



'''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						) ->     Optional[int]:
							UpperCAmelCase	:						Optional[int]					=						CLIPSegTextConfig()
							UpperCAmelCase	:						Any					=						CLIPSegVisionConfig(patch_size=1_6						)

							UpperCAmelCase	:						Any					=						True if """refined""" in model_name else False
							UpperCAmelCase	:						Any					=						1_6 if """rd16""" in model_name else 6_4

							UpperCAmelCase	:						int					=						CLIPSegConfig.from_text_vision_configs(
							    _lowercase      ,							_lowercase      ,							use_complex_transposed_convolution=_lowercase      ,							reduce_dim=_lowercase      ,							)
							return config



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

							return name



def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     Union[str, Any]:
							for key in orig_state_dict.copy().keys():
														UpperCAmelCase	:						List[Any]					=						orig_state_dict.pop(_lowercase						)

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

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

							return orig_state_dict



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



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     List[Any]:
							UpperCAmelCase	:						List[Any]					=						get_clipseg_config(_lowercase						)
							UpperCAmelCase	:						Optional[Any]					=						CLIPSegForImageSegmentation(_lowercase						)
							model.eval()

							UpperCAmelCase	:						List[str]					=						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	:						Optional[int]					=						convert_state_dict(_lowercase      ,							_lowercase						)
							UpperCAmelCase	,	UpperCAmelCase	:						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}'''						)

							UpperCAmelCase	:						Optional[Any]					=						ViTImageProcessor(size=3_5_2						)
							UpperCAmelCase	:						Dict					=						CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""						)
							UpperCAmelCase	:						Dict					=						CLIPSegProcessor(image_processor=_lowercase      ,							tokenizer=_lowercase						)

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

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

							with torch.no_grad():
														UpperCAmelCase	:						Any					=						model(**_lowercase						)

							# verify values
							UpperCAmelCase	:						Tuple					=						torch.tensor([0.1110, -0.1882, 0.1645]						)
							UpperCAmelCase	:						Optional[int]					=						torch.tensor([0.2692, -0.7197, -0.1328]						)
							if model_name == "clipseg-rd64-refined":
														UpperCAmelCase	:						Optional[Any]					=						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":
														UpperCAmelCase	:						Tuple					=						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":
														UpperCAmelCase	:						Union[str, 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  :     Dict       =       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[Any]       =       parser.parse_args()
							convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import sys



def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     int:
							if not numbers:
														return 0
							if target == 0 and target in numbers:
														return 0
							if not isinstance(_lowercase      ,							(list, tuple)						) or not all(
							    isinstance(_lowercase      ,							_lowercase						) for number in numbers						):
														raise ValueError("""numbers must be an iterable of integers"""						)

							UpperCAmelCase	:						Optional[Any]					=						0
							UpperCAmelCase	:						List[str]					=						sys.maxsize

							while right < len(_lowercase						):
														curr_sum += numbers[right]
														while curr_sum >= target and left <= right:
																					UpperCAmelCase	:						Optional[Any]					=						min(_lowercase      ,							right - left + 1						)
																					curr_sum -= numbers[left]
																					left += 1
														right += 1

							return 0 if min_len == sys.maxsize else min_len

