

'''simple docstring'''





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


UpperCamelCase_							=					logging.get_logger(__name__)

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





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				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    :		int		,				UpperCAmelCase__    :		Optional[int]=32128		,				UpperCAmelCase__    :		str=768		,				UpperCAmelCase__    :		Optional[int]=64		,				UpperCAmelCase__    :		Optional[Any]=2048		,				UpperCAmelCase__    :		List[str]=64		,				UpperCAmelCase__    :		List[Any]=12		,				UpperCAmelCase__    :		Tuple=3		,				UpperCAmelCase__    :		List[Any]=12		,				UpperCAmelCase__    :		Any=3		,				UpperCAmelCase__    :		Any=12		,				UpperCAmelCase__    :		Tuple=8		,				UpperCAmelCase__    :		Tuple=False		,				UpperCAmelCase__    :		Union[str, Any]=0.01		,				UpperCAmelCase__    :		Optional[Any]="float32"		,				UpperCAmelCase__    :		Tuple=False		,				UpperCAmelCase__    :		Any=32		,				UpperCAmelCase__    :		str=128		,				UpperCAmelCase__    :		Optional[Any]=0.1		,				UpperCAmelCase__    :		List[Any]=1E-6		,				UpperCAmelCase__    :		Optional[Any]=0.0_01		,				UpperCAmelCase__    :		List[str]=0.0_01		,				UpperCAmelCase__    :		Tuple=1.0		,				UpperCAmelCase__    :		str="relu"		,				UpperCAmelCase__    :		str=True		,				UpperCAmelCase__    :		Tuple=False		,				UpperCAmelCase__    :		int=True		,				UpperCAmelCase__    :		Dict=0		,				UpperCAmelCase__    :		Optional[Any]=1		,				**UpperCAmelCase__    :		List[Any]		,				):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=vocab_size
										lowercase   :       Optional[Any]								=d_model
										lowercase   :       int								=d_kv
										lowercase   :       Optional[int]								=d_ff

										lowercase   :       Union[str, Any]								=num_sparse_encoder_layers

										lowercase   :       List[Any]								=num_layers
										lowercase   :       List[Any]								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowercase   :       Optional[int]								=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:
																lowercase   :       int								=self.num_layers // self.num_sparse_encoder_layers
										else:
																lowercase   :       List[Any]								=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:
																lowercase   :       Any								=self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
																lowercase   :       Union[str, Any]								=self.num_decoder_layers  # HACK: this will create 0 sparse layers

										lowercase   :       Dict								=num_heads
										lowercase   :       Union[str, Any]								=num_experts
										lowercase   :       List[Any]								=expert_capacity
										lowercase   :       List[str]								=router_bias
										lowercase   :       Tuple								=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}'''      )
										lowercase   :       str								=router_dtype

										lowercase   :       List[str]								=router_ignore_padding_tokens
										lowercase   :       List[str]								=relative_attention_num_buckets
										lowercase   :       Optional[Any]								=relative_attention_max_distance

										lowercase   :       Optional[int]								=dropout_rate
										lowercase   :       Any								=layer_norm_epsilon
										lowercase   :       Union[str, Any]								=initializer_factor
										lowercase   :       Dict								=feed_forward_proj
										lowercase   :       Union[str, Any]								=use_cache
										lowercase   :       Union[str, Any]								=add_router_probs

										lowercase   :       str								=router_z_loss_coef
										lowercase   :       Optional[int]								=router_aux_loss_coef

										lowercase   :       Union[str, Any]								=self.feed_forward_proj.split('''-'''      )
										lowercase   :       Optional[int]								=act_info[-1]
										lowercase   :       Tuple								=act_info[0] == '''gated'''

										if len(UpperCAmelCase__      ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase__      ) > 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":
																lowercase   :       Dict								='''gelu_new'''

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








'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.{param_name}''']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.{w}.weight'''
																					UpperCamelCase_							=					state_dict[param_name]
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.LayerNorm.{w}'''
																					UpperCamelCase_							=					state_dict[param_name]

    # Transformer Blocks #
							UpperCamelCase_							=					0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					state_dict["""lm_head.weight"""]

							print(f'''N layers selected for distillation: {std_idx}''')
							print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

							print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
							torch.save(compressed_sd, args.dump_checkpoint)





