

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import torch






class 			A_						(			TensorFormatter[Mapping, '''torch.Tensor''', Mapping]							):
							def __init__(						self						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=None  ,**SCREAMING_SNAKE_CASE__						:     Tuple):
								super().__init__(features=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase							:							List[str]   			=					torch_tensor_kwargs
								import torch  # noqa import torch at initialization
							def        lowerCAmelCase	(						self						:     List[Any]  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]):
								import torch

								if isinstance(SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__) and column:
									if all(
									    isinstance(SCREAMING_SNAKE_CASE__  ,torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column):
										return torch.stack(SCREAMING_SNAKE_CASE__)
								return column
							def        lowerCAmelCase	(						self						:     Any  ,SCREAMING_SNAKE_CASE__						:     List[Any]):
								import torch

								if isinstance(SCREAMING_SNAKE_CASE__  ,(str, bytes, type(SCREAMING_SNAKE_CASE__))):
									return value
								elif isinstance(SCREAMING_SNAKE_CASE__  ,(np.character, np.ndarray)) and np.issubdtype(value.dtype  ,np.character):
									return value.tolist()

								__lowerCamelCase							:							Optional[int]   			=					{}

								if isinstance(SCREAMING_SNAKE_CASE__  ,(np.number, np.ndarray)) and np.issubdtype(value.dtype  ,np.integer):
									__lowerCamelCase							:							List[str]   			=					{'dtype': torch.intaa}
								elif isinstance(SCREAMING_SNAKE_CASE__  ,(np.number, np.ndarray)) and np.issubdtype(value.dtype  ,np.floating):
									__lowerCamelCase							:							Any   			=					{'dtype': torch.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(SCREAMING_SNAKE_CASE__  ,PIL.Image.Image):
										__lowerCamelCase							:							List[Any]   			=					np.asarray(SCREAMING_SNAKE_CASE__)
								return torch.tensor(SCREAMING_SNAKE_CASE__  ,**{**default_dtype, **self.torch_tensor_kwargs})
							def        lowerCAmelCase	(						self						:     Dict  ,SCREAMING_SNAKE_CASE__						:     int):
								import torch

								# support for torch, tf, jax etc.
								if hasattr(SCREAMING_SNAKE_CASE__  ,'__array__') and not isinstance(SCREAMING_SNAKE_CASE__  ,torch.Tensor):
									__lowerCamelCase							:							Any   			=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(SCREAMING_SNAKE_CASE__  ,np.ndarray):
									if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
										return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__) for substruct in data_struct])
								elif isinstance(SCREAMING_SNAKE_CASE__  ,(list, tuple)):
									return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__) for substruct in data_struct])
								return self._tensorize(SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     Any):
								return map_nested(self._recursive_tensorize  ,SCREAMING_SNAKE_CASE__  ,map_list=SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Optional[Any]  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]):
								__lowerCamelCase							:							Optional[Any]   			=					self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase							:							Optional[Any]   			=					self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE__)
								return self.recursive_tensorize(SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Optional[int]  ,SCREAMING_SNAKE_CASE__						:     Tuple):
								__lowerCamelCase							:							Union[str, Any]   			=					self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase							:							int   			=					self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE__  ,pa_table.column_names[0])
								__lowerCamelCase							:							Union[str, Any]   			=					self.recursive_tensorize(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase							:							Optional[Any]   			=					self._consolidate(SCREAMING_SNAKE_CASE__)
								return column


							def        lowerCAmelCase	(						self						:     Dict  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]):
								__lowerCamelCase							:							str   			=					self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase							:							List[Any]   			=					self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase							:							Any   			=					self.recursive_tensorize(SCREAMING_SNAKE_CASE__)
								for column_name in batch:
									__lowerCamelCase							:							Tuple   			=					self._consolidate(batch[column_name])
								return batch








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()
SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    # initialize config
    if "resnet-50" in model_name:
        _lowercase			:				Union[str, Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-50'					)
    elif "resnet-101" in model_name:
        _lowercase			:				Optional[Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-101'					)
    else:
        raise ValueError('Model name should include either resnet50 or resnet101'					)

    _lowercase			:				Tuple 						=						DetrConfig(use_timm_backbone=lowerCamelCase_				,					backbone_config=lowerCamelCase_					)

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

    return config, is_panoptic



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    _lowercase			:				List[str] 						=						[]

    # 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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				str 						=						state_dict.pop(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						val



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    _lowercase			:				Any 						=						''
    if is_panoptic:
        _lowercase			:				Optional[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)
        _lowercase			:				int 						=						state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
        _lowercase			:				Tuple 						=						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
        _lowercase			:				List[str] 						=						in_proj_weight[:256, :]
        _lowercase			:				Tuple 						=						in_proj_bias[:256]
        _lowercase			:				List[Any] 						=						in_proj_weight[256:512, :]
        _lowercase			:				Any 						=						in_proj_bias[256:512]
        _lowercase			:				int 						=						in_proj_weight[-256:, :]
        _lowercase			:				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
        _lowercase			:				str 						=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
        _lowercase			:				Optional[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
        _lowercase			:				Union[str, Any] 						=						in_proj_weight[:256, :]
        _lowercase			:				Dict 						=						in_proj_bias[:256]
        _lowercase			:				Tuple 						=						in_proj_weight[256:512, :]
        _lowercase			:				Dict 						=						in_proj_bias[256:512]
        _lowercase			:				str 						=						in_proj_weight[-256:, :]
        _lowercase			:				Optional[int] 						=						in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _lowercase			:				Tuple 						=						state_dict.pop(
            F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
        _lowercase			:				Dict 						=						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
        _lowercase			:				List[str] 						=						in_proj_weight_cross_attn[:256, :]
        _lowercase			:				Tuple 						=						in_proj_bias_cross_attn[:256]
        _lowercase			:				str 						=						in_proj_weight_cross_attn[256:512, :]
        _lowercase			:				Union[str, Any] 						=						in_proj_bias_cross_attn[256:512]
        _lowercase			:				List[Any] 						=						in_proj_weight_cross_attn[-256:, :]
        _lowercase			:				Dict 						=						in_proj_bias_cross_attn[-256:]



def 							UpperCamelCase_( )       ->		List[Any]:
    _lowercase			:				Dict 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				str 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)

    return im



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=False					)       ->		List[Any]:
    _lowercase						,							_lowercase			:				int 						=						get_detr_config(lowerCamelCase_					)

    # load original model from torch hub
    _lowercase			:				int 						=						{
        'detr-resnet-50': 'detr_resnet50',
        'detr-resnet-101': 'detr_resnet101',
    }
    logger.info(F'''Converting model {model_name}...'''					)
    _lowercase			:				Optional[Any] 						=						torch.hub.load('facebookresearch/detr'				,					model_name_to_original_name[model_name]				,					pretrained=lowerCamelCase_					).eval()
    _lowercase			:				str 						=						detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(lowerCamelCase_					):
        if is_panoptic:
            _lowercase			:				str 						=						'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
    _lowercase			:				List[Any] 						=						'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'					)
            ):
                _lowercase			:				Tuple 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				int 						=						val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				Optional[Any] 						=						val
            elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
                continue
            else:
                _lowercase			:				Optional[Any] 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				Union[str, Any] 						=						val
        else:
            if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
                _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				List[str] 						=						val

    # finally, create HuggingFace model and load state dict
    _lowercase			:				Optional[Any] 						=						DetrForSegmentation(lowerCamelCase_					) if is_panoptic else DetrForObjectDetection(lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    # verify our conversion on an image
    _lowercase			:				str 						=						'coco_panoptic' if is_panoptic else 'coco_detection'
    _lowercase			:				Optional[int] 						=						DetrImageProcessor(format=lowerCamelCase_					)

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

    _lowercase			:				int 						=						detr(lowerCamelCase_					)
    _lowercase			:				Tuple 						=						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__":
 SCREAMING_SNAKE_CASE			: List[Any]				   =					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.")
 SCREAMING_SNAKE_CASE			: Tuple				   =					parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


