


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 __UpperCAmelCase       (		__A			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     {}
       state_dict.pop("pixel_mean" ,						__A			)
       state_dict.pop("pixel_std" ,						__A			)

       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(__A ,						__A			)

              if re.match(__A ,						__A			):
                     UpperCAmelCase__										=     int(re.match(__A ,						__A			).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 __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A="ybelkada/segment-anything"			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     hf_hub_download(__A ,						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=1_0_2_4 ,						num_hidden_layers=2_4 ,						num_attention_heads=1_6 ,						global_attn_indexes=[5, 1_1, 1_7, 2_3] ,						)

              UpperCAmelCase__										=     SamConfig(
                  vision_config=__A ,						)
       elif "sam_vit_h" in model_name:
              UpperCAmelCase__										=     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] ,						)

              UpperCAmelCase__										=     SamConfig(
                  vision_config=__A ,						)

       UpperCAmelCase__										=     torch.load(__A ,						map_location="cpu"			)
       UpperCAmelCase__										=     replace_keys(__A			)

       UpperCAmelCase__										=     SamImageProcessor()

       UpperCAmelCase__										=     SamProcessor(image_processor=__A			)
       UpperCAmelCase__										=     SamModel(__A			)

       hf_model.load_state_dict(__A			)
       UpperCAmelCase__										=     hf_model.to("cuda"			)

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

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

       UpperCAmelCase__										=     processor(images=np.array(__A			) ,						return_tensors="pt"			).to("cuda"			)

       with torch.no_grad():
              UpperCAmelCase__										=     hf_model(**__A			)
       UpperCAmelCase__										=     output.iou_scores.squeeze()

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

              UpperCAmelCase__										=     processor(
                  images=np.array(__A			) ,						input_points=__A ,						input_labels=__A ,						return_tensors="pt"			).to("cuda"			)

              with torch.no_grad():
                     UpperCAmelCase__										=     hf_model(**__A			)
              UpperCAmelCase__										=     output.iou_scores.squeeze()

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

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

              UpperCAmelCase__										=     processor(images=np.array(__A			) ,						input_boxes=__A ,						return_tensors="pt"			).to("cuda"			)

              with torch.no_grad():
                     UpperCAmelCase__										=     hf_model(**__A			)
              UpperCAmelCase__										=     output.iou_scores.squeeze()

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

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

              UpperCAmelCase__										=     processor(
                  images=np.array(__A			) ,						input_points=__A ,						input_labels=__A ,						return_tensors="pt"			).to("cuda"			)

              with torch.no_grad():
                     UpperCAmelCase__										=     hf_model(**__A			)
              UpperCAmelCase__										=     output.iou_scores.squeeze()

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


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)









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





