





import argparse

import requests
import torch
from PIL import Image

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





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       Dict:
			UpperCAmelCase     	=       CLIPSegTextConfig()
			UpperCAmelCase     	=       CLIPSegVisionConfig(patch_size=16				)

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

			UpperCAmelCase     	=       CLIPSegConfig.from_text_vision_configs(
			    UpperCamelCase__    ,    UpperCamelCase__    ,    use_complex_transposed_convolution=UpperCamelCase__    ,    reduce_dim=UpperCamelCase__    ,    )
			return config





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

			return name





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       str:
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase     	=       orig_state_dict.pop(UpperCamelCase__				)

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

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

			return orig_state_dict





def 		__SCREAMING_SNAKE_CASE						(       )						->       List[Any]:
			UpperCAmelCase     	=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			UpperCAmelCase     	=       Image.open(requests.get(UpperCamelCase__    ,    stream=UpperCamelCase__				).raw				)
			return image





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       List[str]:
			UpperCAmelCase     	=       get_clipseg_config(UpperCamelCase__				)
			UpperCAmelCase     	=       CLIPSegForImageSegmentation(UpperCamelCase__				)
			model.eval()

			UpperCAmelCase     	=       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
			UpperCAmelCase     	=       convert_state_dict(UpperCamelCase__    ,    UpperCamelCase__				)
			UpperCAmelCase     	=       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}"""				)

			UpperCAmelCase     	=       ViTImageProcessor(size=352				)
			UpperCAmelCase     	=       CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''				)
			UpperCAmelCase     	=       CLIPSegProcessor(image_processor=UpperCamelCase__    ,    tokenizer=UpperCamelCase__				)

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

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

			with torch.no_grad():
						UpperCAmelCase     	=       model(**UpperCamelCase__				)

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

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










import numpy as np





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__="utf-8"    ,    UpperCamelCase__="surrogatepass"				)						->       Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       bin(int.from_bytes(text.encode(UpperCamelCase__    ,    UpperCamelCase__				)    ,    '''big'''				)				)[2:]
			return bits.zfill(8 * ((len(UpperCamelCase__				) + 7) // 8)				)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__="utf-8"    ,    UpperCamelCase__="surrogatepass"				)						->       str:







			'''simple docstring'''

			UpperCAmelCase     	=       int(UpperCamelCase__    ,    2				)
			return n.to_bytes((n.bit_length() + 7) // 8    ,    '''big'''				).decode(UpperCamelCase__    ,    UpperCamelCase__				) or "\0"





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       Tuple:







			'''simple docstring'''

			if size_par + len(UpperCamelCase__				) <= 2**size_par - (len(UpperCamelCase__				) - 1):
						raise ValueError('''size of parity don\'t match with size of data'''				)

			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       [bin(UpperCamelCase__				)[2:] for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				)]

			# sorted information data for the size of the output data
			UpperCAmelCase     	=       []
			# data position template + parity
			UpperCAmelCase     	=       []
			# parity bit counter
			UpperCAmelCase     	=       0
			# counter position of data bits
			UpperCAmelCase     	=       0

			for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
												data_out_gab.append('''P'''				)
												UpperCAmelCase     	=       qtd_bp + 1
									else:
												data_out_gab.append('''D'''				)
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]				)
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__				)

    # Calculates parity
			UpperCAmelCase     	=       0  # parity bit counter
			for bp in range(1    ,    size_par + 1				):
						# Bit counter one for a given parity
						UpperCAmelCase     	=       0
						# counter to control the loop reading
						UpperCAmelCase     	=       0
						for x in data_ord:
									if x is not None:
												try:
															UpperCAmelCase     	=       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCAmelCase     	=       '''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2				)

						qtd_bp += 1

			# Mount the message
			UpperCAmelCase     	=       0  # parity bit counter
			for x in range(0    ,    size_par + len(UpperCamelCase__				)				):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]				)				)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]				)

			return data_out





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       Optional[int]:







			'''simple docstring'''

			UpperCAmelCase     	=       []
			# Parity bit counter
			UpperCAmelCase     	=       0
			# Counter p data bit reading
			UpperCAmelCase     	=       0
			# list of parity received
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []

			for x in range(1    ,    len(UpperCamelCase__				) + 1				):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
									data_out_gab.append('''P'''				)
									UpperCAmelCase     	=       qtd_bp + 1
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]				)
						else:
									parity_received.append(data[cont_data]				)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       [bin(UpperCamelCase__				)[2:] for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				)]

			#  sorted information data for the size of the output data
			UpperCAmelCase     	=       []
			# Data position feedback + parity
			UpperCAmelCase     	=       []
			# Parity bit counter
			UpperCAmelCase     	=       0
			# Counter p data bit reading
			UpperCAmelCase     	=       0

			for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
									data_out_gab.append('''P'''				)
									UpperCAmelCase     	=       qtd_bp + 1
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]				)
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__				)

    # Calculates parity
			UpperCAmelCase     	=       0  # parity bit counter
			for bp in range(1    ,    size_par + 1				):
						# Bit counter one for a certain parity
						UpperCAmelCase     	=       0
						# Counter to control loop reading
						UpperCAmelCase     	=       0
						for x in data_ord:
									if x is not None:
												try:
															UpperCAmelCase     	=       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCAmelCase     	=       '''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2				)				)

						qtd_bp += 1

			# Mount the message
			UpperCAmelCase     	=       0  # Parity bit counter
			for x in range(0    ,    size_par + len(UpperCamelCase__				)				):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]				)				)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]				)

			UpperCAmelCase     	=       parity_received == parity
			return data_output, ack



