



"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def        A_  (      _lowercase     ):







						'''simple docstring'''


						snake_case_			:int						=      CLIPSegTextConfig()
						snake_case_			:str						=      CLIPSegVisionConfig(patch_size=16     )

						snake_case_			:Any						=      True if """refined""" in model_name else False
						snake_case_			:int						=      16 if """rd16""" in model_name else 64

						snake_case_			:Union[str, Any]						=      CLIPSegConfig.from_text_vision_configs(
						    _lowercase, _lowercase, use_complex_transposed_convolution=_lowercase, reduce_dim=_lowercase, )
						return config
def        A_  (      _lowercase     ):







						'''simple docstring'''


						if "clip_model" in name:
												snake_case_			:Optional[Any]						=      name.replace("""clip_model""", """clip"""     )
						if "transformer" in name:
												if "visual" in name:
																		snake_case_			:Optional[int]						=      name.replace("""visual.transformer""", """vision_model"""     )
												else:
																		snake_case_			:Optional[Any]						=      name.replace("""transformer""", """text_model"""     )
						if "resblocks" in name:
												snake_case_			:Union[str, Any]						=      name.replace("""resblocks""", """encoder.layers"""     )
						if "ln_1" in name:
												snake_case_			:List[Any]						=      name.replace("""ln_1""", """layer_norm1"""     )
						if "ln_2" in name:
												snake_case_			:Optional[Any]						=      name.replace("""ln_2""", """layer_norm2"""     )
						if "c_fc" in name:
												snake_case_			:Union[str, Any]						=      name.replace("""c_fc""", """fc1"""     )
						if "c_proj" in name:
												snake_case_			:Any						=      name.replace("""c_proj""", """fc2"""     )
						if "attn" in name and "self" not in name:
												snake_case_			:List[str]						=      name.replace("""attn""", """self_attn"""     )
						# text encoder
						if "token_embedding" in name:
												snake_case_			:Tuple						=      name.replace("""token_embedding""", """text_model.embeddings.token_embedding"""     )
						if "positional_embedding" in name and "visual" not in name:
												snake_case_			:List[str]						=      name.replace("""positional_embedding""", """text_model.embeddings.position_embedding.weight"""     )
						if "ln_final" in name:
												snake_case_			:Any						=      name.replace("""ln_final""", """text_model.final_layer_norm"""     )
						# vision encoder
						if "visual.class_embedding" in name:
												snake_case_			:Optional[Any]						=      name.replace("""visual.class_embedding""", """vision_model.embeddings.class_embedding"""     )
						if "visual.conv1" in name:
												snake_case_			:Optional[int]						=      name.replace("""visual.conv1""", """vision_model.embeddings.patch_embedding"""     )
						if "visual.positional_embedding" in name:
												snake_case_			:Tuple						=      name.replace("""visual.positional_embedding""", """vision_model.embeddings.position_embedding.weight"""     )
						if "visual.ln_pre" in name:
												snake_case_			:Union[str, Any]						=      name.replace("""visual.ln_pre""", """vision_model.pre_layrnorm"""     )
						if "visual.ln_post" in name:
												snake_case_			:Optional[Any]						=      name.replace("""visual.ln_post""", """vision_model.post_layernorm"""     )
						# projection layers
						if "visual.proj" in name:
												snake_case_			:List[Any]						=      name.replace("""visual.proj""", """visual_projection.weight"""     )
						if "text_projection" in name:
												snake_case_			:Dict						=      name.replace("""text_projection""", """text_projection.weight"""     )
						# decoder
						if "trans_conv" in name:
												snake_case_			:Union[str, 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:
												snake_case_			:Optional[Any]						=      """decoder.""" + name
						if "blocks" in name:
												snake_case_			:List[Any]						=      name.replace("""blocks""", """decoder.layers"""     )
						if "linear1" in name:
												snake_case_			:Optional[int]						=      name.replace("""linear1""", """mlp.fc1"""     )
						if "linear2" in name:
												snake_case_			:Union[str, Any]						=      name.replace("""linear2""", """mlp.fc2"""     )
						if "norm1" in name and "layer_" not in name:
												snake_case_			:List[str]						=      name.replace("""norm1""", """layer_norm1"""     )
						if "norm2" in name and "layer_" not in name:
												snake_case_			:str						=      name.replace("""norm2""", """layer_norm2"""     )

						return name
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case_			:Any						=      orig_state_dict.pop(_lowercase     )

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

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

						return orig_state_dict
def        A_  (      ):







						'''simple docstring'''


						snake_case_			:Optional[int]						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_			:Optional[int]						=      Image.open(requests.get(_lowercase, stream=_lowercase     ).raw     )
						return image
def        A_  (      _lowercase, _lowercase, _lowercase, _lowercase     ):







						'''simple docstring'''


						snake_case_			:Union[str, Any]						=      get_clipseg_config(_lowercase     )
						snake_case_			:str						=      CLIPSegForImageSegmentation(_lowercase     )
						model.eval()

						snake_case_			:Any						=      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
						snake_case_			:Dict						=      convert_state_dict(_lowercase, _lowercase     )
						snake_case_,	snake_case_			:List[str]						=      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}"""     )

						snake_case_			:Optional[int]						=      ViTImageProcessor(size=352     )
						snake_case_			:Union[str, Any]						=      CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""     )
						snake_case_			:List[Any]						=      CLIPSegProcessor(image_processor=_lowercase, tokenizer=_lowercase     )

						snake_case_			:Optional[int]						=      prepare_img()
						snake_case_			:List[Any]						=      ["""a glass""", """something to fill""", """wood""", """a jar"""]

						snake_case_			:str						=      processor(text=_lowercase, images=[image] * len(_lowercase     ), padding="""max_length""", return_tensors="""pt"""     )

						with torch.no_grad():
												snake_case_			:int						=      model(**_lowercase     )

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





"""simple docstring"""
from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
						import pyximport

						pyximport.install(setup_args={"include_dirs": np.get_include()})
						from . import algos_graphormer  # noqa E402
def        A_  (      _lowercase, _lowercase = 512     ):







						'''simple docstring'''


						snake_case_			:int						=      x.shape[1] if len(x.shape     ) > 1 else 1
						snake_case_			:List[Any]						=      1 + np.arange(0, feature_num * offset, _lowercase, dtype=np.intaa     )
						snake_case_			:int						=      x + feature_offset
						return x
def        A_  (      _lowercase, _lowercase=True     ):







						'''simple docstring'''


						requires_backends(_lowercase, ["""cython"""]     )

						if keep_features and "edge_attr" in item.keys():  # edge_attr
												snake_case_			:Tuple						=      np.asarray(item["""edge_attr"""], dtype=np.intaa     )
						else:
												snake_case_			:List[str]						=      np.ones((len(item["""edge_index"""][0]     ), 1), dtype=np.intaa     )  # same embedding for all

						if keep_features and "node_feat" in item.keys():  # input_nodes
												snake_case_			:Dict						=      np.asarray(item["""node_feat"""], dtype=np.intaa     )
						else:
												snake_case_			:Tuple						=      np.ones((item["""num_nodes"""], 1), dtype=np.intaa     )  # same embedding for all

						snake_case_			:Any						=      np.asarray(item["""edge_index"""], dtype=np.intaa     )

						snake_case_			:str						=      convert_to_single_emb(_lowercase     ) + 1
						snake_case_			:str						=      item["""num_nodes"""]

						if len(edge_attr.shape     ) == 1:
												snake_case_			:Any						=      edge_attr[:, None]
						snake_case_			:Dict						=      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]], dtype=np.intaa     )
						snake_case_			:str						=      convert_to_single_emb(_lowercase     ) + 1

						# node adj matrix [num_nodes, num_nodes] bool
						snake_case_			:List[str]						=      np.zeros([num_nodes, num_nodes], dtype=_lowercase     )
						snake_case_			:int						=      True

						snake_case_,	snake_case_			:Tuple						=      algos_graphormer.floyd_warshall(_lowercase     )
						snake_case_			:List[str]						=      np.amax(_lowercase     )

						snake_case_			:Dict						=      algos_graphormer.gen_edge_input(_lowercase, _lowercase, _lowercase     )
						snake_case_			:Tuple						=      np.zeros([num_nodes + 1, num_nodes + 1], dtype=np.single     )  # with graph token

						# combine
						snake_case_			:str						=      input_nodes + 1  # we shift all indices by one for padding
						snake_case_			:str						=      attn_bias
						snake_case_			:Optional[Any]						=      attn_edge_type
						snake_case_			:Any						=      shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
						snake_case_			:int						=      np.sum(_lowercase, axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
						snake_case_			:List[str]						=      item["""in_degree"""]  # for undirected graph
						snake_case_			:List[str]						=      input_edges + 1  # we shift all indices by one for padding
						if "labels" not in item:
												snake_case_			:Optional[Any]						=      item["""y"""]

						return item

class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Dict						,					snake_case:							Optional[int]=20						,					snake_case:							List[Any]=False						)       ->     Union[str, Any]:
											if not is_cython_available():
																	raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""						)

											snake_case_			:List[str]						=      spatial_pos_max
											snake_case_			:int						=      on_the_fly_processing






					def __call__(	self:							str						,					snake_case:							List[dict]						)       ->     Dict[str, Any]:
											if self.on_the_fly_processing:
																	snake_case_			:int						=      [preprocess_item(snake_case						) for i in features]

											if not isinstance(features[0]						,					snake_case						):
																	snake_case_			:Tuple						=      [vars(snake_case						) for f in features]
											snake_case_			:Any						=      {}

											snake_case_			:Dict						=      max(len(i["""input_nodes"""]						) for i in features						)
											snake_case_			:List[Any]						=      len(features[0]["""input_nodes"""][0]						)
											snake_case_			:Dict						=      len(features[0]["""attn_edge_type"""][0][0]						)
											snake_case_			:List[Any]						=      max(len(i["""input_edges"""][0][0]						) for i in features						)
											snake_case_			:Any						=      len(features[0]["""input_edges"""][0][0][0]						)
											snake_case_			:List[str]						=      len(snake_case						)

											snake_case_			:Union[str, Any]						=      torch.zeros(snake_case						,					max_node_num + 1						,					max_node_num + 1						,					dtype=torch.float						)
											snake_case_			:int						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Optional[Any]						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Tuple						=      torch.zeros(snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Dict						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Union[str, Any]						=      torch.zeros(
											    snake_case						,					snake_case						,					snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)

											for ix, f in enumerate(snake_case						):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																							snake_case_			:List[str]						=      torch.tensor(f[k]						)

																	if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]						) > 0:
																							snake_case_			:Union[str, Any]						=      float("""-inf"""						)

																	snake_case_			:Optional[Any]						=      f["""attn_bias"""]
																	snake_case_			:int						=      f[
																	    """attn_edge_type"""
																	]
																	snake_case_			:Dict						=      f["""spatial_pos"""]
																	snake_case_			:Tuple						=      f["""in_degree"""]
																	snake_case_			:List[Any]						=      f["""input_nodes"""]
																	snake_case_			:List[Any]						=      f["""input_edges"""]

											snake_case_			:int						=      batch["""in_degree"""]

											snake_case_			:Any						=      features[0]["""labels"""]
											if len(snake_case						) == 1:  # one task
																	if isinstance(sample[0]						,					snake_case						):  # regression
																							snake_case_			:Dict						=      torch.from_numpy(np.concatenate([i["""labels"""] for i in features]						)						)
																	else:  # binary classification
																							snake_case_			:str						=      torch.from_numpy(np.concatenate([i["""labels"""] for i in features]						)						)
											else:  # multi task classification, left to float to keep the NaNs
																	snake_case_			:Optional[int]						=      torch.from_numpy(np.stack([i["""labels"""] for i in features]						,					axis=0						)						)

											return batch
