


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 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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

		lowercase__            =      DetaConfig(
		    backbone_config=SCREAMING_SNAKE_CASE						,				num_queries=9_00						,				encoder_ffn_dim=20_48						,				decoder_ffn_dim=20_48						,				num_feature_levels=5						,				assign_first_stage=SCREAMING_SNAKE_CASE						,				with_box_refine=SCREAMING_SNAKE_CASE						,				two_stage=SCREAMING_SNAKE_CASE						,				)

		# set labels
		lowercase__            =      '''huggingface/label-files'''
		if "o365" in model_name:
				lowercase__            =      3_66
				lowercase__            =      '''object365-id2label.json'''
		else:
				lowercase__            =      91
				lowercase__            =      '''coco-detection-id2label.json'''

		lowercase__            =      num_labels
		lowercase__            =      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				repo_type='''dataset'''     )     )						,				'''r'''     )     )
		lowercase__            =      {int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
		lowercase__            =      idalabel
		lowercase__            =      {v: k for k, v in idalabel.items()}

		return config
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      []

		# 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 (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      dct.pop(SCREAMING_SNAKE_CASE     )
		lowercase__            =      val
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
		for i in range(len(backbone_config.depths     )     ):
				lowercase__            =      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)
						lowercase__            =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'     )
						lowercase__            =      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
						lowercase__            =      in_proj_weight[:dim, :]
						lowercase__            =      in_proj_bias[: dim]
						lowercase__            =      in_proj_weight[
						    dim : dim * 2, :
						]
						lowercase__            =      in_proj_bias[
						    dim : dim * 2
						]
						lowercase__            =      in_proj_weight[
						    -dim :, :
						]
						lowercase__            =      in_proj_bias[-dim :]
						# fmt: on
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__            =      Image.open(requests.get(SCREAMING_SNAKE_CASE						,				stream=SCREAMING_SNAKE_CASE     ).raw     )

		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      get_deta_config(SCREAMING_SNAKE_CASE     )

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

		lowercase__            =      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
		lowercase__            =      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:
						lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
						lowercase__            =      val
				if "input_proj" in key:
						lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
						lowercase__            =      val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
						lowercase__            =      val

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

		lowercase__            =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(SCREAMING_SNAKE_CASE     )

		# load image processor
		lowercase__            =      DetaImageProcessor(format='''coco_detection'''     )

		# verify our conversion on image
		lowercase__            =      prepare_img()
		lowercase__            =      processor(images=SCREAMING_SNAKE_CASE						,				return_tensors='''pt'''     )
		lowercase__            =      encoding['''pixel_values''']
		lowercase__            =      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":
				lowercase__            =      torch.tensor(
				    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]     )
				lowercase__            =      torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]     )
		elif model_name == "deta-swin-large-o365":
				lowercase__            =      torch.tensor(
				    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]     )
				lowercase__            =      torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]     )
		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__":
			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)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase       =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TapasForMaskedLM',
			    'TapasForQuestionAnswering',
			    'TapasForSequenceClassification',
			    'TapasModel',
			    'TapasPreTrainedModel',
			    'load_tf_weights_in_tapas',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFTapasForMaskedLM',
			    'TFTapasForQuestionAnswering',
			    'TFTapasForSequenceClassification',
			    'TFTapasModel',
			    'TFTapasPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
			from .tokenization_tapas import TapasTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tapas import (
						    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TapasForMaskedLM,
						    TapasForQuestionAnswering,
						    TapasForSequenceClassification,
						    TapasModel,
						    TapasPreTrainedModel,
						    load_tf_weights_in_tapas,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_tapas import (
						    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTapasForMaskedLM,
						    TFTapasForQuestionAnswering,
						    TFTapasForSequenceClassification,
						    TFTapasModel,
						    TFTapasPreTrainedModel,
						)


else:
			import sys

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


