



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()
_lowercase:				str       =	logging.get_logger(__name__)






def 					a(   A :					Any )	-> int:
					"""simple docstring"""





					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(   A :					List[str] )	-> Tuple:
					"""simple docstring"""





					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[:256, :]
										a        =     in_proj_bias[:256]
										a        =     in_proj_weight[256:512, :]
										a        =     in_proj_bias[256:512]
										a        =     in_proj_weight[-256:, :]
										a        =     in_proj_bias[-256:]






def 					a(   )	-> Tuple:
					"""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 					a(   A :					Tuple				,  A :					int				,  A :					Optional[Any]				,  A :					Optional[Any]				,  A :					Any				,  A :					List[Any]				,  A :					Any				,  )	-> Tuple:
					"""simple docstring"""





					a        =     DeformableDetrConfig()
					# set config attributes
					if single_scale:
										a        =     1
					a        =     dilation
					a        =     with_box_refine
					a        =     two_stage
					# set labels
					a        =     91
					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()}

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

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

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

					# load original state dict
					a        =     torch.load(A				,  map_location="cpu" )["model"]
					# rename keys
					for key in state_dict.copy().keys():
										a        =     state_dict.pop(A )
										a        =     val
					# query, key and value matrices need special treatment
					read_in_q_k_v(A )
					# 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(A )
															a        =     val
    # finally, create HuggingFace model and load state dict
					a        =     DeformableDetrForObjectDetection(A )
					model.load_state_dict(A )
					model.eval()

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

					a        =     torch.tensor(
					    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]] )
					a        =     torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]] )

					if single_scale:
										a        =     torch.tensor(
										    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]] )
										a        =     torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]] )

					if single_scale and dilation:
										a        =     torch.tensor(
										    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]] )
										a        =     torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]] )

					if with_box_refine:
										a        =     torch.tensor(
										    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]] )
										a        =     torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]] )

					if with_box_refine and two_stage:
										a        =     torch.tensor(
										    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]] )
										a        =     torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]] )

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

					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!" )

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

					# 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=A				,  organization="nielsr"				,  commit_message="Add model" )


if __name__ == "__main__":
				_lowercase:				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."
				)
				_lowercase:				List[Any]       =	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,
				)





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
