


'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)

def       __UpperCamelCase     (     _A     :					Optional[Any]					)     ->   Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase       :     Optional[int]   				=			CLIPSegTextConfig()
	lowerCAmelCase       :     int   				=			CLIPSegVisionConfig(patch_size=16					)

	lowerCAmelCase       :     Optional[Any]   				=			True if 'refined' in model_name else False
	lowerCAmelCase       :     Any   				=			16 if 'rd16' in model_name else 64

	lowerCAmelCase       :     List[Any]   				=			CLIPSegConfig.from_text_vision_configs(
	    _A       , _A       , use_complex_transposed_convolution=_A       , reduce_dim=_A       , )
	return config

def       __UpperCamelCase     (     _A     :					str					)     ->   str:




	"""simple docstring"""





	if "clip_model" in name:
		lowerCAmelCase       :     Any   				=			name.replace('clip_model'       , 'clip'					)
	if "transformer" in name:
		if "visual" in name:
			lowerCAmelCase       :     Union[str, Any]   				=			name.replace('visual.transformer'       , 'vision_model'					)
		else:
			lowerCAmelCase       :     List[Any]   				=			name.replace('transformer'       , 'text_model'					)
	if "resblocks" in name:
		lowerCAmelCase       :     str   				=			name.replace('resblocks'       , 'encoder.layers'					)
	if "ln_1" in name:
		lowerCAmelCase       :     Any   				=			name.replace('ln_1'       , 'layer_norm1'					)
	if "ln_2" in name:
		lowerCAmelCase       :     Tuple   				=			name.replace('ln_2'       , 'layer_norm2'					)
	if "c_fc" in name:
		lowerCAmelCase       :     Union[str, Any]   				=			name.replace('c_fc'       , 'fc1'					)
	if "c_proj" in name:
		lowerCAmelCase       :     Tuple   				=			name.replace('c_proj'       , 'fc2'					)
	if "attn" in name and "self" not in name:
		lowerCAmelCase       :     Any   				=			name.replace('attn'       , 'self_attn'					)
	# text encoder
	if "token_embedding" in name:
		lowerCAmelCase       :     str   				=			name.replace('token_embedding'       , 'text_model.embeddings.token_embedding'					)
	if "positional_embedding" in name and "visual" not in name:
		lowerCAmelCase       :     Tuple   				=			name.replace('positional_embedding'       , 'text_model.embeddings.position_embedding.weight'					)
	if "ln_final" in name:
		lowerCAmelCase       :     str   				=			name.replace('ln_final'       , 'text_model.final_layer_norm'					)
	# vision encoder
	if "visual.class_embedding" in name:
		lowerCAmelCase       :     Dict   				=			name.replace('visual.class_embedding'       , 'vision_model.embeddings.class_embedding'					)
	if "visual.conv1" in name:
		lowerCAmelCase       :     List[Any]   				=			name.replace('visual.conv1'       , 'vision_model.embeddings.patch_embedding'					)
	if "visual.positional_embedding" in name:
		lowerCAmelCase       :     str   				=			name.replace('visual.positional_embedding'       , 'vision_model.embeddings.position_embedding.weight'					)
	if "visual.ln_pre" in name:
		lowerCAmelCase       :     int   				=			name.replace('visual.ln_pre'       , 'vision_model.pre_layrnorm'					)
	if "visual.ln_post" in name:
		lowerCAmelCase       :     Dict   				=			name.replace('visual.ln_post'       , 'vision_model.post_layernorm'					)
	# projection layers
	if "visual.proj" in name:
		lowerCAmelCase       :     Optional[int]   				=			name.replace('visual.proj'       , 'visual_projection.weight'					)
	if "text_projection" in name:
		lowerCAmelCase       :     List[str]   				=			name.replace('text_projection'       , 'text_projection.weight'					)
	# decoder
	if "trans_conv" in name:
		lowerCAmelCase       :     Tuple   				=			name.replace('trans_conv'       , 'transposed_convolution'					)
	if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
		lowerCAmelCase       :     List[Any]   				=			'decoder.' + name
	if "blocks" in name:
		lowerCAmelCase       :     str   				=			name.replace('blocks'       , 'decoder.layers'					)
	if "linear1" in name:
		lowerCAmelCase       :     List[str]   				=			name.replace('linear1'       , 'mlp.fc1'					)
	if "linear2" in name:
		lowerCAmelCase       :     Optional[Any]   				=			name.replace('linear2'       , 'mlp.fc2'					)
	if "norm1" in name and "layer_" not in name:
		lowerCAmelCase       :     Tuple   				=			name.replace('norm1'       , 'layer_norm1'					)
	if "norm2" in name and "layer_" not in name:
		lowerCAmelCase       :     Dict   				=			name.replace('norm2'       , 'layer_norm2'					)

	return name

def       __UpperCamelCase     (     _A     :					Any       , _A     :					List[str]					)     ->   Optional[int]:




	"""simple docstring"""





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

		if key.startswith('clip_model'					) and "attn.in_proj" in key:
			lowerCAmelCase       :     Any   				=			key.split('.'					)
			if "visual" in key:
				lowerCAmelCase       :     Optional[int]   				=			int(key_split[4]					)
				lowerCAmelCase       :     Any   				=			config.vision_config.hidden_size
				lowerCAmelCase       :     Any   				=			'vision_model'
			else:
				lowerCAmelCase       :     Optional[Any]   				=			int(key_split[3]					)
				lowerCAmelCase       :     str   				=			config.text_config.hidden_size
				lowerCAmelCase       :     Optional[int]   				=			'text_model'

			if "weight" in key:
				lowerCAmelCase       :     Any   				=			val[:dim, :]
				lowerCAmelCase       :     Optional[int]   				=			val[
				    dim : dim * 2, :
				]
				lowerCAmelCase       :     List[Any]   				=			val[-dim:, :]
			else:
				lowerCAmelCase       :     List[str]   				=			val[:dim]
				lowerCAmelCase       :     Union[str, Any]   				=			val[dim : dim * 2]
				lowerCAmelCase       :     Tuple   				=			val[-dim:]
		elif "self_attn" in key and "out_proj" not in key:
			lowerCAmelCase       :     Any   				=			key.split('.'					)
			lowerCAmelCase       :     Dict   				=			int(key_split[1]					)
			lowerCAmelCase       :     Tuple   				=			config.reduce_dim
			if "weight" in key:
				lowerCAmelCase       :     str   				=			val[:dim, :]
				lowerCAmelCase       :     Union[str, Any]   				=			val[dim : dim * 2, :]
				lowerCAmelCase       :     Dict   				=			val[-dim:, :]
			else:
				lowerCAmelCase       :     Union[str, Any]   				=			val[:dim]
				lowerCAmelCase       :     str   				=			val[dim : dim * 2]
				lowerCAmelCase       :     int   				=			val[-dim:]
		else:
			lowerCAmelCase       :     Optional[int]   				=			rename_key(_A					)
			if "visual_projection" in new_name or "text_projection" in new_name:
				lowerCAmelCase       :     Union[str, Any]   				=			val.T
			lowerCAmelCase       :     Tuple   				=			val

	return orig_state_dict

def       __UpperCamelCase     (     )     ->   List[str]:




	"""simple docstring"""





	lowerCAmelCase       :     List[str]   				=			'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCAmelCase       :     Dict   				=			Image.open(requests.get(_A       , stream=_A					).raw					)
	return image

def       __UpperCamelCase     (     _A     :					Dict       , _A     :					Dict       , _A     :					int       , _A     :					Dict					)     ->   List[Any]:




	"""simple docstring"""





	lowerCAmelCase       :     List[str]   				=			get_clipseg_config(_A					)
	lowerCAmelCase       :     str   				=			CLIPSegForImageSegmentation(_A					)
	model.eval()

	lowerCAmelCase       :     Tuple   				=			torch.load(_A       , map_location='cpu'					)

	# remove some keys
	for key in state_dict.copy().keys():
		if key.startswith('model'					):
			state_dict.pop(_A       , _A					)

    # rename some keys
	lowerCAmelCase       :     Any   				=			convert_state_dict(_A       , _A					)
	lowerCAmelCase				, lowerCAmelCase       :     Any   				=			model.load_state_dict(_A       , strict=_A					)

	if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
		raise ValueError('Missing keys that are not expected: {}'.format(_A					)					)
	if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
		raise ValueError(F"Unexpected keys: {unexpected_keys}"					)

	lowerCAmelCase       :     Tuple   				=			ViTImageProcessor(size=3_52					)
	lowerCAmelCase       :     Any   				=			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'					)
	lowerCAmelCase       :     List[Any]   				=			CLIPSegProcessor(image_processor=_A       , tokenizer=_A					)

	lowerCAmelCase       :     Tuple   				=			prepare_img()
	lowerCAmelCase       :     Union[str, Any]   				=			['a glass', 'something to fill', 'wood', 'a jar']

	lowerCAmelCase       :     Optional[int]   				=			processor(text=_A       , images=[image] * len(_A					)       , padding='max_length'       , return_tensors='pt'					)

	with torch.no_grad():
		lowerCAmelCase       :     int   				=			model(**_A					)

	# verify values
	lowerCAmelCase       :     str   				=			torch.tensor([0.11_10, -0.18_82, 0.16_45]					)
	lowerCAmelCase       :     str   				=			torch.tensor([0.26_92, -0.71_97, -0.13_28]					)
	if model_name == "clipseg-rd64-refined":
		lowerCAmelCase       :     List[str]   				=			torch.tensor(
		    [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]					)
	elif model_name == "clipseg-rd64":
		lowerCAmelCase       :     Dict   				=			torch.tensor(
		    [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]					)
	elif model_name == "clipseg-rd16":
		lowerCAmelCase       :     int   				=			torch.tensor(
		    [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]					)
	else:
		raise ValueError(F"Model name {model_name} not supported."					)

	assert torch.allclose(outputs.logits[0, :3, :3]       , _A       , atol=1e-3					)
	assert torch.allclose(outputs.conditional_embeddings[0, :3]       , _A       , atol=1e-3					)
	assert torch.allclose(outputs.pooled_output[0, :3]       , _A       , atol=1e-3					)
	print('Looks ok!'					)

	if pytorch_dump_folder_path is not None:
		print(F"Saving model and processor to {pytorch_dump_folder_path}"					)
		model.save_pretrained(_A					)
		processor.save_pretrained(_A					)

	if push_to_hub:
		print(F"Pushing model and processor for {model_name} to the hub"					)
		model.push_to_hub(F"CIDAS/{model_name}"					)
		processor.push_to_hub(F"CIDAS/{model_name}"					)


if __name__ == "__main__":
				_lowerCAmelCase	:		List[Any]		  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='clipseg-rd64',
				    type=str,
				    choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
				    help=(
				        'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
				        ' reduce dimension)'
				    ),
				)
				parser.add_argument(
				    '--checkpoint_path',
				    default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
				    type=str,
				    help=(
				        'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
				        ' the decoder weights.'
				    ),
				)
				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.'
				)

				_lowerCAmelCase	:		str		  =						parser.parse_args()
				convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




class 			lowerCAmelCase					:



	def __init__(       self			,       snake_case__     ):
		lowerCAmelCase       :     Dict   				=			data
		lowerCAmelCase       :     Dict   				=			None
		lowerCAmelCase       :     Optional[int]   				=			None



	def __str__(       self     ):
		return f"{self.data}"



class 			lowerCAmelCase					:



	def __init__(       self     ):
		lowerCAmelCase       :     int   				=			None
		lowerCAmelCase       :     Dict   				=			None



	def __iter__(       self     ):
		lowerCAmelCase       :     Any   				=			self.head
		while node:
			yield node.data
			lowerCAmelCase       :     Tuple   				=			node.next



	def __str__(       self     ):
		return "->".join([str(snake_case__     ) for item in self]     )



	def __len__(       self     ):
		return sum(1 for _ in self     )



	def 					lowercase  (       self			,       snake_case__     ):
		self.insert_at_nth(0			,       snake_case__     )



	def 					lowercase  (       self			,       snake_case__     ):
		self.insert_at_nth(len(self     )			,       snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     Dict   				=			len(self     )

		if not 0 <= index <= length:
			raise IndexError('list index out of range'     )
		lowerCAmelCase       :     Dict   				=			Node(snake_case__     )
		if self.head is None:
			lowerCAmelCase       :     Any   				=			new_node
		elif index == 0:
			lowerCAmelCase       :     Dict   				=			new_node
			lowerCAmelCase       :     List[str]   				=			self.head
			lowerCAmelCase       :     Any   				=			new_node
		elif index == length:
			lowerCAmelCase       :     List[str]   				=			new_node
			lowerCAmelCase       :     int   				=			self.tail
			lowerCAmelCase       :     List[str]   				=			new_node
		else:
			lowerCAmelCase       :     Optional[Any]   				=			self.head
			for _ in range(0			,       snake_case__     ):
				lowerCAmelCase       :     Dict   				=			temp.next
			lowerCAmelCase       :     Tuple   				=			new_node
			lowerCAmelCase       :     Union[str, Any]   				=			temp.previous
			lowerCAmelCase       :     int   				=			temp
			lowerCAmelCase       :     Optional[int]   				=			new_node



	def 					lowercase  (       self     ):
		return self.delete_at_nth(0     )



	def 					lowercase  (       self     ):
		return self.delete_at_nth(len(self     ) - 1     )



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Optional[Any]   				=			len(self     )

		if not 0 <= index <= length - 1:
			raise IndexError('list index out of range'     )
		lowerCAmelCase       :     List[Any]   				=			self.head  # default first node
		if length == 1:
			lowerCAmelCase       :     int   				=			None
		elif index == 0:
			lowerCAmelCase       :     str   				=			self.head.next
			lowerCAmelCase       :     Dict   				=			None
		elif index == length - 1:
			lowerCAmelCase       :     int   				=			self.tail
			lowerCAmelCase       :     Dict   				=			self.tail.previous
			lowerCAmelCase       :     str   				=			None
		else:
			lowerCAmelCase       :     str   				=			self.head
			for _ in range(0			,       snake_case__     ):
				lowerCAmelCase       :     Dict   				=			temp.next
			lowerCAmelCase       :     Tuple   				=			temp
			lowerCAmelCase       :     List[str]   				=			temp.previous
			lowerCAmelCase       :     Tuple   				=			temp.next
		return delete_node.data



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Dict   				=			self.head

		while current.data != data:  # Find the position to delete
			if current.next:
				lowerCAmelCase       :     str   				=			current.next
			else:  # We have reached the end an no value matches
				raise ValueError('No data matching given value'     )

		if current == self.head:
			self.delete_head()

		elif current == self.tail:
			self.delete_tail()

		else:  # Before: 1 <--> 2(current) <--> 3
			lowerCAmelCase       :     Optional[Any]   				=			current.next  # 1 --> 3
			lowerCAmelCase       :     str   				=			current.previous  # 1 <--> 3
		return data



	def 					lowercase  (       self     ):
		return len(self     ) == 0


def       __UpperCamelCase     (     )     ->   None:




	"""simple docstring"""





	lowerCAmelCase       :     str   				=			DoublyLinkedList()
	assert linked_list.is_empty() is True
	assert str(_A					) == ""

	try:
		linked_list.delete_head()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	try:
		linked_list.delete_tail()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	for i in range(10					):
		assert len(_A					) == i
		linked_list.insert_at_nth(_A       , i + 1					)
	assert str(_A					) == "->".join(str(_A					) for i in range(1       , 11					)					)

	linked_list.insert_at_head(0					)
	linked_list.insert_at_tail(11					)
	assert str(_A					) == "->".join(str(_A					) for i in range(0       , 12					)					)

	assert linked_list.delete_head() == 0
	assert linked_list.delete_at_nth(9					) == 10
	assert linked_list.delete_tail() == 11
	assert len(_A					) == 9
	assert str(_A					) == "->".join(str(_A					) for i in range(1       , 10					)					)


if __name__ == "__main__":
				from doctest import testmod

				testmod()
