code: 
  
  
  
  
  
  
  """simple docstring"""
  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()
  snake_case   :   List[Any]    		=		logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  snake_case   :   Any    		=		[]
  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        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Dict   ,    __snake_case:   int     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						state_dict.pop(lowercase_     )
         __magic_name__										=						val
  
  
  def        A	(							__snake_case:   Dict     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						OrderedDict()
         for key, value in state_dict.items():
                if "backbone.0.body" in key:
                       __magic_name__										=						key.replace('backbone.0.body'   ,    'backbone.conv_encoder.model'     )
                       __magic_name__										=						value
                else:
                       __magic_name__										=						value
  
         return new_state_dict
  
  
  def        A	(							__snake_case:   int   ,    __snake_case:   Optional[int]=False     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						''
         if is_panoptic:
                __magic_name__										=						'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)
                __magic_name__										=						state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
                __magic_name__										=						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
                __magic_name__										=						in_proj_weight[:2_5_6, :]
                __magic_name__										=						in_proj_bias[:2_5_6]
                __magic_name__										=						in_proj_weight[2_5_6:5_1_2, :]
                __magic_name__										=						in_proj_bias[2_5_6:5_1_2]
                __magic_name__										=						in_proj_weight[-2_5_6:, :]
                __magic_name__										=						in_proj_bias[-2_5_6:]
         # 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
                __magic_name__										=						state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
                __magic_name__										=						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
                __magic_name__										=						in_proj_weight[:2_5_6, :]
                __magic_name__										=						in_proj_bias[:2_5_6]
                __magic_name__										=						in_proj_weight[2_5_6:5_1_2, :]
                __magic_name__										=						in_proj_bias[2_5_6:5_1_2]
                __magic_name__										=						in_proj_weight[-2_5_6:, :]
                __magic_name__										=						in_proj_bias[-2_5_6:]
                # read in weights + bias of input projection layer of cross-attention
                __magic_name__										=						state_dict.pop(
                    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""     )
                __magic_name__										=						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
                __magic_name__										=						in_proj_weight_cross_attn[:2_5_6, :]
                __magic_name__										=						in_proj_bias_cross_attn[:2_5_6]
                __magic_name__										=						in_proj_weight_cross_attn[2_5_6:5_1_2, :]
                __magic_name__										=						in_proj_bias_cross_attn[2_5_6:5_1_2]
                __magic_name__										=						in_proj_weight_cross_attn[-2_5_6:, :]
                __magic_name__										=						in_proj_bias_cross_attn[-2_5_6:]
  
  
  def        A	(							)						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						'http://images.cocodataset.org/val2017/000000039769.jpg'
         __magic_name__										=						Image.open(requests.get(lowercase_   ,    stream=lowercase_     ).raw     )
  
         return im
  
  
  @torch.no_grad()
  def        A	(							__snake_case:   Tuple   ,    __snake_case:   Optional[Any]     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						DetrConfig()
         # set backbone and dilation attributes
         if "resnet101" in model_name:
                __magic_name__										=						'resnet101'
         if "dc5" in model_name:
                __magic_name__										=						True
         __magic_name__										=						'panoptic' in model_name
         if is_panoptic:
                __magic_name__										=						2_5_0
         else:
                __magic_name__										=						9_1
                __magic_name__										=						'huggingface/label-files'
                __magic_name__										=						'coco-detection-id2label.json'
                __magic_name__										=						json.load(open(hf_hub_download(lowercase_   ,    lowercase_   ,    repo_type='dataset'     )   ,    'r'     )     )
                __magic_name__										=						{int(lowercase_     ): v for k, v in idalabel.items()}
                __magic_name__										=						idalabel
                __magic_name__										=						{v: k for k, v in idalabel.items()}
  
         # load image processor
         __magic_name__										=						'coco_panoptic' if is_panoptic else 'coco_detection'
         __magic_name__										=						DetrImageProcessor(format=lowercase_     )
  
         # prepare image
         __magic_name__										=						prepare_img()
         __magic_name__										=						image_processor(images=lowercase_   ,    return_tensors='pt'     )
         __magic_name__										=						encoding['pixel_values']
  
         logger.info(F"""Converting model {model_name}..."""     )
  
         # load original model from torch hub
         __magic_name__										=						torch.hub.load('facebookresearch/detr'   ,    lowercase_   ,    pretrained=lowercase_     ).eval()
         __magic_name__										=						detr.state_dict()
         # rename keys
         for src, dest in rename_keys:
                if is_panoptic:
                       __magic_name__										=						'detr.' + src
                rename_key(lowercase_   ,    lowercase_   ,    lowercase_     )
         __magic_name__										=						rename_backbone_keys(lowercase_     )
         # query, key and value matrices need special treatment
         read_in_q_k_v(lowercase_   ,    is_panoptic=lowercase_     )
         # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
         __magic_name__										=						'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'     )
                       ):
                              __magic_name__										=						state_dict.pop(lowercase_     )
                              __magic_name__										=						val
                       elif "class_labels_classifier" in key or "bbox_predictor" in key:
                              __magic_name__										=						state_dict.pop(lowercase_     )
                              __magic_name__										=						val
                       elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
                              continue
                       else:
                              __magic_name__										=						state_dict.pop(lowercase_     )
                              __magic_name__										=						val
                else:
                       if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
                              __magic_name__										=						state_dict.pop(lowercase_     )
                              __magic_name__										=						val
      # finally, create HuggingFace model and load state dict
         __magic_name__										=						DetrForSegmentation(lowercase_     ) if is_panoptic else DetrForObjectDetection(lowercase_     )
         model.load_state_dict(lowercase_     )
         model.eval()
         # verify our conversion
         __magic_name__										=						detr(lowercase_     )
         __magic_name__										=						model(lowercase_     )
         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(lowercase_     ).mkdir(exist_ok=lowercase_     )
         model.save_pretrained(lowercase_     )
         image_processor.save_pretrained(lowercase_     )
  
  
  if __name__ == "__main__":
        snake_case   :   int    		=		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."""
        )
        snake_case   :   Union[str, Any]    		=		parser.parse_args()
        convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  import qiskit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						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:
                __magic_name__										=						np.random.randint(1   ,    2**num_qubits     )
                # Next, format 'b' as a binary string of length 'n', padded with zeros:
                __magic_name__										=						format(__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(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
          # Do the controlled-NOT gates for each qubit, using the output qubit
          # as the target:
                for index in range(__snake_case     ):
                       oracle_qc.cx(__snake_case   ,    __snake_case     )
                # Next, place the final X-gates
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__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)
                __magic_name__										=						np.random.randint(2     )
                if output == 1:
                       oracle_qc.x(__snake_case     )
  
         __magic_name__										=						oracle_qc.to_gate()
         __magic_name__										=						'Oracle'  # To show when we display the circuit
         return oracle_gate
  
  
  def        A	(							__snake_case:   qiskit.QuantumCircuit   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1   ,    __snake_case     )
         # Set up the output qubit:
         dj_circuit.x(__snake_case     )
         dj_circuit.h(__snake_case     )
         # And set up the input register:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
         # Let's append the oracle gate to our circuit:
         dj_circuit.append(__snake_case   ,    range(num_qubits + 1     )     )
         # Finally, perform the H-gates again and measure:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
  
         for i in range(__snake_case     ):
                dj_circuit.measure(__snake_case   ,    __snake_case     )
  
         return dj_circuit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.result.counts.Counts:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.Aer.get_backend('aer_simulator'     )
  
         __magic_name__										=						dj_oracle(__snake_case   ,    __snake_case     )
         __magic_name__										=						dj_algorithm(__snake_case   ,    __snake_case     )
  
         # Execute the circuit on the simulator
         __magic_name__										=						qiskit.execute(__snake_case   ,    __snake_case   ,    shots=1_0_0_0     )
  
         # Return the histogram data of the results of the experiment.
         return job.result().get_counts(__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)}""")