

'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	if "backbone.0.body" in orig_key:
		_lowercase							=     orig_key.replace("""backbone.0.body"""							,       """backbone.conv_encoder.model"""	)
	if "transformer" in orig_key:
		_lowercase							=     orig_key.replace("""transformer."""							,       """"""	)
	if "norm1" in orig_key:
		if "encoder" in orig_key:
			_lowercase							=     orig_key.replace("""norm1"""							,       """self_attn_layer_norm"""	)
		else:
			_lowercase							=     orig_key.replace("""norm1"""							,       """encoder_attn_layer_norm"""	)
	if "norm2" in orig_key:
		if "encoder" in orig_key:
			_lowercase							=     orig_key.replace("""norm2"""							,       """final_layer_norm"""	)
		else:
			_lowercase							=     orig_key.replace("""norm2"""							,       """self_attn_layer_norm"""	)
	if "norm3" in orig_key:
		_lowercase							=     orig_key.replace("""norm3"""							,       """final_layer_norm"""	)
	if "linear1" in orig_key:
		_lowercase							=     orig_key.replace("""linear1"""							,       """fc1"""	)
	if "linear2" in orig_key:
		_lowercase							=     orig_key.replace("""linear2"""							,       """fc2"""	)
	if "query_embed" in orig_key:
		_lowercase							=     orig_key.replace("""query_embed"""							,       """query_position_embeddings"""	)
	if "cross_attn" in orig_key:
		_lowercase							=     orig_key.replace("""cross_attn"""							,       """encoder_attn"""	)

	return orig_key






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	# transformer decoder self-attention layers
	for i in range(6	):
		# read in weights + bias of input projection layer of self-attention
		_lowercase							=     state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""	)
		_lowercase							=     state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""	)
		# next, add query, keys and values (in that order) to the state dict
		_lowercase							=     in_proj_weight[:256, :]
		_lowercase							=     in_proj_bias[:256]
		_lowercase							=     in_proj_weight[256:512, :]
		_lowercase							=     in_proj_bias[256:512]
		_lowercase							=     in_proj_weight[-256:, :]
		_lowercase							=     in_proj_bias[-256:]






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
	_lowercase							=     Image.open(requests.get(snake_case_							,       stream=snake_case_	).raw	)

	return im






@torch.no_grad()
def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       ):
	_lowercase							=     DeformableDetrConfig()
	# set config attributes
	if single_scale:
		_lowercase							=     1
	_lowercase							=     dilation
	_lowercase							=     with_box_refine
	_lowercase							=     two_stage
	# set labels
	_lowercase							=     91
	_lowercase							=     """huggingface/label-files"""
	_lowercase							=     """coco-detection-id2label.json"""
	_lowercase							=     json.load(open(cached_download(hf_hub_url(snake_case_							,       snake_case_							,       repo_type="""dataset"""	)	)							,       """r"""	)	)
	_lowercase							=     {int(snake_case_	): v for k, v in idalabel.items()}
	_lowercase							=     idalabel
	_lowercase							=     {v: k for k, v in idalabel.items()}

	# load image processor
	_lowercase							=     DeformableDetrImageProcessor(format="""coco_detection"""	)

	# prepare image
	_lowercase							=     prepare_img()
	_lowercase							=     image_processor(images=snake_case_							,       return_tensors="""pt"""	)
	_lowercase							=     encoding["""pixel_values"""]

	logger.info("""Converting model..."""	)

	# load original state dict
	_lowercase							=     torch.load(snake_case_							,       map_location="""cpu"""	)["""model"""]
	# rename keys
	for key in state_dict.copy().keys():
		_lowercase							=     state_dict.pop(snake_case_	)
		_lowercase							=     val
	# query, key and value matrices need special treatment
	read_in_q_k_v(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
	_lowercase							=     """model."""
	for key in state_dict.copy().keys():
		if not key.startswith("""class_embed"""	) and not key.startswith("""bbox_embed"""	):
			_lowercase							=     state_dict.pop(snake_case_	)
			_lowercase							=     val
    # finally, create HuggingFace model and load state dict
	_lowercase							=     DeformableDetrForObjectDetection(snake_case_	)
	model.load_state_dict(snake_case_	)
	model.eval()

	_lowercase							=     """cuda""" if torch.cuda.is_available() else """cpu"""
	model.to(snake_case_	)
	# verify our conversion
	_lowercase							=     model(pixel_values.to(snake_case_	)	)

	_lowercase							=     torch.tensor(
	    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]	)
	_lowercase							=     torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]	)

	if single_scale:
		_lowercase							=     torch.tensor(
		    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]	)
		_lowercase							=     torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]	)

	if single_scale and dilation:
		_lowercase							=     torch.tensor(
		    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]	)
		_lowercase							=     torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]	)

	if with_box_refine:
		_lowercase							=     torch.tensor(
		    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]	)
		_lowercase							=     torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]	)

	if with_box_refine and two_stage:
		_lowercase							=     torch.tensor(
		    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]	)
		_lowercase							=     torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]	)

	print("""Logits:"""							,       outputs.logits[0, :3, :3]	)

	assert torch.allclose(outputs.logits[0, :3, :3]							,       expected_logits.to(snake_case_	)							,       atol=1e-4	)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,       expected_boxes.to(snake_case_	)							,       atol=1e-4	)

	print("""Everything ok!"""	)

	# 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_	)
	image_processor.save_pretrained(snake_case_	)

	# Push to hub
	if push_to_hub:
		_lowercase							=     """deformable-detr"""
		model_name += "-single-scale" if single_scale else ""
		model_name += "-dc5" if dilation else ""
		model_name += "-with-box-refine" if with_box_refine else ""
		model_name += "-two-stage" if two_stage else ""
		print("""Pushing model to hub..."""	)
		model.push_to_hub(repo_path_or_name=snake_case_							,       organization="""nielsr"""							,       commit_message="""Add model"""	)


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

							parser.add_argument(
							    '--checkpoint_path',
							    type=str,
							    default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
							    help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
							)
							parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
							parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
							parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
							parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to the folder to output PyTorch model.',
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)
							_lowerCamelCase    =  parser.parse_args()
							convert_deformable_detr_checkpoint(
							    args.checkpoint_path,
							    args.single_scale,
							    args.dilation,
							    args.with_box_refine,
							    args.two_stage,
							    args.pytorch_dump_folder_path,
							    args.push_to_hub,
							)







'''simple docstring'''







from .balanced_parentheses import balanced_parentheses
from .stack import Stack






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(snake_case_							,       -1	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	if not balanced_parentheses(snake_case_	):
		raise ValueError("""Mismatched parentheses"""	)
	_lowercase							=     Stack()
	_lowercase							=     []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(snake_case_	)
		elif char == "(":
			stack.push(snake_case_	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(snake_case_	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(snake_case_	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(snake_case_	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							_lowerCamelCase    =  'a+b*(c^d-e)^(f+g*h)-i'

							print('Infix to Postfix Notation demonstration:\n')
							print('Infix notation: ' + expression)
							print('Postfix notation: ' + infix_to_postfix(expression))




