




"""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_        =       {
    '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     ( __UpperCamelCase	):
			__lowercase			:  Optional[int]								=       {}
			state_dict.pop('''pixel_mean'''	,				__UpperCamelCase	)
			state_dict.pop('''pixel_std'''	,				__UpperCamelCase	)

			__lowercase			:  List[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:
												__lowercase			:  Any								=       key.replace(__UpperCamelCase	,				__UpperCamelCase	)

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

						__lowercase			:  Tuple								=       value

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

			return model_state_dict







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase="ybelkada/segment-anything"	):
			__lowercase			:  int								=       hf_hub_download(__UpperCamelCase	,				f"""checkpoints/{model_name}.pth"""	)

			if "sam_vit_b" in model_name:
						__lowercase			:  int								=       SamConfig()
			elif "sam_vit_l" in model_name:
						__lowercase			:  Dict								=       SamVisionConfig(
						    hidden_size=10_24	,				num_hidden_layers=24	,				num_attention_heads=16	,				global_attn_indexes=[5, 11, 17, 23]	,				)

						__lowercase			:  List[Any]								=       SamConfig(
						    vision_config=__UpperCamelCase	,				)
			elif "sam_vit_h" in model_name:
						__lowercase			:  Dict								=       SamVisionConfig(
						    hidden_size=12_80	,				num_hidden_layers=32	,				num_attention_heads=16	,				global_attn_indexes=[7, 15, 23, 31]	,				)

						__lowercase			:  List[str]								=       SamConfig(
						    vision_config=__UpperCamelCase	,				)

			__lowercase			:  List[Any]								=       torch.load(__UpperCamelCase	,				map_location='''cpu'''	)
			__lowercase			:  List[Any]								=       replace_keys(__UpperCamelCase	)

			__lowercase			:  Optional[Any]								=       SamImageProcessor()

			__lowercase			:  Union[str, Any]								=       SamProcessor(image_processor=__UpperCamelCase	)
			__lowercase			:  List[Any]								=       SamModel(__UpperCamelCase	)

			hf_model.load_state_dict(__UpperCamelCase	)
			__lowercase			:  Dict								=       hf_model.to('''cuda'''	)

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

			__lowercase			:  Union[str, Any]								=       [[[4_00, 6_50]]]
			__lowercase			:  List[str]								=       [[1]]

			__lowercase			:  Optional[int]								=       processor(images=np.array(__UpperCamelCase	)	,				return_tensors='''pt'''	).to('''cuda'''	)

			with torch.no_grad():
						__lowercase			:  Dict								=       hf_model(**__UpperCamelCase	)
			__lowercase			:  List[str]								=       output.iou_scores.squeeze()

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

						__lowercase			:  Optional[int]								=       processor(
						    images=np.array(__UpperCamelCase	)	,				input_points=__UpperCamelCase	,				input_labels=__UpperCamelCase	,				return_tensors='''pt'''	).to('''cuda'''	)

						with torch.no_grad():
									__lowercase			:  str								=       hf_model(**__UpperCamelCase	)
						__lowercase			:  Optional[Any]								=       output.iou_scores.squeeze()

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

						__lowercase			:  List[str]								=       ((75, 2_75, 17_25, 8_50),)

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

						with torch.no_grad():
									__lowercase			:  str								=       hf_model(**__UpperCamelCase	)
						__lowercase			:  Optional[Any]								=       output.iou_scores.squeeze()

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

						# Test with 2 points and 1 image.
						__lowercase			:  Optional[Any]								=       [[[4_00, 6_50], [8_00, 6_50]]]
						__lowercase			:  Union[str, Any]								=       [[1, 1]]

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

						with torch.no_grad():
									__lowercase			:  Optional[int]								=       hf_model(**__UpperCamelCase	)
						__lowercase			:  Any								=       output.iou_scores.squeeze()

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


if __name__ == "__main__":
			a_        =       argparse.ArgumentParser()
			a_        =       ['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_        =       parser.parse_args()

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






import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						Any     ,				__A  :						Dict     ,				__A  :						Optional[int]=None			)					->Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
      __A    =nn.Parameter(__A			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
            __A    =nn.Parameter(__A			)





def      A__					(			__A  :						List[Any]     ,				__A  :						Tuple     ,				__A  :						List[Any]			)					->Dict:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)

      set_param(
          torch_layer.self_attention.query_key     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.value     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.output.dense     ,				torch.tensor(__A			).view(-1     ,				__A			).contiguous().transpose(0     ,				1			)     ,				)





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple     ,				__A  :						Optional[Any]			)					->int:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)
      __A    =np.asarray(weights[3]			)

      set_param(
          torch_layer.self_attention.query     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.key     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.value     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.output.dense     ,				torch.tensor(__A			).view(-1     ,				__A			).contiguous().transpose(0     ,				1			)     ,				)





def      A__					(			__A  :						int     ,				__A  :						List[str]     ,				__A  :						Optional[Any]			)					->Any:
      # layernorm 1
      __A    =weights[0][0][0]
      __A    =np.asarray(layer_norm_a[0]			)
      __A    =np.asarray(layer_norm_a[1]			)
      set_param(
          torch_block.attention.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # lsh weights + output
      __A    =weights[0][1]
      if len(__A			) < 4:
            set_layer_weights_in_torch_lsh(__A     ,				torch_block.attention     ,				__A			)
      else:
            set_layer_weights_in_torch_local(__A     ,				torch_block.attention     ,				__A			)

      # intermediate weighs
      __A    =weights[2][0][1][2]

      # Chunked Feed Forward
      if len(__A			) == 4:
            __A    =intermediate_weights[2]

      # layernorm 2
      __A    =np.asarray(intermediate_weights[0][0]			)
      __A    =np.asarray(intermediate_weights[0][1]			)
      set_param(
          torch_block.feed_forward.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # intermediate dense
      __A    =np.asarray(intermediate_weights[1][0]			)
      __A    =np.asarray(intermediate_weights[1][1]			)
      set_param(
          torch_block.feed_forward.dense.dense     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)

      # intermediate out
      __A    =np.asarray(intermediate_weights[4][0]			)
      __A    =np.asarray(intermediate_weights[4][1]			)
      set_param(
          torch_block.feed_forward.output.dense     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						Optional[int]			)					->List[Any]:
      # reformer model
      __A    =torch_model.reformer

      # word embeds
      __A    =np.asarray(weights[1]			)
      set_param(
          torch_model_reformer.embeddings.word_embeddings     ,				torch.tensor(__A			)     ,				)

      if isinstance(weights[3]     ,				__A			):
            __A    =torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights			)			):
                  __A    =np.asarray(weights[3][emb_idx][0]			)
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), F'''{position_embeddings[emb_idx]} emb does not match'''
                  __A    =nn.Parameter(torch.tensor(__A			)			)

      __A    =weights[5]
      assert len(torch_model_reformer.encoder.layers			) * 4 == len(
          __A			), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
            __A    =trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(__A     ,				__A     ,				__A			)

      # output layer norm
      __A    =np.asarray(weights[7][0]			)
      __A    =np.asarray(weights[7][1]			)
      set_param(
          torch_model_reformer.encoder.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # output embeddings
      __A    =np.asarray(weights[9][0]			)
      __A    =np.asarray(weights[9][1]			)
      set_param(
          torch_model.lm_head.decoder     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Tuple			)					->Union[str, Any]:
      # Initialise PyTorch model
      __A    =ReformerConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      __A    =ReformerModelWithLMHead(__A			)

      with open(__A     ,				'''rb'''			) as f:
            __A    =pickle.load(__A			)['''weights''']

      set_model_weights_in_torch(__A     ,				__A     ,				config.hidden_size			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      torch.save(model.state_dict()     ,				__A			)


if __name__ == "__main__":
   _lowerCamelCase      :					Union[str, Any]					    =      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained Reformer model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _lowerCamelCase      :					Tuple					    =      parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


