

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_						=					logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[int]       )      ->				str:
				if "backbone.0.body" in orig_key:
								_UpperCAmelCase				:						Any       						=			orig_key.replace("backbone.0.body"     ,					"backbone.conv_encoder.model"       )
				if "transformer" in orig_key:
								_UpperCAmelCase				:						Union[str, Any]       						=			orig_key.replace("transformer."     ,					""       )
				if "norm1" in orig_key:
								if "encoder" in orig_key:
												_UpperCAmelCase				:						int       						=			orig_key.replace("norm1"     ,					"self_attn_layer_norm"       )
								else:
												_UpperCAmelCase				:						Any       						=			orig_key.replace("norm1"     ,					"encoder_attn_layer_norm"       )
				if "norm2" in orig_key:
								if "encoder" in orig_key:
												_UpperCAmelCase				:						Dict       						=			orig_key.replace("norm2"     ,					"final_layer_norm"       )
								else:
												_UpperCAmelCase				:						Dict       						=			orig_key.replace("norm2"     ,					"self_attn_layer_norm"       )
				if "norm3" in orig_key:
								_UpperCAmelCase				:						Any       						=			orig_key.replace("norm3"     ,					"final_layer_norm"       )
				if "linear1" in orig_key:
								_UpperCAmelCase				:						str       						=			orig_key.replace("linear1"     ,					"fc1"       )
				if "linear2" in orig_key:
								_UpperCAmelCase				:						Any       						=			orig_key.replace("linear2"     ,					"fc2"       )
				if "query_embed" in orig_key:
								_UpperCAmelCase				:						Tuple       						=			orig_key.replace("query_embed"     ,					"query_position_embeddings"       )
				if "cross_attn" in orig_key:
								_UpperCAmelCase				:						List[Any]       						=			orig_key.replace("cross_attn"     ,					"encoder_attn"       )

				return orig_key

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     str       )      ->				Optional[int]:
				# transformer decoder self-attention layers
				for i in range(6       ):
								# read in weights + bias of input projection layer of self-attention
								_UpperCAmelCase				:						Tuple       						=			state_dict.pop(F'decoder.layers.{i}.self_attn.in_proj_weight'       )
								_UpperCAmelCase				:						Optional[Any]       						=			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
								_UpperCAmelCase				:						Tuple       						=			in_proj_weight[:256, :]
								_UpperCAmelCase				:						Dict       						=			in_proj_bias[:256]
								_UpperCAmelCase				:						int       						=			in_proj_weight[256:512, :]
								_UpperCAmelCase				:						Dict       						=			in_proj_bias[256:512]
								_UpperCAmelCase				:						Optional[int]       						=			in_proj_weight[-256:, :]
								_UpperCAmelCase				:						Optional[int]       						=			in_proj_bias[-256:]

def 							__SCREAMING_SNAKE_CASE							( )      ->				List[str]:
				_UpperCAmelCase				:						List[str]       						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_UpperCAmelCase				:						Dict       						=			Image.open(requests.get(lowerCAmelCase     ,					stream=lowerCAmelCase       ).raw       )

				return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     List[str]     ,					lowerCAmelCase:     Optional[int]     ,					lowerCAmelCase:     int     ,					lowerCAmelCase:     Optional[Any]     ,					lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     int     ,					)      ->				int:
				_UpperCAmelCase				:						Dict       						=			DeformableDetrConfig()
				# set config attributes
				if single_scale:
								_UpperCAmelCase				:						List[str]       						=			1
				_UpperCAmelCase				:						Optional[Any]       						=			dilation
				_UpperCAmelCase				:						int       						=			with_box_refine
				_UpperCAmelCase				:						int       						=			two_stage
				# set labels
				_UpperCAmelCase				:						Union[str, Any]       						=			91
				_UpperCAmelCase				:						Optional[int]       						=			"huggingface/label-files"
				_UpperCAmelCase				:						Tuple       						=			"coco-detection-id2label.json"
				_UpperCAmelCase				:						Optional[int]       						=			json.load(open(cached_download(hf_hub_url(lowerCAmelCase     ,					lowerCAmelCase     ,					repo_type="dataset"       )       )     ,					"r"       )       )
				_UpperCAmelCase				:						str       						=			{int(lowerCAmelCase       ): v for k, v in idalabel.items()}
				_UpperCAmelCase				:						Tuple       						=			idalabel
				_UpperCAmelCase				:						Any       						=			{v: k for k, v in idalabel.items()}

				# load image processor
				_UpperCAmelCase				:						List[Any]       						=			DeformableDetrImageProcessor(format="coco_detection"       )

				# prepare image
				_UpperCAmelCase				:						str       						=			prepare_img()
				_UpperCAmelCase				:						Union[str, Any]       						=			image_processor(images=lowerCAmelCase     ,					return_tensors="pt"       )
				_UpperCAmelCase				:						Any       						=			encoding["pixel_values"]

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

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

				_UpperCAmelCase				:						Tuple       						=			"cuda" if torch.cuda.is_available() else "cpu"
				model.to(lowerCAmelCase       )
				# verify our conversion
				_UpperCAmelCase				:						List[Any]       						=			model(pixel_values.to(lowerCAmelCase       )       )

				_UpperCAmelCase				:						Dict       						=			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]]       )
				_UpperCAmelCase				:						Optional[Any]       						=			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:
								_UpperCAmelCase				:						Optional[int]       						=			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]]       )
								_UpperCAmelCase				:						List[Any]       						=			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:
								_UpperCAmelCase				:						List[Any]       						=			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]]       )
								_UpperCAmelCase				:						Any       						=			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:
								_UpperCAmelCase				:						Any       						=			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]]       )
								_UpperCAmelCase				:						Optional[int]       						=			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:
								_UpperCAmelCase				:						Union[str, Any]       						=			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]]       )
								_UpperCAmelCase				:						List[Any]       						=			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(lowerCAmelCase       )     ,					atol=1E-4       )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,					expected_boxes.to(lowerCAmelCase       )     ,					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(lowerCAmelCase       ).mkdir(exist_ok=lowerCAmelCase       )
				model.save_pretrained(lowerCAmelCase       )
				image_processor.save_pretrained(lowerCAmelCase       )

				# Push to hub
				if push_to_hub:
								_UpperCAmelCase				:						str       						=			"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=lowerCAmelCase     ,					organization="nielsr"     ,					commit_message="Add model"       )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					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_						=					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,
					)



import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowerCAmelCase__							= datasets.utils.logging.get_logger(__name__)
@dataclass
class 				a_ (						datasets.BuilderConfig						):






 '''simple docstring'''






 UpperCAmelCase_	 =					None






class 				a_ (						datasets.ArrowBasedBuilder						):






 '''simple docstring'''






 UpperCAmelCase_	 =					ArrowConfig




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        return datasets.DatasetInfo(features=self.config.features)




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  str):







        '''simple docstring'''



        if not self.config.data_files:
               raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""")
        lowerCAmelCase__     	=					dl_manager.download_and_extract(self.config.data_files)
        if isinstance(lowercase__			,    (str, list, tuple)):
               lowerCAmelCase__     	=					data_files
               if isinstance(lowercase__			,    lowercase__):
                      lowerCAmelCase__     	=					[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowerCAmelCase__     	=					[dl_manager.iter_files(lowercase__) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,    gen_kwargs={'files': files})]
        lowerCAmelCase__     	=					[]
        for split_name, files in data_files.items():
               if isinstance(lowercase__			,    lowercase__):
                      lowerCAmelCase__     	=					[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowerCAmelCase__     	=					[dl_manager.iter_files(lowercase__) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(lowercase__):
                             with open(lowercase__			,    'rb') as f:
                                    lowerCAmelCase__     	=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(lowercase__).schema)
                             break
               splits.append(datasets.SplitGenerator(name=lowercase__			,    gen_kwargs={'files': files}))
        return splits




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  pa.Table):







        '''simple docstring'''



        if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               lowerCAmelCase__     	=					table_cast(lowercase__			,    self.info.features.arrow_schema)
        return pa_table




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  str):







        '''simple docstring'''



        for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase__)):
               with open(lowercase__			,    'rb') as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(lowercase__)):
                                    lowerCAmelCase__     	=					pa.Table.from_batches([record_batch])
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield F"""{file_idx}_{batch_idx}""", self._cast_table(lowercase__)
                      except ValueError as e:
                             logger.error(F"""Failed to read file '{file}' with error {type(lowercase__)}: {e}""")
                             raise
