






import argparse

import requests
import torch
from PIL import Image

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




def    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       CLIPSegTextConfig()
					_A									=       CLIPSegVisionConfig(patch_size=16       )

					_A									=       True if '''refined''' in model_name else False
					_A									=       16 if '''rd16''' in model_name else 64

					_A									=       CLIPSegConfig.from_text_vision_configs(
					    _lowercase    ,	_lowercase    ,	use_complex_transposed_convolution=_lowercase    ,	reduce_dim=_lowercase    ,	)
					return config




def    __A    (   _lowercase       ):

					'''simple docstring'''







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

					return name




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					for key in orig_state_dict.copy().keys():
										_A									=       orig_state_dict.pop(_lowercase       )

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

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

					return orig_state_dict




def    __A    (   ):

					'''simple docstring'''







					_A									=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_A									=       Image.open(requests.get(_lowercase    ,	stream=_lowercase       ).raw       )
					return image




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       get_clipseg_config(_lowercase       )
					_A									=       CLIPSegForImageSegmentation(_lowercase       )
					model.eval()

					_A									=       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
					_A									=       convert_state_dict(_lowercase    ,	_lowercase       )
					_A							,_A									=       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}"""       )

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

					_A									=       prepare_img()
					_A									=       ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

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

					with torch.no_grad():
										_A									=       model(**_lowercase       )

					# verify values
					_A									=       torch.tensor([0.11_10, -0.18_82, 0.16_45]       )
					_A									=       torch.tensor([0.26_92, -0.71_97, -0.13_28]       )
					if model_name == "clipseg-rd64-refined":
										_A									=       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":
										_A									=       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":
										_A									=       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          = 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 math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


if __name__ == "__main__":
				print(solution())



