
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      :				Optional[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				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  {}
		state_dict.pop('pixel_mean'					, lowercase				)
		state_dict.pop('pixel_std'					, lowercase				)

		UpperCamelCase						=  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:
								UpperCamelCase						=  key.replace(lowercase					, lowercase				)

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

				UpperCamelCase						=  value

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

		return model_state_dict


def    A (      lowercase					, lowercase					, lowercase					, lowercase="ybelkada/segment-anything"				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  hf_hub_download(lowercase					, f'''checkpoints/{model_name}.pth'''				)

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

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

				UpperCamelCase						=  SamConfig(
				    vision_config=lowercase					, )

		UpperCamelCase						=  torch.load(lowercase					, map_location='cpu'				)
		UpperCamelCase						=  replace_keys(lowercase				)

		UpperCamelCase						=  SamImageProcessor()

		UpperCamelCase						=  SamProcessor(image_processor=lowercase				)
		UpperCamelCase						=  SamModel(lowercase				)

		hf_model.load_state_dict(lowercase				)
		UpperCamelCase						=  hf_model.to('cuda'				)

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

		UpperCamelCase						=  [[[400, 650]]]
		UpperCamelCase						=  [[1]]

		UpperCamelCase						=  processor(images=np.array(lowercase				)					, return_tensors='pt'				).to('cuda'				)

		with torch.no_grad():
				UpperCamelCase						=  hf_model(**lowercase				)
		UpperCamelCase						=  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

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

				with torch.no_grad():
						UpperCamelCase						=  hf_model(**lowercase				)
				UpperCamelCase						=  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

				UpperCamelCase						=  ((75, 275, 1_725, 850),)

				UpperCamelCase						=  processor(images=np.array(lowercase				)					, input_boxes=lowercase					, return_tensors='pt'				).to('cuda'				)

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

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

				with torch.no_grad():
						UpperCamelCase						=  hf_model(**lowercase				)
				UpperCamelCase						=  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__":
					_UpperCAmelCase      :				Optional[Any]          = argparse.ArgumentParser()
					_UpperCAmelCase      :				Dict          = ["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      :				int          = parser.parse_args()

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


from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

_UpperCAmelCase      :				Tuple          = _symbol_database.Default()


_UpperCAmelCase      :				List[Any]          = _descriptor_pool.Default().AddSerializedFile(
    b"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)

_UpperCAmelCase      :				int          = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
					_UpperCAmelCase      :				int          = None
					_UpperCAmelCase      :				List[str]          = b"H\003"
					# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
					_UpperCAmelCase      :				Optional[Any]          = 45
					_UpperCAmelCase      :				Any          = 1_581
					_UpperCAmelCase      :				Tuple          = 1_517
					_UpperCAmelCase      :				List[str]          = 1_570
					_UpperCAmelCase      :				int          = 1_584
					_UpperCAmelCase      :				List[Any]          = 1_793
					_UpperCAmelCase      :				Optional[int]          = 1_795
					_UpperCAmelCase      :				Any          = 1_916
					_UpperCAmelCase      :				Tuple          = 1_864
					_UpperCAmelCase      :				List[Any]          = 1_905
					_UpperCAmelCase      :				Union[str, Any]          = 1_919
					_UpperCAmelCase      :				str          = 2_429
					_UpperCAmelCase      :				Any          = 2_208
					_UpperCAmelCase      :				Dict          = 2_418
					_UpperCAmelCase      :				Optional[Any]          = 2_323
					_UpperCAmelCase      :				Tuple          = 2_407
# @@protoc_insertion_point(module_scope)
