






'''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			:	Dict						)  -> Optional[int]:
			UpperCAmelCase_     :   List[Any]       =   CLIPSegTextConfig()
			UpperCAmelCase_     :   Optional[Any]       =   CLIPSegVisionConfig(patch_size=1_6						)

			UpperCAmelCase_     :   Tuple       =   True if '''refined''' in model_name else False
			UpperCAmelCase_     :   Any       =   1_6 if '''rd16''' in model_name else 6_4

			UpperCAmelCase_     :   str       =   CLIPSegConfig.from_text_vision_configs(
			    A	,   A	,   use_complex_transposed_convolution=A	,   reduce_dim=A	,   )
			return config
def __UpperCAmelCase (			A			:	Optional[int]						)  -> List[str]:
			# update prefixes
			if "clip_model" in name:
						UpperCAmelCase_     :   List[Any]       =   name.replace('''clip_model'''	,   '''clip'''						)
			if "transformer" in name:
						if "visual" in name:
									UpperCAmelCase_     :   Optional[int]       =   name.replace('''visual.transformer'''	,   '''vision_model'''						)
						else:
									UpperCAmelCase_     :   int       =   name.replace('''transformer'''	,   '''text_model'''						)
			if "resblocks" in name:
						UpperCAmelCase_     :   Optional[Any]       =   name.replace('''resblocks'''	,   '''encoder.layers'''						)
			if "ln_1" in name:
						UpperCAmelCase_     :   List[str]       =   name.replace('''ln_1'''	,   '''layer_norm1'''						)
			if "ln_2" in name:
						UpperCAmelCase_     :   Dict       =   name.replace('''ln_2'''	,   '''layer_norm2'''						)
			if "c_fc" in name:
						UpperCAmelCase_     :   Dict       =   name.replace('''c_fc'''	,   '''fc1'''						)
			if "c_proj" in name:
						UpperCAmelCase_     :   Optional[Any]       =   name.replace('''c_proj'''	,   '''fc2'''						)
			if "attn" in name and "self" not in name:
						UpperCAmelCase_     :   Optional[int]       =   name.replace('''attn'''	,   '''self_attn'''						)
			# text encoder
			if "token_embedding" in name:
						UpperCAmelCase_     :   Union[str, Any]       =   name.replace('''token_embedding'''	,   '''text_model.embeddings.token_embedding'''						)
			if "positional_embedding" in name and "visual" not in name:
						UpperCAmelCase_     :   Any       =   name.replace('''positional_embedding'''	,   '''text_model.embeddings.position_embedding.weight'''						)
			if "ln_final" in name:
						UpperCAmelCase_     :   Optional[Any]       =   name.replace('''ln_final'''	,   '''text_model.final_layer_norm'''						)
			# vision encoder
			if "visual.class_embedding" in name:
						UpperCAmelCase_     :   List[str]       =   name.replace('''visual.class_embedding'''	,   '''vision_model.embeddings.class_embedding'''						)
			if "visual.conv1" in name:
						UpperCAmelCase_     :   List[Any]       =   name.replace('''visual.conv1'''	,   '''vision_model.embeddings.patch_embedding'''						)
			if "visual.positional_embedding" in name:
						UpperCAmelCase_     :   int       =   name.replace('''visual.positional_embedding'''	,   '''vision_model.embeddings.position_embedding.weight'''						)
			if "visual.ln_pre" in name:
						UpperCAmelCase_     :   Any       =   name.replace('''visual.ln_pre'''	,   '''vision_model.pre_layrnorm'''						)
			if "visual.ln_post" in name:
						UpperCAmelCase_     :   Tuple       =   name.replace('''visual.ln_post'''	,   '''vision_model.post_layernorm'''						)
			# projection layers
			if "visual.proj" in name:
						UpperCAmelCase_     :   Dict       =   name.replace('''visual.proj'''	,   '''visual_projection.weight'''						)
			if "text_projection" in name:
						UpperCAmelCase_     :   Tuple       =   name.replace('''text_projection'''	,   '''text_projection.weight'''						)
			# decoder
			if "trans_conv" in name:
						UpperCAmelCase_     :   Dict       =   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_     :   List[Any]       =   '''decoder.''' + name
			if "blocks" in name:
						UpperCAmelCase_     :   Union[str, Any]       =   name.replace('''blocks'''	,   '''decoder.layers'''						)
			if "linear1" in name:
						UpperCAmelCase_     :   Optional[Any]       =   name.replace('''linear1'''	,   '''mlp.fc1'''						)
			if "linear2" in name:
						UpperCAmelCase_     :   str       =   name.replace('''linear2'''	,   '''mlp.fc2'''						)
			if "norm1" in name and "layer_" not in name:
						UpperCAmelCase_     :   Any       =   name.replace('''norm1'''	,   '''layer_norm1'''						)
			if "norm2" in name and "layer_" not in name:
						UpperCAmelCase_     :   List[Any]       =   name.replace('''norm2'''	,   '''layer_norm2'''						)

			return name
def __UpperCAmelCase (			A			:	str	,   A			:	Optional[Any]						)  -> Any:
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase_     :   List[Any]       =   orig_state_dict.pop(A						)

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

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

			return orig_state_dict
def __UpperCAmelCase (			)  -> int:
			UpperCAmelCase_     :   Any       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			UpperCAmelCase_     :   str       =   Image.open(requests.get(A	,   stream=A						).raw						)
			return image
def __UpperCAmelCase (			A			:	List[str]	,   A			:	Tuple	,   A			:	str	,   A			:	Union[str, Any]						)  -> Any:
			UpperCAmelCase_     :   int       =   get_clipseg_config(A						)
			UpperCAmelCase_     :   Union[str, Any]       =   CLIPSegForImageSegmentation(A						)
			model.eval()

			UpperCAmelCase_     :   Dict       =   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
			UpperCAmelCase_     :   Dict       =   convert_state_dict(A	,   A						)
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Union[str, 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}"						)

			UpperCAmelCase_     :   int       =   ViTImageProcessor(size=3_5_2						)
			UpperCAmelCase_     :   List[Any]       =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
			UpperCAmelCase_     :   Optional[Any]       =   CLIPSegProcessor(image_processor=A	,   tokenizer=A						)

			UpperCAmelCase_     :   str       =   prepare_img()
			UpperCAmelCase_     :   int       =   ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

			UpperCAmelCase_     :   Dict       =   processor(text=A	,   images=[image] * len(A						)	,   padding='''max_length'''	,   return_tensors='''pt'''						)

			with torch.no_grad():
						UpperCAmelCase_     :   Tuple       =   model(**A						)

			# verify values
			UpperCAmelCase_     :   Optional[Any]       =   torch.tensor([0.1110, -0.1882, 0.1645]						)
			UpperCAmelCase_     :   List[Any]       =   torch.tensor([0.2692, -0.7197, -0.1328]						)
			if model_name == "clipseg-rd64-refined":
						UpperCAmelCase_     :   str       =   torch.tensor(
						    [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]						)
			elif model_name == "clipseg-rd64":
						UpperCAmelCase_     :   List[str]       =   torch.tensor(
						    [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]						)
			elif model_name == "clipseg-rd16":
						UpperCAmelCase_     :   int       =   torch.tensor(
						    [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]						)
			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__":
						_UpperCamelCase		:		List[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.'
						)

						_UpperCamelCase		:		Any								=			parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize


class 				snake_case__     (			UpperCamelCase):
					a_       =			1
					a_       =			2
					a_       =			3




					def __repr__(       self       :						str     )  ->    Optional[int]:
								return F"{self.__class__.__name__}.{self.name}"






def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	int	,   A			:	int						)  -> float:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   point
			return degrees(atana(y - miny	,   x - minx						)						)
def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	tuple[int, int]	,   A			:	tuple[int, int]						)  -> Direction:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   starting
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[Any]       =   via
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   target
			UpperCAmelCase_     :   Optional[int]       =   degrees(atana(ya - ya	,   xa - xa						)						)
			via_angle %= 3_6_0
			UpperCAmelCase_     :   Dict       =   degrees(atana(ya - ya	,   xa - xa						)						)
			target_angle %= 3_6_0
			# t-
			#  \ \
			#   \ v
			#    \|
			#     s
			# via_angle is always lower than target_angle, if direction is left.
			# If they are same, it means they are on a same line of convex hull.
			if target_angle > via_angle:
						return Direction.left
			elif target_angle == via_angle:
						return Direction.straight
			else:
						return Direction.right







def __UpperCAmelCase (			A			:	list[tuple[int, int]]						)  -> list[tuple[int, int]]:

			if len(A						) <= 2:
						# There is no convex hull
						raise ValueError('''graham_scan: argument must contain more than 3 points.'''						)
			if len(A						) == 3:
						return points
			# find the lowest and the most left point
			UpperCAmelCase_     :   List[str]       =   0
			UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   maxsize, maxsize
			for i, point in enumerate(A						):
						UpperCAmelCase_     :   str       =   point[0]
						UpperCAmelCase_     :   List[Any]       =   point[1]
						if y < miny:
									UpperCAmelCase_     :   int       =   y
									UpperCAmelCase_     :   Dict       =   x
									UpperCAmelCase_     :   Any       =   i
						if y == miny and x < minx:
									UpperCAmelCase_     :   str       =   x
									UpperCAmelCase_     :   Optional[int]       =   i

    # remove the lowest and the most left point from points for preparing for sort
			points.pop(A						)

			UpperCAmelCase_     :   Optional[Any]       =   sorted(A	,   key=lambda A						: angle_comparer(A	,   A	,   A						)						)
			# This insert actually costs complexity,
			# and you should instead add (minx, miny) into stack later.
			# I'm using insert just for easy understanding.
			sorted_points.insert(0	,   (minx, miny)						)

			UpperCAmelCase_     :   deque[tuple[int, int]]       =   deque()
			stack.append(sorted_points[0]						)
			stack.append(sorted_points[1]						)
			stack.append(sorted_points[2]						)
			# In any ways, the first 3 points line are towards left.
			# Because we sort them the angle from minx, miny.
			UpperCAmelCase_     :   Any       =   Direction.left

			for i in range(3	,   len(A						)						):
						while True:
									UpperCAmelCase_     :   Any       =   stack[-2]
									UpperCAmelCase_     :   List[Any]       =   stack[-1]
									UpperCAmelCase_     :   Any       =   sorted_points[i]
									UpperCAmelCase_     :   Tuple       =   check_direction(A	,   A	,   A						)

									if next_direction == Direction.left:
												UpperCAmelCase_     :   Optional[Any]       =   Direction.left
												break
									if next_direction == Direction.straight:
												if current_direction == Direction.left:
															# We keep current_direction as left.
															# Because if the straight line keeps as straight,
															# we want to know if this straight line is towards left.
															break
												elif current_direction == Direction.right:
															# If the straight line is towards right,
															# every previous points on those straigh line is not convex hull.
															stack.pop()
									if next_direction == Direction.right:
												stack.pop()
						stack.append(sorted_points[i]						)
			return list(A						)


