



import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)





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)
