
"""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				 =					{
    '''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					(		snake_case__ :  Optional[Any]  ):
	A     = {}
	state_dict.pop('pixel_mean'						,   snake_case__  )
	state_dict.pop('pixel_std'						,   snake_case__  )

	A     = 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     = key.replace(snake_case__						,   snake_case__  )

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

		A     = value

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

	return model_state_dict






def    _snake_case					(		snake_case__ :  Any						,   snake_case__ :  List[str]						,   snake_case__ :  Dict						,   snake_case__ :  int="ybelkada/segment-anything"  ):
	A     = hf_hub_download(snake_case__						,   F'checkpoints/{model_name}.pth'  )

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

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

		A     = SamConfig(
		    vision_config=snake_case__						,   )

	A     = torch.load(snake_case__						,   map_location='cpu'  )
	A     = replace_keys(snake_case__  )

	A     = SamImageProcessor()

	A     = SamProcessor(image_processor=snake_case__  )
	A     = SamModel(snake_case__  )

	hf_model.load_state_dict(snake_case__  )
	A     = hf_model.to('cuda'  )

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

	A     = [[[400, 650]]]
	A     = [[1]]

	A     = processor(images=np.array(snake_case__  )						,   return_tensors='pt'  ).to('cuda'  )

	with torch.no_grad():
		A     = hf_model(**snake_case__  )
	A     = output.iou_scores.squeeze()

	if model_name == "sam_vit_h_4b8939":
		assert scores[-1].item() == 0.579890251159668

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

		with torch.no_grad():
			A     = hf_model(**snake_case__  )
		A     = output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9712603092193604

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

		A     = processor(images=np.array(snake_case__  )						,   input_boxes=snake_case__						,   return_tensors='pt'  ).to('cuda'  )

		with torch.no_grad():
			A     = hf_model(**snake_case__  )
		A     = output.iou_scores.squeeze()

		assert scores[-1].item() == 0.8686015605926514

		# Test with 2 points and 1 image.
		A     = [[[400, 650], [800, 650]]]
		A     = [[1, 1]]

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

		with torch.no_grad():
			A     = hf_model(**snake_case__  )
		A     = output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
		_lowercase				 =					argparse.ArgumentParser()
		_lowercase				 =					['''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				 =					parser.parse_args()

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

"""simple docstring"""


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


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''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 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''mobilenet_v1'''


						def __init__(			self  :   Optional[int]	,A_  :   Optional[int]=3	,A_  :   Any=224	,A_  :   List[Any]=1.0	,A_  :   Union[str, Any]=8	,A_  :   Union[str, Any]="relu6"	,A_  :   Optional[Any]=True	,A_  :   List[str]=0.9_99	,A_  :   int=0.02	,A_  :   int=0.0_01	,**A_  :   Union[str, Any]	,)  -> Dict:
							super().__init__(**A_   )

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

							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = hidden_act
							A     = tf_padding
							A     = classifier_dropout_prob
							A     = initializer_range
							A     = layer_norm_eps







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     version.parse('''1.11'''     )


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


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> 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     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> float:
							return 1e-4