


import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase      						=    []
for i in range(6):
						# 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'),
    ]
)
def     UpperCamelCase_(			_A				:Dict			,       _A				:str			,       _A				:List[Any]					)->						List[str]:
			UpperCamelCase__   =    state_dict.pop(_A					)
			UpperCamelCase__   =    val
def     UpperCamelCase_(			_A				:int					)->						Dict:
			UpperCamelCase__   =    OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									UpperCamelCase__   =    key.replace("backbone.0.body"			,       "backbone.conv_encoder.model"					)
									UpperCamelCase__   =    value
						else:
									UpperCamelCase__   =    value

			return new_state_dict
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:Any=False					)->						Optional[int]:
			UpperCamelCase__   =    ""
			if is_panoptic:
						UpperCamelCase__   =    "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__   =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
						UpperCamelCase__   =    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__   =    in_proj_weight[:2_56, :]
						UpperCamelCase__   =    in_proj_bias[:2_56]
						UpperCamelCase__   =    in_proj_weight[2_56:5_12, :]
						UpperCamelCase__   =    in_proj_bias[2_56:5_12]
						UpperCamelCase__   =    in_proj_weight[-2_56:, :]
						UpperCamelCase__   =    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
						UpperCamelCase__   =    state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
						UpperCamelCase__   =    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__   =    in_proj_weight[:2_56, :]
						UpperCamelCase__   =    in_proj_bias[:2_56]
						UpperCamelCase__   =    in_proj_weight[2_56:5_12, :]
						UpperCamelCase__   =    in_proj_bias[2_56:5_12]
						UpperCamelCase__   =    in_proj_weight[-2_56:, :]
						UpperCamelCase__   =    in_proj_bias[-2_56:]
						# read in weights + bias of input projection layer of cross-attention
						UpperCamelCase__   =    state_dict.pop(
						    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
						UpperCamelCase__   =    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__   =    in_proj_weight_cross_attn[:2_56, :]
						UpperCamelCase__   =    in_proj_bias_cross_attn[:2_56]
						UpperCamelCase__   =    in_proj_weight_cross_attn[2_56:5_12, :]
						UpperCamelCase__   =    in_proj_bias_cross_attn[2_56:5_12]
						UpperCamelCase__   =    in_proj_weight_cross_attn[-2_56:, :]
						UpperCamelCase__   =    in_proj_bias_cross_attn[-2_56:]
def     UpperCamelCase_(			)->						Union[str, Any]:
			UpperCamelCase__   =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__   =    Image.open(requests.get(_A			,       stream=_A					).raw					)

			return im
@torch.no_grad()
def     UpperCamelCase_(			_A				:str			,       _A				:List[str]					)->						List[str]:
			UpperCamelCase__   =    DetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						UpperCamelCase__   =    "resnet101"
			if "dc5" in model_name:
						UpperCamelCase__   =    True
			UpperCamelCase__   =    "panoptic" in model_name
			if is_panoptic:
						UpperCamelCase__   =    2_50
			else:
						UpperCamelCase__   =    91
						UpperCamelCase__   =    "huggingface/label-files"
						UpperCamelCase__   =    "coco-detection-id2label.json"
						UpperCamelCase__   =    json.load(open(hf_hub_download(_A			,       _A			,       repo_type="dataset"					)			,       "r"					)					)
						UpperCamelCase__   =    {int(_A					): v for k, v in idalabel.items()}
						UpperCamelCase__   =    idalabel
						UpperCamelCase__   =    {v: k for k, v in idalabel.items()}

			# load image processor
			UpperCamelCase__   =    "coco_panoptic" if is_panoptic else "coco_detection"
			UpperCamelCase__   =    DetrImageProcessor(format=_A					)

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

			logger.info(F'''Converting model {model_name}...'''					)

			# load original model from torch hub
			UpperCamelCase__   =    torch.hub.load("facebookresearch/detr"			,       _A			,       pretrained=_A					).eval()
			UpperCamelCase__   =    detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									UpperCamelCase__   =    "detr." + src
						rename_key(_A			,       _A			,       _A					)
			UpperCamelCase__   =    rename_backbone_keys(_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
			UpperCamelCase__   =    "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__   =    state_dict.pop(_A					)
												UpperCamelCase__   =    val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												UpperCamelCase__   =    state_dict.pop(_A					)
												UpperCamelCase__   =    val
									elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
												continue
									else:
												UpperCamelCase__   =    state_dict.pop(_A					)
												UpperCamelCase__   =    val
						else:
									if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
												UpperCamelCase__   =    state_dict.pop(_A					)
												UpperCamelCase__   =    val
    # finally, create HuggingFace model and load state dict
			UpperCamelCase__   =    DetrForSegmentation(_A					) if is_panoptic else DetrForObjectDetection(_A					)
			model.load_state_dict(_A					)
			model.eval()
			# verify our conversion
			UpperCamelCase__   =    detr(_A					)
			UpperCamelCase__   =    model(_A					)
			assert torch.allclose(outputs.logits			,       original_outputs["pred_logits"]			,       atol=1e-4					)
			assert torch.allclose(outputs.pred_boxes			,       original_outputs["pred_boxes"]			,       atol=1e-4					)
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks			,       original_outputs["pred_masks"]			,       atol=1e-4					)

			# 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					)
			image_processor.save_pretrained(_A					)


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

						parser.add_argument(
						    '--model_name', default='detr_resnet50', type=str, 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.'
						)
						__UpperCamelCase      						=    parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






import qiskit
from qiskit.providers import Backend
def     UpperCamelCase_(			_A				:int			,       _A				:int					)->						tuple[qiskit.QuantumCircuit, str, str]:
			UpperCamelCase__, UpperCamelCase__   =    bin(_A					)[2:], bin(_A					)[2:]  # Remove leading '0b'

			# Ensure that both strings are of the same length
			if len(_A					) > len(_A					):
						UpperCamelCase__   =    y.zfill(len(_A					)					)
			else:
						UpperCamelCase__   =    x.zfill(len(_A					)					)

			# We need (3 * number of bits in the larger number)+1 qBits
			# The second parameter is the number of classical registers, to measure the result
			UpperCamelCase__   =    qiskit.QuantumCircuit((len(_A					) * 3) + 1			,       len(_A					) + 1					)

			# We are essentially "not-ing" the bits that are 1
			# Reversed because it's easier to perform ops on more significant bits
			for i in range(len(_A					)					):
						if x[::-1][i] == "1":
									circuit.x(_A					)
			for j in range(len(_A					)					):
						if y[::-1][j] == "1":
									circuit.x(len(_A					) + j					)

			return circuit, x, y
def     UpperCamelCase_(			_A				:qiskit.QuantumCircuit			,       _A				:int			,       _A				:int			,       _A				:int			,       _A				:int			,       )->						Union[str, Any]:
			circuit.ccx(_A			,       _A			,       _A					)
			circuit.cx(_A			,       _A					)
			circuit.ccx(_A			,       _A			,       _A					)
			circuit.cx(_A			,       _A					)
			circuit.cx(_A			,       _A					)
def     UpperCamelCase_(			_A				:int			,       _A				:int			,       _A				:Backend = qiskit.Aer.get_backend("aer_simulator"					)			,       )->						int:

			if vala < 0 or vala < 0:
						raise ValueError("Both Integers must be positive!"					)

			# Store the Integers
			UpperCamelCase__, UpperCamelCase__, UpperCamelCase__   =    store_two_classics(_A			,       _A					)
			for i in range(len(_A					)					):
						full_adder(_A			,       _A			,       len(_A					) + i			,       len(_A					) + len(_A					) + i			,       len(_A					) + len(_A					) + i + 1					)
						circuit.barrier()  # Optional, just for aesthetics

			# Measure the resultant qBits
			for i in range(len(_A					) + 1					):
						circuit.measure([(len(_A					) * 2) + i]			,       [i]					)

			UpperCamelCase__   =    qiskit.execute(_A			,       _A			,       shots=1					).result()

			# The result is in binary. Convert it back to int
			return int(list(res.get_counts()					)[0]			,       2					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


