


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       lowercase_		(		_A			:    Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  SwinConfig()
      lowerCamelCase__		:				Any    =  swin_name.split("_"						)

      lowerCamelCase__		:				Optional[int]    =  name_split[1]
      lowerCamelCase__		:				Optional[int]    =  int(name_split[4]						)
      lowerCamelCase__		:				Any    =  int(name_split[3][-1]						)

      if model_size == "tiny":
            lowerCamelCase__		:				Tuple    =  96
            lowerCamelCase__		:				Optional[int]    =  (2, 2, 6, 2)
            lowerCamelCase__		:				Optional[Any]    =  (3, 6, 12, 24)
      elif model_size == "small":
            lowerCamelCase__		:				Optional[int]    =  96
            lowerCamelCase__		:				Tuple    =  (2, 2, 18, 2)
            lowerCamelCase__		:				str    =  (3, 6, 12, 24)
      elif model_size == "base":
            lowerCamelCase__		:				Optional[Any]    =  128
            lowerCamelCase__		:				Optional[Any]    =  (2, 2, 18, 2)
            lowerCamelCase__		:				Tuple    =  (4, 8, 16, 32)
      else:
            lowerCamelCase__		:				List[str]    =  192
            lowerCamelCase__		:				Tuple    =  (2, 2, 18, 2)
            lowerCamelCase__		:				int    =  (6, 12, 24, 48)

      if "in22k" in swin_name:
            lowerCamelCase__		:				Optional[Any]    =  21841
      else:
            lowerCamelCase__		:				Union[str, Any]    =  1000
            lowerCamelCase__		:				Tuple    =  "huggingface/label-files"
            lowerCamelCase__		:				Tuple    =  "imagenet-1k-id2label.json"
            lowerCamelCase__		:				Any    =  json.load(open(hf_hub_download(_A    ,					_A    ,					repo_type="dataset"						)    ,					"r"						)						)
            lowerCamelCase__		:				List[Any]    =  {int(_A						): v for k, v in idalabel.items()}
            lowerCamelCase__		:				List[Any]    =  idalabel
            lowerCamelCase__		:				str    =  {v: k for k, v in idalabel.items()}

      lowerCamelCase__		:				str    =  img_size
      lowerCamelCase__		:				Union[str, Any]    =  num_classes
      lowerCamelCase__		:				str    =  embed_dim
      lowerCamelCase__		:				str    =  depths
      lowerCamelCase__		:				Any    =  num_heads
      lowerCamelCase__		:				Union[str, Any]    =  window_size

      return config

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      if "patch_embed.proj" in name:
            lowerCamelCase__		:				Union[str, Any]    =  name.replace("patch_embed.proj"    ,					"embeddings.patch_embeddings.projection"						)
      if "patch_embed.norm" in name:
            lowerCamelCase__		:				Union[str, Any]    =  name.replace("patch_embed.norm"    ,					"embeddings.norm"						)
      if "layers" in name:
            lowerCamelCase__		:				Union[str, Any]    =  "encoder." + name
      if "attn.proj" in name:
            lowerCamelCase__		:				Optional[Any]    =  name.replace("attn.proj"    ,					"attention.output.dense"						)
      if "attn" in name:
            lowerCamelCase__		:				Union[str, Any]    =  name.replace("attn"    ,					"attention.self"						)
      if "norm1" in name:
            lowerCamelCase__		:				int    =  name.replace("norm1"    ,					"layernorm_before"						)
      if "norm2" in name:
            lowerCamelCase__		:				Tuple    =  name.replace("norm2"    ,					"layernorm_after"						)
      if "mlp.fc1" in name:
            lowerCamelCase__		:				List[str]    =  name.replace("mlp.fc1"    ,					"intermediate.dense"						)
      if "mlp.fc2" in name:
            lowerCamelCase__		:				Dict    =  name.replace("mlp.fc2"    ,					"output.dense"						)

      if name == "norm.weight":
            lowerCamelCase__		:				Union[str, Any]    =  "layernorm.weight"
      if name == "norm.bias":
            lowerCamelCase__		:				str    =  "layernorm.bias"

      if "head" in name:
            lowerCamelCase__		:				Optional[int]    =  name.replace("head"    ,					"classifier"						)
      else:
            lowerCamelCase__		:				Optional[Any]    =  "swin." + name

      return name

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    List[str]						):

      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            lowerCamelCase__		:				Tuple    =  orig_state_dict.pop(_A						)

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

                  if "weight" in key:
                        lowerCamelCase__		:				str    =  val[:dim, :]
                        lowerCamelCase__		:				List[str]    =  val[
                            dim : dim * 2, :
                        ]
                        lowerCamelCase__		:				List[Any]    =  val[-dim:, :]
                  else:
                        lowerCamelCase__		:				str    =  val[
                            :dim
                        ]
                        lowerCamelCase__		:				Tuple    =  val[
                            dim : dim * 2
                        ]
                        lowerCamelCase__		:				int    =  val[
                            -dim:
                        ]
            else:
                  lowerCamelCase__		:				Tuple    =  val

      return orig_state_dict

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  timm.create_model(_A    ,					pretrained=_A						)
      timm_model.eval()

      lowerCamelCase__		:				List[Any]    =  get_swin_config(_A						)
      lowerCamelCase__		:				Dict    =  SwinForImageClassification(_A						)
      model.eval()

      lowerCamelCase__		:				List[Any]    =  convert_state_dict(timm_model.state_dict()    ,					_A						)
      model.load_state_dict(_A						)

      lowerCamelCase__		:				Union[str, Any]    =  "http://images.cocodataset.org/val2017/000000039769.jpg"

      lowerCamelCase__		:				int    =  AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,					"-"						)						)						)
      lowerCamelCase__		:				Any    =  Image.open(requests.get(_A    ,					stream=_A						).raw						)
      lowerCamelCase__		:				int    =  image_processor(images=_A    ,					return_tensors="pt"						)

      lowerCamelCase__		:				List[Any]    =  timm_model(inputs["pixel_values"]						)
      lowerCamelCase__		:				List[str]    =  model(**_A						).logits

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

      print(F"Saving model {swin_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						)


if __name__ == "__main__":
    A      :    Optional[int]		=					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      :    Optional[Any]		=					parser.parse_args()
    convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A      :    Union[str, Any]		=					logging.get_logger(__name__)

A      :    Optional[Any]		=					{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"xlm-roberta"





       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Optional[Any]=30522					,      __lowerCamelCase							:			List[Any]=768					,      __lowerCamelCase							:			Union[str, Any]=12					,      __lowerCamelCase							:			int=12					,      __lowerCamelCase							:			Dict=3072					,      __lowerCamelCase							:			Optional[int]="gelu"					,      __lowerCamelCase							:			Optional[Any]=0.1					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			str=512					,      __lowerCamelCase							:			Optional[Any]=2					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			List[str]=1E-1_2					,      __lowerCamelCase							:			Optional[int]=1					,      __lowerCamelCase							:			Union[str, Any]=0					,      __lowerCamelCase							:			Optional[int]=2					,      __lowerCamelCase							:			Dict="absolute"					,      __lowerCamelCase							:			Optional[Any]=True					,      __lowerCamelCase							:			Tuple=None					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase       )

             lowerCamelCase__		:				str    =  vocab_size
             lowerCamelCase__		:				Any    =  hidden_size
             lowerCamelCase__		:				Optional[Any]    =  num_hidden_layers
             lowerCamelCase__		:				Union[str, Any]    =  num_attention_heads
             lowerCamelCase__		:				Any    =  hidden_act
             lowerCamelCase__		:				str    =  intermediate_size
             lowerCamelCase__		:				Optional[int]    =  hidden_dropout_prob
             lowerCamelCase__		:				Any    =  attention_probs_dropout_prob
             lowerCamelCase__		:				Union[str, Any]    =  max_position_embeddings
             lowerCamelCase__		:				List[str]    =  type_vocab_size
             lowerCamelCase__		:				Tuple    =  initializer_range
             lowerCamelCase__		:				Tuple    =  layer_norm_eps
             lowerCamelCase__		:				Any    =  position_embedding_type
             lowerCamelCase__		:				Any    =  use_cache
             lowerCamelCase__		:				Any    =  classifier_dropout







class 			_lowercase (							lowercase__):


       """simple docstring"""




       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__		:				List[str]    =  {0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__		:				Any    =  {0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]       )


