


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			__snake_case            =   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")
			__snake_case            =   parser.parse_args()

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

			__snake_case            =   model.state_dict()
			__snake_case            =   {}

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

    # Transformer Blocks #
			__snake_case            =   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"]:
															__snake_case            =   state_dict[
															    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
															]
									__snake_case            =   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"]:
															__snake_case            =   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"]:
									__snake_case            =   state_dict[F'''{layer}''']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												__snake_case            =   state_dict[F'''lm_head.dense.{w}''']
												__snake_case            =   state_dict[F'''lm_head.layer_norm.{w}''']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									__snake_case            =   state_dict[F'''{prefix}.ln_f.{w}''']
						__snake_case            =   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)








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


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """markuplm"""




			def __init__(						self							:		Optional[Any]       ,       __magic_name__							:		List[Any]=3_0_5_2_2       ,       __magic_name__							:		int=7_6_8       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		str=3_0_7_2       ,       __magic_name__							:		Optional[int]="gelu"       ,       __magic_name__							:		Dict=0.1       ,       __magic_name__							:		Optional[int]=0.1       ,       __magic_name__							:		Any=5_1_2       ,       __magic_name__							:		List[str]=2       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		List[str]=1e-12       ,       __magic_name__							:		str=0       ,       __magic_name__							:		List[str]=0       ,       __magic_name__							:		Optional[Any]=2       ,       __magic_name__							:		Dict=2_5_6       ,       __magic_name__							:		Tuple=1_0_2_4       ,       __magic_name__							:		Any=2_1_6       ,       __magic_name__							:		str=1_0_0_1       ,       __magic_name__							:		Dict=3_2       ,       __magic_name__							:		Optional[int]=5_0       ,       __magic_name__							:		List[Any]="absolute"       ,       __magic_name__							:		Any=True       ,       __magic_name__							:		Optional[Any]=None       ,       **__magic_name__							:		List[str]       ,       ):







						"""simple docstring"""







						super().__init__(
						    pad_token_id=__magic_name__       ,       bos_token_id=__magic_name__       ,       eos_token_id=__magic_name__       ,       **__magic_name__       ,       )
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					type_vocab_size
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					layer_norm_eps
						UpperCamelCase         =					position_embedding_type
						UpperCamelCase         =					use_cache
						UpperCamelCase         =					classifier_dropout
						# additional properties
						UpperCamelCase         =					max_depth
						UpperCamelCase         =					max_xpath_tag_unit_embeddings
						UpperCamelCase         =					max_xpath_subs_unit_embeddings
						UpperCamelCase         =					tag_pad_id
						UpperCamelCase         =					subs_pad_id
						UpperCamelCase         =					xpath_unit_hidden_size




