






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()
__UpperCAmelCase								=						logging.get_logger(__name__)




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					if "resnet-50" in model_name:
										UpperCAmelCase_    :      Union[str, Any]            =						ResNetConfig.from_pretrained('microsoft/resnet-50'  )
					elif "resnet-101" in model_name:
										UpperCAmelCase_    :      Dict            =						ResNetConfig.from_pretrained('microsoft/resnet-101'  )
					else:
										raise ValueError('Model name should include either resnet50 or resnet101'  )

					UpperCAmelCase_    :      List[Any]            =						DetrConfig(use_timm_backbone=__snake_case     ,       backbone_config=__snake_case  )

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

					return config, is_panoptic




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      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      lowercase__      (     __snake_case    :							List[Any]     ,       __snake_case    :							Tuple     ,       __snake_case    :							List[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						state_dict.pop(__snake_case  )
					UpperCAmelCase_    :      int            =						val




def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							Union[str, Any]=False  ):


					'''simple docstring'''


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




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[Any]            =						'http://images.cocodataset.org/val2017/000000039769.jpg'
					UpperCAmelCase_    :      str            =						Image.open(requests.get(__snake_case     ,       stream=__snake_case  ).raw  )

					return im




@torch.no_grad()
def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							List[Any]=None     ,       __snake_case    :							Optional[Any]=False  ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						get_detr_config(__snake_case  )

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

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

					# verify our conversion on an image
					UpperCAmelCase_    :      int            =						'coco_panoptic' if is_panoptic else 'coco_detection'
					UpperCAmelCase_    :      Dict            =						DetrImageProcessor(format=__snake_case  )

					UpperCAmelCase_    :      Any            =						processor(images=prepare_img()     ,       return_tensors='pt'  )
					UpperCAmelCase_    :      Optional[int]            =						encoding['pixel_values']

					UpperCAmelCase_    :      int            =						detr(__snake_case  )
					UpperCAmelCase_    :      Any            =						model(__snake_case  )

					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(__snake_case  ).mkdir(exist_ok=__snake_case  )
										model.save_pretrained(__snake_case  )
										processor.save_pretrained(__snake_case  )

					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__":
		__UpperCAmelCase								=						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.')
		__UpperCAmelCase								=						parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













"""simple docstring"""




from decimal import Decimal, getcontext
from math import ceil, factorial






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int					) ->str:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("Undefined for non-integers"					)
	elif precision < 1:
		raise ValueError("Undefined for non-natural numbers"					)

	a      :		Tuple	=				precision
	a      :		str	=				ceil(precision / 14					)
	a      :		List[Any]	=				42_6880 * Decimal(1_0005					).sqrt()
	a      :		Union[str, Any]	=				1
	a      :		Dict	=				1359_1409
	a      :		Optional[int]	=				Decimal(_lowercase					)
	for k in range(1    ,       _lowercase					):
		a      :		int	=				factorial(6 * k					) // (factorial(3 * k					) * factorial(_lowercase					) ** 3)
		linear_term += 5_4514_0134
		exponential_term *= -26_2537_4126_4076_8000
		partial_sum += Decimal(multinomial_term * linear_term					) / exponential_term
	return str(constant_term / partial_sum					)[:-1]


if __name__ == "__main__":
		a						: Optional[Any]     	=			50
		print(F'''The first {n} digits of pi is: {pi(n)}''')


