






'''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,
)


lowerCAmelCase__          =						{
    '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       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		UpperCamelCase__				:					Union[str, Any]					=					{}
		state_dict.pop('pixel_mean'     ,      lowerCamelCase_)
		state_dict.pop('pixel_std'     ,      lowerCamelCase_)

		UpperCamelCase__				:					str					=					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__				:					int					=					key.replace(lowerCamelCase_     ,      lowerCamelCase_)

				if re.match(lowerCamelCase_     ,      lowerCamelCase_):
						UpperCamelCase__				:					int					=					int(re.match(lowerCamelCase_     ,      lowerCamelCase_).group(2))
						if layer_nb == 0:
								UpperCamelCase__				:					Optional[Any]					=					key.replace('layers.0'     ,      'proj_in')
						elif layer_nb == 1:
								UpperCamelCase__				:					List[str]					=					key.replace('layers.1'     ,      'layers.0')
						elif layer_nb == 2:
								UpperCamelCase__				:					List[Any]					=					key.replace('layers.2'     ,      'proj_out')

				UpperCamelCase__				:					Any					=					value

		UpperCamelCase__				:					int					=					model_state_dict[
		    'prompt_encoder.shared_embedding.positional_embedding'
		]

		return model_state_dict







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_="ybelkada/segment-anything")							->     Any:
		UpperCamelCase__				:					Dict					=					hf_hub_download(lowerCamelCase_     ,      f'checkpoints/{model_name}.pth')

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

				UpperCamelCase__				:					Tuple					=					SamConfig(
				    vision_config=lowerCamelCase_     ,      )
		elif "sam_vit_h" in model_name:
				UpperCamelCase__				:					Optional[Any]					=					SamVisionConfig(
				    hidden_size=1_280     ,      num_hidden_layers=32     ,      num_attention_heads=16     ,      global_attn_indexes=[7, 15, 23, 31]     ,      )

				UpperCamelCase__				:					str					=					SamConfig(
				    vision_config=lowerCamelCase_     ,      )

		UpperCamelCase__				:					Optional[int]					=					torch.load(lowerCamelCase_     ,      map_location='cpu')
		UpperCamelCase__				:					Union[str, Any]					=					replace_keys(lowerCamelCase_)

		UpperCamelCase__				:					int					=					SamImageProcessor()

		UpperCamelCase__				:					Dict					=					SamProcessor(image_processor=lowerCamelCase_)
		UpperCamelCase__				:					Union[str, Any]					=					SamModel(lowerCamelCase_)

		hf_model.load_state_dict(lowerCamelCase_)
		UpperCamelCase__				:					Dict					=					hf_model.to('cuda')

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

		UpperCamelCase__				:					Dict					=					[[[400, 650]]]
		UpperCamelCase__				:					List[str]					=					[[1]]

		UpperCamelCase__				:					Any					=					processor(images=np.array(lowerCamelCase_)     ,      return_tensors='pt').to('cuda')

		with torch.no_grad():
				UpperCamelCase__				:					Tuple					=					hf_model(**lowerCamelCase_)
		UpperCamelCase__				:					str					=					output.iou_scores.squeeze()

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.579_890_251_159_668

				UpperCamelCase__				:					Union[str, Any]					=					processor(
				    images=np.array(lowerCamelCase_)     ,      input_points=lowerCamelCase_     ,      input_labels=lowerCamelCase_     ,      return_tensors='pt').to('cuda')

				with torch.no_grad():
						UpperCamelCase__				:					List[str]					=					hf_model(**lowerCamelCase_)
				UpperCamelCase__				:					List[Any]					=					output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9_712_603_092_193_604

				UpperCamelCase__				:					Tuple					=					((75, 275, 1_725, 850),)

				UpperCamelCase__				:					str					=					processor(images=np.array(lowerCamelCase_)     ,      input_boxes=lowerCamelCase_     ,      return_tensors='pt').to('cuda')

				with torch.no_grad():
						UpperCamelCase__				:					Tuple					=					hf_model(**lowerCamelCase_)
				UpperCamelCase__				:					Union[str, Any]					=					output.iou_scores.squeeze()

				assert scores[-1].item() == 0.8_686_015_605_926_514

				# Test with 2 points and 1 image.
				UpperCamelCase__				:					str					=					[[[400, 650], [800, 650]]]
				UpperCamelCase__				:					Optional[int]					=					[[1, 1]]

				UpperCamelCase__				:					Any					=					processor(
				    images=np.array(lowerCamelCase_)     ,      input_points=lowerCamelCase_     ,      input_labels=lowerCamelCase_     ,      return_tensors='pt').to('cuda')

				with torch.no_grad():
						UpperCamelCase__				:					List[str]					=					hf_model(**lowerCamelCase_)
				UpperCamelCase__				:					Optional[int]					=					output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		lowerCAmelCase__          =						['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__          =						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 __future__ import annotations







class       __lowercase					:

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						list[list[int]]):
			UpperCamelCase__				:					int					=					TypeError(
			    'Matrices must be formed from a list of zero or more lists containing at '
			    'least one and the same number of values, each of which must be of type '
			    'int or float.')
			if len(UpperCAmelCase_) != 0:
					UpperCamelCase__				:					str					=					len(rows[0])
					if cols == 0:
							raise error
					for row in rows:
							if len(UpperCAmelCase_) != cols:
									raise error
							for value in row:
									if not isinstance(UpperCAmelCase_  ,					(int, float)):
											raise error
					UpperCamelCase__				:					Optional[int]					=					rows
			else:
					UpperCamelCase__				:					Optional[Any]					=					[]

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return len(self.rows)

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return len(self.rows[0])

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return (self.num_rows, self.num_columns)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self.order[0] == self.order[1]

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Optional[int]					=					[
			    [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
			    for row_num in range(self.num_rows)
			]
			return Matrix(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict):
			if not self.is_square:
					return 0
			if self.order == (0, 0):
					return 1
			if self.order == (1, 1):
					return int(self.rows[0][0])
			if self.order == (2, 2):
					return int(
					    (self.rows[0][0] * self.rows[1][1])
					    - (self.rows[0][1] * self.rows[1][0]))
			else:
					return sum(
					    self.rows[0][column] * self.cofactors().rows[0][column]
					    for column in range(self.num_columns))

	def      __UpperCamelCase							(      self   :						str):
			return bool(self.determinant())

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					Optional[Any]					=					[
			    [
			        self.rows[other_row][other_column]
			        for other_column in range(self.num_columns)
			        if other_column != column
			    ]
			    for other_row in range(self.num_rows)
			    if other_row != row
			]
			return Matrix(UpperCAmelCase_).determinant()

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			if (row + column) % 2 == 0:
					return self.get_minor(UpperCAmelCase_  ,					UpperCAmelCase_)
			return -1 * self.get_minor(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[Any]):
			return Matrix(
			    [
			        [self.get_minor(UpperCAmelCase_  ,					UpperCAmelCase_) for column in range(self.num_columns)]
			        for row in range(self.num_rows)
			    ])

	def      __UpperCamelCase							(      self   :						Optional[int]):
			return Matrix(
			    [
			        [
			            self.minors().rows[row][column]
			            if (row + column) % 2 == 0
			            else self.minors().rows[row][column] * -1
			            for column in range(self.minors().num_columns)
			        ]
			        for row in range(self.minors().num_rows)
			    ])

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Dict					=					[
			    [self.cofactors().rows[column][row] for column in range(self.num_columns)]
			    for row in range(self.num_rows)
			]
			return Matrix(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					List[Any]					=					self.determinant()
			if not determinant:
					raise TypeError('Only matrices with a non-zero determinant have an inverse')
			return self.adjugate() * (1 / determinant)

	def __repr__(      self   :						Any):
			return str(self.rows)

	def __str__(      self   :						List[Any]):
			if self.num_rows == 0:
					return "[]"
			if self.num_rows == 1:
					return "[[" + ". ".join(str(self.rows[0])) + "]]"
			return (
			    "["
			    + "\n ".join(
			        [
			            '[' + '. '.join([str(UpperCAmelCase_) for value in row]) + '.]'
			            for row in self.rows
			        ])
			    + "]"
			)

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						list[int]  ,					UpperCAmelCase_   :						int | None = None):
			UpperCamelCase__				:					List[str]					=					TypeError('Row must be a list containing all ints and/or floats')
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					raise type_error
			for value in row:
					if not isinstance(UpperCAmelCase_  ,					(int, float)):
							raise type_error
			if len(UpperCAmelCase_) != self.num_columns:
					raise ValueError(
					    'Row must be equal in length to the other rows in the matrix')
			if position is None:
					self.rows.append(UpperCAmelCase_)
			else:
					UpperCamelCase__				:					Tuple					=					self.rows[0:position] + [row] + self.rows[position:]

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						list[int]  ,					UpperCAmelCase_   :						int | None = None):
			UpperCamelCase__				:					int					=					TypeError(
			    'Column must be a list containing all ints and/or floats')
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					raise type_error
			for value in column:
					if not isinstance(UpperCAmelCase_  ,					(int, float)):
							raise type_error
			if len(UpperCAmelCase_) != self.num_rows:
					raise ValueError(
					    'Column must be equal in length to the other columns in the matrix')
			if position is None:
					UpperCamelCase__				:					Optional[int]					=					[self.rows[i] + [column[i]] for i in range(self.num_rows)]
			else:
					UpperCamelCase__				:					str					=					[
					    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
					    for i in range(self.num_rows)
					]

	def __eq__(      self   :						List[Any]  ,					UpperCAmelCase_   :						object):
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					return NotImplemented
			return self.rows == other.rows

	def __ne__(      self   :						Any  ,					UpperCAmelCase_   :						object):
			return not self == other

	def __neg__(      self   :						Union[str, Any]):
			return self * -1

	def __add__(      self   :						Optional[int]  ,					UpperCAmelCase_   :						Matrix):
			if self.order != other.order:
					raise ValueError('Addition requires matrices of the same order')
			return Matrix(
			    [
			        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
			        for i in range(self.num_rows)
			    ])

	def __sub__(      self   :						Tuple  ,					UpperCAmelCase_   :						Matrix):
			if self.order != other.order:
					raise ValueError('Subtraction requires matrices of the same order')
			return Matrix(
			    [
			        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
			        for i in range(self.num_rows)
			    ])

	def __mul__(      self   :						Any  ,					UpperCAmelCase_   :						Matrix | int | float):
			if isinstance(UpperCAmelCase_  ,					(int, float)):
					return Matrix(
					    [[int(element * other) for element in row] for row in self.rows])
			elif isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					if self.num_columns != other.num_rows:
							raise ValueError(
							    'The number of columns in the first matrix must '
							    'be equal to the number of rows in the second')
					return Matrix(
					    [
					        [Matrix.dot_product(UpperCAmelCase_  ,					UpperCAmelCase_) for column in other.columns()]
					        for row in self.rows
					    ])
			else:
					raise TypeError(
					    'A Matrix can only be multiplied by an int, float, or another matrix')

	def __pow__(      self   :						Dict  ,					UpperCAmelCase_   :						int):
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					raise TypeError('A Matrix can only be raised to the power of an int')
			if not self.is_square:
					raise ValueError('Only square matrices can be raised to a power')
			if other == 0:
					return self.identity()
			if other < 0:
					if self.is_invertable():
							return self.inverse() ** (-other)
					raise ValueError(
					    'Only invertable matrices can be raised to a negative power')
			UpperCamelCase__				:					str					=					self
			for _ in range(other - 1):
					result *= self
			return result





	@classmethod
	def      __UpperCamelCase							(      cls   :						Optional[int]  ,					UpperCAmelCase_   :						list[int]  ,					UpperCAmelCase_   :						list[int]):
			return sum(row[i] * column[i] for i in range(len(UpperCAmelCase_)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()




