






"""simple docstring"""





import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    torch.load(SCREAMING_SNAKE_CASE ,	map_location='cpu'					)

    UpperCamelCase__         =    chkpt['model']

    # We have the base model one level deeper than the original XLM repository
    UpperCamelCase__         =    {}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            UpperCamelCase__         =    v
        else:
            UpperCamelCase__         =    v

    UpperCamelCase__         =    chkpt['params']
    UpperCamelCase__         =    {n: v for n, v in config.items() if not isinstance(SCREAMING_SNAKE_CASE ,	(torch.FloatTensor, numpy.ndarray)					)}

    UpperCamelCase__         =    chkpt['dico_word2id']
    UpperCamelCase__         =    {s + '</w>' if s.find('@@'					) == -1 and i > 13 else s.replace('@@' ,	''					): i for s, i in vocab.items()}

    # Save pytorch-model
    UpperCamelCase__         =    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
    UpperCamelCase__         =    pytorch_dump_folder_path + '/' + CONFIG_NAME
    UpperCamelCase__         =    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

    print(F'Save PyTorch model to {pytorch_weights_dump_path}'					)
    torch.save(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    print(F'Save configuration file to {pytorch_config_dump_path}'					)
    with open(SCREAMING_SNAKE_CASE ,	'w' ,	encoding='utf-8'					) as f:
        f.write(json.dumps(SCREAMING_SNAKE_CASE ,	indent=2					) + '\n'					)

    print(F'Save vocab file to {pytorch_config_dump_path}'					)
    with open(SCREAMING_SNAKE_CASE ,	'w' ,	encoding='utf-8'					) as f:
        f.write(json.dumps(SCREAMING_SNAKE_CASE ,	indent=2					) + '\n'					)


if __name__ == "__main__":
  A__					: Optional[Any]=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  A__					: Optional[Any]=	parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)












"""simple docstring"""





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    SwinConfig()
    UpperCamelCase__         =    swin_name.split('_'					)

    UpperCamelCase__         =    name_split[1]
    UpperCamelCase__         =    int(name_split[4]					)
    UpperCamelCase__         =    int(name_split[3][-1]					)

    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__         =    1_28
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (4, 8, 16, 32)
    else:
        UpperCamelCase__         =    1_92
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (6, 12, 24, 48)

    if "in22k" in swin_name:
        UpperCamelCase__         =    2_18_41
    else:
        UpperCamelCase__         =    10_00
        UpperCamelCase__         =    'huggingface/label-files'
        UpperCamelCase__         =    'imagenet-1k-id2label.json'
        UpperCamelCase__         =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	repo_type='dataset'					) ,	'r'					)					)
        UpperCamelCase__         =    {int(SCREAMING_SNAKE_CASE					): 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   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """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 name == "norm.weight":
        UpperCamelCase__         =    'layernorm.weight'
    if name == "norm.bias":
        UpperCamelCase__         =    'layernorm.bias'

    if "head" in name:
        UpperCamelCase__         =    name.replace('head' ,	'classifier'					)
    else:
        UpperCamelCase__         =    'swin.' + name

    return name







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        UpperCamelCase__         =    orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

        if "mask" in key:
            continue
        elif "qkv" in key:
            UpperCamelCase__         =    key.split('.'					)
            UpperCamelCase__         =    int(key_split[1]					)
            UpperCamelCase__         =    int(key_split[3]					)
            UpperCamelCase__         =    model.swin.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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    timm.create_model(SCREAMING_SNAKE_CASE ,	pretrained=SCREAMING_SNAKE_CASE					)
    timm_model.eval()

    UpperCamelCase__         =    get_swin_config(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    SwinForImageClassification(SCREAMING_SNAKE_CASE					)
    model.eval()

    UpperCamelCase__         =    convert_state_dict(timm_model.state_dict() ,	SCREAMING_SNAKE_CASE					)
    model.load_state_dict(SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    'http://images.cocodataset.org/val2017/000000039769.jpg'

    UpperCamelCase__         =    AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' ,	'-'					)					)					)
    UpperCamelCase__         =    Image.open(requests.get(SCREAMING_SNAKE_CASE ,	stream=SCREAMING_SNAKE_CASE					).raw					)
    UpperCamelCase__         =    image_processor(images=SCREAMING_SNAKE_CASE ,	return_tensors='pt'					)

    UpperCamelCase__         =    timm_model(inputs['pixel_values']					)
    UpperCamelCase__         =    model(**SCREAMING_SNAKE_CASE					).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	atol=1E-3					)

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(SCREAMING_SNAKE_CASE					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: Optional[Any]=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin 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."""
  )

  A__					: Tuple=	parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




