




import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def    snake_case   (				A		,  A		,  A     )				->				Dict:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				json.load(open(A		,  'r'     )     )
	lowerCamelCase_				:						str				  =				GPTNeoConfig(
	    hidden_size=config_json['n_embd']		,  num_layers=config_json['n_layer']		,  num_heads=config_json['n_head']		,  attention_types=config_json['attention_types']		,  max_position_embeddings=config_json['n_positions']		,  resid_dropout=config_json['res_dropout']		,  embed_dropout=config_json['embed_dropout']		,  attention_dropout=config_json['attn_dropout']		,  )
	print(f"Building PyTorch model from configuration: {config}"     )
	lowerCamelCase_				:						Dict				  =				GPTNeoForCausalLM(A     )

	# Load weights from tf checkpoint
	load_tf_weights_in_gpt_neo(A		,  A		,  A     )

	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"     )
	model.save_pretrained(A     )


if __name__ == "__main__":
							UpperCamelCase__						=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_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 mesh-tf 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.'
							)
							UpperCamelCase__						=		parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=256,       lowercase_=1024,       lowercase_=16,       lowercase_=16,       lowercase_=4,       lowercase_="gelu",       lowercase_=1E-6,       **lowercase_,       )				->       int:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =hidden_size
     a__    =image_size
     a__    =patch_size
     a__    =image_size // patch_size
     a__    =mask_input_channels
     a__    =num_point_embeddings
     a__    =hidden_act
     a__    =layer_norm_eps







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=256,       lowercase_="relu",       lowercase_=2048,       lowercase_=2,       lowercase_=8,       lowercase_=2,       lowercase_=3,       lowercase_=3,       lowercase_=256,       lowercase_=1E-6,       **lowercase_,       )				->       int:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =hidden_size
     a__    =hidden_act
     a__    =mlp_dim
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =attention_downsample_rate
     a__    =num_multimask_outputs
     a__    =iou_head_depth
     a__    =iou_head_hidden_dim
     a__    =layer_norm_eps







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=768,       lowercase_=256,       lowercase_=12,       lowercase_=12,       lowercase_=3,       lowercase_=1024,       lowercase_=16,       lowercase_="gelu",       lowercase_=1E-06,       lowercase_=0.0,       lowercase_=1E-10,       lowercase_=True,       lowercase_=4.0,       lowercase_=True,       lowercase_=True,       lowercase_=14,       lowercase_=[2, 5, 8, 11],       lowercase_=128,       lowercase_=None,       **lowercase_,       )				->       Union[str, Any]:






     """simple docstring"""






     super().__init__(**lowercase_			)

     a__    =hidden_size
     a__    =output_channels
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =num_channels
     a__    =image_size
     a__    =patch_size
     a__    =hidden_act
     a__    =layer_norm_eps
     a__    =attention_dropout
     a__    =initializer_range
     a__    =qkv_bias
     a__    =mlp_ratio
     a__    =use_abs_pos
     a__    =use_rel_pos
     a__    =window_size
     a__    =global_attn_indexes
     a__    =num_pos_feats
     a__    =int(hidden_size * mlp_ratio			) if mlp_dim is None else mlp_dim







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[int]        =  'sam'
   lowerCamelCase__					:      str        =  True



   def __init__(							self,       lowercase_=None,       lowercase_=None,       lowercase_=None,       lowercase_=0.02,       **lowercase_,       )				->       Tuple:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =vision_config if vision_config is not None else {}
     a__    =prompt_encoder_config if prompt_encoder_config is not None else {}
     a__    =mask_decoder_config if mask_decoder_config is not None else {}

     if isinstance(lowercase_,       lowercase_			):
       a__    =vision_config.to_dict()
     if isinstance(lowercase_,       lowercase_			):
       a__    =prompt_encoder_config.to_dict()
     if isinstance(lowercase_,       lowercase_			):
       a__    =mask_decoder_config.to_dict()

     a__    =SamVisionConfig(**lowercase_			)
     a__    =SamPromptEncoderConfig(**lowercase_			)
     a__    =SamMaskDecoderConfig(**lowercase_			)
     a__    =initializer_range



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =copy.deepcopy(self.__dict__			)
     a__    =self.vision_config.to_dict()
     a__    =self.prompt_encoder_config.to_dict()
     a__    =self.mask_decoder_config.to_dict()
     a__    =self.__class__.model_type
     return output
