






import argparse

import requests
import torch
from PIL import Image

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


def __A					(   __lowerCAmelCase			)->						List[Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    CLIPSegTextConfig()
						_UpperCAmelCase             =    CLIPSegVisionConfig(patch_size=16			)

						_UpperCAmelCase             =    True if "refined" in model_name else False
						_UpperCAmelCase             =    16 if "rd16" in model_name else 64

						_UpperCAmelCase             =    CLIPSegConfig.from_text_vision_configs(
						    __lowerCAmelCase				,	__lowerCAmelCase				,	use_complex_transposed_convolution=__lowerCAmelCase				,	reduce_dim=__lowerCAmelCase				,	)
						return config


def __A					(   __lowerCAmelCase			)->						Optional[int]:





						"""simple docstring"""



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

						return name


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						int:





						"""simple docstring"""



						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase             =    orig_state_dict.pop(__lowerCAmelCase			)

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

																		if "weight" in key:
																								_UpperCAmelCase             =    val[:dim, :]
																								_UpperCAmelCase             =    val[
																								    dim : dim * 2, :
																								]
																								_UpperCAmelCase             =    val[-dim:, :]
																		else:
																								_UpperCAmelCase             =    val[:dim]
																								_UpperCAmelCase             =    val[dim : dim * 2]
																								_UpperCAmelCase             =    val[-dim:]
												elif "self_attn" in key and "out_proj" not in key:
																		_UpperCAmelCase             =    key.split('.'			)
																		_UpperCAmelCase             =    int(key_split[1]			)
																		_UpperCAmelCase             =    config.reduce_dim
																		if "weight" in key:
																								_UpperCAmelCase             =    val[:dim, :]
																								_UpperCAmelCase             =    val[dim : dim * 2, :]
																								_UpperCAmelCase             =    val[-dim:, :]
																		else:
																								_UpperCAmelCase             =    val[:dim]
																								_UpperCAmelCase             =    val[dim : dim * 2]
																								_UpperCAmelCase             =    val[-dim:]
												else:
																		_UpperCAmelCase             =    rename_key(__lowerCAmelCase			)
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								_UpperCAmelCase             =    val.T
																		_UpperCAmelCase             =    val

						return orig_state_dict


def __A					(   )->						List[Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    "http://images.cocodataset.org/val2017/000000039769.jpg"
						_UpperCAmelCase             =    Image.open(requests.get(__lowerCAmelCase				,	stream=__lowerCAmelCase			).raw			)
						return image


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						int:





						"""simple docstring"""



						_UpperCAmelCase             =    get_clipseg_config(__lowerCAmelCase			)
						_UpperCAmelCase             =    CLIPSegForImageSegmentation(__lowerCAmelCase			)
						model.eval()

						_UpperCAmelCase             =    torch.load(__lowerCAmelCase				,	map_location='cpu'			)

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

    # rename some keys
						_UpperCAmelCase             =    convert_state_dict(__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    model.load_state_dict(__lowerCAmelCase				,	strict=__lowerCAmelCase			)

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

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

						_UpperCAmelCase             =    prepare_img()
						_UpperCAmelCase             =    ["a glass", "something to fill", "wood", "a jar"]

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

						with torch.no_grad():
												_UpperCAmelCase             =    model(**__lowerCAmelCase			)

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

						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__":
		_a   			=						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.'''
		)

		_a   			=						parser.parse_args()
		convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








from typing import Any





class __lowerCamelCase				:





	"""simple docstring"""






	def __init__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    []
							_UpperCAmelCase             =    0

	def __str__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    '<' + str(self.stack					)[1:-1] + '>'
							return printed

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							self.stack.append(UpperCAmelCase					)
							_UpperCAmelCase             =    self.length + 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							self.rotate(1					)
							_UpperCAmelCase             =    self.stack[self.length - 1]
							_UpperCAmelCase             =    self.stack[:-1]
							self.rotate(self.length - 1					)
							_UpperCAmelCase             =    self.length - 1
							return dequeued

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							for _ in range(UpperCAmelCase					):
													_UpperCAmelCase             =    self.stack[0]
													_UpperCAmelCase             =    self.stack[1:]
													self.put(UpperCAmelCase					)
													_UpperCAmelCase             =    self.length - 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    self.get()
							self.put(UpperCAmelCase					)
							self.rotate(self.length - 1					)
							return front

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return self.length
