



"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor




class  _UpperCAmelCase (    unittest.TestCase):

 def __init__(  self	: Union[str, Any]						,  lowercase_	: str						,  lowercase_	: Optional[int]=7						,  lowercase_	: List[str]=3						,  lowercase_	: str=18						,  lowercase_	: Tuple=30						,  lowercase_	: Optional[int]=400						,  lowercase_	: Optional[int]=True						,  lowercase_	: Any=None						,  lowercase_	: Any=True						,  lowercase_	: str=None						,  lowercase_	: int=True						,  lowercase_	: List[Any]=[0.5, 0.5, 0.5]						,  lowercase_	: Any=[0.5, 0.5, 0.5]						,  ):
     snake_case_				:  Dict       =			size if size is not None else {'''shortest_edge''': 18}
     snake_case_				:  Optional[int]       =			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
     snake_case_				:  Any       =			parent
     snake_case_				:  Any       =			batch_size
     snake_case_				:  str       =			num_channels
     snake_case_				:  List[Any]       =			image_size
     snake_case_				:  Dict       =			min_resolution
     snake_case_				:  Dict       =			max_resolution
     snake_case_				:  int       =			do_resize
     snake_case_				:  Any       =			size
     snake_case_				:  Optional[Any]       =			do_center_crop
     snake_case_				:  int       =			crop_size
     snake_case_				:  List[str]       =			do_normalize
     snake_case_				:  List[Any]       =			image_mean
     snake_case_				:  Any       =			image_std






 def 		_snake_case      (  self	: Optional[int]       ):
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "do_center_crop": self.do_center_crop,
         "size": self.size,
         "crop_size": self.crop_size,
     }





@require_torch
@require_vision
class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Any	      =	LevitImageProcessor if is_vision_available() else None

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Any       =			LevitImageProcessingTester(self       )

 @property
 def 		_snake_case      (  self	: str       ):
     return self.image_processor_tester.prepare_image_processor_dict()

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Optional[int]       =			self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(lowercase_						,  '''image_mean'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''image_std'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_normalize'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_resize'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_center_crop'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''size'''       )       )

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Optional[int]       =			self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size						,  {'''shortest_edge''': 18}       )
     self.assertEqual(image_processor.crop_size						,  {'''height''': 18, '''width''': 18}       )

     snake_case_				:  Optional[int]       =			self.image_processing_class.from_dict(self.image_processor_dict						,  size=42						,  crop_size=84       )
     self.assertEqual(image_processor.size						,  {'''shortest_edge''': 42}       )
     self.assertEqual(image_processor.crop_size						,  {'''height''': 84, '''width''': 84}       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     pass

 def 		_snake_case      (  self	: Union[str, Any]       ):
     # Initialize image_processing
     snake_case_				:  List[Any]       =			self.image_processing_class(**self.image_processor_dict       )
     # create random PIL images
     snake_case_				:  Any       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowercase_       )
     for image in image_inputs:
         self.assertIsInstance(lowercase_						,  Image.Image       )

     # Test not batched input
     snake_case_				:  Optional[int]       =			image_processing(image_inputs[0]						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

     # Test batched
     snake_case_				:  Optional[int]       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

 def 		_snake_case      (  self	: Union[str, Any]       ):
     # Initialize image_processing
     snake_case_				:  List[Any]       =			self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     snake_case_				:  Optional[int]       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowercase_						,  numpify=lowercase_       )
     for image in image_inputs:
         self.assertIsInstance(lowercase_						,  np.ndarray       )

     # Test not batched input
     snake_case_				:  str       =			image_processing(image_inputs[0]						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

     # Test batched
     snake_case_				:  Dict       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )






 def 		_snake_case      (  self	: Union[str, Any]       ):
     # Initialize image_processing
     snake_case_				:  Union[str, Any]       =			self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     snake_case_				:  List[Any]       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowercase_						,  torchify=lowercase_       )
     for image in image_inputs:
         self.assertIsInstance(lowercase_						,  torch.Tensor       )

     # Test not batched input
     snake_case_				:  Optional[int]       =			image_processing(image_inputs[0]						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

     # Test batched
     snake_case_				:  List[Any]       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )










"""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,
)


lowercase__					:						List[str]												=			{
    '''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 __lowercase    (							_a					):
    snake_case_				:  List[str]       =			{}
    state_dict.pop('''pixel_mean'''	,    _a					)
    state_dict.pop('''pixel_std'''	,    _a					)

    snake_case_				:  Union[str, Any]       =			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:
                snake_case_				:  Optional[int]       =			key.replace(_a	,    _a					)

        if re.match(_a	,    _a					):
            snake_case_				:  Union[str, Any]       =			int(re.match(_a	,    _a					).group(2					)					)
            if layer_nb == 0:
                snake_case_				:  Optional[int]       =			key.replace('''layers.0'''	,    '''proj_in'''					)
            elif layer_nb == 1:
                snake_case_				:  Union[str, Any]       =			key.replace('''layers.1'''	,    '''layers.0'''					)
            elif layer_nb == 2:
                snake_case_				:  List[Any]       =			key.replace('''layers.2'''	,    '''proj_out'''					)

        snake_case_				:  Optional[Any]       =			value

    snake_case_				:  Tuple       =			model_state_dict[
        '''prompt_encoder.shared_embedding.positional_embedding'''
    ]

    return model_state_dict




def __lowercase    (							_a	,    _a	,    _a	,    _a="ybelkada/segment-anything"					):
    snake_case_				:  Optional[Any]       =			hf_hub_download(_a	,    f"checkpoints/{model_name}.pth"					)

    if "sam_vit_b" in model_name:
        snake_case_				:  Tuple       =			SamConfig()
    elif "sam_vit_l" in model_name:
        snake_case_				:  Optional[Any]       =			SamVisionConfig(
            hidden_size=1_024	,    num_hidden_layers=24	,    num_attention_heads=16	,    global_attn_indexes=[5, 11, 17, 23]	,    )

        snake_case_				:  Union[str, Any]       =			SamConfig(
            vision_config=_a	,    )
    elif "sam_vit_h" in model_name:
        snake_case_				:  Tuple       =			SamVisionConfig(
            hidden_size=1_280	,    num_hidden_layers=32	,    num_attention_heads=16	,    global_attn_indexes=[7, 15, 23, 31]	,    )

        snake_case_				:  List[str]       =			SamConfig(
            vision_config=_a	,    )

    snake_case_				:  Tuple       =			torch.load(_a	,    map_location='''cpu'''					)
    snake_case_				:  Optional[Any]       =			replace_keys(_a					)

    snake_case_				:  Any       =			SamImageProcessor()

    snake_case_				:  Optional[Any]       =			SamProcessor(image_processor=_a					)
    snake_case_				:  Tuple       =			SamModel(_a					)

    hf_model.load_state_dict(_a					)
    snake_case_				:  Tuple       =			hf_model.to('''cuda'''					)

    snake_case_				:  Union[str, Any]       =			'''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
    snake_case_				:  Union[str, Any]       =			Image.open(requests.get(_a	,    stream=_a					).raw					).convert('''RGB'''					)

    snake_case_				:  Tuple       =			[[[400, 650]]]
    snake_case_				:  List[str]       =			[[1]]

    snake_case_				:  Optional[int]       =			processor(images=np.array(_a					)	,    return_tensors='''pt'''					).to('''cuda'''					)

    with torch.no_grad():
        snake_case_				:  Optional[Any]       =			hf_model(**_a					)
    snake_case_				:  Any       =			output.iou_scores.squeeze()

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579_8902_5115_9668

        snake_case_				:  Optional[Any]       =			processor(
            images=np.array(_a					)	,    input_points=_a	,    input_labels=_a	,    return_tensors='''pt'''					).to('''cuda'''					)

        with torch.no_grad():
            snake_case_				:  Optional[Any]       =			hf_model(**_a					)
        snake_case_				:  Tuple       =			output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9712_6030_9219_3604

        snake_case_				:  Tuple       =			((75, 275, 1_725, 850),)

        snake_case_				:  Optional[Any]       =			processor(images=np.array(_a					)	,    input_boxes=_a	,    return_tensors='''pt'''					).to('''cuda'''					)

        with torch.no_grad():
            snake_case_				:  Dict       =			hf_model(**_a					)
        snake_case_				:  Any       =			output.iou_scores.squeeze()

        assert scores[-1].item() == 0.8686_0156_0592_6514

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

        snake_case_				:  Tuple       =			processor(
            images=np.array(_a					)	,    input_points=_a	,    input_labels=_a	,    return_tensors='''pt'''					).to('''cuda'''					)

        with torch.no_grad():
            snake_case_				:  Dict       =			hf_model(**_a					)
        snake_case_				:  Dict       =			output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
     lowercase__					:						Optional[int]												=			argparse.ArgumentParser()
     lowercase__					:						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''',
     )

     lowercase__					:						Tuple												=			parser.parse_args()

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





