





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=SwinvaConfig()
   UpperCAmelCase__						=swinva_name.split("_"     )

   UpperCAmelCase__						=name_split[1]
   if "to" in name_split[3]:
      UpperCAmelCase__						=int(name_split[3][-3:]     )
   else:
      UpperCAmelCase__						=int(name_split[3]     )
   if "to" in name_split[2]:
      UpperCAmelCase__						=int(name_split[2][-2:]     )
   else:
      UpperCAmelCase__						=int(name_split[2][6:]     )

   if model_size == "tiny":
      UpperCAmelCase__						=96
      UpperCAmelCase__						=(2, 2, 6, 2)
      UpperCAmelCase__						=(3, 6, 12, 24)
   elif model_size == "small":
      UpperCAmelCase__						=96
      UpperCAmelCase__						=(2, 2, 18, 2)
      UpperCAmelCase__						=(3, 6, 12, 24)
   elif model_size == "base":
      UpperCAmelCase__						=128
      UpperCAmelCase__						=(2, 2, 18, 2)
      UpperCAmelCase__						=(4, 8, 16, 32)
   else:
      UpperCAmelCase__						=192
      UpperCAmelCase__						=(2, 2, 18, 2)
      UpperCAmelCase__						=(6, 12, 24, 48)

   if "to" in swinva_name:
      UpperCAmelCase__						=(12, 12, 12, 6)

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      UpperCAmelCase__						=21841
      UpperCAmelCase__						="huggingface/label-files"
      UpperCAmelCase__						="imagenet-22k-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()}

   else:
      UpperCAmelCase__						=1000
      UpperCAmelCase__						="huggingface/label-files"
      UpperCAmelCase__						="imagenet-1k-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()}

   UpperCAmelCase__						=img_size
   UpperCAmelCase__						=num_classes
   UpperCAmelCase__						=embed_dim
   UpperCAmelCase__						=depths
   UpperCAmelCase__						=num_heads
   UpperCAmelCase__						=window_size

   return config






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if "patch_embed.proj" in name:
      UpperCAmelCase__						=name.replace("patch_embed.proj"				,							"embeddings.patch_embeddings.projection"     )
   if "patch_embed.norm" in name:
      UpperCAmelCase__						=name.replace("patch_embed.norm"				,							"embeddings.norm"     )
   if "layers" in name:
      UpperCAmelCase__						="encoder." + name
   if "attn.proj" in name:
      UpperCAmelCase__						=name.replace("attn.proj"				,							"attention.output.dense"     )
   if "attn" in name:
      UpperCAmelCase__						=name.replace("attn"				,							"attention.self"     )
   if "norm1" in name:
      UpperCAmelCase__						=name.replace("norm1"				,							"layernorm_before"     )
   if "norm2" in name:
      UpperCAmelCase__						=name.replace("norm2"				,							"layernorm_after"     )
   if "mlp.fc1" in name:
      UpperCAmelCase__						=name.replace("mlp.fc1"				,							"intermediate.dense"     )
   if "mlp.fc2" in name:
      UpperCAmelCase__						=name.replace("mlp.fc2"				,							"output.dense"     )
   if "q_bias" in name:
      UpperCAmelCase__						=name.replace("q_bias"				,							"query.bias"     )
   if "k_bias" in name:
      UpperCAmelCase__						=name.replace("k_bias"				,							"key.bias"     )
   if "v_bias" in name:
      UpperCAmelCase__						=name.replace("v_bias"				,							"value.bias"     )
   if "cpb_mlp" in name:
      UpperCAmelCase__						=name.replace("cpb_mlp"				,							"continuous_position_bias_mlp"     )
   if name == "norm.weight":
      UpperCAmelCase__						="layernorm.weight"
   if name == "norm.bias":
      UpperCAmelCase__						="layernorm.bias"

   if "head" in name:
      UpperCAmelCase__						=name.replace("head"				,							"classifier"     )
   else:
      UpperCAmelCase__						="swinv2." + name

   return name






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   for key in orig_state_dict.copy().keys():
      UpperCAmelCase__						=orig_state_dict.pop(A     )

      if "mask" in key:
         continue
      elif "qkv" in key:
         UpperCAmelCase__						=key.split("."     )
         UpperCAmelCase__						=int(key_split[1]     )
         UpperCAmelCase__						=int(key_split[3]     )
         UpperCAmelCase__						=model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            UpperCAmelCase__						=val[:dim, :]
            UpperCAmelCase__						=val[dim : dim * 2, :]
            UpperCAmelCase__						=val[-dim:, :]
         else:
            UpperCAmelCase__						=val[:dim]
            UpperCAmelCase__						=val[
                dim : dim * 2
            ]
            UpperCAmelCase__						=val[-dim:]
      else:
         UpperCAmelCase__						=val

   return orig_state_dict






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=timm.create_model(A				,							pretrained=A     )
   timm_model.eval()

   UpperCAmelCase__						=get_swinva_config(A     )
   UpperCAmelCase__						=SwinvaForImageClassification(A     )
   model.eval()

   UpperCAmelCase__						=convert_state_dict(timm_model.state_dict()				,							A     )
   model.load_state_dict(A     )

   UpperCAmelCase__						="http://images.cocodataset.org/val2017/000000039769.jpg"

   UpperCAmelCase__						=AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"				,							"-"     )     )     )
   UpperCAmelCase__						=Image.open(requests.get(A				,							stream=A     ).raw     )
   UpperCAmelCase__						=image_processor(images=A				,							return_tensors="pt"     )

   UpperCAmelCase__						=timm_model(inputs["pixel_values"]     )
   UpperCAmelCase__						=model(**A     ).logits

   assert torch.allclose(A				,							A				,							atol=1e-3     )

   print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(A     )

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(A     )

   model.push_to_hub(
       repo_path_or_name=Path(A				,							A     )				,							organization="nandwalritik"				,							commit_message="Add model"				,							)


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--swinv2_name',
        default='swinv2_tiny_patch4_window8_256',
        type=str,
        help='Name of the Swinv2 timm model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    UpperCamelCase_												=	parser.parse_args()
    convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=sorted_examples[0]

   def is_too_big(A     ):
      return tok(A				,							return_tensors="pt"     ).input_ids.shape[1] > max_tokens

   for src, tgt in tqdm(sorted_examples[1:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=new_tgt + " " + tgt
      if is_too_big(A     ) or is_too_big(A     ):  # cant fit, finalize example
         finished_src.append(A     )
         finished_tgt.append(A     )
         UpperCAmelCase__			,							UpperCAmelCase__						=src, tgt
      else:  # can fit, keep adding
         UpperCAmelCase__			,							UpperCAmelCase__						=cand_src, cand_tgt

    # cleanup
   if new_src:
      assert new_tgt
      finished_src.append(A     )
      finished_tgt.append(A     )
   return finished_src, finished_tgt






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=pack_examples(A				,							A				,							A				,							A     )
      print(F"""packed {split} split from {len(A     )} examples -> {len(A     )}."""     )
      Path(save_path / F"""{split}.source"""     ).open("w"     ).write("\n".join(A     )     )
      Path(save_path / F"""{split}.target"""     ).open("w"     ).write("\n".join(A     )     )
   for split in ["val", "test"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      shutil.copyfile(A				,							save_path / F"""{split}.source"""     )
      shutil.copyfile(A				,							save_path / F"""{split}.target"""     )






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser()
   parser.add_argument("--tok_name"				,							type=A				,							help="like facebook/bart-large-cnn,t5-base, etc."     )
   parser.add_argument("--max_seq_len"				,							type=A				,							default=128     )
   parser.add_argument("--data_dir"				,							type=A     )
   parser.add_argument("--save_path"				,							type=A     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=AutoTokenizer.from_pretrained(args.tok_name     )
   return pack_data_dir(A				,							Path(args.data_dir     )				,							args.max_seq_len				,							args.save_path     )


if __name__ == "__main__":
    packer_cli()



