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,
)


__UpperCamelCase	: Union[str, Any]    		=  {
    '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			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							{}
						state_dict.pop('''pixel_mean'''	,						lowerCAmelCase__		)
						state_dict.pop('''pixel_std'''	,						lowerCAmelCase__		)

						SCREAMING_SNAKE_CASE							:					Optional[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:
																								SCREAMING_SNAKE_CASE							:					Any           =							key.replace(lowerCAmelCase__	,						lowerCAmelCase__		)

												if re.match(lowerCAmelCase__	,						lowerCAmelCase__		):
																		SCREAMING_SNAKE_CASE							:					Dict           =							int(re.match(lowerCAmelCase__	,						lowerCAmelCase__		).group(2		)		)
																		if layer_nb == 0:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							key.replace('''layers.0'''	,						'''proj_in'''		)
																		elif layer_nb == 1:
																								SCREAMING_SNAKE_CASE							:					int           =							key.replace('''layers.1'''	,						'''layers.0'''		)
																		elif layer_nb == 2:
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							key.replace('''layers.2'''	,						'''proj_out'''		)

												SCREAMING_SNAKE_CASE							:					Any           =							value

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

						return model_state_dict
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase="ybelkada/segment-anything"		):
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							hf_hub_download(lowerCAmelCase__	,						f"""checkpoints/{model_name}.pth"""		)

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

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

												SCREAMING_SNAKE_CASE							:					Tuple           =							SamConfig(
												    vision_config=lowerCAmelCase__	,						)

						SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.load(lowerCAmelCase__	,						map_location='''cpu'''		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							replace_keys(lowerCAmelCase__		)

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							SamImageProcessor()

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							SamProcessor(image_processor=lowerCAmelCase__		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							SamModel(lowerCAmelCase__		)

						hf_model.load_state_dict(lowerCAmelCase__		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							hf_model.to('''cuda'''		)

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

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							[[[400, 650]]]
						SCREAMING_SNAKE_CASE							:					int           =							[[1]]

						SCREAMING_SNAKE_CASE							:					List[str]           =							processor(images=np.array(lowerCAmelCase__		)	,						return_tensors='''pt'''		).to('''cuda'''		)

						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:					Dict           =							hf_model(**lowerCAmelCase__		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							output.iou_scores.squeeze()

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

												SCREAMING_SNAKE_CASE							:					str           =							processor(
												    images=np.array(lowerCAmelCase__		)	,						input_points=lowerCAmelCase__	,						input_labels=lowerCAmelCase__	,						return_tensors='''pt'''		).to('''cuda'''		)

												with torch.no_grad():
																		SCREAMING_SNAKE_CASE							:					List[str]           =							hf_model(**lowerCAmelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							output.iou_scores.squeeze()

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

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

												SCREAMING_SNAKE_CASE							:					List[str]           =							processor(images=np.array(lowerCAmelCase__		)	,						input_boxes=lowerCAmelCase__	,						return_tensors='''pt'''		).to('''cuda'''		)

												with torch.no_grad():
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							hf_model(**lowerCAmelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							output.iou_scores.squeeze()

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

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

												SCREAMING_SNAKE_CASE							:					str           =							processor(
												    images=np.array(lowerCAmelCase__		)	,						input_points=lowerCAmelCase__	,						input_labels=lowerCAmelCase__	,						return_tensors='''pt'''		).to('''cuda'''		)

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

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


if __name__ == "__main__":
					__UpperCamelCase	: Any    		=  argparse.ArgumentParser()
					__UpperCamelCase	: Tuple    		=  ['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',
					)

					__UpperCamelCase	: Dict    		=  parser.parse_args()

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






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





