

'''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__      		=			{
    '''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 	_A  (					A__   ):






	"""simple docstring"""



	__lowercase													=				{}
	state_dict.pop('''pixel_mean'''  ,       _lowerCamelCase   )
	state_dict.pop('''pixel_std'''  ,       _lowerCamelCase   )

	__lowercase													=				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:
				__lowercase													=				key.replace(_lowerCamelCase  ,       _lowerCamelCase   )

		if re.match(_lowerCamelCase  ,       _lowerCamelCase   ):
			__lowercase													=				int(re.match(_lowerCamelCase  ,       _lowerCamelCase   ).group(2   )   )
			if layer_nb == 0:
				__lowercase													=				key.replace('''layers.0'''  ,       '''proj_in'''   )
			elif layer_nb == 1:
				__lowercase													=				key.replace('''layers.1'''  ,       '''layers.0'''   )
			elif layer_nb == 2:
				__lowercase													=				key.replace('''layers.2'''  ,       '''proj_out'''   )

		__lowercase													=				value

	__lowercase													=				model_state_dict[
	    'prompt_encoder.shared_embedding.positional_embedding'
	]

	return model_state_dict


def 	_A  (					A__  ,       A__  ,       A__  ,       A__="ybelkada/segment-anything"   ):






	"""simple docstring"""



	__lowercase													=				hf_hub_download(_lowerCamelCase  ,       F"checkpoints/{model_name}.pth"   )

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

		__lowercase													=				SamConfig(
		    vision_config=_lowerCamelCase  ,       )
	elif "sam_vit_h" in model_name:
		__lowercase													=				SamVisionConfig(
		    hidden_size=1280  ,       num_hidden_layers=32  ,       num_attention_heads=16  ,       global_attn_indexes=[7, 15, 23, 31]  ,       )

		__lowercase													=				SamConfig(
		    vision_config=_lowerCamelCase  ,       )

	__lowercase													=				torch.load(_lowerCamelCase  ,       map_location='''cpu'''   )
	__lowercase													=				replace_keys(_lowerCamelCase   )

	__lowercase													=				SamImageProcessor()

	__lowercase													=				SamProcessor(image_processor=_lowerCamelCase   )
	__lowercase													=				SamModel(_lowerCamelCase   )

	hf_model.load_state_dict(_lowerCamelCase   )
	__lowercase													=				hf_model.to('''cuda'''   )

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

	__lowercase													=				[[[400, 650]]]
	__lowercase													=				[[1]]

	__lowercase													=				processor(images=np.array(_lowerCamelCase   )  ,       return_tensors='''pt'''   ).to('''cuda'''   )

	with torch.no_grad():
		__lowercase													=				hf_model(**_lowerCamelCase   )
	__lowercase													=				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

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

		with torch.no_grad():
			__lowercase													=				hf_model(**_lowerCamelCase   )
		__lowercase													=				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

		__lowercase													=				((75, 275, 1725, 850),)

		__lowercase													=				processor(images=np.array(_lowerCamelCase   )  ,       input_boxes=_lowerCamelCase  ,       return_tensors='''pt'''   ).to('''cuda'''   )

		with torch.no_grad():
			__lowercase													=				hf_model(**_lowerCamelCase   )
		__lowercase													=				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.
		__lowercase													=				[[[400, 650], [800, 650]]]
		__lowercase													=				[[1, 1]]

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

		with torch.no_grad():
			__lowercase													=				hf_model(**_lowerCamelCase   )
		__lowercase													=				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__      		=			argparse.ArgumentParser()
	lowerCAmelCase__      		=			['''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__      		=			parser.parse_args()

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




'''simple docstring'''






def 	_A  (					A__ = 50   ):






 """simple docstring"""



 __lowercase													=				[1] * (length + 1)

 for row_length in range(3  ,       length + 1   ):
  for block_length in range(3  ,       row_length + 1   ):
   for block_start in range(row_length - block_length   ):
    ways_number[row_length] += ways_number[
        row_length - block_start - block_length - 1
    ]

   ways_number[row_length] += 1

 return ways_number[length]


if __name__ == "__main__":
 print(f'{solution() = }')

