

import argparse

import requests
import torch
from PIL import Image

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







def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				int							= CLIPSegTextConfig()
						SCREAMING_SNAKE_CASE_:				Optional[int]							= CLIPSegVisionConfig(patch_size=16					)

						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= True if "refined" in model_name else False
						SCREAMING_SNAKE_CASE_:				str							= 16 if "rd16" in model_name else 64

						SCREAMING_SNAKE_CASE_:				str							= CLIPSegConfig.from_text_vision_configs(
						    _UpperCAmelCase       ,			_UpperCAmelCase       ,			use_complex_transposed_convolution=_UpperCAmelCase       ,			reduce_dim=_UpperCAmelCase       ,			)
						return config







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

						return name







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= orig_state_dict.pop(_UpperCAmelCase					)

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

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

						return orig_state_dict







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= "http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:				Optional[int]							= Image.open(requests.get(_UpperCAmelCase       ,			stream=_UpperCAmelCase					).raw					)
						return image







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= get_clipseg_config(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Dict							= CLIPSegForImageSegmentation(_UpperCAmelCase					)
						model.eval()

						SCREAMING_SNAKE_CASE_:				List[str]							= torch.load(_UpperCAmelCase       ,			map_location="cpu"					)

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

    # rename some keys
						SCREAMING_SNAKE_CASE_:				Optional[int]							= convert_state_dict(_UpperCAmelCase       ,			_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				List[str]							= model.load_state_dict(_UpperCAmelCase       ,			strict=_UpperCAmelCase					)

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

						SCREAMING_SNAKE_CASE_:				Tuple							= ViTImageProcessor(size=3_52					)
						SCREAMING_SNAKE_CASE_:				str							= CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= CLIPSegProcessor(image_processor=_UpperCAmelCase       ,			tokenizer=_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= prepare_img()
						SCREAMING_SNAKE_CASE_:				int							= ["a glass", "something to fill", "wood", "a jar"]

						SCREAMING_SNAKE_CASE_:				Tuple							= processor(text=_UpperCAmelCase       ,			images=[image] * len(_UpperCAmelCase					)       ,			padding="max_length"       ,			return_tensors="pt"					)

						with torch.no_grad():
												SCREAMING_SNAKE_CASE_:				Dict							= model(**_UpperCAmelCase					)

						# verify values
						SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]					)
						SCREAMING_SNAKE_CASE_:				Tuple							= torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]					)
						if model_name == "clipseg-rd64-refined":
												SCREAMING_SNAKE_CASE_:				Any							= torch.tensor(
												    [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]					)
						elif model_name == "clipseg-rd64":
												SCREAMING_SNAKE_CASE_:				Dict							= torch.tensor(
												    [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]					)
						elif model_name == "clipseg-rd16":
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.tensor(
												    [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]					)
						else:
												raise ValueError(f"Model name {model_name} not supported."					)

						assert torch.allclose(outputs.logits[0, :3, :3]       ,			_UpperCAmelCase       ,			atol=1e-3					)
						assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,			_UpperCAmelCase       ,			atol=1e-3					)
						assert torch.allclose(outputs.pooled_output[0, :3]       ,			_UpperCAmelCase       ,			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(_UpperCAmelCase					)
												processor.save_pretrained(_UpperCAmelCase					)

						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[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."""
						)

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









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






