

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'switch_transformers'
			__lowerCamelCase       				=				['past_key_values']
			__lowerCamelCase       				=				{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


			def __init__(			self					,					lowercase=32128					,					lowercase=768					,					lowercase=64					,					lowercase=2048					,					lowercase=64					,					lowercase=12					,					lowercase=3					,					lowercase=12					,					lowercase=3					,					lowercase=12					,					lowercase=8					,					lowercase=False					,					lowercase=0.01					,					lowercase="float32"					,					lowercase=False					,					lowercase=32					,					lowercase=128					,					lowercase=0.1					,					lowercase=1e-6					,					lowercase=0.001					,					lowercase=0.001					,					lowercase=1.0					,					lowercase="relu"					,					lowercase=True					,					lowercase=False					,					lowercase=True					,					lowercase=0					,					lowercase=1					,					**lowercase					,					)     ->  Tuple:
							'''simple docstring'''






							A__						=	vocab_size
							A__						=	d_model
							A__						=	d_kv
							A__						=	d_ff

							A__						=	num_sparse_encoder_layers

							A__						=	num_layers
							A__						=	(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							A__						=	num_sparse_decoder_layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_encoder_layers > 0:
											A__						=	self.num_layers // self.num_sparse_encoder_layers
							else:
											A__						=	self.num_layers  # HACK: this will create 0 sparse layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_decoder_layers > 0:
											A__						=	self.num_decoder_layers // self.num_sparse_decoder_layers
							else:
											A__						=	self.num_decoder_layers  # HACK: this will create 0 sparse layers

							A__						=	num_heads
							A__						=	num_experts
							A__						=	expert_capacity
							A__						=	router_bias
							A__						=	router_jitter_noise
							if router_dtype not in ["float32", "float16", "bfloat16"]:
											raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'				)
							A__						=	router_dtype

							A__						=	router_ignore_padding_tokens
							A__						=	relative_attention_num_buckets
							A__						=	relative_attention_max_distance

							A__						=	dropout_rate
							A__						=	layer_norm_epsilon
							A__						=	initializer_factor
							A__						=	feed_forward_proj
							A__						=	use_cache
							A__						=	add_router_probs

							A__						=	router_z_loss_coef
							A__						=	router_aux_loss_coef

							A__						=	self.feed_forward_proj.split("-"				)
							A__						=	act_info[-1]
							A__						=	act_info[0] == "gated"

							if len(lowercase				) > 1 and act_info[0] != "gated" or len(lowercase				) > 2:
											raise ValueError(
											    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
											    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
											    "'gated-gelu' or 'relu'"				)

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
											A__						=	"gelu_new"

							super().__init__(
							    pad_token_id=lowercase					,					eos_token_id=lowercase					,					is_encoder_decoder=lowercase					,					**lowercase					,					)






import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				BigBirdConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)

    if is_trivia_qa:
        __a						       =				BigBirdForQuestionAnswering(_UpperCAmelCase				)
    else:
        __a						       =				BigBirdForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_big_bird(_UpperCAmelCase   , _UpperCAmelCase   , is_trivia_qa=_UpperCAmelCase				)

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


if __name__ == "__main__":
 __snake_case						:Tuple      		=     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(
     '''--big_bird_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained BERT 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.'''
 )
 parser.add_argument(
     '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
 )
 __snake_case						:Any      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
 )





