

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,
)


a__												=						{
    '''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   __UpperCAmelCase					(      __a :     Tuple      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Dict   =						{}
					state_dict.pop('''pixel_mean'''						,__a      )
					state_dict.pop('''pixel_std'''						,__a      )

					_a      :       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:
																				_a      :       Tuple   =						key.replace(__a						,__a      )

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

										_a      :       List[str]   =						value

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

					return model_state_dict



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     List[Any]						,__a :     List[str]						,__a :     Dict="ybelkada/segment-anything"      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       List[str]   =						hf_hub_download(__a						,F"""checkpoints/{model_name}.pth"""      )

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

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

										_a      :       Dict   =						SamConfig(
										    vision_config=__a						,)

					_a      :       int   =						torch.load(__a						,map_location='''cpu'''      )
					_a      :       List[str]   =						replace_keys(__a      )

					_a      :       List[Any]   =						SamImageProcessor()

					_a      :       Optional[int]   =						SamProcessor(image_processor=__a      )
					_a      :       str   =						SamModel(__a      )

					hf_model.load_state_dict(__a      )
					_a      :       Optional[Any]   =						hf_model.to('''cuda'''      )

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

					_a      :       Dict   =						[[[400, 650]]]
					_a      :       Dict   =						[[1]]

					_a      :       int   =						processor(images=np.array(__a      )						,return_tensors='''pt'''      ).to('''cuda'''      )

					with torch.no_grad():
										_a      :       List[Any]   =						hf_model(**__a      )
					_a      :       List[Any]   =						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      :       Optional[int]   =						processor(
										    images=np.array(__a      )						,input_points=__a						,input_labels=__a						,return_tensors='''pt'''      ).to('''cuda'''      )

										with torch.no_grad():
															_a      :       Tuple   =						hf_model(**__a      )
										_a      :       List[str]   =						output.iou_scores.squeeze()

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

										_a      :       List[Any]   =						((75, 275, 1_725, 850),)

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

										with torch.no_grad():
															_a      :       Union[str, Any]   =						hf_model(**__a      )
										_a      :       Optional[Any]   =						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      :       List[str]   =						[[[400, 650], [800, 650]]]
										_a      :       Optional[Any]   =						[[1, 1]]

										_a      :       List[str]   =						processor(
										    images=np.array(__a      )						,input_points=__a						,input_labels=__a						,return_tensors='''pt'''      ).to('''cuda'''      )

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

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


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()
			a__												=						['''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''',
			)

			a__												=						parser.parse_args()

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






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "mobilenet_v1"


							def __init__(     self			,				_a=3			,				_a=2_2_4			,				_a=1.0			,				_a=8			,				_a="relu6"			,				_a=True			,				_a=0.999			,				_a=0.02			,				_a=0.001			,				**_a			,				)       ->       List[Any]:
												super().__init__(**_a			)

												if depth_multiplier <= 0:
																	raise ValueError('''depth_multiplier must be greater than zero.'''			)

												_a      :       Tuple   =						num_channels
												_a      :       str   =						image_size
												_a      :       Tuple   =						depth_multiplier
												_a      :       Any   =						min_depth
												_a      :       int   =						hidden_act
												_a      :       Optional[Any]   =						tf_padding
												_a      :       str   =						classifier_dropout_prob
												_a      :       Optional[int]   =						initializer_range
												_a      :       Any   =						layer_norm_eps



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict([('''pixel_values''', {0: '''batch'''})]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																	return OrderedDict([('''logits''', {0: '''batch'''})]			)
												else:
																	return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4



