






"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

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







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[Any]			)				->    Any:
		lowerCamelCase_   					=						CLIPSegTextConfig()
		lowerCamelCase_   					=						CLIPSegVisionConfig(patch_size=16			)

		lowerCamelCase_   					=						True if 'refined' in model_name else False
		lowerCamelCase_   					=						16 if 'rd16' in model_name else 64

		lowerCamelCase_   					=						CLIPSegConfig.from_text_vision_configs(
		    lowercase_		,    lowercase_		,    use_complex_transposed_convolution=lowercase_		,    reduce_dim=lowercase_		,    )
		return config







def 					lowerCamelCase__	(							_lowerCamelCase	:		int			)				->    Optional[int]:







		if "clip_model" in name:
				lowerCamelCase_   					=						name.replace('clip_model'		,    'clip'			)
		if "transformer" in name:
				if "visual" in name:
						lowerCamelCase_   					=						name.replace('visual.transformer'		,    'vision_model'			)
				else:
						lowerCamelCase_   					=						name.replace('transformer'		,    'text_model'			)
		if "resblocks" in name:
				lowerCamelCase_   					=						name.replace('resblocks'		,    'encoder.layers'			)
		if "ln_1" in name:
				lowerCamelCase_   					=						name.replace('ln_1'		,    'layer_norm1'			)
		if "ln_2" in name:
				lowerCamelCase_   					=						name.replace('ln_2'		,    'layer_norm2'			)
		if "c_fc" in name:
				lowerCamelCase_   					=						name.replace('c_fc'		,    'fc1'			)
		if "c_proj" in name:
				lowerCamelCase_   					=						name.replace('c_proj'		,    'fc2'			)
		if "attn" in name and "self" not in name:
				lowerCamelCase_   					=						name.replace('attn'		,    'self_attn'			)
		# text encoder
		if "token_embedding" in name:
				lowerCamelCase_   					=						name.replace('token_embedding'		,    'text_model.embeddings.token_embedding'			)
		if "positional_embedding" in name and "visual" not in name:
				lowerCamelCase_   					=						name.replace('positional_embedding'		,    'text_model.embeddings.position_embedding.weight'			)
		if "ln_final" in name:
				lowerCamelCase_   					=						name.replace('ln_final'		,    'text_model.final_layer_norm'			)
		# vision encoder
		if "visual.class_embedding" in name:
				lowerCamelCase_   					=						name.replace('visual.class_embedding'		,    'vision_model.embeddings.class_embedding'			)
		if "visual.conv1" in name:
				lowerCamelCase_   					=						name.replace('visual.conv1'		,    'vision_model.embeddings.patch_embedding'			)
		if "visual.positional_embedding" in name:
				lowerCamelCase_   					=						name.replace('visual.positional_embedding'		,    'vision_model.embeddings.position_embedding.weight'			)
		if "visual.ln_pre" in name:
				lowerCamelCase_   					=						name.replace('visual.ln_pre'		,    'vision_model.pre_layrnorm'			)
		if "visual.ln_post" in name:
				lowerCamelCase_   					=						name.replace('visual.ln_post'		,    'vision_model.post_layernorm'			)
		# projection layers
		if "visual.proj" in name:
				lowerCamelCase_   					=						name.replace('visual.proj'		,    'visual_projection.weight'			)
		if "text_projection" in name:
				lowerCamelCase_   					=						name.replace('text_projection'		,    'text_projection.weight'			)
		# decoder
		if "trans_conv" in name:
				lowerCamelCase_   					=						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_   					=						'decoder.' + name
		if "blocks" in name:
				lowerCamelCase_   					=						name.replace('blocks'		,    'decoder.layers'			)
		if "linear1" in name:
				lowerCamelCase_   					=						name.replace('linear1'		,    'mlp.fc1'			)
		if "linear2" in name:
				lowerCamelCase_   					=						name.replace('linear2'		,    'mlp.fc2'			)
		if "norm1" in name and "layer_" not in name:
				lowerCamelCase_   					=						name.replace('norm1'		,    'layer_norm1'			)
		if "norm2" in name and "layer_" not in name:
				lowerCamelCase_   					=						name.replace('norm2'		,    'layer_norm2'			)

		return name







def 					lowerCamelCase__	(							_lowerCamelCase	:		str		,    _lowerCamelCase	:		List[Any]			)				->    Optional[Any]:







		for key in orig_state_dict.copy().keys():
				lowerCamelCase_   					=						orig_state_dict.pop(lowercase_			)

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

						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:]
				elif "self_attn" in key and "out_proj" not in key:
						lowerCamelCase_   					=						key.split('.'			)
						lowerCamelCase_   					=						int(key_split[1]			)
						lowerCamelCase_   					=						config.reduce_dim
						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_   					=						rename_key(lowercase_			)
						if "visual_projection" in new_name or "text_projection" in new_name:
								lowerCamelCase_   					=						val.T
						lowerCamelCase_   					=						val

		return orig_state_dict







def 					lowerCamelCase__	(							)				->    List[str]:
		lowerCamelCase_   					=						'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase_   					=						Image.open(requests.get(lowercase_		,    stream=lowercase_			).raw			)
		return image







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Dict			)				->    Any:
		lowerCamelCase_   					=						get_clipseg_config(lowercase_			)
		lowerCamelCase_   					=						CLIPSegForImageSegmentation(lowercase_			)
		model.eval()

		lowerCamelCase_   					=						torch.load(lowercase_		,    map_location='cpu'			)

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

    # rename some keys
		lowerCamelCase_   					=						convert_state_dict(lowercase_		,    lowercase_			)
		lowerCamelCase_					,     lowerCamelCase_   					=						model.load_state_dict(lowercase_		,    strict=lowercase_			)

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

		lowerCamelCase_   					=						ViTImageProcessor(size=352			)
		lowerCamelCase_   					=						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'			)
		lowerCamelCase_   					=						CLIPSegProcessor(image_processor=lowercase_		,    tokenizer=lowercase_			)

		lowerCamelCase_   					=						prepare_img()
		lowerCamelCase_   					=						['a glass', 'something to fill', 'wood', 'a jar']

		lowerCamelCase_   					=						processor(text=lowercase_		,    images=[image] * len(lowercase_			)		,    padding='max_length'		,    return_tensors='pt'			)

		with torch.no_grad():
				lowerCamelCase_   					=						model(**lowercase_			)

		# verify values
		lowerCamelCase_   					=						torch.tensor([0.11_10, -0.18_82, 0.16_45]			)
		lowerCamelCase_   					=						torch.tensor([0.26_92, -0.71_97, -0.13_28]			)
		if model_name == "clipseg-rd64-refined":
				lowerCamelCase_   					=						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_   					=						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_   					=						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]		,    lowercase_		,    atol=1e-3			)
		assert torch.allclose(outputs.conditional_embeddings[0, :3]		,    lowercase_		,    atol=1e-3			)
		assert torch.allclose(outputs.pooled_output[0, :3]		,    lowercase_		,    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(lowercase_			)
				processor.save_pretrained(lowercase_			)

		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__":
		_SCREAMING_SNAKE_CASE :    str    =						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.'''
		)

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












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int			)				->    int:
  if not root or root not in binary_tree_mirror_dictionary:
    return
  lowerCamelCase_					,     lowerCamelCase_   					=						binary_tree_mirror_dictionary[root][:2]
  lowerCamelCase_   					=						[right_child, left_child]
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int = 1			)				->    dict:
  if not binary_tree:
    raise ValueError('binary tree cannot be empty'			)
  if root not in binary_tree:
    lowerCamelCase_   					=						F'''root {root} is not present in the binary_tree'''
    raise ValueError(_lowerCamelCase			)
  lowerCamelCase_   					=						dict(_lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Tuple    =						{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'''Binary tree: {binary_tree}''')
  _SCREAMING_SNAKE_CASE :    Dict    =						binary_tree_mirror(binary_tree, 5)
  print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')




