

'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_							=					logging.get_logger(__name__)

UpperCamelCase_							=					{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						'ibert'







				def __init__(					self    :		int		,				UpperCAmelCase__    :		Any=30522		,				UpperCAmelCase__    :		Tuple=768		,				UpperCAmelCase__    :		Dict=12		,				UpperCAmelCase__    :		List[Any]=12		,				UpperCAmelCase__    :		List[Any]=3072		,				UpperCAmelCase__    :		List[Any]="gelu"		,				UpperCAmelCase__    :		int=0.1		,				UpperCAmelCase__    :		Union[str, Any]=0.1		,				UpperCAmelCase__    :		Tuple=512		,				UpperCAmelCase__    :		int=2		,				UpperCAmelCase__    :		Optional[Any]=0.02		,				UpperCAmelCase__    :		str=1E-12		,				UpperCAmelCase__    :		Any=1		,				UpperCAmelCase__    :		str=0		,				UpperCAmelCase__    :		str=2		,				UpperCAmelCase__    :		Union[str, Any]="absolute"		,				UpperCAmelCase__    :		str=False		,				UpperCAmelCase__    :		str="none"		,				**UpperCAmelCase__    :		int		,				):
										'''simple docstring'''
										super().__init__(pad_token_id=UpperCAmelCase__		,				bos_token_id=UpperCAmelCase__		,				eos_token_id=UpperCAmelCase__		,				**UpperCAmelCase__      )

										lowercase   :       Dict								=vocab_size
										lowercase   :       int								=hidden_size
										lowercase   :       str								=num_hidden_layers
										lowercase   :       Any								=num_attention_heads
										lowercase   :       List[str]								=hidden_act
										lowercase   :       str								=intermediate_size
										lowercase   :       int								=hidden_dropout_prob
										lowercase   :       Optional[int]								=attention_probs_dropout_prob
										lowercase   :       List[str]								=max_position_embeddings
										lowercase   :       List[str]								=type_vocab_size
										lowercase   :       Dict								=initializer_range
										lowercase   :       Union[str, Any]								=layer_norm_eps
										lowercase   :       Any								=position_embedding_type
										lowercase   :       List[Any]								=quant_mode
										lowercase   :       int								=force_dequant





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										if self.task == "multiple-choice":
																lowercase   :       Dict								={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																lowercase   :       List[Any]								={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]      )








'''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)





