




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)


"""simple docstring"""


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


__A					=			logging.get_logger(__name__)

__A					=			{
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						'''nystromformer'''



			def __init__(				self	,	__UpperCAmelCase=3_00_00	,	__UpperCAmelCase=7_68	,	__UpperCAmelCase=12	,	__UpperCAmelCase=12	,	__UpperCAmelCase=30_72	,	__UpperCAmelCase="gelu_new"	,	__UpperCAmelCase=0.1	,	__UpperCAmelCase=0.1	,	__UpperCAmelCase=5_10	,	__UpperCAmelCase=2	,	__UpperCAmelCase=64	,	__UpperCAmelCase=64	,	__UpperCAmelCase=65	,	__UpperCAmelCase=False	,	__UpperCAmelCase=0.0_2	,	__UpperCAmelCase=1e-5	,	__UpperCAmelCase=1	,	__UpperCAmelCase=0	,	__UpperCAmelCase=2	,	**__UpperCAmelCase	,	)					->	Union[str, Any]:
				_lowerCAmelCase								=vocab_size
				_lowerCAmelCase								=max_position_embeddings
				_lowerCAmelCase								=hidden_size
				_lowerCAmelCase								=num_hidden_layers
				_lowerCAmelCase								=num_attention_heads
				_lowerCAmelCase								=intermediate_size
				_lowerCAmelCase								=hidden_act
				_lowerCAmelCase								=hidden_dropout_prob
				_lowerCAmelCase								=attention_probs_dropout_prob
				_lowerCAmelCase								=initializer_range
				_lowerCAmelCase								=type_vocab_size
				_lowerCAmelCase								=segment_means_seq_len
				_lowerCAmelCase								=num_landmarks
				_lowerCAmelCase								=conv_kernel_size
				_lowerCAmelCase								=inv_coeff_init_option
				_lowerCAmelCase								=layer_norm_eps
				super().__init__(pad_token_id=__UpperCAmelCase	,	bos_token_id=__UpperCAmelCase	,	eos_token_id=__UpperCAmelCase	,	**__UpperCAmelCase    )



