


'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE				:							Any		       =		logging.get_logger(__name__)







def 			a_							(  UpperCamelCase_   ):
							if "backbone.0.body" in orig_key:
														A_ 						=		orig_key.replace("backbone.0.body"	,     "backbone.conv_encoder.model"   )
							if "transformer" in orig_key:
														A_ 						=		orig_key.replace("transformer."	,     ""   )
							if "norm1" in orig_key:
														if "encoder" in orig_key:
																					A_ 						=		orig_key.replace("norm1"	,     "self_attn_layer_norm"   )
														else:
																					A_ 						=		orig_key.replace("norm1"	,     "encoder_attn_layer_norm"   )
							if "norm2" in orig_key:
														if "encoder" in orig_key:
																					A_ 						=		orig_key.replace("norm2"	,     "final_layer_norm"   )
														else:
																					A_ 						=		orig_key.replace("norm2"	,     "self_attn_layer_norm"   )
							if "norm3" in orig_key:
														A_ 						=		orig_key.replace("norm3"	,     "final_layer_norm"   )
							if "linear1" in orig_key:
														A_ 						=		orig_key.replace("linear1"	,     "fc1"   )
							if "linear2" in orig_key:
														A_ 						=		orig_key.replace("linear2"	,     "fc2"   )
							if "query_embed" in orig_key:
														A_ 						=		orig_key.replace("query_embed"	,     "query_position_embeddings"   )
							if "cross_attn" in orig_key:
														A_ 						=		orig_key.replace("cross_attn"	,     "encoder_attn"   )

							return orig_key







def 			a_							(  UpperCamelCase_   ):
							# transformer decoder self-attention layers
							for i in range(6   ):
														# read in weights + bias of input projection layer of self-attention
														A_ 						=		state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_weight"   )
														A_ 						=		state_dict.pop(f"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[:2_5_6, :]
														A_ 						=		in_proj_bias[:2_5_6]
														A_ 						=		in_proj_weight[2_5_6:5_1_2, :]
														A_ 						=		in_proj_bias[2_5_6:5_1_2]
														A_ 						=		in_proj_weight[-2_5_6:, :]
														A_ 						=		in_proj_bias[-2_5_6:]







def 			a_							(  ):
							A_ 						=		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_ 						=		Image.open(requests.get(UpperCamelCase_	,     stream=UpperCamelCase_   ).raw   )

							return im







@torch.no_grad()
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     ):
							A_ 						=		DeformableDetrConfig()
							# set config attributes
							if single_scale:
														A_ 						=		1
							A_ 						=		dilation
							A_ 						=		with_box_refine
							A_ 						=		two_stage
							# set labels
							A_ 						=		9_1
							A_ 						=		"huggingface/label-files"
							A_ 						=		"coco-detection-id2label.json"
							A_ 						=		json.load(open(cached_download(hf_hub_url(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )   )	,     "r"   )   )
							A_ 						=		{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
							A_ 						=		idalabel
							A_ 						=		{v: k for k, v in idalabel.items()}

							# load image processor
							A_ 						=		DeformableDetrImageProcessor(format="coco_detection"   )

							# prepare image
							A_ 						=		prepare_img()
							A_ 						=		image_processor(images=UpperCamelCase_	,     return_tensors="pt"   )
							A_ 						=		encoding["pixel_values"]

							logger.info("Converting model..."   )

							# load original state dict
							A_ 						=		torch.load(UpperCamelCase_	,     map_location="cpu"   )["model"]
							# rename keys
							for key in state_dict.copy().keys():
														A_ 						=		state_dict.pop(UpperCamelCase_   )
														A_ 						=		val
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase_   )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							A_ 						=		"model."
							for key in state_dict.copy().keys():
														if not key.startswith("class_embed"   ) and not key.startswith("bbox_embed"   ):
																					A_ 						=		state_dict.pop(UpperCamelCase_   )
																					A_ 						=		val
    # finally, create HuggingFace model and load state dict
							A_ 						=		DeformableDetrForObjectDetection(UpperCamelCase_   )
							model.load_state_dict(UpperCamelCase_   )
							model.eval()

							A_ 						=		"cuda" if torch.cuda.is_available() else "cpu"
							model.to(UpperCamelCase_   )
							# verify our conversion
							A_ 						=		model(pixel_values.to(UpperCamelCase_   )   )

							A_ 						=		torch.tensor(
							    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]   )
							A_ 						=		torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]   )

							if single_scale:
														A_ 						=		torch.tensor(
														    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]   )
														A_ 						=		torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]   )

							if single_scale and dilation:
														A_ 						=		torch.tensor(
														    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]   )
														A_ 						=		torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]   )

							if with_box_refine:
														A_ 						=		torch.tensor(
														    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]   )
														A_ 						=		torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]   )

							if with_box_refine and two_stage:
														A_ 						=		torch.tensor(
														    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]   )
														A_ 						=		torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]   )

							print("Logits:"	,     outputs.logits[0, :3, :3]   )

							assert torch.allclose(outputs.logits[0, :3, :3]	,     expected_logits.to(UpperCamelCase_   )	,     atol=1e-4   )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,     expected_boxes.to(UpperCamelCase_   )	,     atol=1e-4   )

							print("Everything ok!"   )

							# Save model and image processor
							logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."   )
							Path(UpperCamelCase_   ).mkdir(exist_ok=UpperCamelCase_   )
							model.save_pretrained(UpperCamelCase_   )
							image_processor.save_pretrained(UpperCamelCase_   )

							# Push to hub
							if push_to_hub:
														A_ 						=		"deformable-detr"
														model_name += "-single-scale" if single_scale else ""
														model_name += "-dc5" if dilation else ""
														model_name += "-with-box-refine" if with_box_refine else ""
														model_name += "-two-stage" if two_stage else ""
														print("Pushing model to hub..."   )
														model.push_to_hub(repo_path_or_name=UpperCamelCase_	,     organization="nielsr"	,     commit_message="Add model"   )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE				:							str		       =		argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''',
							    type=str,
							    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
							    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
							)
							parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
							parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
							parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
							parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    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				:							int		       =		parser.parse_args()
							convert_deformable_detr_checkpoint(
							    args.checkpoint_path,
							    args.single_scale,
							    args.dilation,
							    args.with_box_refine,
							    args.two_stage,
							    args.pytorch_dump_folder_path,
							    args.push_to_hub,
							)









'''simple docstring'''
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers.activations_tf import get_tf_activation






@require_tf
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		tf.constant([-1_00, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]  )
													A_ 						=		get_tf_activation("gelu"  )
													A_ 						=		get_tf_activation("gelu_10"  )

													A_ 						=		gelu(lowerCAmelCase  )
													A_ 						=		geluaa(lowerCAmelCase  )

													A_ 						=		tf.where(y_gelu_aa < 1_0.0   ,     1.0   ,     0.0  )

													self.assertEqual(tf.math.reduce_max(lowerCAmelCase  ).numpy().item()   ,     1_0.0  )
													self.assertTrue(np.allclose(y_gelu * clipped_mask   ,     y_gelu_aa * clipped_mask  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													get_tf_activation("gelu"  )
													get_tf_activation("gelu_10"  )
													get_tf_activation("gelu_fast"  )
													get_tf_activation("gelu_new"  )
													get_tf_activation("glu"  )
													get_tf_activation("mish"  )
													get_tf_activation("quick_gelu"  )
													get_tf_activation("relu"  )
													get_tf_activation("sigmoid"  )
													get_tf_activation("silu"  )
													get_tf_activation("swish"  )
													get_tf_activation("tanh"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation("bogus"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation(lowerCAmelCase  )





