


import argparse

import requests
import torch
from PIL import Image

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




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				__magic_name__	  = CLIPSegTextConfig()
				__magic_name__	  = CLIPSegVisionConfig(patch_size=16						)

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

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




def      __snake_case       (       lowerCamelCase_ :				Union[str, Any]						):
				'''simple docstring'''




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

				return name




def      __snake_case       (       lowerCamelCase_ :				List[str]	,  lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




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

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

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

				return orig_state_dict




def      __snake_case       (       ):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				Dict	,  lowerCamelCase_ :				Optional[Any]	,  lowerCamelCase_ :				Dict	,  lowerCamelCase_ :				Any						):
				'''simple docstring'''




				__magic_name__	  = get_clipseg_config(lowerCamelCase_						)
				__magic_name__	  = CLIPSegForImageSegmentation(lowerCamelCase_						)
				model.eval()

				__magic_name__	  = 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
				__magic_name__	  = convert_state_dict(lowerCamelCase_	,  lowerCamelCase_						)
				__magic_name__  ,      __magic_name__	  = 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}'						)

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

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

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

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

				# verify values
				__magic_name__	  = torch.tensor([0.1110, -0.1882, 0.1645]						)
				__magic_name__	  = torch.tensor([0.2692, -0.7197, -0.1328]						)
				if model_name == "clipseg-rd64-refined":
								__magic_name__	  = 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":
								__magic_name__	  = 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":
								__magic_name__	  = 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]	,  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__":
						__magic_name__   :   Optional[int]  							=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.'
						)

						__magic_name__   :   Tuple  							=parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int = 100						):
				'''simple docstring'''




				__magic_name__	  = [[0 for _ in range(lowerCamelCase_						)] for _ in range(m + 1						)]
				for i in range(m + 1						):
								__magic_name__	  = 1

				for n in range(m + 1						):
								for k in range(1	,  lowerCamelCase_						):
												memo[n][k] += memo[n][k - 1]
												if n > k:
																memo[n][k] += memo[n - k - 1][k]

				return memo[m][m - 1] - 1


if __name__ == "__main__":
						print(solution(int(input('Enter a number: ').strip())))



