

"""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__: Union[str, Any]			)				->			Optional[int]:




					"""simple docstring"""



					__lowercase												=			CLIPSegTextConfig()
					__lowercase												=			CLIPSegVisionConfig(patch_size=16			)

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

					__lowercase												=			CLIPSegConfig.from_text_vision_configs(
					    lowercase__ ,	lowercase__ ,	use_complex_transposed_convolution=lowercase__ ,	reduce_dim=lowercase__ ,	)
					return config





def 				__UpperCamelCase   (    a__: Optional[Any]			)				->			Dict:




					"""simple docstring"""



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

					return name





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




					"""simple docstring"""



					for key in orig_state_dict.copy().keys():
										__lowercase												=			orig_state_dict.pop(lowercase__			)

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

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

					return orig_state_dict





def 				__UpperCamelCase   (    )				->			List[Any]:




					"""simple docstring"""



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





def 				__UpperCamelCase   (    a__: Dict ,	a__: Tuple ,	a__: str ,	a__: Tuple			)				->			Optional[int]:




					"""simple docstring"""



					__lowercase												=			get_clipseg_config(lowercase__			)
					__lowercase												=			CLIPSegForImageSegmentation(lowercase__			)
					model.eval()

					__lowercase												=			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
					__lowercase												=			convert_state_dict(lowercase__ ,	lowercase__			)
					__lowercase												=			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}'''			)

					__lowercase												=			ViTImageProcessor(size=3_52			)
					__lowercase												=			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			)
					__lowercase												=			CLIPSegProcessor(image_processor=lowercase__ ,	tokenizer=lowercase__			)

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

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

					with torch.no_grad():
										__lowercase												=			model(**lowercase__			)

					# verify values
					__lowercase												=			torch.tensor([0.11_10, -0.18_82, 0.16_45]			)
					__lowercase												=			torch.tensor([0.26_92, -0.71_97, -0.13_28]			)
					if model_name == "clipseg-rd64-refined":
										__lowercase												=			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":
										__lowercase												=			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":
										__lowercase												=			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__":
							a__ :	Union[str, 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."""
							)

							a__ :	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 				__UpperCamelCase   (    a__: int ,	a__: int = 10_00			)				->			str:




					"""simple docstring"""



					if (not isinstance(a__ ,	a__			)) or (digit_position <= 0):
										raise ValueError('''Digit position must be a positive integer'''			)
					elif (not isinstance(a__ ,	a__			)) or (precision < 0):
										raise ValueError('''Precision must be a nonnegative integer'''			)

					# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
					# accurate
					__lowercase												=			(
					    4 * _subsum(a__ ,	1 ,	a__			)
					    - 2 * _subsum(a__ ,	4 ,	a__			)
					    - _subsum(a__ ,	5 ,	a__			)
					    - _subsum(a__ ,	6 ,	a__			)
					)

					# return the first hex digit of the fractional part of the result
					return hex(int((sum_result % 1) * 16			)			)[2:]





def 				__UpperCamelCase   (    a__: int ,	a__: int ,	a__: int			)				->			float:




					"""simple docstring"""



					__lowercase												=			0.0
					for sum_index in range(digit_pos_to_extract + precision			):
										__lowercase												=			8 * sum_index + denominator_addend
										if sum_index < digit_pos_to_extract:
															# if the exponential term is an integer and we mod it by the denominator
															# before dividing, only the integer part of the sum will change;
															# the fractional part will not
															__lowercase												=			pow(
															    16 ,	digit_pos_to_extract - 1 - sum_index ,	a__			)
										else:
															__lowercase												=			pow(16 ,	digit_pos_to_extract - 1 - sum_index			)
										total += exponential_term / denominator
					return total


if __name__ == "__main__":
							import doctest

							doctest.testmod()


