



"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

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

def 					_lowerCAmelCase (  lowercase_		):
						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(
						    lowercase_						,      lowercase_						,      use_complex_transposed_convolution=lowercase_						,      reduce_dim=lowercase_						,      )
						return config

def 					_lowerCAmelCase (  lowercase_		):
						# update prefixes
						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 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						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 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					'http://images.cocodataset.org/val2017/000000039769.jpg'
						UpperCAmelCase			 =					Image.open(requests.get(lowercase_						,      stream=lowercase_		).raw		)
						return image

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						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=352		)
						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([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]		)
						UpperCAmelCase			 =					torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]		)
						if model_name == "clipseg-rd64-refined":
												UpperCAmelCase			 =					torch.tensor(
												    [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-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":
												UpperCAmelCase			 =					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":
												UpperCAmelCase			 =					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]						,      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__":
				snake_case_								=			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."""
				)

				snake_case_								=			parser.parse_args()
				convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""


from itertools import permutations

def 					_lowerCAmelCase (  lowercase_ = 5		):
						if gon_side < 3 or gon_side > 5:
												raise ValueError('gon_side must be in the range [3, 5]'		)

						# Since it's 16, we know 10 is on the outer ring
						# Put the big numbers at the end so that they are never the first number
						UpperCAmelCase			 =					list(range(gon_side + 1						,      0						,      -1		)		)
						UpperCAmelCase			 =					list(range(gon_side + 2						,      gon_side * 2 + 1		)		)

						for perm in permutations(small_numbers + big_numbers		):
												UpperCAmelCase			 =					generate_gon_ring(lowercase_						,      list(lowercase_		)		)
												if is_magic_gon(lowercase_		):
																		return int(''.join(str(lowercase_		) for n in numbers		)		)

						UpperCAmelCase			 =					F"""Magic {gon_side}-gon ring is impossible"""
						raise ValueError(lowercase_		)

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					[0] * (gon_side * 3)
						UpperCAmelCase			 =					perm[0:3]
						perm.append(perm[1]		)

						UpperCAmelCase			 =					1 if gon_side < 5 else 2

						for i in range(1						,      len(lowercase_		) // 3 + magic_number		):
												UpperCAmelCase			 =					perm[2 * i + 1]
												UpperCAmelCase			 =					result[3 * i - 1]
												UpperCAmelCase			 =					perm[2 * i + 2]

						return result

def 					_lowerCAmelCase (  lowercase_		):
						if len(lowercase_		) % 3 != 0:
												raise ValueError('a gon ring should have a length that is a multiple of 3'		)

						if min(numbers[::3]		) != numbers[0]:
												return False

						UpperCAmelCase			 =					sum(numbers[:3]		)

						return all(sum(numbers[i : i + 3]		) == total for i in range(3						,      len(lowercase_		)						,      3		)		)


if __name__ == "__main__":
				print(solution())





