






'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

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







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						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(
						    _A    ,   _A    ,   use_complex_transposed_convolution=_A    ,   reduce_dim=_A    ,   )
						return config







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						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 						__magic_name__(			_A    ,   _A       ):
						'''simple docstring'''





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

												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(_A       )
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								UpperCamelCase__       =			val.T
																		UpperCamelCase__       =			val

						return orig_state_dict







def 						__magic_name__(			):
						'''simple docstring'''





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







def 						__magic_name__(			_A    ,   _A    ,   _A    ,   _A       ):
						'''simple docstring'''





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

						UpperCamelCase__       =			torch.load(_A    ,   map_location="""cpu"""       )

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

    # rename some keys
						UpperCamelCase__       =			convert_state_dict(_A    ,   _A       )
						UpperCamelCase__     , UpperCamelCase__       =			model.load_state_dict(_A    ,   strict=_A       )

						if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
												raise ValueError("""Missing keys that are not expected: {}""".format(_A       )       )
						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=_A    ,   tokenizer=_A       )

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

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

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

						# verify values
						UpperCamelCase__       =			torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]       )
						UpperCamelCase__       =			torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]       )
						if model_name == "clipseg-rd64-refined":
												UpperCamelCase__       =			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":
												UpperCamelCase__       =			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":
												UpperCamelCase__       =			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]    ,   _A    ,   atol=1e-3       )
						assert torch.allclose(outputs.conditional_embeddings[0, :3]    ,   _A    ,   atol=1e-3       )
						assert torch.allclose(outputs.pooled_output[0, :3]    ,   _A    ,   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(_A       )
												processor.save_pretrained(_A       )

						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_					:			Union[str, Any]     						=				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_					:			Any     						=				parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''


def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )

						# Set the gap value to be decreased by a factor of 1.3
						# after each iteration
						UpperCamelCase__       =			1.3

						# Continue sorting until the gap is 1
						while gap > 1:
												# Decrease the gap value
												UpperCamelCase__       =			int(gap / shrink       )

												# Sort the elements using insertion sort
												for i in range(_A    ,   len(_A       )       ):
																		UpperCamelCase__       =			collection[i]
																		UpperCamelCase__       =			i
																		while j >= gap and collection[j - gap] > temp:
																								UpperCamelCase__       =			collection[j - gap]
																								j -= gap
																		UpperCamelCase__       =			temp

						return collection


if __name__ == "__main__":
					import doctest

					doctest.testmod()
