

"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase       						=		logging.get_logger(__name__)

_lowerCAmelCase       						=		{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}







class 						__UpperCamelCase     (     UpperCAmelCase_    ):
  _UpperCAmelCase        =		"beit"

  def __init__(		self     ,_A=8192     ,_A=768     ,_A=12     ,_A=12     ,_A=3072     ,_A="gelu"     ,_A=0.0     ,_A=0.0     ,_A=0.0_2     ,_A=1E-12     ,_A=224     ,_A=16     ,_A=3     ,_A=False     ,_A=False     ,_A=False     ,_A=False     ,_A=0.1     ,_A=0.1     ,_A=True     ,_A=[3, 5, 7, 11]     ,_A=[1, 2, 3, 6]     ,_A=True     ,_A=0.4     ,_A=256     ,_A=1     ,_A=False     ,_A=255     ,**_A     ,):
      '''simple docstring'''

      super().__init__(**__lowerCamelCase   )

      _lowerCAmelCase							: Optional[Any]	=     vocab_size
      _lowerCAmelCase							: Optional[int]	=     hidden_size
      _lowerCAmelCase							: List[str]	=     num_hidden_layers
      _lowerCAmelCase							: List[str]	=     num_attention_heads
      _lowerCAmelCase							: Any	=     intermediate_size
      _lowerCAmelCase							: List[Any]	=     hidden_act
      _lowerCAmelCase							: Optional[int]	=     hidden_dropout_prob
      _lowerCAmelCase							: Dict	=     attention_probs_dropout_prob
      _lowerCAmelCase							: Dict	=     initializer_range
      _lowerCAmelCase							: Tuple	=     layer_norm_eps

      _lowerCAmelCase							: Union[str, Any]	=     image_size
      _lowerCAmelCase							: Any	=     patch_size
      _lowerCAmelCase							: Union[str, Any]	=     num_channels
      _lowerCAmelCase							: Dict	=     use_mask_token
      _lowerCAmelCase							: Tuple	=     use_absolute_position_embeddings
      _lowerCAmelCase							: Tuple	=     use_relative_position_bias
      _lowerCAmelCase							: Tuple	=     use_shared_relative_position_bias
      _lowerCAmelCase							: Dict	=     layer_scale_init_value
      _lowerCAmelCase							: List[Any]	=     drop_path_rate
      _lowerCAmelCase							: int	=     use_mean_pooling
      # decode head attributes (semantic segmentation)
      _lowerCAmelCase							: Union[str, Any]	=     out_indices
      _lowerCAmelCase							: Dict	=     pool_scales
      # auxiliary head attributes (semantic segmentation)
      _lowerCAmelCase							: int	=     use_auxiliary_head
      _lowerCAmelCase							: int	=     auxiliary_loss_weight
      _lowerCAmelCase							: Dict	=     auxiliary_channels
      _lowerCAmelCase							: Dict	=     auxiliary_num_convs
      _lowerCAmelCase							: Tuple	=     auxiliary_concat_input
      _lowerCAmelCase							: str	=     semantic_loss_ignore_index







class 						__UpperCamelCase     (     UpperCAmelCase_    ):
  _UpperCAmelCase        =		version.parse("1.11"    )

  @property
  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]   )







  @property
  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      return 1E-4












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,
)


SCREAMING_SNAKE_CASE_   :      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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     {}
		state_dict.pop('pixel_mean'						,   snake_case							)
		state_dict.pop('pixel_std'						,   snake_case							)

		__lowercase											=     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:
								__lowercase											=     key.replace(snake_case						,   snake_case							)

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

				__lowercase											=     value

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

		return model_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case="ybelkada/segment-anything"							)		->    int:
		__lowercase											=     hf_hub_download(snake_case						,   F"checkpoints/{model_name}.pth"							)

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

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

				__lowercase											=     SamConfig(
				    vision_config=snake_case						,   )

		__lowercase											=     torch.load(snake_case						,   map_location='cpu'							)
		__lowercase											=     replace_keys(snake_case							)

		__lowercase											=     SamImageProcessor()

		__lowercase											=     SamProcessor(image_processor=snake_case							)
		__lowercase											=     SamModel(snake_case							)

		hf_model.load_state_dict(snake_case							)
		__lowercase											=     hf_model.to('cuda'							)

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

		__lowercase											=     [[[400, 650]]]
		__lowercase											=     [[1]]

		__lowercase											=     processor(images=np.array(snake_case							)						,   return_tensors='pt'							).to('cuda'							)

		with torch.no_grad():
				__lowercase											=     hf_model(**snake_case							)
		__lowercase											=     output.iou_scores.squeeze()

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

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

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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

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

				__lowercase											=     processor(images=np.array(snake_case							)						,   input_boxes=snake_case						,   return_tensors='pt'							).to('cuda'							)

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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

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

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

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   argparse.ArgumentParser()
		SCREAMING_SNAKE_CASE_   :      Union[str, 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''',
		)

		SCREAMING_SNAKE_CASE_   :      List[Any]			=   parser.parse_args()

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




