


'''simple docstring'''


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()
UpperCAmelCase_		:   Optional[Any]							   =		logging.get_logger(__name__)







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[str]     		=      SwinConfig(
				    embed_dim=192							,      depths=(2, 2, 18, 2)							,      num_heads=(6, 12, 24, 48)							,      window_size=12							,      out_features=["""stage2""", """stage3""", """stage4"""]							,      )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      DetaConfig(
				    backbone_config=SCREAMING_SNAKE_CASE__							,      num_queries=900							,      encoder_ffn_dim=2048							,      decoder_ffn_dim=2048							,      num_feature_levels=5							,      assign_first_stage=SCREAMING_SNAKE_CASE__							,      with_box_refine=SCREAMING_SNAKE_CASE__							,      two_stage=SCREAMING_SNAKE_CASE__							,      )

				# set labels
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """huggingface/label-files"""
				if "o365" in model_name:
								_SCREAMING_SNAKE_CASE :       Dict     		=      366
								_SCREAMING_SNAKE_CASE :       List[str]     		=      """object365-id2label.json"""
				else:
								_SCREAMING_SNAKE_CASE :       str     		=      91
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """coco-detection-id2label.json"""

				_SCREAMING_SNAKE_CASE :       str     		=      num_labels
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      repo_type="""dataset"""     )     )							,      """r"""     )     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {int(SCREAMING_SNAKE_CASE__     ): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE :       str     		=      idalabel
				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      {v: k for k, v in idalabel.items()}

				return config







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      []

				# 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 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Any     		=      dct.pop(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      val







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Any     		=      [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
				for i in range(len(backbone_config.depths     )     ):
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      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)
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
												_SCREAMING_SNAKE_CASE :       int     		=      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
												_SCREAMING_SNAKE_CASE :       str     		=      in_proj_weight[:dim, :]
												_SCREAMING_SNAKE_CASE :       int     		=      in_proj_bias[: dim]
												_SCREAMING_SNAKE_CASE :       int     		=      in_proj_weight[
												    dim : dim * 2, :
												]
												_SCREAMING_SNAKE_CASE :       List[str]     		=      in_proj_bias[
												    dim : dim * 2
												]
												_SCREAMING_SNAKE_CASE :       int     		=      in_proj_weight[
												    -dim :, :
												]
												_SCREAMING_SNAKE_CASE :       List[str]     		=      in_proj_bias[-dim :]
												# fmt: on







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
				_SCREAMING_SNAKE_CASE :       int     		=      Image.open(requests.get(SCREAMING_SNAKE_CASE__							,      stream=SCREAMING_SNAKE_CASE__     ).raw     )

				return im







@torch.no_grad()
def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      get_deta_config(SCREAMING_SNAKE_CASE__     )

				# load original state dict
				if model_name == "deta-swin-large":
								_SCREAMING_SNAKE_CASE :       Dict     		=      hf_hub_download(repo_id="""nielsr/deta-checkpoints"""							,      filename="""adet_swin_ft.pth"""     )
				elif model_name == "deta-swin-large-o365":
								_SCREAMING_SNAKE_CASE :       str     		=      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"""     )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.load(SCREAMING_SNAKE_CASE__							,      map_location="""cpu"""     )["""model"""]

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

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

				# 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 :       int     		=      state_dict.pop(SCREAMING_SNAKE_CASE__     )
												_SCREAMING_SNAKE_CASE :       List[str]     		=      val
								if "input_proj" in key:
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      state_dict.pop(SCREAMING_SNAKE_CASE__     )
												_SCREAMING_SNAKE_CASE :       List[str]     		=      val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      state_dict.pop(SCREAMING_SNAKE_CASE__     )
												_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      val

    # finally, create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE :       Tuple     		=      DetaForObjectDetection(SCREAMING_SNAKE_CASE__     )
				model.load_state_dict(SCREAMING_SNAKE_CASE__     )
				model.eval()

				_SCREAMING_SNAKE_CASE :       Dict     		=      """cuda""" if torch.cuda.is_available() else """cpu"""
				model.to(SCREAMING_SNAKE_CASE__     )

				# load image processor
				_SCREAMING_SNAKE_CASE :       Any     		=      DetaImageProcessor(format="""coco_detection"""     )

				# verify our conversion on image
				_SCREAMING_SNAKE_CASE :       str     		=      prepare_img()
				_SCREAMING_SNAKE_CASE :       Dict     		=      processor(images=SCREAMING_SNAKE_CASE__							,      return_tensors="""pt"""     )
				_SCREAMING_SNAKE_CASE :       List[str]     		=      encoding["""pixel_values"""]
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model(pixel_values.to(SCREAMING_SNAKE_CASE__     )     )

				# verify logits
				print("""Logits:"""							,      outputs.logits[0, :3, :3]     )
				print("""Boxes:"""							,      outputs.pred_boxes[0, :3, :3]     )
				if model_name == "deta-swin-large":
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.tensor(
								    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]     )
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]     )
				elif model_name == "deta-swin-large-o365":
								_SCREAMING_SNAKE_CASE :       List[str]     		=      torch.tensor(
								    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]     )
				assert torch.allclose(outputs.logits[0, :3, :3]							,      expected_logits.to(SCREAMING_SNAKE_CASE__     )							,      atol=1E-4     )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,      expected_boxes.to(SCREAMING_SNAKE_CASE__     )							,      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(SCREAMING_SNAKE_CASE__     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )
								model.save_pretrained(SCREAMING_SNAKE_CASE__     )
								processor.save_pretrained(SCREAMING_SNAKE_CASE__     )

				# 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__":
				UpperCAmelCase_		:   Tuple							   =		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.'
				)
				UpperCAmelCase_		:   Dict							   =		parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


if __name__ == "__main__":
				print(F"{solution() = }")




