'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

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



def  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[Any]       =CLIPSegTextConfig()
     _UpperCamelCase  :List[str]       =CLIPSegVisionConfig(patch_size=16      )

     _UpperCamelCase  :List[Any]       =True if """refined""" in model_name else False
     _UpperCamelCase  :str       =16 if """rd16""" in model_name else 64

     _UpperCamelCase  :str       =CLIPSegConfig.from_text_vision_configs(
         __a			,      __a			,      use_complex_transposed_convolution=__a			,      reduce_dim=__a			,      )
     return config



def  _lowerCAmelCase				(				__a      )  ->      List[str]:
     '''simple docstring'''







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

     return name



def  _lowerCAmelCase				(				__a			,      __a      )  ->      Tuple:
     '''simple docstring'''







     for key in orig_state_dict.copy().keys():
          _UpperCamelCase  :str       =orig_state_dict.pop(__a      )

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

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

     return orig_state_dict



def  _lowerCAmelCase				(				)  ->      str:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a      )  ->      Dict:
     '''simple docstring'''







     _UpperCamelCase  :str       =get_clipseg_config(__a      )
     _UpperCamelCase  :Dict       =CLIPSegForImageSegmentation(__a      )
     model.eval()

     _UpperCamelCase  :List[str]       =torch.load(__a			,      map_location="""cpu"""      )

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

    # rename some keys
     _UpperCamelCase  :Tuple       =convert_state_dict(__a			,      __a      )
     _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =model.load_state_dict(__a			,      strict=__a      )

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

     _UpperCamelCase  :Dict       =ViTImageProcessor(size=3_52      )
     _UpperCamelCase  :Optional[int]       =CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""      )
     _UpperCamelCase  :str       =CLIPSegProcessor(image_processor=__a			,      tokenizer=__a      )

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

     _UpperCamelCase  :Optional[Any]       =processor(text=__a			,      images=[image] * len(__a      )			,      padding="""max_length"""			,      return_tensors="""pt"""      )

     with torch.no_grad():
          _UpperCamelCase  :List[Any]       =model(**__a      )

     # verify values
     _UpperCamelCase  :List[Any]       =torch.tensor([0.11_10, -0.18_82, 0.16_45]      )
     _UpperCamelCase  :List[Any]       =torch.tensor([0.26_92, -0.71_97, -0.13_28]      )
     if model_name == "clipseg-rd64-refined":
          _UpperCamelCase  :List[str]       =torch.tensor(
              [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]      )
     elif model_name == "clipseg-rd64":
          _UpperCamelCase  :Dict       =torch.tensor(
              [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]      )
     elif model_name == "clipseg-rd16":
          _UpperCamelCase  :Optional[int]       =torch.tensor(
              [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]      )
     else:
          raise ValueError(F'''Model name {model_name} not supported.'''      )

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

     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__":
      _lowerCamelCase :				Optional[Any]														= 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."""
      )

      _lowerCamelCase :				int														= parser.parse_args()
      convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]			)   ->					Tuple:
									A				:       Dict														=    val
									# Number of nodes in left subtree
									A				:       List[Any]														=    0
									A				:       List[str]														=    None
									A				:       Union[str, Any]														=    None
									A				:       Dict														=    None







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      Union[str, Any]			)   ->					Optional[Any]:
									assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

									if self.val < other.val:
												A				:       List[str]														=    self.right
												A				:       Any														=    None
												if self.right:
															A				:       Dict														=    other
												A				:       Optional[int]														=    other
												A				:       int														=    self.left_tree_size * 2 + 1
												return self
									else:
												A				:       Optional[Any]														=    other.right
												A				:       Dict														=    None
												if other.right:
															A				:       str														=    self
												A				:       Any														=    self
												A				:       int														=    other.left_tree_size * 2 + 1
												return other







class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[Any]=None						,						__lowerCamelCase   :      Union[str, Any]=None						,						__lowerCamelCase   :      Union[str, Any]=0			)   ->					Optional[Any]:
									A				:       List[str]														=    heap_size
									A				:       Optional[int]														=    bottom_root
									A				:       str														=    min_node







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[int]			)   ->					Optional[int]:

									# Empty heaps corner cases
									if other.size == 0:
												return None
									if self.size == 0:
												A				:       int														=    other.size
												A				:       Dict														=    other.bottom_root
												A				:       Tuple														=    other.min_node
												return None
									# Update size
									A				:       Optional[Any]														=    self.size + other.size

									# Update min.node
									if self.min_node.val > other.min_node.val:
												A				:       Tuple														=    other.min_node
									# Merge

									# Order roots by left_subtree_size
									A				:       Tuple														=    []
									A							,			A				:       List[str]														=    self.bottom_root, other.bottom_root
									while i or j:
												if i and ((not j) or i.left_tree_size < j.left_tree_size):
															combined_roots_list.append((i, True)			)
															A				:       List[Any]														=    i.parent
												else:
															combined_roots_list.append((j, False)			)
															A				:       Optional[Any]														=    j.parent
        # Insert links between them
									for i in range(len(__lowerCamelCase			) - 1			):
												if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
															A				:       str														=    combined_roots_list[i + 1][0]
															A				:       Any														=    combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
									A				:       Dict														=    combined_roots_list[0][0]
									while i.parent:
												if (
												    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
												) or (
												    i.left_tree_size == i.parent.left_tree_size
												    and i.left_tree_size != i.parent.parent.left_tree_size
												):
															# Neighbouring Nodes
															A				:       Union[str, Any]														=    i.left
															A				:       Tuple														=    i.parent.parent

															# Merging trees
															A				:       Optional[int]														=    i.merge_trees(i.parent			)

															# Updating links
															A				:       List[str]														=    previous_node
															A				:       int														=    next_node
															if previous_node:
																		A				:       int														=    i
															if next_node:
																		A				:       Optional[int]														=    i
												else:
															A				:       str														=    i.parent
        # Updating self.bottom_root
									while i.left:
												A				:       List[str]														=    i.left
									A				:       List[str]														=    i

									# Update other
									A				:       Optional[Any]														=    self.size
									A				:       Any														=    self.bottom_root
									A				:       Optional[Any]														=    self.min_node

									# Return the merged heap
									return self







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      int			)   ->					Optional[Any]:
									if self.size == 0:
												A				:       Dict														=    Node(__lowerCamelCase			)
												A				:       List[Any]														=    1
												A				:       Optional[Any]														=    self.bottom_root
									else:
												# Create new node
												A				:       Tuple														=    Node(__lowerCamelCase			)

												# Update size
												self.size += 1

												# update min_node
												if val < self.min_node.val:
															A				:       List[Any]														=    new_node
												# Put new_node as a bottom_root in heap
												A				:       int														=    new_node
												A				:       Tuple														=    self.bottom_root
												A				:       Any														=    new_node

												# Consecutively merge roots with same left_tree_size
												while (
												    self.bottom_root.parent
												    and self.bottom_root.left_tree_size
												    == self.bottom_root.parent.left_tree_size
												):
															# Next node
															A				:       List[Any]														=    self.bottom_root.parent.parent

															# Merge
															A				:       Optional[Any]														=    self.bottom_root.merge_trees(self.bottom_root.parent			)

															# Update Links
															A				:       List[Any]														=    next_node
															A				:       List[str]														=    None
															if next_node:
																		A				:       List[Any]														=    self.bottom_root







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									return self.min_node.val







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Tuple:
									return self.size == 0







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Optional[int]:
									A				:       List[str]														=    self.min_node.val

									# Last element in heap corner case
									if self.size == 1:
												# Update size
												A				:       Union[str, Any]														=    0

												# Update bottom root
												A				:       int														=    None

												# Update min_node
												A				:       Optional[int]														=    None

												return min_value
									# No right subtree corner case
									# The structure of the tree implies that this should be the bottom root
									# and there is at least one other root
									if self.min_node.right is None:
												# Update size
												self.size -= 1

												# Update bottom root
												A				:       List[str]														=    self.bottom_root.parent
												A				:       Dict														=    None

												# Update min_node
												A				:       List[str]														=    self.bottom_root
												A				:       Dict														=    self.bottom_root.parent
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       List[Any]														=    i.parent
												return min_value
									# General case
									# Find the BinomialHeap of the right subtree of min_node
									A				:       List[str]														=    self.min_node.right
									A				:       List[Any]														=    None
									A				:       Tuple														=    bottom_of_new
									A				:       Optional[Any]														=    1

									# Size, min_node and bottom_root
									while bottom_of_new.left:
												A				:       List[str]														=    size_of_new * 2 + 1
												A				:       int														=    bottom_of_new.left
												if bottom_of_new.val < min_of_new.val:
															A				:       int														=    bottom_of_new
        # Corner case of single root on top left path
									if (not self.min_node.left) and (not self.min_node.parent):
												A				:       Optional[Any]														=    size_of_new
												A				:       List[str]														=    bottom_of_new
												A				:       Optional[int]														=    min_of_new
												# print("Single root, multiple nodes case")
												return min_value
									# Remaining cases
									# Construct heap of right subtree
									A				:       List[str]														=    BinomialHeap(
									    bottom_root=__lowerCamelCase						,						min_node=__lowerCamelCase						,						heap_size=__lowerCamelCase			)

									# Update size
									A				:       Tuple														=    self.size - 1 - size_of_new

									# Neighbour nodes
									A				:       List[str]														=    self.min_node.left
									A				:       List[str]														=    self.min_node.parent

									# Initialize new bottom_root and min_node
									A				:       List[Any]														=    previous_node or next_node
									A				:       int														=    next_node

									# Update links of previous_node and search below for new min_node and
									# bottom_root
									if previous_node:
												A				:       Optional[int]														=    next_node

												# Update bottom_root and search for min_node below
												A				:       Optional[Any]														=    previous_node
												A				:       Dict														=    previous_node
												while self.bottom_root.left:
															A				:       List[str]														=    self.bottom_root.left
															if self.bottom_root.val < self.min_node.val:
																		A				:       Optional[Any]														=    self.bottom_root
									if next_node:
												A				:       Any														=    previous_node

												# Search for new min_node above min_node
												A				:       int														=    next_node
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       Any														=    i.parent
        # Merge heaps
									self.merge_heaps(__lowerCamelCase			)

									return min_value







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Optional[Any]:
									A				:       Tuple														=    self.bottom_root
									while top_root.parent:
												A				:       Optional[int]														=    top_root.parent
									# preorder
									A				:       Union[str, Any]														=    []
									self.__traversal(__lowerCamelCase						,						__lowerCamelCase			)
									return heap_pre_order







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]						,						__lowerCamelCase   :      Optional[int]						,						__lowerCamelCase   :      Any=0			)   ->					List[Any]:
									if curr_node:
												preorder.append((curr_node.val, level)			)
												self.__traversal(curr_node.left						,						__lowerCamelCase						,						level + 1			)
												self.__traversal(curr_node.right						,						__lowerCamelCase						,						level + 1			)
									else:
												preorder.append(("#", level)			)







						def __str__(						self   :      List[str]			)   ->					Optional[Any]:
									if self.is_empty():
												return ""
									A				:       Optional[int]														=    self.pre_order()

									return "\n".join(("-" * level + str(__lowerCamelCase			)) for value, level in preorder_heap			)


# Unit Tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()