


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
    A      :    Union[str, Any]		=					argparse.ArgumentParser(
        description=(
            "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
            " Distillation"
        )
    )
    parser.add_argument("--model_type", default="bert", choices=["bert"])
    parser.add_argument("--model_name", default="bert-base-uncased", type=str)
    parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
    parser.add_argument("--vocab_transform", action="store_true")
    A      :    Optional[Any]		=					parser.parse_args()

    if args.model_type == "bert":
        A      :    Dict		=					BertForMaskedLM.from_pretrained(args.model_name)
        A      :    Union[str, Any]		=					"bert"
    else:
        raise ValueError("args.model_type should be \"bert\".")

    A      :    Dict		=					model.state_dict()
    A      :    Optional[Any]		=					{}

    for w in ["word_embeddings", "position_embeddings"]:
        A      :    int		=					state_dict[f'{prefix}.embeddings.{w}.weight']
    for w in ["weight", "bias"]:
        A      :    List[str]		=					state_dict[f'{prefix}.embeddings.LayerNorm.{w}']

    A      :    List[Any]		=					0
    for teacher_idx in [0, 2, 4, 7, 9, 11]:
        for w in ["weight", "bias"]:
            A      :    Dict		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
            ]
            A      :    Union[str, Any]		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
            ]
            A      :    Union[str, Any]		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
            ]

            A      :    Tuple		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
            ]
            A      :    Tuple		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
            ]

            A      :    Dict		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
            ]
            A      :    str		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
            ]
            A      :    str		=					state_dict[
                f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
            ]
        std_idx += 1

    A      :    Any		=					state_dict["cls.predictions.decoder.weight"]
    A      :    Tuple		=					state_dict["cls.predictions.bias"]
    if args.vocab_transform:
        for w in ["weight", "bias"]:
            A      :    str		=					state_dict[f'cls.predictions.transform.dense.{w}']
            A      :    Tuple		=					state_dict[f'cls.predictions.transform.LayerNorm.{w}']

    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


A      :    str		=					logging.get_logger(__name__)

A      :    int		=					{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"mgp-str"





       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			List[Any]=[32, 128]					,      __lowerCamelCase							:			Optional[int]=4					,      __lowerCamelCase							:			str=3					,      __lowerCamelCase							:			Optional[Any]=27					,      __lowerCamelCase							:			List[str]=38					,      __lowerCamelCase							:			Dict=50257					,      __lowerCamelCase							:			List[Any]=30522					,      __lowerCamelCase							:			Optional[Any]=768					,      __lowerCamelCase							:			str=12					,      __lowerCamelCase							:			Optional[int]=12					,      __lowerCamelCase							:			List[str]=4.0					,      __lowerCamelCase							:			Union[str, Any]=True					,      __lowerCamelCase							:			Any=False					,      __lowerCamelCase							:			Dict=1E-5					,      __lowerCamelCase							:			int=0.0					,      __lowerCamelCase							:			Optional[int]=0.0					,      __lowerCamelCase							:			List[Any]=0.0					,      __lowerCamelCase							:			Union[str, Any]=False					,      __lowerCamelCase							:			str=0.0_2					,      **__lowerCamelCase							:			Dict					,      ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase       )

             lowerCamelCase__		:				int    =  image_size
             lowerCamelCase__		:				Union[str, Any]    =  patch_size
             lowerCamelCase__		:				Dict    =  num_channels
             lowerCamelCase__		:				Union[str, Any]    =  max_token_length
             lowerCamelCase__		:				Optional[int]    =  num_character_labels
             lowerCamelCase__		:				Union[str, Any]    =  num_bpe_labels
             lowerCamelCase__		:				Optional[int]    =  num_wordpiece_labels
             lowerCamelCase__		:				List[str]    =  hidden_size
             lowerCamelCase__		:				Dict    =  num_hidden_layers
             lowerCamelCase__		:				Any    =  num_attention_heads
             lowerCamelCase__		:				Any    =  mlp_ratio
             lowerCamelCase__		:				List[Any]    =  distilled
             lowerCamelCase__		:				Optional[Any]    =  layer_norm_eps
             lowerCamelCase__		:				Union[str, Any]    =  drop_rate
             lowerCamelCase__		:				List[Any]    =  qkv_bias
             lowerCamelCase__		:				int    =  attn_drop_rate
             lowerCamelCase__		:				List[Any]    =  drop_path_rate
             lowerCamelCase__		:				List[str]    =  output_aa_attentions
             lowerCamelCase__		:				Dict    =  initializer_range


