x, y]						)  # we get the reverse path from here
		while x != init[0] or y != init[1]:
				__UpperCAmelCase    :     Dict													=	x - DIRECTIONS[action[x][y]][0]
				__UpperCAmelCase    :     str													=	y - DIRECTIONS[action[x][y]][1]
				__UpperCAmelCase    :     List[Any]													=	xa
				__UpperCAmelCase    :     Dict													=	ya
				invpath.append([x, y]						)

		__UpperCAmelCase    :     Union[str, Any]													=	[]
		for i in range(len(_UpperCAmelCase						)						):
				path.append(invpath[len(_UpperCAmelCase						) - 1 - i]						)
		return path, action


if __name__ == "__main__":
				__A   			=[
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				__A   			=[0, 0]
				# all coordinates are given in format [y,x]
				__A   			=[len(grid) - 1, len(grid[0]) - 1]
				__A   			=1

				# the cost map which pushes the path closer to the goal
				__A   			=[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												__A   			=abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																__A   			=9_9

				__A			,			__A   			=search(grid, init, goal, cost, heuristic)

				print("ACTION MAP")
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])










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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__A   			=logging.get_logger(__name__)

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	SwinConfig(
		    embed_dim=1_92 ,   depths=(2, 2, 18, 2) ,   num_heads=(6, 12, 24, 48) ,   window_size=12 ,   out_features=['''stage2''', '''stage3''', '''stage4'''] ,   )

		__UpperCAmelCase    :     Optional[int]													=	DetaConfig(
		    backbone_config=_UpperCAmelCase ,   num_queries=9_00 ,   encoder_ffn_dim=20_48 ,   decoder_ffn_dim=20_48 ,   num_feature_levels=5 ,   assign_first_stage=_UpperCAmelCase ,   with_box_refine=_UpperCAmelCase ,   two_stage=_UpperCAmelCase ,   )

		# set labels
		__UpperCAmelCase    :     Optional[int]													=	'''huggingface/label-files'''
		if "o365" in model_name:
				__UpperCAmelCase    :     Tuple													=	3_66
				__UpperCAmelCase    :     List[str]													=	'''object365-id2label.json'''
		else:
				__UpperCAmelCase    :     Any													=	91
				__UpperCAmelCase    :     int													=	'''coco-detection-id2label.json'''

		__UpperCAmelCase    :     Optional[int]													=	num_labels
		__UpperCAmelCase    :     List[str]													=	json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,   _UpperCAmelCase ,   repo_type='''dataset'''						)						) ,   '''r'''						)						)
		__UpperCAmelCase    :     str													=	{int(_UpperCAmelCase						): v for k, v in idalabel.items()}
		__UpperCAmelCase    :     Optional[int]													=	idalabel
		__UpperCAmelCase    :     Tuple													=	{v: k for k, v in idalabel.items()}

		return config

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	[]

		# stem
		# fmt: off
		rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')						)
		rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')						)
		rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')						)
		rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')						)
		# stages
		for i in range(len(config.backbone_config.depths						)						):
				for j in range(config.backbone_config.depths[i]						):
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')						)

				if i < 3:
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')						)

		rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')						)
		rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')						)
		rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')						)
		rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')						)
		rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')						)
		rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')						)

		# 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        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	dct.pop(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	val

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
		for i in range(len(backbone_config.depths						)						):
				__UpperCAmelCase    :     str													=	num_features[i]
				for j in range(backbone_config.depths[i]						):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						__UpperCAmelCase    :     List[str]													=	state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'						)
						__UpperCAmelCase    :     List[Any]													=	state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'						)
						# next, add query, keys and values (in that order) to the state dict
						__UpperCAmelCase    :     Dict													=	in_proj_weight[:dim, :]
						__UpperCAmelCase    :     List[str]													=	in_proj_bias[: dim]
						__UpperCAmelCase    :     str													=	in_proj_weight[
						    dim : dim * 2, :
						]
						__UpperCAmelCase    :     Any													=	in_proj_bias[
						    dim : dim * 2
						]
						__UpperCAmelCase    :     Tuple													=	in_proj_weight[
						    -dim :, :
						]
						__UpperCAmelCase    :     int													=	in_proj_bias[-dim :]
						# fmt: on

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	config.d_model
		for i in range(config.decoder_layers						):
				# read in weights + bias of input projection layer of self-attention
				__UpperCAmelCase    :     List[str]													=	state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'						)
				__UpperCAmelCase    :     Tuple													=	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
				__UpperCAmelCase    :     Union[str, Any]													=	in_proj_weight[:hidden_size, :]
				__UpperCAmelCase    :     List[Any]													=	in_proj_bias[:hidden_size]
				__UpperCAmelCase    :     int													=	in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				__UpperCAmelCase    :     str													=	in_proj_bias[hidden_size : hidden_size * 2]
				__UpperCAmelCase    :     Tuple													=	in_proj_weight[-hidden_size:, :]
				__UpperCAmelCase    :     Optional[Any]													=	in_proj_bias[-hidden_size:]

def        a						(      ):







		'''simple docstring'''





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

		return im

@torch.no_grad()
def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	get_deta_config(_UpperCAmelCase						)

		# load original state dict
		if model_name == "deta-swin-large":
				__UpperCAmelCase    :     Dict													=	hf_hub_download(repo_id='''nielsr/deta-checkpoints''' ,   filename='''adet_swin_ft.pth'''						)
		elif model_name == "deta-swin-large-o365":
				__UpperCAmelCase    :     Any													=	hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365''' ,   filename='''deta_swin_pt_o365.pth'''						)
		else:
				raise ValueError(f'Model name {model_name} not supported'						)

		__UpperCAmelCase    :     str													=	torch.load(_UpperCAmelCase ,   map_location='''cpu'''						)['''model''']

		# original state dict
		for name, param in state_dict.items():
				print(_UpperCAmelCase ,   param.shape						)

		# rename keys
		__UpperCAmelCase    :     int													=	create_rename_keys(_UpperCAmelCase						)
		for src, dest in rename_keys:
				rename_key(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
		read_in_swin_q_k_v(_UpperCAmelCase ,   config.backbone_config						)
		read_in_decoder_q_k_v(_UpperCAmelCase ,   _UpperCAmelCase						)

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						__UpperCAmelCase    :     Optional[Any]													=	state_dict.pop(_UpperCAmelCase						)
						__UpperCAmelCase    :     Union[str, Any]													=	val
				if "input_proj" in key:
						__UpperCAmelCase    :     Union[str, Any]													=	state_dict.pop(_UpperCAmelCase						)
						__UpperCAmelCase    :     List[str]													=	val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						__UpperCAmelCase    :     Union[str, Any]													=	state_dict.pop(_UpperCAmelCase						)
						__UpperCAmelCase    :     Union[str, Any]													=	val

    # finally, create HuggingFace model and load state dict
		__UpperCAmelCase    :     Union[str, Any]													=	DetaForObjectDetection(_UpperCAmelCase						)
		model.load_state_dict(_UpperCAmelCase						)
		model.eval()

		__UpperCAmelCase    :     Optional[int]													=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(_UpperCAmelCase						)

		# load image processor
		__UpperCAmelCase    :     str													=	DetaImageProcessor(format='''coco_detection'''						)

		# verify our conversion on image
		__UpperCAmelCase    :     str													=	prepare_img()
		__UpperCAmelCase    :     Optional[int]													=	processor(images=_UpperCAmelCase ,   return_tensors='''pt'''						)
		__UpperCAmelCase    :     List[Any]													=	encoding['''pixel_values''']
		__UpperCAmelCase    :     List[str]													=	model(pixel_values.to(_UpperCAmelCase						)						)

		# verify logits
		print('''Logits:''' ,   outputs.logits[0, :3, :3]						)
		print('''Boxes:''' ,   outputs.pred_boxes[0, :3, :3]						)
		if model_name == "deta-swin-large":
				__UpperCAmelCase    :     str													=	torch.tensor(
				    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]						)
				__UpperCAmelCase    :     Union[str, Any]													=	torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]						)
		elif model_name == "deta-swin-large-o365":
				__UpperCAmelCase    :     Optional[Any]													=	torch.tensor(
				    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]						)
				__UpperCAmelCase    :     str													=	torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]						)
		assert torch.allclose(outputs.logits[0, :3, :3] ,   expected_logits.to(_UpperCAmelCase						) ,   atol=1e-4						)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,   expected_boxes.to(_UpperCAmelCase						) ,   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(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)
				model.save_pretrained(_UpperCAmelCase						)
				processor.save_pretrained(_UpperCAmelCase						)

		# 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-swin-large",
				    choices=["deta-swin-large", "deta-swin-large-o365"],
				    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)





