
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

_lowercase											=    TypeVar("""T""")


class 			lowercase_		(      Generic[T]       ):


       def __init__(    self						,       __A						,       __A				)						->    None:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=None

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=len(__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=[any_type for _ in range(self.N				)] + arr
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=fnc
              self.build()


       def 	_snake_case				(    self				)						->    None:

              for p in range(self.N - 1						,       0						,       -1				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.fn(self.st[p * 2]						,       self.st[p * 2 + 1]				)


       def 	_snake_case				(    self						,       __A						,       __A				)						->    None:
              p += self.N
              SCREAMING_SNAKE_CASE_							:				Any		=v
              while p > 1:
                     SCREAMING_SNAKE_CASE_							:				Dict		=p // 2
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.fn(self.st[p * 2]						,       self.st[p * 2 + 1]				)





       def 	_snake_case				(    self						,       __A						,       __A				)						->    T | None:  # noqa: E741
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=l + self.N, r + self.N

              SCREAMING_SNAKE_CASE_							:				List[str]		=None
              while l <= r:
                     if l % 2 == 1:
                            SCREAMING_SNAKE_CASE_							:				Any		=self.st[l] if res is None else self.fn(__A						,       self.st[l]				)
                     if r % 2 == 0:
                            SCREAMING_SNAKE_CASE_							:				int		=self.st[r] if res is None else self.fn(__A						,       self.st[r]				)
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				int		=(l + 1) // 2, (r - 1) // 2
              return res


if __name__ == "__main__":
 from functools import reduce

 _lowercase											=    [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

 _lowercase											=    {
     0: 7,
     1: 2,
     2: 6,
     3: -14,
     4: 5,
     5: 4,
     6: 7,
     7: -10,
     8: 9,
     9: 10,
     10: 12,
     11: 1,
 }

 _lowercase											=    SegmentTree(test_array, min)
 _lowercase											=    SegmentTree(test_array, max)
 _lowercase											=    SegmentTree(test_array, lambda a, b: a + b)
 def 							SCREAMING_SNAKE_CASE_	(					)			->						List[Any]:
        for i in range(len(A__				)				):
               for j in range(A__			,							len(A__				)				):
                      SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=reduce(A__			,							test_array[i : j + 1]				)
                      SCREAMING_SNAKE_CASE_							:				List[str]		=reduce(A__			,							test_array[i : j + 1]				)
                      SCREAMING_SNAKE_CASE_							:				List[Any]		=reduce(lambda UpperCAmelCase_			,							UpperCAmelCase_				: a + b			,							test_array[i : j + 1]				)
                      assert min_range == min_segment_tree.query(A__			,							A__				)
                      assert max_range == max_segment_tree.query(A__			,							A__				)
                      assert sum_range == sum_segment_tree.query(A__			,							A__				)

 test_all_segments()

 for index, value in test_updates.items():
  _lowercase											=    value
  min_segment_tree.update(index, value)
  max_segment_tree.update(index, value)
  sum_segment_tree.update(index, value)
  test_all_segments()





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	SwinConfig(image_size=192						)

      if "base" in model_name:
            lowercase__  =	6
            lowercase__  =	128
            lowercase__  =	(2, 2, 18, 2)
            lowercase__  =	(4, 8, 16, 32)
      elif "large" in model_name:
            lowercase__  =	12
            lowercase__  =	192
            lowercase__  =	(2, 2, 18, 2)
            lowercase__  =	(6, 12, 24, 48)
      else:
            raise ValueError('Model not supported, only supports base and large variants'						)

      lowercase__  =	window_size
      lowercase__  =	embed_dim
      lowercase__  =	depths
      lowercase__  =	num_heads

      return config

def 	_lowerCAmelCase (    A__						):
      if "encoder.mask_token" in name:
            lowercase__  =	name.replace('encoder.mask_token'    ,	'embeddings.mask_token'						)
      if "encoder.patch_embed.proj" in name:
            lowercase__  =	name.replace('encoder.patch_embed.proj'    ,	'embeddings.patch_embeddings.projection'						)
      if "encoder.patch_embed.norm" in name:
            lowercase__  =	name.replace('encoder.patch_embed.norm'    ,	'embeddings.norm'						)
      if "attn.proj" in name:
            lowercase__  =	name.replace('attn.proj'    ,	'attention.output.dense'						)
      if "attn" in name:
            lowercase__  =	name.replace('attn'    ,	'attention.self'						)
      if "norm1" in name:
            lowercase__  =	name.replace('norm1'    ,	'layernorm_before'						)
      if "norm2" in name:
            lowercase__  =	name.replace('norm2'    ,	'layernorm_after'						)
      if "mlp.fc1" in name:
            lowercase__  =	name.replace('mlp.fc1'    ,	'intermediate.dense'						)
      if "mlp.fc2" in name:
            lowercase__  =	name.replace('mlp.fc2'    ,	'output.dense'						)

      if name == "encoder.norm.weight":
            lowercase__  =	'layernorm.weight'
      if name == "encoder.norm.bias":
            lowercase__  =	'layernorm.bias'

      if "decoder" in name:
            pass
      else:
            lowercase__  =	'swin.' + name

      return name

def 	_lowerCAmelCase (    A__    ,	A__						):
      for key in orig_state_dict.copy().keys():
            lowercase__  =	orig_state_dict.pop(A__						)

            if "attn_mask" in key:
                  pass
            elif "qkv" in key:
                  lowercase__  =	key.split('.'						)
                  lowercase__  =	int(key_split[2]						)
                  lowercase__  =	int(key_split[4]						)
                  lowercase__  =	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

      return orig_state_dict

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__						):
      lowercase__  =	torch.load(A__    ,	map_location='cpu'						)['model']

      lowercase__  =	get_swin_config(A__						)
      lowercase__  =	SwinForMaskedImageModeling(A__						)
      model.eval()

      lowercase__  =	convert_state_dict(A__    ,	A__						)
      model.load_state_dict(A__						)

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

      lowercase__  =	ViTImageProcessor(size={'height': 192, 'width': 192}						)
      lowercase__  =	Image.open(requests.get(A__    ,	stream=A__						).raw						)
      lowercase__  =	image_processor(images=A__    ,	return_tensors='pt'						)

      with torch.no_grad():
            lowercase__  =	model(**A__						).logits

      print(outputs.keys()						)
      print('Looks ok!'						)

      if pytorch_dump_folder_path is not None:
            print(F'''Saving model {model_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 push_to_hub:
            print(F'''Pushing model and image processor for {model_name} to hub'''						)
            model.push_to_hub(F'''microsoft/{model_name}'''						)
            image_processor.push_to_hub(F'''microsoft/{model_name}'''						)


if __name__ == "__main__":
       a__   :					Optional[Any] 						=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="swin-base-simmim-window6-192",
           type=str,
           choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
           help="Name of the Swin SimMIM model you'd like to convert.",
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       a__   :					Optional[int] 						=      parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
