



import argparse

import requests
import torch
from PIL import Image

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



def        __a     (    lowerCAmelCase_   :					List[str]				)      -> int:






						'''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(
						    lowerCAmelCase_			,lowerCAmelCase_			,use_complex_transposed_convolution=lowerCAmelCase_			,reduce_dim=lowerCAmelCase_			,)
						return config



def        __a     (    lowerCAmelCase_   :					Union[str, Any]				)      -> Union[str, Any]:






						'''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        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Union[str, Any]				)      -> List[Any]:






						'''simple docstring'''



						for key in orig_state_dict.copy().keys():
												UpperCAmelCase_=   orig_state_dict.pop(lowerCAmelCase_				)

												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(lowerCAmelCase_				)
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								UpperCAmelCase_=   val.T
																		UpperCAmelCase_=   val

						return orig_state_dict



def        __a     (    )      -> Optional[Any]:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					List[str]				)      -> Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=   get_clipseg_config(lowerCAmelCase_				)
						UpperCAmelCase_=   CLIPSegForImageSegmentation(lowerCAmelCase_				)
						model.eval()

						UpperCAmelCase_=   torch.load(lowerCAmelCase_			,map_location="""cpu"""				)

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

    # rename some keys
						UpperCAmelCase_=   convert_state_dict(lowerCAmelCase_			,lowerCAmelCase_				)
						UpperCAmelCase_,   UpperCAmelCase_=   model.load_state_dict(lowerCAmelCase_			,strict=lowerCAmelCase_				)

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

						UpperCAmelCase_=   ViTImageProcessor(size=3_52				)
						UpperCAmelCase_=   CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""				)
						UpperCAmelCase_=   CLIPSegProcessor(image_processor=lowerCAmelCase_			,tokenizer=lowerCAmelCase_				)

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

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

						with torch.no_grad():
												UpperCAmelCase_=   model(**lowerCAmelCase_				)

						# verify values
						UpperCAmelCase_=   torch.tensor([0.1_110, -0.1_882, 0.1_645]				)
						UpperCAmelCase_=   torch.tensor([0.2_692, -0.7_197, -0.1_328]				)
						if model_name == "clipseg-rd64-refined":
												UpperCAmelCase_=   torch.tensor(
												    [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]				)
						elif model_name == "clipseg-rd64":
												UpperCAmelCase_=   torch.tensor(
												    [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]				)
						elif model_name == "clipseg-rd16":
												UpperCAmelCase_=   torch.tensor(
												    [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]				)
						else:
												raise ValueError(F"""Model name {model_name} not supported."""				)

						assert torch.allclose(outputs.logits[0, :3, :3]			,lowerCAmelCase_			,atol=1E-3				)
						assert torch.allclose(outputs.conditional_embeddings[0, :3]			,lowerCAmelCase_			,atol=1E-3				)
						assert torch.allclose(outputs.pooled_output[0, :3]			,lowerCAmelCase_			,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(lowerCAmelCase_				)
												processor.save_pretrained(lowerCAmelCase_				)

						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)









from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
__A				    =						{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}



def        __a     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



						UpperCAmelCase_=   {node: {node} for node in graph}

						UpperCAmelCase_=   {node: graph[node][:] for node in graph}

						while len(lowerCAmelCase_				) > 2:
												# Choose a random edge.
												UpperCAmelCase_=   random.choice(list(graph_copy.keys()				)				)
												UpperCAmelCase_=   random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												UpperCAmelCase_=   u + v
												UpperCAmelCase_=   list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(lowerCAmelCase_				)
												uv_neighbors.remove(lowerCAmelCase_				)
												UpperCAmelCase_=   uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(lowerCAmelCase_				)

												UpperCAmelCase_=   set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)

    # Find cutset.
						UpperCAmelCase_=   [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))




