



import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Any			,							UpperCamelCase_					:							Any			,							UpperCamelCase_					:							str			,							UpperCamelCase_					:							Dict						)->	List[str]:
    if isinstance(UpperCamelCase_			,							UpperCamelCase_						):
        A__					=       np.full((len(UpperCamelCase_						), sequence_length, 2)			,							UpperCamelCase_						)
    else:
        A__					=       np.full((len(UpperCamelCase_						), sequence_length)			,							UpperCamelCase_						)

    for i, tensor in enumerate(UpperCamelCase_						):
        if padding_side == "right":
            if isinstance(UpperCamelCase_			,							UpperCamelCase_						):
                A__					=       tensor[:sequence_length]
            else:
                A__					=       tensor[:sequence_length]
        else:
            if isinstance(UpperCamelCase_			,							UpperCamelCase_						):
                A__					=       tensor[:sequence_length]
            else:
                A__					=       tensor[:sequence_length]

    return out_tensor.tolist()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Dict						)->	Union[str, Any]:
    A__					=       ord(UpperCamelCase_						)
    if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
        return True
    A__					=       unicodedata.category(UpperCamelCase_						)
    if cat.startswith('''P'''						):
        return True
    return False



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					42
     UpperCamelCase__		     =					True
     UpperCamelCase__		     =					None
     UpperCamelCase__		     =					None
     UpperCamelCase__		     =					-100
     UpperCamelCase__		     =					"pt"




     def 		snake_case_		(					self			,    a__):
         import torch

         A__					=       '''label''' if '''label''' in features[0].keys() else '''labels'''
         A__					=       [feature[label_name] for feature in features] if label_name in features[0].keys() else None
         A__					=       self.tokenizer.pad(
             a__			,    padding=self.padding			,    max_length=self.max_length			,    pad_to_multiple_of=self.pad_to_multiple_of			,    return_tensors='''pt''' if labels is None else None			,    )

         if labels is None:
             return batch

         A__					=       torch.tensor(batch['''entity_ids''']).shape[1]
         A__					=       self.tokenizer.padding_side
         if padding_side == "right":
             A__					=       [
                 list(a__) + [self.label_pad_token_id] * (sequence_length - len(a__)) for label in labels
             ]
         else:
             A__					=       [
                 [self.label_pad_token_id] * (sequence_length - len(a__)) + list(a__) for label in labels
             ]

         A__					=       [feature['''ner_tags'''] for feature in features]
         A__					=       padding_tensor(a__			,    -1			,    a__			,    a__)
         A__					=       [feature['''original_entity_spans'''] for feature in features]
         A__					=       padding_tensor(a__			,    (-1, -1)			,    a__			,    a__)
         A__					=       {k: torch.tensor(a__			,    dtype=torch.intaa) for k, v in batch.items()}

         return batch





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,
)


_lowercase    	=		{
    "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 		lowerCAmelCase__     (       UpperCamelCase_					:							Any						)->	int:
    A__					=       {}
    state_dict.pop('''pixel_mean'''			,							UpperCamelCase_						)
    state_dict.pop('''pixel_std'''			,							UpperCamelCase_						)

    A__					=       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:
                A__					=       key.replace(UpperCamelCase_			,							UpperCamelCase_						)

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

        A__					=       value

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

    return model_state_dict



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Any			,							UpperCamelCase_					:							Union[str, Any]="ybelkada/segment-anything"						)->	Optional[int]:
    A__					=       hf_hub_download(UpperCamelCase_			,							f"checkpoints/{model_name}.pth"						)

    if "sam_vit_b" in model_name:
        A__					=       SamConfig()
    elif "sam_vit_l" in model_name:
        A__					=       SamVisionConfig(
            hidden_size=1_0_2_4			,							num_hidden_layers=2_4			,							num_attention_heads=1_6			,							global_attn_indexes=[5, 1_1, 1_7, 2_3]			,							)

        A__					=       SamConfig(
            vision_config=UpperCamelCase_			,							)
    elif "sam_vit_h" in model_name:
        A__					=       SamVisionConfig(
            hidden_size=1_2_8_0			,							num_hidden_layers=3_2			,							num_attention_heads=1_6			,							global_attn_indexes=[7, 1_5, 2_3, 3_1]			,							)

        A__					=       SamConfig(
            vision_config=UpperCamelCase_			,							)

    A__					=       torch.load(UpperCamelCase_			,							map_location='''cpu'''						)
    A__					=       replace_keys(UpperCamelCase_						)

    A__					=       SamImageProcessor()

    A__					=       SamProcessor(image_processor=UpperCamelCase_						)
    A__					=       SamModel(UpperCamelCase_						)

    hf_model.load_state_dict(UpperCamelCase_						)
    A__					=       hf_model.to('''cuda'''						)

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

    A__					=       [[[4_0_0, 6_5_0]]]
    A__					=       [[1]]

    A__					=       processor(images=np.array(UpperCamelCase_						)			,							return_tensors='''pt'''						).to('''cuda'''						)

    with torch.no_grad():
        A__					=       hf_model(**UpperCamelCase_						)
    A__					=       output.iou_scores.squeeze()

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579890251159668

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

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9712603092193604

        A__					=       ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

        A__					=       processor(images=np.array(UpperCamelCase_						)			,							input_boxes=UpperCamelCase_			,							return_tensors='''pt'''						).to('''cuda'''						)

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

        assert scores[-1].item() == 0.8686015605926514

        # Test with 2 points and 1 image.
        A__					=       [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
        A__					=       [[1, 1]]

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

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   _lowercase    	=		["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",
   )

   _lowercase    	=		parser.parse_args()

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