





'''simple docstring'''

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]	):
    A__    =      []
    embed.append(
        (
            F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
            F"stage{idx}.patch_embed.proj.weight",
        )	)
    embed.append(
        (
            F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
            F"stage{idx}.patch_embed.proj.bias",
        )	)
    embed.append(
        (
            F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
            F"stage{idx}.patch_embed.norm.weight",
        )	)
    embed.append(
        (
            F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
            F"stage{idx}.patch_embed.norm.bias",
        )	)
    return embed





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Optional[int]	):
    A__    =      []
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
            F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
            F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
            F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
            F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
            F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
            F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
            F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
            F"stage{idx}.blocks.{cnt}.attn.proj.weight",
        )	)
    attention_weights.append(
        (
            F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
            F"stage{idx}.blocks.{cnt}.attn.proj.bias",
        )	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")	)
    attention_weights.append(
        (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")	)
    return attention_weights





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      []
    token.append((F"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token")	)
    return token





def 						UpperCamelCase    (							):
    A__    =      []
    head.append(("layernorm.weight", "norm.weight")	)
    head.append(("layernorm.bias", "norm.bias")	)
    head.append(("classifier.weight", "head.weight")	)
    head.append(("classifier.bias", "head.bias")	)
    return head





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : str	):
    A__    =      "imagenet-1k-id2label.json"
    A__    =      10_00

    A__    =      "huggingface/label-files"
    A__    =      num_labels
    A__    =      json.load(open(cached_download(hf_hub_url(_lowerCamelCase						,	_lowerCamelCase						,	repo_type="dataset"	)	)						,	"r"	)	)
    A__    =      {int(_lowerCamelCase	): v for k, v in idalabel.items()}

    A__    =      idalabel
    A__    =      {v: k for k, v in idalabel.items()}

    A__    =      A__    =      CvtConfig(num_labels=_lowerCamelCase						,	idalabel=_lowerCamelCase						,	labelaid=_lowerCamelCase	)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/"						,	1	)[-1][4:6] == "13":
        A__    =      [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/"						,	1	)[-1][4:6] == "21":
        A__    =      [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        A__    =      [2, 2, 20]
        A__    =      [3, 12, 16]
        A__    =      [1_92, 7_68, 10_24]

    A__    =      CvtForImageClassification(_lowerCamelCase	)
    A__    =      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"	)
    A__    =      image_size
    A__    =      torch.load(_lowerCamelCase						,	map_location=torch.device("cpu"	)	)

    A__    =      OrderedDict()
    A__    =      []

    for idx in range(len(config.depth	)	):
        if config.cls_token[idx]:
            A__    =      list_of_state_dict + cls_token(_lowerCamelCase	)
        A__    =      list_of_state_dict + embeddings(_lowerCamelCase	)
        for cnt in range(config.depth[idx]	):
            A__    =      list_of_state_dict + attention(_lowerCamelCase						,	_lowerCamelCase	)

    A__    =      list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_lowerCamelCase	)
    for i in range(len(_lowerCamelCase	)	):
        A__    =      original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_lowerCamelCase	)
    model.save_pretrained(_lowerCamelCase	)
    image_processor.save_pretrained(_lowerCamelCase	)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  __lowerCAmelCase							:     int		     =argparse.ArgumentParser()
  parser.add_argument(
      "--cvt_model",
      default="cvt-w24",
      type=str,
      help="Name of the cvt model you'd like to convert.",
  )
  parser.add_argument(
      "--image_size",
      default=384,
      type=int,
      help="Input Image Size",
  )
  parser.add_argument(
      "--cvt_file_name",
      default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
      type=str,
      help="Input Image Size",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  __lowerCAmelCase							:     Dict		     =parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)










'''simple docstring'''

import qiskit





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int	):
    A__    =      qiskit.Aer.get_backend("aer_simulator"	)

    # Create a Quantum Circuit acting on the q register
    A__    =      qiskit.QuantumCircuit(_lowerCamelCase						,	_lowerCamelCase	)

    # Map the quantum measurement to the classical bits
    circuit.measure([0]						,	[0]	)

    # Execute the circuit on the simulator
    A__    =      qiskit.execute(_lowerCamelCase						,	_lowerCamelCase						,	shots=10_00	)

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


if __name__ == "__main__":
  print(f"""Total count for various states are: {single_qubit_measure(1, 1)}""")



