






"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


lowerCAmelCase__    						=			logging.get_logger(__name__)


@dataclass
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					str 					=				field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()       )}       )
						SCREAMING_SNAKE_CASE_:					str 					=				field(
						    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}       )
						SCREAMING_SNAKE_CASE_:					int 					=				field(
						    default=128   ,  metadata={
						        'help': (
						            'The maximum total input sequence length after tokenization. Sequences longer '
						            'than this will be truncated, sequences shorter will be padded.'
						        )
						    }   ,  )
						SCREAMING_SNAKE_CASE_:					bool 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Overwrite the cached training and evaluation sets'}       )







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.task_name.lower()







class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				'train'
						SCREAMING_SNAKE_CASE_:					int 					=				'dev'
						SCREAMING_SNAKE_CASE_:					int 					=				'test'







class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					GlueDataTrainingArguments
						SCREAMING_SNAKE_CASE_:					str
						SCREAMING_SNAKE_CASE_:					List[InputFeatures]







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = Split.train   ,				lowerCAmelCase_ = None   ,				)		->							Tuple:
										warnings.warn(
										    'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
										    'library. You can have a look at this example script for pointers: '
										    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'   ,				lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		int       		=    args
										_SCREAMING_SNAKE_CASE   :		int       		=    glue_processors[args.task_name]()
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    glue_output_modes[args.task_name]
										if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							):
														try:
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    Split[mode]
														except KeyError:
																		raise KeyError('mode is not a valid split name'							)
        # Load data features from cache or dataset file
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    os.path.join(
										    cache_dir if cache_dir is not None else args.data_dir   ,				F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""   ,				)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.processor.get_labels()
										if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
										    "RobertaTokenizer",
										    "RobertaTokenizerFast",
										    "XLMRobertaTokenizer",
										    "BartTokenizer",
										    "BartTokenizerFast",
										):
														# HACK(label indices are swapped in RoBERTa pretrained model)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    label_list[2], label_list[1]
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    label_list

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    cached_features_file + '.lock'
										with FileLock(lowerCAmelCase_							):
														if os.path.exists(lowerCAmelCase_							) and not args.overwrite_cache:
																		_SCREAMING_SNAKE_CASE   :		str       		=    time.time()
																		_SCREAMING_SNAKE_CASE   :		Any       		=    torch.load(lowerCAmelCase_							)
																		logger.info(
																		    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""   ,				time.time() - start							)
														else:
																		logger.info(F"""Creating features from dataset file at {args.data_dir}"""							)

																		if mode == Split.dev:
																						_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.processor.get_dev_examples(args.data_dir							)
																		elif mode == Split.test:
																						_SCREAMING_SNAKE_CASE   :		int       		=    self.processor.get_test_examples(args.data_dir							)
																		else:
																						_SCREAMING_SNAKE_CASE   :		Any       		=    self.processor.get_train_examples(args.data_dir							)
																		if limit_length is not None:
																						_SCREAMING_SNAKE_CASE   :		List[str]       		=    examples[:limit_length]
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    glue_convert_examples_to_features(
																		    lowerCAmelCase_   ,				lowerCAmelCase_   ,				max_length=args.max_seq_length   ,				label_list=lowerCAmelCase_   ,				output_mode=self.output_mode   ,				)
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    time.time()
																		torch.save(self.features   ,				lowerCAmelCase_							)
																		# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																		logger.info(
																		    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""							)







						def __len__(    self							)		->							List[Any]:
										return len(self.features							)







						def __getitem__(    self   ,				lowerCAmelCase_							)		->							InputFeatures:
										return self.features[i]







						def 			A      (    self							)		->							str:
										return self.label_list









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

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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





def 				lowercase__		(					lowerCamelCase       ):
				# initialize config
				if "resnet-50" in model_name:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ResNetConfig.from_pretrained('microsoft/resnet-50'       )
				elif "resnet-101" in model_name:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ResNetConfig.from_pretrained('microsoft/resnet-101'       )
				else:
								raise ValueError('Model name should include either resnet50 or resnet101'       )

				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    DetrConfig(use_timm_backbone=lowerCamelCase,						backbone_config=lowerCamelCase       )

				# set label attributes
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'panoptic' in model_name
				if is_panoptic:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    250
				else:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    91
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'huggingface/label-files'
								_SCREAMING_SNAKE_CASE   :		Dict       		=    'coco-detection-id2label.json'
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    json.load(open(hf_hub_download(lowerCamelCase,						lowerCamelCase,						repo_type='dataset'       ),						'r'       )       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    {int(lowerCamelCase       ): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    idalabel
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    {v: k for k, v in idalabel.items()}

				return config, is_panoptic





def 				lowercase__		(					lowerCamelCase       ):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    []

				# stem
				# fmt: off
				rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')       )
				rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')       )
				rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')       )
				rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')       )
				rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																    )       )
    # fmt: on

				for i in range(config.encoder_layers       ):
								# 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}.multihead_attn.out_proj.weight""",
								        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
								    )       )
								rename_keys.append(
								    (
								        f"""transformer.decoder.layers.{i}.multihead_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""")       )

				# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
				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'),
				    ]       )

				return rename_keys





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    state_dict.pop(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    val





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ''
				if is_panoptic:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    '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)
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    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
								_SCREAMING_SNAKE_CASE   :		Any       		=    in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias[-256:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6       ):
								# read in weights + bias of input projection layer of self-attention
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		int       		=    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""       )
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   :		str       		=    in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   :		str       		=    in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   :		int       		=    in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								_SCREAMING_SNAKE_CASE   :		str       		=    state_dict.pop(
								    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""       )
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    in_proj_weight_cross_attn[:256, :]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias_cross_attn[:256]
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    in_proj_weight_cross_attn[256:512, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias_cross_attn[256:512]
								_SCREAMING_SNAKE_CASE   :		int       		=    in_proj_weight_cross_attn[-256:, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias_cross_attn[-256:]





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_SCREAMING_SNAKE_CASE   :		Dict       		=    Image.open(requests.get(lowerCamelCase,						stream=lowerCamelCase       ).raw       )

				return im





@torch.no_grad()
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=None,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    get_detr_config(lowerCamelCase       )

				# load original model from torch hub
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
				    'detr-resnet-50': 'detr_resnet50',
				    'detr-resnet-101': 'detr_resnet101',
				}
				logger.info(f"""Converting model {model_name}..."""       )
				_SCREAMING_SNAKE_CASE   :		int       		=    torch.hub.load('facebookresearch/detr',						model_name_to_original_name[model_name],						pretrained=lowerCamelCase       ).eval()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(lowerCamelCase       ):
								if is_panoptic:
												_SCREAMING_SNAKE_CASE   :		int       		=    'detr.' + src
								rename_key(lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowerCamelCase,						is_panoptic=lowerCamelCase       )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_SCREAMING_SNAKE_CASE   :		str       		=    'detr.model.' if is_panoptic else 'model.'
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('detr'       )
												    and not key.startswith('class_labels_classifier'       )
												    and not key.startswith('bbox_predictor'       )
												):
																_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		int       		=    val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_SCREAMING_SNAKE_CASE   :		Tuple       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		Dict       		=    val
												elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
																continue
												else:
																_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    val
								else:
												if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		int       		=    val

    # finally, create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE   :		Dict       		=    DetrForSegmentation(lowerCamelCase       ) if is_panoptic else DetrForObjectDetection(lowerCamelCase       )
				model.load_state_dict(lowerCamelCase       )
				model.eval()

				# verify our conversion on an image
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'coco_panoptic' if is_panoptic else 'coco_detection'
				_SCREAMING_SNAKE_CASE   :		int       		=    DetrImageProcessor(format=lowerCamelCase       )

				_SCREAMING_SNAKE_CASE   :		Any       		=    processor(images=prepare_img(),						return_tensors='pt'       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    encoding['pixel_values']

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    detr(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		int       		=    model(lowerCamelCase       )

				assert torch.allclose(outputs.logits,						original_outputs['pred_logits'],						atol=1E-3       )
				assert torch.allclose(outputs.pred_boxes,						original_outputs['pred_boxes'],						atol=1E-3       )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks,						original_outputs['pred_masks'],						atol=1E-4       )
				print('Looks ok!'       )

				if pytorch_dump_folder_path is not None:
								# 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       )
								processor.save_pretrained(lowerCamelCase       )

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info('Uploading PyTorch model and image processor to the hub...'       )
								model.push_to_hub(f"""nielsr/{model_name}"""       )
								processor.push_to_hub(f"""nielsr/{model_name}"""       )


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

			parser.add_argument(
			    '--model_name',
			    default='detr-resnet-50',
			    type=str,
			    choices=['detr-resnet-50', 'detr-resnet-101'],
			    help='Name of the 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.'
			)
			parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
			lowerCAmelCase__    						=			parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

