"""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()
_a					:					List[str]=			logging.get_logger(__name__)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]  )							->      Tuple:

	'''simple docstring'''





	if "resnet-50" in model_name:
		__snake_case      :							int						=			ResNetConfig.from_pretrained('microsoft/resnet-50'  )
	elif "resnet-101" in model_name:
		__snake_case      :							int						=			ResNetConfig.from_pretrained('microsoft/resnet-101'  )
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'  )

	__snake_case      :							Optional[Any]						=			DetrConfig(use_timm_backbone=A_      ,							backbone_config=A_  )

	# set label attributes
	__snake_case      :							Union[str, Any]						=			'''panoptic''' in model_name
	if is_panoptic:
		__snake_case      :							Any						=			2_50
	else:
		__snake_case      :							List[str]						=			91
		__snake_case      :							Tuple						=			'''huggingface/label-files'''
		__snake_case      :							Union[str, Any]						=			'''coco-detection-id2label.json'''
		__snake_case      :							str						=			json.load(open(hf_hub_download(A_      ,							A_      ,							repo_type='dataset'  )      ,							'r'  )  )
		__snake_case      :							Tuple						=			{int(A_  ): v for k, v in idalabel.items()}
		__snake_case      :							Optional[int]						=			idalabel
		__snake_case      :							Optional[int]						=			{v: k for k, v in idalabel.items()}

	return config, is_panoptic
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	__snake_case      :							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   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Union[str, Any]  )							->      str:

	'''simple docstring'''





	__snake_case      :							Optional[Any]						=			state_dict.pop(A_  )
	__snake_case      :							int						=			val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    Optional[int]=False  )							->      Optional[Any]:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			''''''
	if is_panoptic:
		__snake_case      :							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)
		__snake_case      :							List[str]						=			state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"  )
		__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
		__snake_case      :							str						=			in_proj_weight[:2_56, :]
		__snake_case      :							Any						=			in_proj_bias[:2_56]
		__snake_case      :							Tuple						=			in_proj_weight[2_56:5_12, :]
		__snake_case      :							List[Any]						=			in_proj_bias[2_56:5_12]
		__snake_case      :							Any						=			in_proj_weight[-2_56:, :]
		__snake_case      :							List[Any]						=			in_proj_bias[-2_56:]
	# 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
		__snake_case      :							Any						=			state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"  )
		__snake_case      :							str						=			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
		__snake_case      :							int						=			in_proj_weight[:2_56, :]
		__snake_case      :							Optional[int]						=			in_proj_bias[:2_56]
		__snake_case      :							Optional[Any]						=			in_proj_weight[2_56:5_12, :]
		__snake_case      :							Tuple						=			in_proj_bias[2_56:5_12]
		__snake_case      :							Optional[int]						=			in_proj_weight[-2_56:, :]
		__snake_case      :							List[Any]						=			in_proj_bias[-2_56:]
		# read in weights + bias of input projection layer of cross-attention
		__snake_case      :							List[Any]						=			state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"  )
		__snake_case      :							str						=			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
		__snake_case      :							List[Any]						=			in_proj_weight_cross_attn[:2_56, :]
		__snake_case      :							Any						=			in_proj_bias_cross_attn[:2_56]
		__snake_case      :							str						=			in_proj_weight_cross_attn[2_56:5_12, :]
		__snake_case      :							Tuple						=			in_proj_bias_cross_attn[2_56:5_12]
		__snake_case      :							Tuple						=			in_proj_weight_cross_attn[-2_56:, :]
		__snake_case      :							Union[str, Any]						=			in_proj_bias_cross_attn[-2_56:]
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	__snake_case      :							int						=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__snake_case      :							Tuple						=			Image.open(requests.get(A_      ,							stream=A_  ).raw  )

	return im
@torch.no_grad()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    Optional[Any]=None      ,							UpperCAmelCase_   :    Tuple=False  )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							Any						=			get_detr_config(A_  )

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

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

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

	__snake_case      :							List[str]						=			processor(images=prepare_img()      ,							return_tensors='pt'  )
	__snake_case      :							Optional[int]						=			encoding['''pixel_values''']

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

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

	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__":
	_a					:					int=			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.")
	_a					:					List[str]=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





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

	__snake_case      :							Any						=			precision
	__snake_case      :							Optional[int]						=			ceil(precision / 14  )
	__snake_case      :							List[Any]						=			42_68_80 * Decimal(1_00_05  ).sqrt()
	__snake_case      :							Optional[Any]						=			1
	__snake_case      :							Union[str, Any]						=			13_59_14_09
	__snake_case      :							int						=			Decimal(UpperCAmelCase_  )
	for k in range(1      ,							UpperCAmelCase_  ):
		__snake_case      :							Union[str, Any]						=			factorial(6 * k  ) // (factorial(3 * k  ) * factorial(UpperCAmelCase_  ) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
	return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
	_a					:					List[Any]=			50
	print(f'''The first {n} digits of pi is: {pi(n)}''')





