



"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
      A:     Dict										=	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:     Tuple										=	parser.parse_args()

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

      A:     str										=	model.state_dict()
      A:     Optional[int]										=	{}

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

      A:     List[str]										=	0
      for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
            for w in ["weight", "bias"]:
                  A:     List[Any]										=	state_dict[
                      f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
                  ]
                  A:     str										=	state_dict[
                      f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
                  ]
                  A:     Optional[int]										=	state_dict[
                      f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
                  ]

                  A:     Any										=	state_dict[
                      f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
                  ]
                  A:     str										=	state_dict[
                      f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
                  ]

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





"""simple docstring"""
import math
from datetime import datetime, timedelta

def    _snake_case		(		UpperCamelCase		:	int		):
 UpperCAmelCase			:      Any			=			year % 19
 UpperCAmelCase			:      Any			=			year % 4
 UpperCAmelCase			:      str			=			year % 7
 UpperCAmelCase			:      Union[str, Any]			=			math.floor(year / 100		)
 UpperCAmelCase			:      Optional[Any]			=			math.floor((13 + 8 * leap_day_inhibits) / 25		)
 UpperCAmelCase			:      int			=			leap_day_inhibits / 4
 UpperCAmelCase			:      int			=			(
     15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
 ) % 30
 UpperCAmelCase			:      Tuple			=			(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

 # days to be added to March 21
 UpperCAmelCase			:      int			=			(19 * metonic_cycle + secular_moon_shift) % 30

 # PHM -> Paschal Full Moon
 UpperCAmelCase			:      Tuple			=			(
     2 * julian_leap_year
     + 4 * non_leap_year
     + 6 * days_to_add
     + century_starting_point
 ) % 7

 if days_to_add == 29 and days_from_phm_to_sunday == 6:
  return datetime(UpperCamelCase    ,	4    ,	19		)
 elif days_to_add == 28 and days_from_phm_to_sunday == 6:
  return datetime(UpperCamelCase    ,	4    ,	18		)
 else:
  return datetime(UpperCamelCase    ,	3    ,	22		) + timedelta(
      days=int(days_to_add + days_from_phm_to_sunday		)		)


if __name__ == "__main__":
      for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
            A:     Any										=	"will be" if year > datetime.now().year else "was"
            print(f"""Easter in {year} {tense} {gauss_easter(year)}""")
