






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,
)


__lowerCamelCase      :    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   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   List[Any]     =						{}
	state_dict.pop("pixel_mean"  ,						lowerCAmelCase__     )
	state_dict.pop("pixel_std"  ,						lowerCAmelCase__     )

	snake_case__				:   List[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:
				snake_case__				:   Union[str, Any]     =						key.replace(lowerCAmelCase__  ,						lowerCAmelCase__     )

		if re.match(lowerCAmelCase__  ,						lowerCAmelCase__     ):
			snake_case__				:   Optional[int]     =						int(re.match(lowerCAmelCase__  ,						lowerCAmelCase__     ).group(2     )     )
			if layer_nb == 0:
				snake_case__				:   Union[str, Any]     =						key.replace("layers.0"  ,						"proj_in"     )
			elif layer_nb == 1:
				snake_case__				:   str     =						key.replace("layers.1"  ,						"layers.0"     )
			elif layer_nb == 2:
				snake_case__				:   List[str]     =						key.replace("layers.2"  ,						"proj_out"     )

		snake_case__				:   Any     =						value

	snake_case__				:   str     =						model_state_dict[
	    "prompt_encoder.shared_embedding.positional_embedding"
	]

	return model_state_dict







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							Tuple  ,						snake_case_      :							List[str]  ,						snake_case_      :							Dict="ybelkada/segment-anything"     ):
	snake_case__				:   Dict     =						hf_hub_download(lowerCAmelCase__  ,						F'''checkpoints/{model_name}.pth'''     )

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

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

		snake_case__				:   Optional[int]     =						SamConfig(
		    vision_config=lowerCAmelCase__  ,						)

	snake_case__				:   Optional[int]     =						torch.load(lowerCAmelCase__  ,						map_location="cpu"     )
	snake_case__				:   Union[str, Any]     =						replace_keys(lowerCAmelCase__     )

	snake_case__				:   Dict     =						SamImageProcessor()

	snake_case__				:   List[str]     =						SamProcessor(image_processor=lowerCAmelCase__     )
	snake_case__				:   Optional[int]     =						SamModel(lowerCAmelCase__     )

	hf_model.load_state_dict(lowerCAmelCase__     )
	snake_case__				:   Optional[Any]     =						hf_model.to("cuda"     )

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

	snake_case__				:   List[Any]     =						[[[400, 650]]]
	snake_case__				:   str     =						[[1]]

	snake_case__				:   Union[str, Any]     =						processor(images=np.array(lowerCAmelCase__     )  ,						return_tensors="pt"     ).to("cuda"     )

	with torch.no_grad():
		snake_case__				:   Any     =						hf_model(**lowerCAmelCase__     )
	snake_case__				:   Optional[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

		snake_case__				:   List[str]     =						processor(
		    images=np.array(lowerCAmelCase__     )  ,						input_points=lowerCAmelCase__  ,						input_labels=lowerCAmelCase__  ,						return_tensors="pt"     ).to("cuda"     )

		with torch.no_grad():
			snake_case__				:   int     =						hf_model(**lowerCAmelCase__     )
		snake_case__				:   List[str]     =						output.iou_scores.squeeze()

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

		snake_case__				:   List[Any]     =						((75, 275, 1725, 850),)

		snake_case__				:   Dict     =						processor(images=np.array(lowerCAmelCase__     )  ,						input_boxes=lowerCAmelCase__  ,						return_tensors="pt"     ).to("cuda"     )

		with torch.no_grad():
			snake_case__				:   Dict     =						hf_model(**lowerCAmelCase__     )
		snake_case__				:   Optional[int]     =						output.iou_scores.squeeze()

		assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

		snake_case__				:   Any     =						processor(
		    images=np.array(lowerCAmelCase__     )  ,						input_points=lowerCAmelCase__  ,						input_labels=lowerCAmelCase__  ,						return_tensors="pt"     ).to("cuda"     )

		with torch.no_grad():
			snake_case__				:   List[Any]     =						hf_model(**lowerCAmelCase__     )
		snake_case__				:   Union[str, Any]     =						output.iou_scores.squeeze()

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


if __name__ == "__main__":
	__lowerCamelCase      :    List[str]   		=   argparse.ArgumentParser()
	__lowerCamelCase      :    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""",
	)

	__lowerCamelCase      :    int   		=   parser.parse_args()

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









# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import jax
	import jaxlib

__lowerCamelCase      :    Optional[int]   		=   get_logger()

__lowerCamelCase      :    Optional[dict]   		=   None



class 							SCREAMING_SNAKE_CASE__		(       TensorFormatter[Mapping, "jax.Array", Mapping]		):





					"""simple docstring"""






					def __init__(      self  :						Optional[Any]							,	__A  :						Dict=None							,	__A  :						List[str]=None							,	**__A  :						str ):
						super().__init__(features=__A )
						import jax
						from jaxlib.xla_client import Device

						if isinstance(__A							,	__A ):
							raise ValueError(
							    f'''Expected {device} to be a `str` not {type(__A )}, as `jaxlib.xla_extension.Device` '''
							    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
							    "the device with `str()` to get its string identifier that will be internally mapped "
							    "to the actual `jaxlib.xla_extension.Device`." )
						snake_case__				:   List[Any]     =						device if isinstance(__A							,	__A ) else str(jax.devices()[0] )
						# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
						# with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   Any     =						self._map_devices_to_str()
						if self.device not in list(DEVICE_MAPPING.keys() ):
							logger.warning(
							    f'''Device with string identifier {self.device} not listed among the available '''
							    f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
							    f'''device: {str(jax.devices()[0] )}.''' )
							snake_case__				:   str     =						str(jax.devices()[0] )
						snake_case__				:   str     =						jnp_array_kwargs



					@staticmethod
					def  _lowercase  (      ):
						import jax

						return {str(__A ): device for device in jax.devices()}



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						str ):
						import jax
						import jax.numpy as jnp

						if isinstance(__A							,	__A ) and column:
							if all(
							    isinstance(__A							,	jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
								return jnp.stack(__A							,	axis=0 )
						return column



					def  _lowercase  (      self  :						int							,	__A  :						Tuple ):
						import jax
						import jax.numpy as jnp

						if isinstance(__A							,	(str, bytes, type(__A )) ):
							return value
						elif isinstance(__A							,	(np.character, np.ndarray) ) and np.issubdtype(value.dtype							,	np.character ):
							return value.tolist()

						snake_case__				:   Optional[int]     =						{}

						if isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.integer ):
							# the default int precision depends on the jax config
							# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
							if jax.config.jax_enable_xaa:
								snake_case__				:   Any     =						{"dtype": jnp.intaa}
							else:
								snake_case__				:   Tuple     =						{"dtype": jnp.intaa}
						elif isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.floating ):
							snake_case__				:   str     =						{"dtype": jnp.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
							import PIL.Image

							if isinstance(__A							,	PIL.Image.Image ):
								snake_case__				:   Optional[Any]     =						np.asarray(__A )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   int     =						self._map_devices_to_str()

						with jax.default_device(DEVICE_MAPPING[self.device] ):
							# calling jnp.array on a np.ndarray does copy the data
							# see https://github.com/google/jax/issues/4486
							return jnp.array(__A							,	**{**default_dtype, **self.jnp_array_kwargs} )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[int] ):
						import jax

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
							import torch

							if isinstance(__A							,	torch.Tensor ):
								return self._tensorize(data_struct.detach().cpu().numpy()[()] )
						if hasattr(__A							,	"__array__" ) and not isinstance(__A							,	jax.Array ):
							snake_case__				:   Union[str, Any]     =						data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__A							,	np.ndarray ):
							if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
								return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
						elif isinstance(__A							,	(list, tuple) ):
							return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
						return self._tensorize(__A )



					def  _lowercase  (      self  :						Tuple							,	__A  :						dict ):
						return map_nested(self._recursive_tensorize							,	__A							,	map_list=__A )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						pa.Table ):
						snake_case__				:   int     =						self.numpy_arrow_extractor().extract_row(__A )
						snake_case__				:   Tuple     =						self.python_features_decoder.decode_row(__A )
						return self.recursive_tensorize(__A )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_column(__A )
						snake_case__				:   Optional[int]     =						self.python_features_decoder.decode_column(__A							,	pa_table.column_names[0] )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						snake_case__				:   Dict     =						self._consolidate(__A )
						return column






					def  _lowercase  (      self  :						str							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_batch(__A )
						snake_case__				:   int     =						self.python_features_decoder.decode_batch(__A )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						for column_name in batch:
							snake_case__				:   Any     =						self._consolidate(batch[column_name] )
						return batch



