






"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       SwinConfig(image_size=192				)

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

				A__							 =       window_size
				A__							 =       embed_dim
				A__							 =       depths
				A__							 =       num_heads

				return config



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

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

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

				return name



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				for key in orig_state_dict.copy().keys():
								A__							 =       orig_state_dict.pop(lowerCAmelCase__				)

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

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

				return orig_state_dict



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

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

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

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

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

				with torch.no_grad():
								A__							 =       model(**lowerCAmelCase__				).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(lowerCAmelCase__				)

								print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
								image_processor.save_pretrained(lowerCAmelCase__				)

				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__":
					SCREAMING_SNAKE_CASE						:						Union[str, 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.'''
					)

					SCREAMING_SNAKE_CASE						:						Tuple					   =       parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""






class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       name
					A__							 =       val


	def __str__(      self						):


					"""simple docstring"""

					return f'''{self.__class__.__name__}({self.name}, {self.val})'''






	def __lt__(      self  ,					__a						):


					"""simple docstring"""

					return self.val < other.val





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       {}
					A__							 =       {}
					A__							 =       self.build_heap(__a						)


	def __getitem__(      self  ,					__a						):


					"""simple docstring"""

					return self.get_value(__a						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return (idx - 1) // 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 1


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.heap_dict[key]


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       len(__a						) - 1
					A__							 =       self.get_parent_idx(__a						)

					for idx, i in enumerate(__a						):
									A__							 =       idx
									A__							 =       i.val

					for i in range(__a  ,					-1  ,					-1						):
									self.sift_down(__a  ,					__a						)
					return array


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					while True:
									A__							 =       self.get_left_child_idx(__a						)  # noqa: E741
									A__							 =       self.get_right_child_idx(__a						)

									A__							 =       idx
									if l < len(__a						) and array[l] < array[idx]:
													A__							 =       l
									if r < len(__a						) and array[r] < array[smallest]:
													A__							 =       r

									if smallest != idx:
													A__ ,			A__							 =       array[smallest], array[idx]
													(
													    (
													    A__
													) ,			(
													    A__
													) ,			
													)							 =       (
													    self.idx_of_element[array[smallest]],
													    self.idx_of_element[array[idx]],
													)
													A__							 =       smallest
									else:
													break


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       self.get_parent_idx(__a						)
					while p >= 0 and self.heap[p] > self.heap[idx]:
									A__ ,			A__							 =       self.heap[idx], self.heap[p]
									A__ ,			A__							 =       (
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									A__							 =       p
									A__							 =       self.get_parent_idx(__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.heap[0]


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__ ,			A__							 =       self.heap[-1], self.heap[0]
					A__ ,			A__							 =       (
					    self.idx_of_element[self.heap[-1]],
					    self.idx_of_element[self.heap[0]],
					)

					A__							 =       self.heap.pop()
					del self.idx_of_element[x]
					self.sift_down(0  ,					self.heap						)
					return x


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					self.heap.append(__a						)
					A__							 =       len(self.heap						) - 1
					A__							 =       node.val
					self.sift_up(len(self.heap						) - 1						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.heap						) == 0






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					assert (
					    self.heap[self.idx_of_element[node]].val > new_value
					), "newValue must be less that current value"
					A__							 =       new_value
					A__							 =       new_value
					self.sift_up(self.idx_of_element[node]						)





SCREAMING_SNAKE_CASE						:						List[str]					   =       Node('''R''', -1)
SCREAMING_SNAKE_CASE						:						Tuple					   =       Node('''B''', 6)
SCREAMING_SNAKE_CASE						:						List[Any]					   =       Node('''A''', 3)
SCREAMING_SNAKE_CASE						:						int					   =       Node('''X''', 1)
SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
SCREAMING_SNAKE_CASE						:						str					   =       MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
					print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()


