
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
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:int											=       logging.get_logger(__name__)


def 		UpperCAmelCase    (			)	->			Tuple:







				"""simple docstring"""



				__A         =				DetaConfig(
				    num_queries=9_0_0	,     encoder_ffn_dim=2_0_4_8	,     decoder_ffn_dim=2_0_4_8	,     num_feature_levels=5	,     assign_first_stage=_A	,     with_box_refine=_A	,     two_stage=_A	,     )

				# set labels
				__A         =				9_1
				__A         =				"huggingface/label-files"
				__A         =				"coco-detection-id2label.json"
				__A         =				json.load(open(cached_download(hf_hub_url(_A	,     _A	,     repo_type="dataset"		)		)	,     "r"		)		)
				__A         =				{int(_A		): v for k, v in idalabel.items()}
				__A         =				idalabel
				__A         =				{v: k for k, v in idalabel.items()}

				return config


def 		UpperCAmelCase    (			a_		)	->			Optional[int]:







				"""simple docstring"""



				__A         =				[]

				# stem
				# fmt: off
				rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")		)
				rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")		)
				rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")		)
				rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")		)
				rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")		)
				# stages
				for stage_idx in range(len(config.backbone_config.depths		)		):
								for layer_idx in range(config.backbone_config.depths[stage_idx]		):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																    )		)
												# 3 convs
												for i in range(3		):
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																    )		)
								# 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_	,     a_	,     a_		)	->			List[Any]:







				"""simple docstring"""



				__A         =				dct.pop(_A		)
				__A         =				val


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[Any]:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			)	->			int:







				"""simple docstring"""



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

				return im


@torch.no_grad()
def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Any:







				"""simple docstring"""



				__A         =				get_deta_config()

				# load original state dict
				if model_name == "deta-resnet-50":
								__A         =				"adet_checkpoint0011.pth"
				elif model_name == "deta-resnet-50-24-epochs":
								__A         =				"adet_2x_checkpoint0023.pth"
				else:
								raise ValueError(F'''Model name {model_name} not supported'''		)
				__A         =				hf_hub_download(repo_id="nielsr/deta-checkpoints"	,     filename=_A		)
				__A         =				torch.load(_A	,     map_location="cpu"		)["model"]

				# rename keys
				__A         =				create_rename_keys(_A		)
				for src, dest in rename_keys:
								rename_key(_A	,     _A	,     _A		)
				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         =				state_dict.pop(_A		)
												__A         =				val
								if "input_proj" in key:
												__A         =				state_dict.pop(_A		)
												__A         =				val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												__A         =				state_dict.pop(_A		)
												__A         =				val

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

				__A         =				"cuda" if torch.cuda.is_available() else "cpu"
				model.to(_A		)

				# load image processor
				__A         =				DetaImageProcessor(format="coco_detection"		)

				# verify our conversion on image
				__A         =				prepare_img()
				__A         =				processor(images=_A	,     return_tensors="pt"		)
				__A         =				encoding["pixel_values"]
				__A         =				model(pixel_values.to(_A		)		)

				# verify logits
				if model_name == "deta-resnet-50":
								__A         =				torch.tensor(
								    [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]		)
								__A         =				torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]		)
				elif model_name == "deta-resnet-50-24-epochs":
								__A         =				torch.tensor(
								    [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]		)
								__A         =				torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]		)

				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__":
					SCREAMING_SNAKE_CASE							:int											=       argparse.ArgumentParser()

					parser.add_argument(
					    '--model_name',
					    type=str,
					    default='deta-resnet-50',
					    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
					    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.'
					)
					SCREAMING_SNAKE_CASE							:str											=       parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
					from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
