




'''simple docstring'''





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,
)


_lowerCamelCase			:	Tuple							 =     {
    """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_		)     -> List[str]:



       """simple docstring"""

       A__   				=					{}
       state_dict.pop('''pixel_mean'''				,       _snake_case		)
       state_dict.pop('''pixel_std'''				,       _snake_case		)

       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(_snake_case				,       _snake_case		)

              if re.match(_snake_case				,       _snake_case		):
                     A__   				=					int(re.match(_snake_case				,       _snake_case		).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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_="ybelkada/segment-anything"		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					hf_hub_download(_snake_case				,       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_024				,       num_hidden_layers=24				,       num_attention_heads=16				,       global_attn_indexes=[5, 11, 17, 23]				,       )

              A__   				=					SamConfig(
                  vision_config=_snake_case				,       )
       elif "sam_vit_h" in model_name:
              A__   				=					SamVisionConfig(
                  hidden_size=1_280				,       num_hidden_layers=32				,       num_attention_heads=16				,       global_attn_indexes=[7, 15, 23, 31]				,       )

              A__   				=					SamConfig(
                  vision_config=_snake_case				,       )

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

       A__   				=					SamImageProcessor()

       A__   				=					SamProcessor(image_processor=_snake_case		)
       A__   				=					SamModel(_snake_case		)

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

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

       A__   				=					[[[400, 650]]]
       A__   				=					[[1]]

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

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

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

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

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

              assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

              A__   				=					((75, 275, 1_725, 850),)

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

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

              assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

              # Test with 2 points and 1 image.
              A__   				=					[[[400, 650], [800, 650]]]
              A__   				=					[[1, 1]]

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

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

              assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
  _lowerCamelCase			:	Any							 =     argparse.ArgumentParser()
  _lowerCamelCase			:	List[Any]							 =     ["""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""",
  )

  _lowerCamelCase			:	Union[str, Any]							 =     parser.parse_args()

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








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


