


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,
)


snake_case_						:			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 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  ={}
				state_dict.pop('''pixel_mean'''	,a__)
				state_dict.pop('''pixel_std'''	,a__)

				_SCREAMING_SNAKE_CASE			  =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			  =key.replace(a__	,a__)

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

								_SCREAMING_SNAKE_CASE			  =value

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

				return model_state_dict







def 						lowerCamelCase(   a__	,a__	,a__	,a__="ybelkada/segment-anything"):
				_SCREAMING_SNAKE_CASE			  =hf_hub_download(a__	,f"checkpoints/{model_name}.pth")

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

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

								_SCREAMING_SNAKE_CASE			  =SamConfig(
								    vision_config=a__	,)

				_SCREAMING_SNAKE_CASE			  =torch.load(a__	,map_location='''cpu''')
				_SCREAMING_SNAKE_CASE			  =replace_keys(a__)

				_SCREAMING_SNAKE_CASE			  =SamImageProcessor()

				_SCREAMING_SNAKE_CASE			  =SamProcessor(image_processor=a__)
				_SCREAMING_SNAKE_CASE			  =SamModel(a__)

				hf_model.load_state_dict(a__)
				_SCREAMING_SNAKE_CASE			  =hf_model.to('''cuda''')

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

				_SCREAMING_SNAKE_CASE			  =[[[400, 650]]]
				_SCREAMING_SNAKE_CASE			  =[[1]]

				_SCREAMING_SNAKE_CASE			  =processor(images=np.array(a__)	,return_tensors='''pt''').to('''cuda''')

				with torch.no_grad():
								_SCREAMING_SNAKE_CASE			  =hf_model(**a__)
				_SCREAMING_SNAKE_CASE			  =output.iou_scores.squeeze()

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

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

								with torch.no_grad():
												_SCREAMING_SNAKE_CASE			  =hf_model(**a__)
								_SCREAMING_SNAKE_CASE			  =output.iou_scores.squeeze()

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

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

								_SCREAMING_SNAKE_CASE			  =processor(images=np.array(a__)	,input_boxes=a__	,return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												_SCREAMING_SNAKE_CASE			  =hf_model(**a__)
								_SCREAMING_SNAKE_CASE			  =output.iou_scores.squeeze()

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

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

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

								with torch.no_grad():
												_SCREAMING_SNAKE_CASE			  =hf_model(**a__)
								_SCREAMING_SNAKE_CASE			  =output.iou_scores.squeeze()

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


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

							snake_case_						:			List[str]													=							parser.parse_args()

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


snake_case_						:			Optional[Any]													=							{
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Optional[Any]													=							[
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			List[Any]													=							['''ASTFeatureExtractor''']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							snake_case_						:			Optional[Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)