


'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

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







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[Any]							=				CLIPSegTextConfig()
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				CLIPSegVisionConfig(patch_size=16					)

				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				True if """refined""" in model_name else False
				_SCREAMING_SNAKE_CASE			:	Dict							=				16 if """rd16""" in model_name else 64

				_SCREAMING_SNAKE_CASE			:	int							=				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       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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

				return name







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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

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

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

				return orig_state_dict







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				_SCREAMING_SNAKE_CASE			:	int							=				Image.open(requests.get(SCREAMING_SNAKE_CASE__							,				stream=SCREAMING_SNAKE_CASE__					).raw					)
				return image







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	int							=				get_clipseg_config(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__					)
				model.eval()

				_SCREAMING_SNAKE_CASE			:	Tuple							=				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
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				convert_state_dict(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	str							=				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}"""					)

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

				_SCREAMING_SNAKE_CASE			:	Dict							=				prepare_img()
				_SCREAMING_SNAKE_CASE			:	str							=				["""a glass""", """something to fill""", """wood""", """a jar"""]

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

				with torch.no_grad():
								_SCREAMING_SNAKE_CASE			:	str							=				model(**SCREAMING_SNAKE_CASE__					)

				# verify values
				_SCREAMING_SNAKE_CASE			:	Any							=				torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]					)
				if model_name == "clipseg-rd64-refined":
								_SCREAMING_SNAKE_CASE			:	Dict							=				torch.tensor(
								    [[-10.0407, -9.9_4_3_1, -10.2646], [-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":
								_SCREAMING_SNAKE_CASE			:	Any							=				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":
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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]							,				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__":
				UpperCAmelCase_		:       Optional[int]							       =		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_		:       Union[str, Any]							       =		parser.parse_args()
				convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       List[Any]							       =		{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






							def __init__(   self   , __snake_case = 1   , __snake_case = 512 * 9   , __snake_case = 512 * 3   , __snake_case = 512   , __snake_case = 512   , __snake_case = 512   , __snake_case = 128   , __snake_case = 5   , __snake_case = 1024   , __snake_case = "multi_hop"   , __snake_case = 512   , __snake_case = False   , __snake_case = 12   , __snake_case = 768   , __snake_case = 768   , __snake_case = 32   , __snake_case = 0.1   , __snake_case = 0.1   , __snake_case = 0.0   , __snake_case = False   , __snake_case = False   , __snake_case = False   , __snake_case = "gelu"   , __snake_case = None   , __snake_case = False   , __snake_case = 0   , __snake_case = False   , __snake_case = 0.0   , __snake_case = 8   , __snake_case = None   , __snake_case = None   , __snake_case = True   , __snake_case = True   , __snake_case=0   , __snake_case=1   , __snake_case=2   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_classes
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_atoms
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_in_degree
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				num_out_degree
											_SCREAMING_SNAKE_CASE			:	Any							=				num_edges
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_spatial
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_edge_dis
											_SCREAMING_SNAKE_CASE			:	int							=				edge_type
											_SCREAMING_SNAKE_CASE			:	Tuple							=				multi_hop_max_dist
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				spatial_pos_max
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_nodes
											_SCREAMING_SNAKE_CASE			:	str							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	int							=				ffn_embedding_dim
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dropout
											_SCREAMING_SNAKE_CASE			:	List[str]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	int							=				layerdrop
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				encoder_normalize_before
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				pre_layernorm
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				apply_graphormer_init
											_SCREAMING_SNAKE_CASE			:	List[str]							=				activation_fn
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				embed_scale
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				freeze_embeddings
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_trans_layers_to_freeze
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				share_input_output_embed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				traceable
											_SCREAMING_SNAKE_CASE			:	List[str]							=				q_noise
											_SCREAMING_SNAKE_CASE			:	str							=				qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE			:	Any							=				kdim
											_SCREAMING_SNAKE_CASE			:	int							=				vdim
											_SCREAMING_SNAKE_CASE			:	Any							=				self_attention
											_SCREAMING_SNAKE_CASE			:	Tuple							=				bias

											super().__init__(
											    pad_token_id=__snake_case   , bos_token_id=__snake_case   , eos_token_id=__snake_case   , **__snake_case   , )




