




"""simple docstring"""




import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__	:				Union[str, Any]  =       logging.get_logger(__name__)

def 					A				(				)     ->			Dict:
       lowerCamelCase       :				Union[str, Any]      			= DetaConfig(
           num_queries=900			,encoder_ffn_dim=2048			,decoder_ffn_dim=2048			,num_feature_levels=5			,assign_first_stage=_A			,with_box_refine=_A			,two_stage=_A			,)

       # set labels
       lowerCamelCase       :				Dict      			= 91
       lowerCamelCase       :				List[Any]      			= "huggingface/label-files"
       lowerCamelCase       :				List[str]      			= "coco-detection-id2label.json"
       lowerCamelCase       :				int      			= json.load(open(cached_download(hf_hub_url(_A			,_A			,repo_type="dataset"   )   )			,"r"   )   )
       lowerCamelCase       :				Optional[int]      			= {int(_A   ): v for k, v in idalabel.items()}
       lowerCamelCase       :				int      			= idalabel
       lowerCamelCase       :				Union[str, Any]      			= {v: k for k, v in idalabel.items()}

       return config

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			int:
       lowerCamelCase       :				Optional[int]      			= []

       # stem
       # fmt: off
       rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")   )
       rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")   )
       rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")   )
       rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")   )
       rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                                )   )
    # transformer encoder
       for i in range(config.encoder_layers   ):
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')   )
              rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')   )

       # transformer decoder
       for i in range(config.decoder_layers   ):
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')   )
              rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')   )

       # fmt: on

       return rename_keys

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			int:
       lowerCamelCase       :				int      			= dct.pop(_A   )
       lowerCamelCase       :				List[str]      			= val

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Optional[int]:
       lowerCamelCase       :				Dict      			= config.d_model
       for i in range(config.decoder_layers   ):
              # read in weights + bias of input projection layer of self-attention
              lowerCamelCase       :				Dict      			= state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
              lowerCamelCase       :				Optional[int]      			= state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''   )
              # next, add query, keys and values (in that order) to the state dict
              lowerCamelCase       :				List[str]      			= in_proj_weight[:hidden_size, :]
              lowerCamelCase       :				Optional[int]      			= in_proj_bias[:hidden_size]
              lowerCamelCase       :				Any      			= in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              lowerCamelCase       :				Any      			= in_proj_bias[hidden_size : hidden_size * 2]
              lowerCamelCase       :				int      			= in_proj_weight[-hidden_size:, :]
              lowerCamelCase       :				List[str]      			= in_proj_bias[-hidden_size:]

def 					A				(				)     ->			Tuple:
       lowerCamelCase       :				List[Any]      			= "http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase       :				Dict      			= Image.open(requests.get(_A			,stream=_A   ).raw   )

       return im

@torch.no_grad()
def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Optional[Any]:
       lowerCamelCase       :				Tuple      			= get_deta_config()

       # load original state dict
       if model_name == "deta-resnet-50":
              lowerCamelCase       :				str      			= "adet_checkpoint0011.pth"
       elif model_name == "deta-resnet-50-24-epochs":
              lowerCamelCase       :				str      			= "adet_2x_checkpoint0023.pth"
       else:
              raise ValueError(f'''Model name {model_name} not supported'''   )
       lowerCamelCase       :				Tuple      			= hf_hub_download(repo_id="nielsr/deta-checkpoints"			,filename=_A   )
       lowerCamelCase       :				str      			= torch.load(_A			,map_location="cpu"   )["model"]

       # rename keys
       lowerCamelCase       :				Any      			= create_rename_keys(_A   )
       for src, dest in rename_keys:
              rename_key(_A			,_A			,_A   )
       read_in_decoder_q_k_v(_A			,_A   )

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     lowerCamelCase       :				Any      			= state_dict.pop(_A   )
                     lowerCamelCase       :				str      			= val
              if "input_proj" in key:
                     lowerCamelCase       :				List[str]      			= state_dict.pop(_A   )
                     lowerCamelCase       :				int      			= val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     lowerCamelCase       :				Any      			= state_dict.pop(_A   )
                     lowerCamelCase       :				Optional[Any]      			= val

    # finally, create HuggingFace model and load state dict
       lowerCamelCase       :				Tuple      			= DetaForObjectDetection(_A   )
       model.load_state_dict(_A   )
       model.eval()

       lowerCamelCase       :				List[str]      			= "cuda" if torch.cuda.is_available() else "cpu"
       model.to(_A   )

       # load image processor
       lowerCamelCase       :				Tuple      			= DetaImageProcessor(format="coco_detection"   )

       # verify our conversion on image
       lowerCamelCase       :				Any      			= prepare_img()
       lowerCamelCase       :				List[Any]      			= processor(images=_A			,return_tensors="pt"   )
       lowerCamelCase       :				Tuple      			= encoding["pixel_values"]
       lowerCamelCase       :				Union[str, Any]      			= model(pixel_values.to(_A   )   )

       # verify logits
       if model_name == "deta-resnet-50":
              lowerCamelCase       :				Dict      			= torch.tensor(
                  [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
              lowerCamelCase       :				List[str]      			= torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]   )
       elif model_name == "deta-resnet-50-24-epochs":
              lowerCamelCase       :				Union[str, Any]      			= torch.tensor(
                  [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
              lowerCamelCase       :				Union[str, Any]      			= torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]   )

       assert torch.allclose(outputs.logits[0, :3, :3]			,expected_logits.to(_A   )			,atol=1e-4   )
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,expected_boxes.to(_A   )			,atol=1e-4   )
       print("Everything ok!"   )

       if pytorch_dump_folder_path:
              # Save model and processor
              logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''   )
              Path(_A   ).mkdir(exist_ok=_A   )
              model.save_pretrained(_A   )
              processor.save_pretrained(_A   )

       # Push to hub
       if push_to_hub:
              print("Pushing model and processor to hub..."   )
              model.push_to_hub(f'''jozhang97/{model_name}'''   )
              processor.push_to_hub(f'''jozhang97/{model_name}'''   )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__	:				str  =       argparse.ArgumentParser()

       parser.add_argument(
           '--model_name',
           type=str,
           default='deta-resnet-50',
           choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
           help='Name of the 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 or not to push the converted model to the 🤗 hub.'
       )
       SCREAMING_SNAKE_CASE__	:				Dict  =       parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












import numpy as np
import qiskit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Optional[int]      			= qiskit.QuantumCircuit(num_qubits + 1   )

       # First, let's deal with the case in which oracle is balanced
       if case == "balanced":
              # First generate a random number that tells us which CNOTs to
              # wrap in X-gates:
              lowerCamelCase       :				Optional[int]      			= np.random.randint(1			,2**num_qubits   )
              # Next, format 'b' as a binary string of length 'n', padded with zeros:
              lowerCamelCase       :				str      			= format(_SCREAMING_SNAKE_CASE			,f'''0{num_qubits}b'''   )
              # Next, we place the first X-gates. Each digit in our binary string
              # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
              # we apply an X-gate to that qubit:
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
              for index in range(_SCREAMING_SNAKE_CASE   ):
                     oracle_qc.cx(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
              # Next, place the final X-gates
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )

    # Case in which oracle is constant
       if case == "constant":
              # First decide what the fixed output of the oracle will be
              # (either always 0 or always 1)
              lowerCamelCase       :				str      			= np.random.randint(2   )
              if output == 1:
                     oracle_qc.x(_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				int      			= oracle_qc.to_gate()
       lowerCamelCase       :				Tuple      			= "Oracle"  # To show when we display the circuit
       return oracle_gate

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Union[str, Any]      			= qiskit.QuantumCircuit(num_qubits + 1			,_SCREAMING_SNAKE_CASE   )
       # Set up the output qubit:
       dj_circuit.x(_SCREAMING_SNAKE_CASE   )
       dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # And set up the input register:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # Let's append the oracle gate to our circuit:
       dj_circuit.append(_SCREAMING_SNAKE_CASE			,range(num_qubits + 1   )   )
       # Finally, perform the H-gates again and measure:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )

       for i in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.measure(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       return dj_circuit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.result.counts.Counts:
       lowerCamelCase       :				Tuple      			= qiskit.Aer.get_backend("aer_simulator"   )

       lowerCamelCase       :				Tuple      			= dj_oracle(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= dj_algorithm(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       # Execute the circuit on the simulator
       lowerCamelCase       :				List[Any]      			= qiskit.execute(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,shots=1000   )

       # Return the histogram data of the results of the experiment.
       return job.result().get_counts(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       print(f'''Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}''')
       print(f'''Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}''')






