


"""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   snake_case		(     UpperCamelCase__       :						List[Any]    )   ->      int:
      lowerCamelCase					:					Union[str, Any]        =	{}
      state_dict.pop("""pixel_mean"""			,				lowerCamelCase_    )
      state_dict.pop("""pixel_std"""			,				lowerCamelCase_    )

      lowerCamelCase					:					str        =	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:
                        lowerCamelCase					:					int        =	key.replace(lowerCamelCase_			,				lowerCamelCase_    )

            if re.match(lowerCamelCase_			,				lowerCamelCase_    ):
                  lowerCamelCase					:					int        =	int(re.match(lowerCamelCase_			,				lowerCamelCase_    ).group(2    )    )
                  if layer_nb == 0:
                        lowerCamelCase					:					Optional[Any]        =	key.replace("""layers.0"""			,				"""proj_in"""    )
                  elif layer_nb == 1:
                        lowerCamelCase					:					List[str]        =	key.replace("""layers.1"""			,				"""layers.0"""    )
                  elif layer_nb == 2:
                        lowerCamelCase					:					List[Any]        =	key.replace("""layers.2"""			,				"""proj_out"""    )

            lowerCamelCase					:					Any        =	value

      lowerCamelCase					:					int        =	model_state_dict[
          'prompt_encoder.shared_embedding.positional_embedding'
      ]

      return model_state_dict




def   snake_case		(     UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						int="ybelkada/segment-anything"    )   ->      Any:
      lowerCamelCase					:					Dict        =	hf_hub_download(lowerCamelCase_			,				F'checkpoints/{model_name}.pth'    )

      if "sam_vit_b" in model_name:
            lowerCamelCase					:					Tuple        =	SamConfig()
      elif "sam_vit_l" in model_name:
            lowerCamelCase					:					Union[str, Any]        =	SamVisionConfig(
                hidden_size=1024			,				num_hidden_layers=24			,				num_attention_heads=16			,				global_attn_indexes=[5, 11, 17, 23]			,				)

            lowerCamelCase					:					Tuple        =	SamConfig(
                vision_config=lowerCamelCase_			,				)
      elif "sam_vit_h" in model_name:
            lowerCamelCase					:					Optional[Any]        =	SamVisionConfig(
                hidden_size=1280			,				num_hidden_layers=32			,				num_attention_heads=16			,				global_attn_indexes=[7, 15, 23, 31]			,				)

            lowerCamelCase					:					str        =	SamConfig(
                vision_config=lowerCamelCase_			,				)

      lowerCamelCase					:					Optional[int]        =	torch.load(lowerCamelCase_			,				map_location="""cpu"""    )
      lowerCamelCase					:					Union[str, Any]        =	replace_keys(lowerCamelCase_    )

      lowerCamelCase					:					int        =	SamImageProcessor()

      lowerCamelCase					:					Dict        =	SamProcessor(image_processor=lowerCamelCase_    )
      lowerCamelCase					:					Union[str, Any]        =	SamModel(lowerCamelCase_    )

      hf_model.load_state_dict(lowerCamelCase_    )
      lowerCamelCase					:					Dict        =	hf_model.to("""cuda"""    )

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

      lowerCamelCase					:					Dict        =	[[[400, 650]]]
      lowerCamelCase					:					List[str]        =	[[1]]

      lowerCamelCase					:					Any        =	processor(images=np.array(lowerCamelCase_    )			,				return_tensors="""pt"""    ).to("""cuda"""    )

      with torch.no_grad():
            lowerCamelCase					:					Tuple        =	hf_model(**lowerCamelCase_    )
      lowerCamelCase					:					str        =	output.iou_scores.squeeze()

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

            lowerCamelCase					:					Union[str, Any]        =	processor(
                images=np.array(lowerCamelCase_    )			,				input_points=lowerCamelCase_			,				input_labels=lowerCamelCase_			,				return_tensors="""pt"""    ).to("""cuda"""    )

            with torch.no_grad():
                  lowerCamelCase					:					List[str]        =	hf_model(**lowerCamelCase_    )
            lowerCamelCase					:					List[Any]        =	output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

            lowerCamelCase					:					Tuple        =	((75, 275, 1725, 850),)

            lowerCamelCase					:					str        =	processor(images=np.array(lowerCamelCase_    )			,				input_boxes=lowerCamelCase_			,				return_tensors="""pt"""    ).to("""cuda"""    )

            with torch.no_grad():
                  lowerCamelCase					:					Tuple        =	hf_model(**lowerCamelCase_    )
            lowerCamelCase					:					Union[str, Any]        =	output.iou_scores.squeeze()

            assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

            # Test with 2 points and 1 image.
            lowerCamelCase					:					str        =	[[[400, 650], [800, 650]]]
            lowerCamelCase					:					Optional[int]        =	[[1, 1]]

            lowerCamelCase					:					Any        =	processor(
                images=np.array(lowerCamelCase_    )			,				input_points=lowerCamelCase_			,				input_labels=lowerCamelCase_			,				return_tensors="""pt"""    ).to("""cuda"""    )

            with torch.no_grad():
                  lowerCamelCase					:					List[str]        =	hf_model(**lowerCamelCase_    )
            lowerCamelCase					:					Optional[int]        =	output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
    __lowerCamelCase      :Union[str, Any]	    =		argparse.ArgumentParser()
    __lowerCamelCase      :Dict	    =		['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)








"""simple docstring"""


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase      :str	    =		get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=AlbertTokenizer
     snake_case__	:					Optional[Any]													=AlbertTokenizerFast
     snake_case__	:					Optional[int]													=True
     snake_case__	:					Any													=True
     snake_case__	:					Optional[int]													=True

     def 		a__						(	self:						Dict	)->    Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCamelCase					:					int        =	AlbertTokenizer(__a	)
           tokenizer.save_pretrained(self.tmpdirname	)

     def 		a__						(	self:						Tuple     ,		__a:						Tuple	)->    Union[str, Any]:
           lowerCamelCase					:					List[str]        =	"""this is a test"""
           lowerCamelCase					:					int        =	"""this is a test"""
           return input_text, output_text

     def 		a__						(	self:						Any	)->    List[Any]:
           lowerCamelCase					:					int        =	"""<pad>"""
           lowerCamelCase					:					Optional[int]        =	0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a	)     ,		__a	)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a	)     ,		__a	)

     def 		a__						(	self:						Tuple	)->    str:
           lowerCamelCase					:					Optional[int]        =	list(self.get_tokenizer().get_vocab().keys()	)

           self.assertEqual(vocab_keys[0]     ,		"""<pad>"""	)
           self.assertEqual(vocab_keys[1]     ,		"""<unk>"""	)
           self.assertEqual(vocab_keys[-1]     ,		"""▁eloquent"""	)
           self.assertEqual(len(__a	)     ,		30_000	)

     def 		a__						(	self:						List[str]	)->    Any:
           self.assertEqual(self.get_tokenizer().vocab_size     ,		30_000	)

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           if not self.test_rust_tokenizer:
                 return

           lowerCamelCase					:					str        =	self.get_tokenizer()
           lowerCamelCase					:					Tuple        =	self.get_rust_tokenizer()

           lowerCamelCase					:					Union[str, Any]        =	"""I was born in 92000, and this is falsé."""

           lowerCamelCase					:					List[str]        =	tokenizer.tokenize(__a	)
           lowerCamelCase					:					Tuple        =	rust_tokenizer.tokenize(__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					Dict        =	tokenizer.encode(__a     ,		add_special_tokens=__a	)
           lowerCamelCase					:					List[str]        =	rust_tokenizer.encode(__a     ,		add_special_tokens=__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					Any        =	self.get_rust_tokenizer()
           lowerCamelCase					:					List[str]        =	tokenizer.encode(__a	)
           lowerCamelCase					:					str        =	rust_tokenizer.encode(__a	)
           self.assertListEqual(__a     ,		__a	)

     def 		a__						(	self:						Tuple	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	AlbertTokenizer(__a     ,		keep_accents=__a	)

           lowerCamelCase					:					int        =	tokenizer.tokenize("""This is a test"""	)
           self.assertListEqual(__a     ,		["""▁this""", """▁is""", """▁a""", """▁test"""]	)

           self.assertListEqual(tokenizer.convert_tokens_to_ids(__a	)     ,		[48, 25, 21, 1_289]	)

           lowerCamelCase					:					List[str]        =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
           self.assertListEqual(
               __a     ,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]	)
           lowerCamelCase					:					List[str]        =	tokenizer.convert_tokens_to_ids(__a	)
           self.assertListEqual(__a     ,		[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]	)

           lowerCamelCase					:					Tuple        =	tokenizer.convert_ids_to_tokens(__a	)
           self.assertListEqual(
               __a     ,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]     ,		)

     def 		a__						(	self:						Tuple	)->    str:
           lowerCamelCase					:					str        =	AlbertTokenizer(__a	)

           lowerCamelCase					:					Union[str, Any]        =	tokenizer.encode("""sequence builders"""	)
           lowerCamelCase					:					List[Any]        =	tokenizer.encode("""multi-sequence build"""	)

           lowerCamelCase					:					Any        =	tokenizer.build_inputs_with_special_tokens(__a	)
           lowerCamelCase					:					str        =	tokenizer.build_inputs_with_special_tokens(__a     ,		__a	)

           assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
           assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
               tokenizer.sep_token_id
           ]







     @slow
     def 		a__						(	self:						Any	)->    Dict:
           # fmt: off
           lowerCamelCase					:					Optional[Any]        =	{"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__a     ,		model_name="""albert-base-v2"""     ,		revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""     ,		)




