"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

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


def 					a__ (						UpperCAmelCase__    :     Tuple							)->		Dict:
						UpperCAmelCase__   		=						CLIPSegTextConfig()
						UpperCAmelCase__   		=						CLIPSegVisionConfig(patch_size=1_6							)

						UpperCAmelCase__   		=						True if """refined""" in model_name else False
						UpperCAmelCase__   		=						1_6 if """rd16""" in model_name else 6_4

						UpperCAmelCase__   		=						CLIPSegConfig.from_text_vision_configs(
						    lowercase_						,     lowercase_						,     use_complex_transposed_convolution=lowercase_						,     reduce_dim=lowercase_						,     )
						return config


def 					a__ (						UpperCAmelCase__    :     Optional[int]							)->		str:







						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 					a__ (						UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Tuple							)->		List[str]:







						for key in orig_state_dict.copy().keys():
												UpperCAmelCase__   		=						orig_state_dict.pop(lowercase_							)

												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(lowercase_							)
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								UpperCAmelCase__   		=						val.T
																		UpperCAmelCase__   		=						val

						return orig_state_dict


def 					a__ (						)->		Optional[int]:
						UpperCAmelCase__   		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase__   		=						Image.open(requests.get(lowercase_						,     stream=lowercase_							).raw							)
						return image


def 					a__ (						UpperCAmelCase__    :     List[str]						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     List[Any]						,     UpperCAmelCase__    :     Tuple							)->		int:
						UpperCAmelCase__   		=						get_clipseg_config(lowercase_							)
						UpperCAmelCase__   		=						CLIPSegForImageSegmentation(lowercase_							)
						model.eval()

						UpperCAmelCase__   		=						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
						UpperCAmelCase__   		=						convert_state_dict(lowercase_						,     lowercase_							)
						UpperCAmelCase__		, UpperCAmelCase__   		=						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}'							)

						UpperCAmelCase__   		=						ViTImageProcessor(size=3_5_2							)
						UpperCAmelCase__   		=						CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""							)
						UpperCAmelCase__   		=						CLIPSegProcessor(image_processor=lowercase_						,     tokenizer=lowercase_							)

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

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

						with torch.no_grad():
												UpperCAmelCase__   		=						model(**lowercase_							)

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

						UpperCamelCase_    :    Optional[int]											=						parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""







from collections import namedtuple

UpperCamelCase_    :    Any											=						namedtuple("""from_to""", """from_ to""")

UpperCamelCase_    :    Union[str, Any]											=						{
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

UpperCamelCase_    :    str											=						{
    """mm""": from_to(0.001, 1000),
    """cm""": from_to(0.01, 100),
    """m""": from_to(1, 1),
    """km""": from_to(1000, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.2_8084),
    """yd""": from_to(0.9144, 1.0_9361),
    """mi""": from_to(1609.34, 0.0_0062_1371),
}


def 					a__ (						UpperCAmelCase__    :     float						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     str     )->		float:
						UpperCAmelCase__   		=						from_type.lower().rstrip("""s"""     )
						UpperCAmelCase__   		=						TYPE_CONVERSION.get(UpperCAmelCase__						,     UpperCAmelCase__     )
						UpperCAmelCase__   		=						to_type.lower().rstrip("""s"""     )
						UpperCAmelCase__   		=						TYPE_CONVERSION.get(UpperCAmelCase__						,     UpperCAmelCase__     )
						if new_from not in METRIC_CONVERSION:
												UpperCAmelCase__   		=						(
												    f'Invalid \'from_type\' value: {from_type!r}.\n'
												    f'Conversion abbreviations are: {", ".join(UpperCAmelCase__     )}'
												)
												raise ValueError(UpperCAmelCase__     )
						if new_to not in METRIC_CONVERSION:
												UpperCAmelCase__   		=						(
												    f'Invalid \'to_type\' value: {to_type!r}.\n'
												    f'Conversion abbreviations are: {", ".join(UpperCAmelCase__     )}'
												)
												raise ValueError(UpperCAmelCase__     )
						return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
						import doctest

						doctest.testmod()

