
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__   :			List[str]												=    logging.get_logger(__name__)




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

				__lowerCamelCase    					=	DetrConfig(use_timm_backbone=__lowerCAmelCase		,  backbone_config=__lowerCAmelCase							)

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

				return config, is_panoptic




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   Union[str, Any]:
				# here we list all keys to be renamed (original name on the left, our name on the right)
				__lowerCamelCase    					=	[]

				# 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  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Optional[int]							)			->   int:
				__lowerCamelCase    					=	state_dict.pop(__lowerCAmelCase							)
				__lowerCamelCase    					=	val




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




def  __magic_name__ (   )			->   List[str]:
				__lowerCamelCase    					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowerCamelCase    					=	Image.open(requests.get(__lowerCAmelCase		,  stream=__lowerCAmelCase							).raw							)

				return im




@torch.no_grad()
def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Any=None		,  __lowerCAmelCase	:						Union[str, Any]=False							)			->   List[str]:
				__lowerCamelCase					,    __lowerCamelCase    					=	get_detr_config(__lowerCAmelCase							)

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

    # finally, create HuggingFace model and load state dict
				__lowerCamelCase    					=	DetrForSegmentation(__lowerCAmelCase							) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase							)
				model.load_state_dict(__lowerCAmelCase							)
				model.eval()

				# verify our conversion on an image
				__lowerCamelCase    					=	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
				__lowerCamelCase    					=	DetrImageProcessor(format=__lowerCAmelCase							)

				__lowerCamelCase    					=	processor(images=prepare_img()		,  return_tensors='''pt'''							)
				__lowerCamelCase    					=	encoding['''pixel_values''']

				__lowerCamelCase    					=	detr(__lowerCAmelCase							)
				__lowerCamelCase    					=	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__   :			Optional[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__   :			str												=    parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							NestedDataStructureLike[PathLike]							,			SCREAMING_SNAKE_CASE__		:							Optional[NamedSplit] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Features] = None							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			**SCREAMING_SNAKE_CASE__		:							str							,			)     ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			split=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			cache_dir=SCREAMING_SNAKE_CASE__							,			keep_in_memory=SCREAMING_SNAKE_CASE__							,			streaming=SCREAMING_SNAKE_CASE__							,			num_proc=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	path_or_paths if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else {self.split: path_or_paths}
						__lowerCamelCase    					=	Text(
						    cache_dir=SCREAMING_SNAKE_CASE__							,			data_files=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							int  )     ->							Dict:
						# Build iterable dataset
						if self.streaming:
										__lowerCamelCase    					=	self.builder.as_streaming_dataset(split=self.split  )
						# Build regular (map-style) dataset
						else:
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None

										self.builder.download_and_prepare(
										    download_config=SCREAMING_SNAKE_CASE__							,			download_mode=SCREAMING_SNAKE_CASE__							,			verification_mode=SCREAMING_SNAKE_CASE__							,			base_path=SCREAMING_SNAKE_CASE__							,			num_proc=self.num_proc							,			)
										__lowerCamelCase    					=	self.builder.as_dataset(
										    split=self.split							,			verification_mode=SCREAMING_SNAKE_CASE__							,			in_memory=self.keep_in_memory  )
						return dataset



