




'''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,
)


a  :				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 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				{}
					state_dict.pop('''pixel_mean''',      __UpperCAmelCase	)
					state_dict.pop('''pixel_std''',      __UpperCAmelCase	)

					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:
																				snake_case_									=				key.replace(__UpperCAmelCase,      __UpperCAmelCase	)

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

										snake_case_									=				value

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

					return model_state_dict


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase="ybelkada/segment-anything"	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				hf_hub_download(__UpperCAmelCase,      F"checkpoints/{model_name}.pth"	)

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

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

										snake_case_									=				SamConfig(
										    vision_config=__UpperCAmelCase,      )

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				replace_keys(__UpperCAmelCase	)

					snake_case_									=				SamImageProcessor()

					snake_case_									=				SamProcessor(image_processor=__UpperCAmelCase	)
					snake_case_									=				SamModel(__UpperCAmelCase	)

					hf_model.load_state_dict(__UpperCAmelCase	)
					snake_case_									=				hf_model.to('''cuda'''	)

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

					snake_case_									=				[[[400, 650]]]
					snake_case_									=				[[1]]

					snake_case_									=				processor(images=np.array(__UpperCAmelCase	),      return_tensors='''pt'''	).to('''cuda'''	)

					with torch.no_grad():
										snake_case_									=				hf_model(**__UpperCAmelCase	)
					snake_case_									=				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

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

										with torch.no_grad():
															snake_case_									=				hf_model(**__UpperCAmelCase	)
										snake_case_									=				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

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

										snake_case_									=				processor(images=np.array(__UpperCAmelCase	),      input_boxes=__UpperCAmelCase,      return_tensors='''pt'''	).to('''cuda'''	)

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

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

										with torch.no_grad():
															snake_case_									=				hf_model(**__UpperCAmelCase	)
										snake_case_									=				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__":
							a  :				Union[str, Any]									=  argparse.ArgumentParser()
							a  :				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',
							)

							a  :				Tuple									=  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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :				str									=  logging.get_logger(__name__)

a  :				str									=  {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"big_bird"


				def __init__(		self    :					Union[str, Any]       ,				lowercase_    :					List[Any]=5_0358       ,				lowercase_    :					Tuple=768       ,				lowercase_    :					Dict=12       ,				lowercase_    :					str=12       ,				lowercase_    :					Tuple=3072       ,				lowercase_    :					Any="gelu_new"       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					List[Any]=4096       ,				lowercase_    :					List[Any]=2       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					Optional[int]=1e-12       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Tuple=0       ,				lowercase_    :					str=1       ,				lowercase_    :					Union[str, Any]=2       ,				lowercase_    :					Optional[Any]=66       ,				lowercase_    :					Optional[int]="block_sparse"       ,				lowercase_    :					Any=True       ,				lowercase_    :					List[str]=False       ,				lowercase_    :					Any=64       ,				lowercase_    :					Tuple=3       ,				lowercase_    :					Tuple=None       ,				**lowercase_    :					Tuple       ,				):
									super().__init__(
									    pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				sep_token_id=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_size
									snake_case_									=				max_position_embeddings
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				initializer_range
									snake_case_									=				type_vocab_size
									snake_case_									=				layer_norm_eps
									snake_case_									=				use_cache

									snake_case_									=				rescale_embeddings
									snake_case_									=				attention_type
									snake_case_									=				use_bias
									snake_case_									=				block_size
									snake_case_									=				num_random_blocks
									snake_case_									=				classifier_dropout







class 						a				(   _lowerCamelCase     ):


				@property
				def   A_ (		self    :					str					):
									if self.task == "multiple-choice":
														snake_case_									=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														snake_case_									=				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]					)

