


"""simple docstring"""


import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


a_       							=    logging.getLogger(__name__)

a_       							=    "pytorch_model.bin"
@dataclasses.dataclass
class snake_case      :
		__UpperCamelCase           =			dataclasses.field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'})
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}     ,				)





@dataclasses.dataclass
class snake_case      :
		__UpperCamelCase           =			dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'})
		__UpperCamelCase           =			dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'})
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'A csv or a json file containing the validation data.'})
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'The name of the task to train on.'}     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'The list of labels for the task.'})





@dataclasses.dataclass
class snake_case      :
		__UpperCamelCase           =			dataclasses.field(
		    metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'})
		__UpperCamelCase           =			dataclasses.field(
		    default='accuracy'     ,				metadata={'help': 'The evaluation metric used for the task.'})
		__UpperCamelCase           =			dataclasses.field(
		    default='no'     ,				metadata={
		        'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
		    }     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=10     ,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=0.0     ,				metadata={
		        'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
		    }     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=0.0     ,				metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=100     ,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}     ,				)
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Random seed for initialization.'}     ,				)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	Union[str, Any]:
				_A					  =			datasets.concatenate_datasets([infer_input, infer_output]     ,			axis=1   )

				if args.do_filter_by_confidence:
								_A					  =			dataset.filter(lambda __lowercase   : example["probability"] > args.confidence_threshold   )

				if args.do_filter_by_val_performance:
								assert eval_result >= 0.0 and eval_result <= 1.0
								_A					  =			int(eval_result * len(__lowercase   )   )
								print(__lowercase   )
								_A					  =			dataset.sort("probability"     ,			reverse=__lowercase   )
								_A					  =			dataset.select(range(__lowercase   )   )

				_A					  =			dataset.remove_columns(["label", "probability"]   )
				_A					  =			dataset.rename_column("prediction"     ,			"label"   )
				_A					  =			dataset.map(lambda __lowercase   : {"label": idalabel[example["label"]]}   )
				_A					  =			dataset.shuffle(seed=args.seed   )

				_A					  =			os.path.join(__lowercase     ,			f"""train_pseudo.{args.data_file_extension}"""   )
				if args.data_file_extension == "csv":
								dataset.to_csv(__lowercase     ,			index=__lowercase   )
				else:
								dataset.to_json(__lowercase   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			**__lowercase   )	->	List[Any]:
				_A					  =			Accelerator()
				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,			datefmt="%m/%d/%Y %H:%M:%S"     ,			level=logging.INFO     ,			)
				logger.info(accelerator.state   )

				# Setup logging, we only want one process per machine to log things on the
				# screen. accelerator.is_local_main_process is only True for one process per
				# machine.
				logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR   )

				if accelerator.is_local_main_process:
								datasets.utils.logging.set_verbosity_warning()
								transformers.utils.logging.set_verbosity_info()
				else:
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()

				_A					  =			STModelArguments(model_name_or_path=__lowercase   )
				_A					  =			STDataArguments(train_file=__lowercase     ,			infer_file=__lowercase   )
				_A					  =			STTrainingArguments(output_dir=__lowercase   )
				_A					  =			argparse.Namespace()

				for arg_class in (model_args, data_args, training_args):
								for key, value in vars(__lowercase   ).items():
												setattr(__lowercase     ,			__lowercase     ,			__lowercase   )

				for key, value in kwargs.items():
								if hasattr(__lowercase     ,			__lowercase   ):
												setattr(__lowercase     ,			__lowercase     ,			__lowercase   )

    # Sanity checks
				_A					  =			{}
				_A					  =			None

				# You need to provide the training data and the data to predict on
				assert args.train_file is not None
				assert args.infer_file is not None
				_A					  =			args.train_file
				_A					  =			args.infer_file

				if args.evaluation_strategy != IntervalStrategy.NO.value:
								assert args.eval_file is not None
								_A					  =			args.eval_file

				for key in data_files:
								_A					  =			data_files[key].split("."   )[-1]
								assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
								if args.data_file_extension is None:
												_A					  =			extension
								else:
												assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

				assert (
				    args.eval_metric in datasets.list_metrics()
				), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

				# If passed along, set the training seed now.
				if args.seed is not None:
								set_seed(args.seed   )

				logger.info("Creating the initial data directory for self-training..."   )
				_A					  =			f"""{args.output_dir}/self-train_iter-{{}}""".format
				_A					  =			data_dir_format(0   )

				if accelerator.is_main_process:
								if args.output_dir is not None:
												os.makedirs(args.output_dir     ,			exist_ok=__lowercase   )
												os.makedirs(__lowercase     ,			exist_ok=__lowercase   )
				accelerator.wait_for_everyone()

				_A					  =			None
				_A					  =			None
				_A					  =			0
				_A					  =			False
				# Show the progress bar
				_A					  =			tqdm(range(args.max_selftrain_iterations   )     ,			disable=not accelerator.is_local_main_process   )

				# Self-train
				for iteration in range(0     ,			int(args.max_selftrain_iterations   )   ):
								_A					  =			data_dir_format(__lowercase   )
								assert os.path.exists(__lowercase   )

								# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
								# iteration > 0
								_A					  =			os.path.join(__lowercase     ,			"stage-1"   )
								_A					  =			{
								    "accelerator": accelerator,
								    "model_name_or_path": args.model_name_or_path,
								    "cache_dir": args.cache_dir,
								    "do_train": True,
								    "train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
								    "do_eval": True if args.eval_file is not None else False,
								    "eval_file": data_files["eval"],
								    "do_predict": True,
								    "infer_file": data_files["infer"],
								    "task_name": args.task_name,
								    "label_list": args.label_list,
								    "output_dir": current_output_dir,
								    "eval_metric": args.eval_metric,
								    "evaluation_strategy": args.evaluation_strategy,
								    "early_stopping_patience": args.early_stopping_patience,
								    "early_stopping_threshold": args.early_stopping_threshold,
								    "seed": args.seed,
								}
								# Add additional training arguments
								for key, value in kwargs.items():
												if key not in arguments_dict and not hasattr(__lowercase     ,			__lowercase   ):
																arguments_dict.update({key: value}   )

								_A					  =			os.path.join(__lowercase     ,			"best-checkpoint"     ,			__lowercase   )
								if os.path.exists(__lowercase   ):
												logger.info(
												    "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."     ,			__lowercase     ,			__lowercase     ,			)
								else:
												logger.info("***** Running self-training: iteration: %d, stage: 1 *****"     ,			__lowercase   )
												finetune(**__lowercase   )
												accelerator.wait_for_everyone()
												assert os.path.exists(__lowercase   )
												logger.info("Self-training job completed: iteration: %d, stage: 1."     ,			__lowercase   )

								if iteration > 0 and args.finetune_on_labeled_data:
												# Stage 2 (optional): fine-tuning on the original labeled data
												_A					  =			os.path.join(__lowercase     ,			"best-checkpoint"   )
												_A					  =			os.path.join(__lowercase     ,			"stage-2"   )
												# Update arguments_dict
												_A					  =			model_path
												_A					  =			data_files["train"]
												_A					  =			current_output_dir

												_A					  =			os.path.join(__lowercase     ,			"best-checkpoint"     ,			__lowercase   )
												if os.path.exists(__lowercase   ):
																logger.info(
																    "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."     ,			__lowercase     ,			__lowercase     ,			)
												else:
																logger.info("***** Running self-training: iteration: %d, stage: 2 *****"     ,			__lowercase   )
																finetune(**__lowercase   )
																accelerator.wait_for_everyone()
																assert os.path.exists(__lowercase   )
																logger.info("Self-training job completed: iteration: %d, stage: 2."     ,			__lowercase   )

								_A					  =			iteration
								_A					  =			data_dir_format(iteration + 1   )

								_A					  =			AutoConfig.from_pretrained(os.path.join(__lowercase     ,			"best-checkpoint"   )   )
								_A					  =			config.idalabel
								_A					  =			os.path.join(__lowercase     ,			"eval_results_best-checkpoint.json"   )
								_A					  =			os.path.join(__lowercase     ,			"test_results_best-checkpoint.json"   )
								assert os.path.exists(__lowercase   )

								with open(__lowercase     ,			"r"   ) as f:
												_A					  =			float(json.load(__lowercase   )[args.eval_metric]   )
								_A					  =			os.path.join(__lowercase     ,			"infer_output_best-checkpoint.csv"   )
								assert os.path.exists(__lowercase   )
								# Loading the dataset from local csv or json files.
								_A					  =			load_dataset(args.data_file_extension     ,			data_files={"data": data_files["infer"]}   )["data"]
								_A					  =			load_dataset("csv"     ,			data_files={"data": infer_output_file}   )["data"]

								if accelerator.is_main_process:
												os.makedirs(__lowercase     ,			exist_ok=__lowercase   )
												shutil.copy(__lowercase     ,			os.path.join(__lowercase     ,			f"""eval_results_iter-{iteration}.json"""   )   )
												if os.path.exists(__lowercase   ):
																shutil.copy(__lowercase     ,			os.path.join(__lowercase     ,			f"""test_results_iter-{iteration}.json"""   )   )
												create_pseudo_labeled_data(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )
								accelerator.wait_for_everyone()

								_A					  =			os.path.join(__lowercase     ,			f"""train_pseudo.{args.data_file_extension}"""   )

								if args.evaluation_strategy != IntervalStrategy.NO.value:
												_A					  =			eval_result

												if best_iteration is None:
																_A					  =			new_iteration
																_A					  =			new_eval_result
												else:
																if new_eval_result - best_eval_result > args.early_stopping_threshold:
																				_A					  =			new_iteration
																				_A					  =			new_eval_result
																				_A					  =			0
																else:
																				if new_eval_result == best_eval_result:
																								_A					  =			new_iteration
																								_A					  =			new_eval_result
																				early_stopping_patience_counter += 1

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				_A					  =			True

								progress_bar.update(1   )

								if should_training_stop:
												break

				if best_iteration is not None:
								# Save the best iteration
								logger.info("Best iteration: %d"     ,			__lowercase   )
								logger.info("Best evaluation result: %s = %f"     ,			args.eval_metric     ,			__lowercase   )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(__lowercase     ,			f"""eval_results_iter-{iteration}.json"""   )     ,			os.path.join(__lowercase     ,			"eval_results_best-iteration.json"   )     ,			)
				else:
								# Assume that the last iteration is the best
								logger.info("Best iteration: %d"     ,			args.max_selftrain_iterations - 1   )
								logger.info("Best evaluation result: %s = %f"     ,			args.eval_metric     ,			__lowercase   )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(__lowercase     ,			f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""   )     ,			os.path.join(__lowercase     ,			"eval_results_best-iteration.json"   )     ,			)



"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a_       							=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_       							=    []
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
		    )
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
		)

		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	List[str]:
				_A					  =			state_dict.pop(__lowercase   )
				_A					  =			val






def      a__ (				__lowercase   )	->	List[str]:
				_A					  =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_A					  =			key.replace("backbone.0.body"     ,			"backbone.conv_encoder.model"   )
												_A					  =			value
								else:
												_A					  =			value

				return new_state_dict






def      a__ (				__lowercase     ,			__lowercase=False   )	->	Any:
				_A					  =			""
				if is_panoptic:
								_A					  =			"conditional_detr."

				# first: transformer encoder
				for i in range(6   ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.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__ (				)	->	int:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )

				return im






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase   )	->	Any:
				_A					  =			ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								_A					  =			"resnet101"
				if "dc5" in model_name:
								_A					  =			True
				_A					  =			"panoptic" in model_name
				if is_panoptic:
								_A					  =			250
				else:
								_A					  =			91
								_A					  =			"huggingface/label-files"
								_A					  =			"coco-detection-id2label.json"
								_A					  =			json.load(open(hf_hub_download(__lowercase     ,			__lowercase     ,			repo_type="dataset"   )     ,			"r"   )   )
								_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}
								_A					  =			idalabel
								_A					  =			{v: k for k, v in idalabel.items()}

				# load image processor
				_A					  =			"coco_panoptic" if is_panoptic else "coco_detection"
				_A					  =			ConditionalDetrImageProcessor(format=__lowercase   )

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

				logger.info(f"""Converting model {model_name}..."""   )

				# load original model from torch hub
				_A					  =			torch.hub.load("DeppMeng/ConditionalDETR"     ,			__lowercase     ,			pretrained=__lowercase   ).eval()
				_A					  =			conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												_A					  =			"conditional_detr." + src
								rename_key(__lowercase     ,			__lowercase     ,			__lowercase   )
				_A					  =			rename_backbone_keys(__lowercase   )
				# query, key and value matrices need special treatment
				read_in_q_k_v(__lowercase     ,			is_panoptic=__lowercase   )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_A					  =			"conditional_detr.model." if is_panoptic else "model."
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith("conditional_detr"   )
												    and not key.startswith("class_labels_classifier"   )
												    and not key.startswith("bbox_predictor"   )
												):
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
												elif key.startswith("bbox_attention"   ) or key.startswith("mask_head"   ):
																continue
												else:
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
								else:
												if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
    # finally, create HuggingFace model and load state dict
				_A					  =			ConditionalDetrForSegmentation(__lowercase   ) if is_panoptic else ConditionalDetrForObjectDetection(__lowercase   )
				model.load_state_dict(__lowercase   )
				model.eval()
				model.push_to_hub(repo_id=__lowercase     ,			organization="DepuMeng"     ,			commit_message="Add model"   )
				# verify our conversion
				_A					  =			conditional_detr(__lowercase   )
				_A					  =			model(__lowercase   )
				assert torch.allclose(outputs.logits     ,			original_outputs["pred_logits"]     ,			atol=1E-4   )
				assert torch.allclose(outputs.pred_boxes     ,			original_outputs["pred_boxes"]     ,			atol=1E-4   )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks     ,			original_outputs["pred_masks"]     ,			atol=1E-4   )

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


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    default="conditional_detr_resnet50",
		    type=str,
		    help="Name of the CONDITIONAL_DETR 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."
		)
		a_       							=    parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)