




import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a_   		=     {
    "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
    "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
    "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
    "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
    "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
    "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
    "mask_downscaling.0": "mask_embed.conv1",
    "mask_downscaling.1": "mask_embed.layer_norm1",
    "mask_downscaling.3": "mask_embed.conv2",
    "mask_downscaling.4": "mask_embed.layer_norm2",
    "mask_downscaling.6": "mask_embed.conv3",
    "point_embeddings": "point_embed",
    "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",
    "image_encoder": "vision_encoder",
    "neck.0": "neck.conv1",
    "neck.1": "neck.layer_norm1",
    "neck.2": "neck.conv2",
    "neck.3": "neck.layer_norm2",
    "patch_embed.proj": "patch_embed.projection",
    ".norm": ".layer_norm",
    "blocks": "layers",
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						{}
    state_dict.pop('''pixel_mean'''					,     lowercase_      )
    state_dict.pop('''pixel_std'''					,     lowercase_      )

    __UpperCamelCase  		=						r'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

    for key, value in state_dict.items():
        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
            if key_to_modify in key:
                __UpperCamelCase  		=						key.replace(lowercase_					,     lowercase_      )

        if re.match(lowercase_					,     lowercase_      ):
            __UpperCamelCase  		=						int(re.match(lowercase_					,     lowercase_      ).group(2      )      )
            if layer_nb == 0:
                __UpperCamelCase  		=						key.replace('''layers.0'''					,     '''proj_in'''      )
            elif layer_nb == 1:
                __UpperCamelCase  		=						key.replace('''layers.1'''					,     '''layers.0'''      )
            elif layer_nb == 2:
                __UpperCamelCase  		=						key.replace('''layers.2'''					,     '''proj_out'''      )

        __UpperCamelCase  		=						value

    __UpperCamelCase  		=						model_state_dict[
        '''prompt_encoder.shared_embedding.positional_embedding'''
    ]

    return model_state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_="ybelkada/segment-anything"      )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						hf_hub_download(lowercase_					,     F"checkpoints/{model_name}.pth"      )

    if "sam_vit_b" in model_name:
        __UpperCamelCase  		=						SamConfig()
    elif "sam_vit_l" in model_name:
        __UpperCamelCase  		=						SamVisionConfig(
            hidden_size=10_24					,     num_hidden_layers=24					,     num_attention_heads=16					,     global_attn_indexes=[5, 11, 17, 23]					,     )

        __UpperCamelCase  		=						SamConfig(
            vision_config=lowercase_					,     )
    elif "sam_vit_h" in model_name:
        __UpperCamelCase  		=						SamVisionConfig(
            hidden_size=12_80					,     num_hidden_layers=32					,     num_attention_heads=16					,     global_attn_indexes=[7, 15, 23, 31]					,     )

        __UpperCamelCase  		=						SamConfig(
            vision_config=lowercase_					,     )

    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    __UpperCamelCase  		=						replace_keys(lowercase_      )

    __UpperCamelCase  		=						SamImageProcessor()

    __UpperCamelCase  		=						SamProcessor(image_processor=lowercase_      )
    __UpperCamelCase  		=						SamModel(lowercase_      )

    hf_model.load_state_dict(lowercase_      )
    __UpperCamelCase  		=						hf_model.to('''cuda'''      )

    __UpperCamelCase  		=						'''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
    __UpperCamelCase  		=						Image.open(requests.get(lowercase_					,     stream=lowercase_      ).raw      ).convert('''RGB'''      )

    __UpperCamelCase  		=						[[[4_00, 6_50]]]
    __UpperCamelCase  		=						[[1]]

    __UpperCamelCase  		=						processor(images=np.array(lowercase_      )					,     return_tensors='''pt'''      ).to('''cuda'''      )

    with torch.no_grad():
        __UpperCamelCase  		=						hf_model(**lowercase_      )
    __UpperCamelCase  		=						output.iou_scores.squeeze()

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579_8902_5115_9668

        __UpperCamelCase  		=						processor(
            images=np.array(lowercase_      )					,     input_points=lowercase_					,     input_labels=lowercase_					,     return_tensors='''pt'''      ).to('''cuda'''      )

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9712_6030_9219_3604

        __UpperCamelCase  		=						((75, 2_75, 17_25, 8_50),)

        __UpperCamelCase  		=						processor(images=np.array(lowercase_      )					,     input_boxes=lowercase_					,     return_tensors='''pt'''      ).to('''cuda'''      )

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

        assert scores[-1].item() == 0.8686_0156_0592_6514

        # Test with 2 points and 1 image.
        __UpperCamelCase  		=						[[[4_00, 6_50], [8_00, 6_50]]]
        __UpperCamelCase  		=						[[1, 1]]

        __UpperCamelCase  		=						processor(
            images=np.array(lowercase_      )					,     input_points=lowercase_					,     input_labels=lowercase_					,     return_tensors='''pt'''      ).to('''cuda'''      )

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 a_   		=     ["sam_vit_b_01ec64", "sam_vit_h_4b8939", "sam_vit_l_0b3195"]
 parser.add_argument(
     "--model_name",
     default="sam_vit_h_4b8939",
     choices=choices,
     type=str,
     help="Path to hf config.json of model to convert",
 )
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
     help="Whether to push the model and processor to the hub after converting",
 )
 parser.add_argument(
     "--model_hub_id",
     default="ybelkada/segment-anything",
     choices=choices,
     type=str,
     help="Path to hf config.json of model to convert",
 )

 a_   		=     parser.parse_args()

 convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)












from __future__ import annotations

import queue

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						data
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						None







def 					__SCREAMING_SNAKE_CASE   (  )       ->	TreeNode:

    """simple docstring"""


    print('''\n********Press N to stop entering at any point of time********\n'''      )
    __UpperCamelCase  		=						input('''Enter the value of the root node: '''      ).strip().lower()
    __UpperCamelCase  		=						queue.Queue()
    __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						q.get()
        __UpperCamelCase  		=						F"Enter the left node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						left_node
        q.put(lowercase_      )
        __UpperCamelCase  		=						F"Enter the right node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						right_node
        q.put(lowercase_      )
    raise




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    print(node.data					,     end=''','''      )
    pre_order(node.left      )
    pre_order(node.right      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    in_order(node.left      )
    print(node.data					,     end=''','''      )
    in_order(node.right      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    post_order(node.left      )
    post_order(node.right      )
    print(node.data					,     end=''','''      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						q.get()
        print(node_dequeued.data					,     end=''','''      )
        if node_dequeued.left:
            q.put(node_dequeued.left      )
        if node_dequeued.right:
            q.put(node_dequeued.right      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						[]
        while not q.empty():
            __UpperCamelCase  		=						q.get()
            print(node_dequeued.data					,     end=''','''      )
            if node_dequeued.left:
                list_.append(node_dequeued.left      )
            if node_dequeued.right:
                list_.append(node_dequeued.right      )
        print()
        for node in list_:
            q.put(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data					,     end=''','''      )
            stack.append(lowercase_      )
            __UpperCamelCase  		=						n.left
        # end of while means current node doesn't have left child
        __UpperCamelCase  		=						stack.pop()
        # start to traverse its right child
        __UpperCamelCase  		=						n.right




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						node
    while n or stack:
        while n:
            stack.append(lowercase_      )
            __UpperCamelCase  		=						n.left
        __UpperCamelCase  		=						stack.pop()
        print(n.data					,     end=''','''      )
        __UpperCamelCase  		=						n.right




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase		,      __UpperCamelCase  		=						[], []
    __UpperCamelCase  		=						node
    stacka.append(lowercase_      )
    while stacka:  # to find the reversed order of post order, store it in stack2
        __UpperCamelCase  		=						stacka.pop()
        if n.left:
            stacka.append(n.left      )
        if n.right:
            stacka.append(n.right      )
        stacka.append(lowercase_      )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data					,     end=''','''      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_ = ""					,     lowercase_=50					,     lowercase_="*"      )       ->	str:

    """simple docstring"""


    if not s:
        return "\n" + width * char
    __UpperCamelCase		,      __UpperCamelCase  		=						divmod(width - len(lowercase_      ) - 2					,     2      )
    return F"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(prompt("Binary Tree Traversals"))

 a_   		=     build_tree()
 print(prompt("Pre Order Traversal"))
 pre_order(node)
 print(prompt() + "\n")

 print(prompt("In Order Traversal"))
 in_order(node)
 print(prompt() + "\n")

 print(prompt("Post Order Traversal"))
 post_order(node)
 print(prompt() + "\n")

 print(prompt("Level Order Traversal"))
 level_order(node)
 print(prompt() + "\n")

 print(prompt("Actual Level Order Traversal"))
 level_order_actual(node)
 print("*" * 50 + "\n")

 print(prompt("Pre Order Traversal - Iteration Version"))
 pre_order_iter(node)
 print(prompt() + "\n")

 print(prompt("In Order Traversal - Iteration Version"))
 in_order_iter(node)
 print(prompt() + "\n")

 print(prompt("Post Order Traversal - Iteration Version"))
 post_order_iter(node)
 print(prompt())






