






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase       		=      {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase       		=      [
		    'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimesformerModel',
		    'TimesformerForVideoClassification',
		    'TimesformerPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_timesformer import (
				    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimesformerForVideoClassification,
				    TimesformerModel,
				    TimesformerPreTrainedModel,
				)

else:
		import sys

		__lowerCAmelCase       		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












'''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()
__lowerCAmelCase       		=      logging.get_logger(__name__)





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]      ):
				a_		:  Tuple											=      SwinConfig(
				    embed_dim=1_92						,		depths=(2, 2, 18, 2)						,		num_heads=(6, 12, 24, 48)						,		window_size=12						,		out_features=['''stage2''', '''stage3''', '''stage4''']						,		)

				a_		:  List[Any]											=      DetaConfig(
				    backbone_config=__A						,		num_queries=9_00						,		encoder_ffn_dim=20_48						,		decoder_ffn_dim=20_48						,		num_feature_levels=5						,		assign_first_stage=__A						,		with_box_refine=__A						,		two_stage=__A						,		)

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

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

				return config





def 		_UpperCAmelCase   (							__A							:   List[str]      ):
				a_		:  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 		_UpperCAmelCase   (							__A							:   str						,		__A							:   int						,		__A							:   Tuple      ):
				a_		:  str											=      dct.pop(__A      )
				a_		:  Dict											=      val





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





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





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

				return im





@torch.no_grad()
def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   int						,		__A							:   Any      ):
				a_		:  Union[str, Any]											=      get_deta_config(__A      )

				# load original state dict
				if model_name == "deta-swin-large":
								a_		:  Optional[Any]											=      hf_hub_download(repo_id='''nielsr/deta-checkpoints'''						,		filename='''adet_swin_ft.pth'''      )
				elif model_name == "deta-swin-large-o365":
								a_		:  List[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'      )

				a_		:  List[Any]											=      torch.load(__A						,		map_location='''cpu'''      )['''model''']

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

				# rename keys
				a_		:  Union[str, Any]											=      create_rename_keys(__A      )
				for src, dest in rename_keys:
								rename_key(__A						,		__A						,		__A      )
				read_in_swin_q_k_v(__A						,		config.backbone_config      )
				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:
												a_		:  Optional[Any]											=      state_dict.pop(__A      )
												a_		:  int											=      val
								if "input_proj" in key:
												a_		:  str											=      state_dict.pop(__A      )
												a_		:  Optional[Any]											=      val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												a_		:  List[str]											=      state_dict.pop(__A      )
												a_		:  List[Any]											=      val

    # finally, create HuggingFace model and load state dict
				a_		:  Dict											=      DetaForObjectDetection(__A      )
				model.load_state_dict(__A      )
				model.eval()

				a_		:  int											=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
				model.to(__A      )

				# load image processor
				a_		:  List[Any]											=      DetaImageProcessor(format='''coco_detection'''      )

				# verify our conversion on image
				a_		:  Dict											=      prepare_img()
				a_		:  Optional[int]											=      processor(images=__A						,		return_tensors='''pt'''      )
				a_		:  Any											=      encoding['''pixel_values''']
				a_		:  int											=      model(pixel_values.to(__A      )      )

				# verify logits
				print('''Logits:'''						,		outputs.logits[0, :3, :3]      )
				print('''Boxes:'''						,		outputs.pred_boxes[0, :3, :3]      )
				if model_name == "deta-swin-large":
								a_		:  Optional[int]											=      torch.tensor(
								    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]      )
								a_		:  Tuple											=      torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]      )
				elif model_name == "deta-swin-large-o365":
								a_		:  Union[str, Any]											=      torch.tensor(
								    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]      )
								a_		:  Any											=      torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]      )
				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__":
		__lowerCAmelCase       		=      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.'
		)
		__lowerCAmelCase       		=      parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




