





"""simple docstring"""





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__				(    SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




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

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

      if model_size == "tiny":
            lowerCAmelCase    		=			96
            lowerCAmelCase    		=			(2, 2, 6, 2)
            lowerCAmelCase    		=			(3, 6, 12, 24)
      elif model_size == "small":
            lowerCAmelCase    		=			96
            lowerCAmelCase    		=			(2, 2, 18, 2)
            lowerCAmelCase    		=			(3, 6, 12, 24)
      elif model_size == "base":
            lowerCAmelCase    		=			1_28
            lowerCAmelCase    		=			(2, 2, 18, 2)
            lowerCAmelCase    		=			(4, 8, 16, 32)
      else:
            lowerCAmelCase    		=			1_92
            lowerCAmelCase    		=			(2, 2, 18, 2)
            lowerCAmelCase    		=			(6, 12, 24, 48)

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

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            lowerCAmelCase    		=			2_18_41
            lowerCAmelCase    		=			'huggingface/label-files'
            lowerCAmelCase    		=			'imagenet-22k-id2label.json'
            lowerCAmelCase    		=			json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""     )				,		"""r"""     )     )
            lowerCAmelCase    		=			{int(__SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
            lowerCAmelCase    		=			idalabel
            lowerCAmelCase    		=			{v: k for k, v in idalabel.items()}

      else:
            lowerCAmelCase    		=			10_00
            lowerCAmelCase    		=			'huggingface/label-files'
            lowerCAmelCase    		=			'imagenet-1k-id2label.json'
            lowerCAmelCase    		=			json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""     )				,		"""r"""     )     )
            lowerCAmelCase    		=			{int(__SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
            lowerCAmelCase    		=			idalabel
            lowerCAmelCase    		=			{v: k for k, v in idalabel.items()}

      lowerCAmelCase    		=			img_size
      lowerCAmelCase    		=			num_classes
      lowerCAmelCase    		=			embed_dim
      lowerCAmelCase    		=			depths
      lowerCAmelCase    		=			num_heads
      lowerCAmelCase    		=			window_size

      return config


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




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

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

      return name


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCAmelCase    		=			orig_state_dict.pop(__SCREAMING_SNAKE_CASE     )

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

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

      return orig_state_dict


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			timm.create_model(__SCREAMING_SNAKE_CASE				,		pretrained=__SCREAMING_SNAKE_CASE     )
      timm_model.eval()

      lowerCAmelCase    		=			get_swinva_config(__SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			SwinvaForImageClassification(__SCREAMING_SNAKE_CASE     )
      model.eval()

      lowerCAmelCase    		=			convert_state_dict(timm_model.state_dict()				,		__SCREAMING_SNAKE_CASE     )
      model.load_state_dict(__SCREAMING_SNAKE_CASE     )

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

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

      lowerCAmelCase    		=			timm_model(inputs["""pixel_values"""]     )
      lowerCAmelCase    		=			model(**__SCREAMING_SNAKE_CASE     ).logits

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

      print(F'Saving model {swinva_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     )

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  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."
       )

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












'''simple docstring'''






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *
