

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        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					int    =				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=_a							,	num_queries=900							,	encoder_ffn_dim=2048							,	decoder_ffn_dim=2048							,	num_feature_levels=5							,	assign_first_stage=_a							,	with_box_refine=_a							,	two_stage=_a							,	)

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

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

	return config

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				[]

	# 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__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				dct.pop(_a)
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				val

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

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

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Any    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
	SCREAMING_SNAKE_CASE      :					Tuple    =				Image.open(requests.get(_a							,	stream=_a).raw)

	return im

@torch.no_grad()
def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					int    =				get_deta_config(_a)

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

	SCREAMING_SNAKE_CASE      :					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
	SCREAMING_SNAKE_CASE      :					Optional[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:
			SCREAMING_SNAKE_CASE      :					List[str]    =				state_dict.pop(_a)
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				val
		if "input_proj" in key:
			SCREAMING_SNAKE_CASE      :					Any    =				state_dict.pop(_a)
			SCREAMING_SNAKE_CASE      :					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      :					Any    =				state_dict.pop(_a)
			SCREAMING_SNAKE_CASE      :					str    =				val

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

	SCREAMING_SNAKE_CASE      :					str    =				"cuda" if torch.cuda.is_available() else "cpu"
	model.to(_a)

	# load image processor
	SCREAMING_SNAKE_CASE      :					int    =				DetaImageProcessor(format="coco_detection")

	# verify our conversion on image
	SCREAMING_SNAKE_CASE      :					List[str]    =				prepare_img()
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				processor(images=_a							,	return_tensors="pt")
	SCREAMING_SNAKE_CASE      :					List[Any]    =				encoding["pixel_values"]
	SCREAMING_SNAKE_CASE      :					Dict    =				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":
		SCREAMING_SNAKE_CASE      :					Any    =				torch.tensor(
		    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]])
		SCREAMING_SNAKE_CASE      :					Dict    =				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":
		SCREAMING_SNAKE_CASE      :					Tuple    =				torch.tensor(
		    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]])
		SCREAMING_SNAKE_CASE      :					Union[str, 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__":
			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)



'''simple docstring'''







from typing import TYPE_CHECKING

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


A__:    List[str]					    =       {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]					    =       ['''ChineseCLIPFeatureExtractor''']
   A__:    Any					    =       ['''ChineseCLIPImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict					    =       [
       '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ChineseCLIPModel''',
       '''ChineseCLIPPreTrainedModel''',
       '''ChineseCLIPTextModel''',
       '''ChineseCLIPVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_chinese_clip import (
       CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ChineseCLIPConfig,
       ChineseCLIPOnnxConfig,
       ChineseCLIPTextConfig,
       ChineseCLIPVisionConfig,
   )
   from .processing_chinese_clip import ChineseCLIPProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_chinese_clip import (
          CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ChineseCLIPModel,
          ChineseCLIPPreTrainedModel,
          ChineseCLIPTextModel,
          ChineseCLIPVisionModel,
      )

else:
   import sys

   A__:    str					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


