


import argparse

import requests
import torch
from PIL import Image

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




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""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(
						    SCREAMING_SNAKE_CASE__       ,    SCREAMING_SNAKE_CASE__       ,    use_complex_transposed_convolution=SCREAMING_SNAKE_CASE__       ,    reduce_dim=SCREAMING_SNAKE_CASE__       ,    )
						return config




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						for key in orig_state_dict.copy().keys():
												a_					=		orig_state_dict.pop(SCREAMING_SNAKE_CASE__      )

												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(SCREAMING_SNAKE_CASE__      )
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								a_					=		val.T
																		a_					=		val

						return orig_state_dict




def   lowerCamelCase_       (					):



						"""simple docstring"""




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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		get_clipseg_config(SCREAMING_SNAKE_CASE__      )
						a_					=		CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__      )
						model.eval()

						a_					=		torch.load(SCREAMING_SNAKE_CASE__       ,    map_location="""cpu"""      )

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

    # rename some keys
						a_					=		convert_state_dict(SCREAMING_SNAKE_CASE__       ,    SCREAMING_SNAKE_CASE__      )
						a_					=		model.load_state_dict(SCREAMING_SNAKE_CASE__       ,    strict=SCREAMING_SNAKE_CASE__      )

						if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
												raise ValueError("""Missing keys that are not expected: {}""".format(SCREAMING_SNAKE_CASE__      )      )
						if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
												raise ValueError(F"Unexpected keys: {unexpected_keys}"      )

						a_					=		ViTImageProcessor(size=352      )
						a_					=		CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""      )
						a_					=		CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__       ,    tokenizer=SCREAMING_SNAKE_CASE__      )

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

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

						with torch.no_grad():
												a_					=		model(**SCREAMING_SNAKE_CASE__      )

						# 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]       ,    SCREAMING_SNAKE_CASE__       ,    atol=1e-3      )
						assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,    SCREAMING_SNAKE_CASE__       ,    atol=1e-3      )
						assert torch.allclose(outputs.pooled_output[0, :3]       ,    SCREAMING_SNAKE_CASE__       ,    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(SCREAMING_SNAKE_CASE__      )
												processor.save_pretrained(SCREAMING_SNAKE_CASE__      )

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



import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A_    :					Tuple             =TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=UpperCamelCase__)
class        lowercase_				( abc.ABC):






							"""simple docstring"""







							snake_case_        =		42
							snake_case_        =		42
							snake_case_        =		42


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													return copy.deepcopy(self		)


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return Features(**self.input_schema			,    **self.label_schema		)


							@property
							@abc.abstractmethod
							def 						lowercase__     (   self		):







													"""simple docstring"""


													raise NotImplementedError






							@classmethod
							def 						lowercase__     (   cls			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		{f.name for f in dataclasses.fields(cls		)}
													return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)