


import argparse

import requests
import torch
from PIL import Image

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


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		CLIPSegTextConfig()
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		CLIPSegVisionConfig(patch_size=16				)

						__SCREAMING_SNAKE_CASE						:					Tuple		      =		True if '''refined''' in model_name else False
						__SCREAMING_SNAKE_CASE						:					int		      =		16 if '''rd16''' in model_name else 64

						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		CLIPSegConfig.from_text_vision_configs(
						    lowercase_   ,	lowercase_   ,	use_complex_transposed_convolution=lowercase_   ,	reduce_dim=lowercase_   ,	)
						return config


def 	_UpperCamelCase							(				lowercase__				):







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

						return name


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):







						for key in orig_state_dict.copy().keys():
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		orig_state_dict.pop(lowercase_				)

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

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

						return orig_state_dict


def 	_UpperCamelCase							(				):
						__SCREAMING_SNAKE_CASE						:					str		      =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		Image.open(requests.get(lowercase_   ,	stream=lowercase_				).raw				)
						return image


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Dict		      =		get_clipseg_config(lowercase_				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		CLIPSegForImageSegmentation(lowercase_				)
						model.eval()

						__SCREAMING_SNAKE_CASE						:					Tuple		      =		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
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		convert_state_dict(lowercase_   ,	lowercase_				)
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[int]		      =		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}'''				)

						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		ViTImageProcessor(size=352				)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		CLIPSegProcessor(image_processor=lowercase_   ,	tokenizer=lowercase_				)

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

						__SCREAMING_SNAKE_CASE						:					str		      =		processor(text=lowercase_   ,	images=[image] * len(lowercase_				)   ,	padding='''max_length'''   ,	return_tensors='''pt'''				)

						with torch.no_grad():
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		model(**lowercase_				)

						# verify values
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.tensor([0.1110, -0.1882, 0.1645]				)
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.tensor([0.2692, -0.7197, -0.1328]				)
						if model_name == "clipseg-rd64-refined":
												__SCREAMING_SNAKE_CASE						:					Any		      =		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":
												__SCREAMING_SNAKE_CASE						:					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":
												__SCREAMING_SNAKE_CASE						:					Any		      =		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]   ,	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__":
						__lowerCAmelCase     :			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[int]      =parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
