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    lowerCAmelCase_ (     __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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    lowerCAmelCase_ (     __A,  __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      token.append((f"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")							)
      return token




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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    lowerCAmelCase_ (     __A,  __A,  __A,  __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  1_000

      UpperCAmelCase__								=  "huggingface/label-files"
      UpperCAmelCase__								=  num_labels
      UpperCAmelCase__								=  json.load(open(cached_download(hf_hub_url(__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()}

      UpperCAmelCase__								=  UpperCAmelCase__								=  CvtConfig(num_labels=__A,  idalabel=__A,  labelaid=__A							)

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

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

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            UpperCAmelCase__								=  [2, 2, 20]
            UpperCAmelCase__								=  [3, 12, 16]
            UpperCAmelCase__								=  [192, 768, 1_024]

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

      UpperCAmelCase__								=  OrderedDict()
      UpperCAmelCase__								=  []

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

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

      model.load_state_dict(__A							)
      model.save_pretrained(__A							)
      image_processor.save_pretrained(__A							)


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

if __name__ == "__main__":
      UpperCamelCase__											=					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=3_8_4,
          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.'
      )

      UpperCamelCase__											=					parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def 			lowercase_				(			_lowerCamelCase							:       Dict[str, torch.Tensor]):
    lowercase__ :   Any    					=				[]
    lowercase__ :   Optional[int]    					=				[]
    lowercase__ :   Tuple    					=				[]

    for rt in rc.restypes:
        lowercase__ :   Dict    					=				rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
        restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
        lowercase__ :   str    					=				{name: i for i, name in enumerate(_lowerCamelCase)}
        restype_atomaa_to_atomaa_list.append(
            [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types])

        restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names])

    # Add dummy mapping for restype 'UNK'
    restype_atomaa_to_atomaa_list.append([0] * 14)
    restype_atomaa_to_atomaa_list.append([0] * 37)
    restype_atomaa_mask_list.append([0.0] * 14)

    lowercase__ :   Union[str, Any]    					=				torch.tensor(
        _lowerCamelCase		,	dtype=torch.intaa		,	device=protein["aatype"].device		,	)
    lowercase__ :   str    					=				torch.tensor(
        _lowerCamelCase		,	dtype=torch.intaa		,	device=protein["aatype"].device		,	)
    lowercase__ :   List[str]    					=				torch.tensor(
        _lowerCamelCase		,	dtype=torch.floataa		,	device=protein["aatype"].device		,	)
    lowercase__ :   str    					=				protein["aatype"].to(torch.long)

    # create the mapping for (residx, atom14) --> atom37, i.e. an array
    # with shape (num_res, 14) containing the atom37 indices for this protein
    lowercase__ :   Dict    					=				restype_atomaa_to_atomaa[protein_aatype]
    lowercase__ :   str    					=				restype_atomaa_mask[protein_aatype]

    lowercase__ :   List[Any]    					=				residx_atomaa_mask
    lowercase__ :   Optional[Any]    					=				residx_atomaa_to_atomaa.long()

    # create the gather indices for mapping back
    lowercase__ :   str    					=				restype_atomaa_to_atomaa[protein_aatype]
    lowercase__ :   str    					=				residx_atomaa_to_atomaa.long()

    # create the corresponding mask
    lowercase__ :   Optional[Any]    					=				torch.zeros([21, 37]		,	dtype=torch.floataa		,	device=protein["aatype"].device)
    for restype, restype_letter in enumerate(rc.restypes):
        lowercase__ :   Tuple    					=				rc.restype_atoa[restype_letter]
        lowercase__ :   List[Any]    					=				rc.residue_atoms[restype_name]
        for atom_name in atom_names:
            lowercase__ :   Optional[int]    					=				rc.atom_order[atom_name]
            lowercase__ :   Tuple    					=				1

    lowercase__ :   Dict    					=				restype_atomaa_mask[protein_aatype]
    lowercase__ :   Any    					=				residx_atomaa_mask

    return protein

def 			lowercase_				(			_lowerCamelCase							:       Dict[str, torch.Tensor]):
    lowercase__ :   Tuple    					=				tree_map(lambda _lowerCamelCase: torch.tensor(_lowerCamelCase		,	device=batch["aatype"].device)		,	_lowerCamelCase		,	np.ndarray)
    lowercase__ :   List[str]    					=				tensor_tree_map(lambda _lowerCamelCase: np.array(_lowerCamelCase)		,	make_atomaa_masks(_lowerCamelCase))
    return out



