


from collections.abc import Sequence
from queue import Queue







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase=None)			->				Union[str, Any]:
            _A      :       str				    = start
            _A      :       Optional[int]				    = end
            _A      :       List[str]				    = val
            _A      :       Tuple				    = (start + end) // 2
            _A      :       Any				    = left
            _A      :       List[Any]				    = right


     def __repr__(						self)			->				Any:
            return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            _A      :       str				    = collection
            _A      :       Optional[Any]				    = function
            if self.collection:
                   _A      :       Optional[int]				    = self._build_tree(0 ,						len(__lowerCamelCase) - 1)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Tuple:
            self._update_tree(self.root ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            return self._query_range(self.root ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            if start == end:
                   return SegmentTreeNode(__lowerCamelCase ,						__lowerCamelCase ,						self.collection[start])
            _A      :       List[Any]				    = (start + end) // 2
            _A      :       int				    = self._build_tree(__lowerCamelCase ,						__lowerCamelCase)
            _A      :       Optional[Any]				    = self._build_tree(mid + 1 ,						__lowerCamelCase)
            return SegmentTreeNode(__lowerCamelCase ,						__lowerCamelCase ,						self.fn(left.val ,						right.val) ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				int:
            if node.start == i and node.end == i:
                   _A      :       List[str]				    = val
                   return
            if i <= node.mid:
                   self._update_tree(node.left ,						__lowerCamelCase ,						__lowerCamelCase)
            else:
                   self._update_tree(node.right ,						__lowerCamelCase ,						__lowerCamelCase)
            _A      :       str				    = self.fn(node.left.val ,						node.right.val)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				str:
            if node.start == i and node.end == j:
                   return node.val

            if i <= node.mid:
                   if j <= node.mid:
                          # range in left child tree
                          return self._query_range(node.left ,						__lowerCamelCase ,						__lowerCamelCase)
                   else:
                          # range in left child tree and right child tree
                          return self.fn(
                              self._query_range(node.left ,						__lowerCamelCase ,						node.mid) ,						self._query_range(node.right ,						node.mid + 1 ,						__lowerCamelCase) ,						)
            else:
                   # range in right child tree
                   return self._query_range(node.right ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Dict:
            if self.root is not None:
                   _A      :       Optional[int]				    = Queue()
                   queue.put(self.root)
                   while not queue.empty():
                          _A      :       Tuple				    = queue.get()
                          yield node

                          if node.left is not None:
                                 queue.put(node.left)

                          if node.right is not None:
                                 queue.put(node.right)


if __name__ == "__main__":
    import operator

    for fn in [operator.add, max, min]:
        print('*' * 50)
        lowerCAmelCase__      		= SegmentTree([2, 1, 5, 3, 4], fn)
        for node in arr.traverse():
            print(node)
        print()

        arr.update(1, 5)
        for node in arr.traverse():
            print(node)
        print()

        print(arr.query_range(3, 4))  # 7
        print(arr.query_range(2, 2))  # 5
        print(arr.query_range(1, 3))  # 13
        print()







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   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						SwinConfig()
      SCREAMING_SNAKE_CASE		=						swin_name.split("""_"""	)

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

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

      if "in22k" in swin_name:
            SCREAMING_SNAKE_CASE		=						2_18_41
      else:
            SCREAMING_SNAKE_CASE		=						10_00
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE		=						img_size
      SCREAMING_SNAKE_CASE		=						num_classes
      SCREAMING_SNAKE_CASE		=						embed_dim
      SCREAMING_SNAKE_CASE		=						depths
      SCREAMING_SNAKE_CASE		=						num_heads
      SCREAMING_SNAKE_CASE		=						window_size

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.proj""" ,						"""embeddings.patch_embeddings.projection"""	)
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.norm""" ,						"""embeddings.norm"""	)
      if "layers" in name:
            SCREAMING_SNAKE_CASE		=						"""encoder.""" + name
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn.proj""" ,						"""attention.output.dense"""	)
      if "attn" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn""" ,						"""attention.self"""	)
      if "norm1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm1""" ,						"""layernorm_before"""	)
      if "norm2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm2""" ,						"""layernorm_after"""	)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc1""" ,						"""intermediate.dense"""	)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc2""" ,						"""output.dense"""	)

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE		=						"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						"""layernorm.bias"""

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

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


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

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

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

      return orig_state_dict



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


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

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

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

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

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

      SCREAMING_SNAKE_CASE		=						timm_model(inputs["""pixel_values"""]	)
      SCREAMING_SNAKE_CASE		=						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__":
      SCREAMING_SNAKE_CASE_						   =       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."""
      )

      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


