
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_						=						logging.get_logger(__name__)





def 				_lowerCamelCase      (       lowerCamelCase_:	Optional[Any]		):



				'''simple docstring'''







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

				A  :	List[str]				=					DetaConfig(
				    backbone_config=lowerCamelCase_					,						num_queries=900					,						encoder_ffn_dim=2048					,						decoder_ffn_dim=2048					,						num_feature_levels=5					,						assign_first_stage=lowerCamelCase_					,						with_box_refine=lowerCamelCase_					,						two_stage=lowerCamelCase_					,						)

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

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

				return config





def 				_lowerCamelCase      (       lowerCamelCase_:	str		):



				'''simple docstring'''







				A  :	Optional[int]				=					[]

				# 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 				_lowerCamelCase      (       lowerCamelCase_:	Union[str, Any]					,						lowerCamelCase_:	Union[str, Any]					,						lowerCamelCase_:	str		):



				'''simple docstring'''







				A  :	List[str]				=					dct.pop(lowerCamelCase_		)
				A  :	int				=					val





def 				_lowerCamelCase      (       lowerCamelCase_:	int					,						lowerCamelCase_:	Any		):



				'''simple docstring'''







				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  :	List[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)
												A  :	Any				=					state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""		)
												A  :	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
												A  :	Optional[int]				=					in_proj_weight[:dim, :]
												A  :	Any				=					in_proj_bias[: dim]
												A  :	List[str]				=					in_proj_weight[
												    dim : dim * 2, :
												]
												A  :	str				=					in_proj_bias[
												    dim : dim * 2
												]
												A  :	Tuple				=					in_proj_weight[
												    -dim :, :
												]
												A  :	List[str]				=					in_proj_bias[-dim :]
												# fmt: on





def 				_lowerCamelCase      (       lowerCamelCase_:	Tuple					,						lowerCamelCase_:	Union[str, Any]		):



				'''simple docstring'''







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





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	Optional[Any]				=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				A  :	Dict				=					Image.open(requests.get(lowerCamelCase_					,						stream=lowerCamelCase_		).raw		)

				return im





@torch.no_grad()
def 				_lowerCamelCase      (       lowerCamelCase_:	List[Any]					,						lowerCamelCase_:	Tuple					,						lowerCamelCase_:	Optional[Any]		):



				'''simple docstring'''







				A  :	int				=					get_deta_config(lowerCamelCase_		)

				# load original state dict
				if model_name == "deta-swin-large":
								A  :	Tuple				=					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  :	int				=					torch.load(lowerCamelCase_					,						map_location='''cpu'''		)['''model''']

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

				# rename keys
				A  :	Any				=					create_rename_keys(lowerCamelCase_		)
				for src, dest in rename_keys:
								rename_key(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_		)
				read_in_swin_q_k_v(lowerCamelCase_					,						config.backbone_config		)
				read_in_decoder_q_k_v(lowerCamelCase_					,						lowerCamelCase_		)

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

    # finally, create HuggingFace model and load state dict
				A  :	List[Any]				=					DetaForObjectDetection(lowerCamelCase_		)
				model.load_state_dict(lowerCamelCase_		)
				model.eval()

				A  :	Tuple				=					'''cuda''' if torch.cuda.is_available() else '''cpu'''
				model.to(lowerCamelCase_		)

				# load image processor
				A  :	Union[str, Any]				=					DetaImageProcessor(format='''coco_detection'''		)

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

				# verify logits
				print('''Logits:'''					,						outputs.logits[0, :3, :3]		)
				print('''Boxes:'''					,						outputs.pred_boxes[0, :3, :3]		)
				if model_name == "deta-swin-large":
								A  :	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]]		)
								A  :	Dict				=					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":
								A  :	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]]		)
								A  :	int				=					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(lowerCamelCase_		)					,						atol=1e-4		)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,						expected_boxes.to(lowerCamelCase_		)					,						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(lowerCamelCase_		).mkdir(exist_ok=lowerCamelCase_		)
								model.save_pretrained(lowerCamelCase_		)
								processor.save_pretrained(lowerCamelCase_		)

				# 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_						=						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_						=						parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

											self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,     expected_slice.numpy()    ,     atol=1e-4      )      )




