






import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


lowerCamelCase     :							Tuple    =logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     None:
     UpperCamelCase__      :       Tuple	    =  nn.ModuleList([src_layers[i] for i in layers_to_copy]      )
     assert len(UpperCamelCase__      ) == len(UpperCamelCase__      ), f'{len(UpperCamelCase__      )} != {len(UpperCamelCase__      )}'
     dest_layers.load_state_dict(layers_to_copy.state_dict()      )


lowerCamelCase     :							Optional[int]    ={
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowerCamelCase     :							str    ={
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Tuple:
     try:
          UpperCamelCase__      :       Dict	    =  LAYERS_TO_COPY[n_teacher][n_student]
          return val
     except KeyError:
          if n_student != n_teacher:
               warnings.warn(
                   f'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
                   f' {n_student}'      )
          return list(range(UpperCamelCase__      )      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[int]:
     if n_student > n_teacher:
          raise ValueError(f'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'      )
     elif n_teacher == n_student:
          return list(range(UpperCamelCase__      )      )
     elif n_student == 1:
          return [n_teacher - 1]
     else:
          return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase = "student" ,   __lowerCAmelCase = None ,   __lowerCAmelCase = None ,   __lowerCAmelCase=False ,   __lowerCAmelCase=None ,   __lowerCAmelCase=None ,   **__lowerCAmelCase ,   )			->     Tuple[PreTrainedModel, List[int], List[int]]:
     UpperCamelCase__      :       Dict	    =  "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
     assert (e is not None) or (d is not None), _msg
     if isinstance(UpperCamelCase__ ,   UpperCamelCase__      ):
          AutoTokenizer.from_pretrained(UpperCamelCase__      ).save_pretrained(UpperCamelCase__      )  # purely for convenience
          UpperCamelCase__      :       Any	    =  AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      ).eval()
     else:
          assert isinstance(UpperCamelCase__ ,   UpperCamelCase__      ), f'teacher must be a model or string got type {type(UpperCamelCase__      )}'
     UpperCamelCase__      :       List[Any]	    =  teacher.config.to_diff_dict()

     try:
          UpperCamelCase__    ,					UpperCamelCase__      :       List[str]	    =  teacher.config.encoder_layers, teacher.config.decoder_layers
          if e is None:
               UpperCamelCase__      :       Optional[Any]	    =  teacher_e
          if d is None:
               UpperCamelCase__      :       Dict	    =  teacher_d
          init_kwargs.update({"encoder_layers": e, "decoder_layers": d}      )
     except AttributeError:  # T5
          if hasattr(teacher.config ,   "num_encoder_layers"      ):
               UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
          else:
               UpperCamelCase__    ,					UpperCamelCase__      :       str	    =  teacher.config.num_layers, teacher.config.num_decoder_layers
          if e is None:
               UpperCamelCase__      :       Dict	    =  teacher_e
          if d is None:
               UpperCamelCase__      :       List[Any]	    =  teacher_d
          if hasattr(teacher.config ,   "num_encoder_layers"      ):
               init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d}      )
          else:
               init_kwargs.update({"num_layers": e, "num_decoder_layers": d}      )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
     init_kwargs.update(UpperCamelCase__      )

     # Copy weights
     UpperCamelCase__      :       int	    =  teacher.config_class(**UpperCamelCase__      )
     UpperCamelCase__      :       str	    =  AutoModelForSeqaSeqLM.from_config(UpperCamelCase__      )
     # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
     UpperCamelCase__      :       Optional[int]	    =  student.load_state_dict(teacher.state_dict() ,   strict=UpperCamelCase__      )
     assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

     if copy_first_teacher_layers:  # Our copying is done. We just log and save
          UpperCamelCase__    ,					UpperCamelCase__      :       Optional[Any]	    =  list(range(UpperCamelCase__      )      ), list(range(UpperCamelCase__      )      )
          logger.info(
              f'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
              f' {save_path}'      )
          student.save_pretrained(UpperCamelCase__      )
          return student, e_layers_to_copy, d_layers_to_copy

     # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
     if e_layers_to_copy is None:
          UpperCamelCase__      :       List[Any]	    =  pick_layers_to_copy(UpperCamelCase__ ,   UpperCamelCase__      )
     if d_layers_to_copy is None:
          UpperCamelCase__      :       str	    =  pick_layers_to_copy(UpperCamelCase__ ,   UpperCamelCase__      )

     try:
          if hasattr(
              UpperCamelCase__ ,   "prophetnet"      ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
               copy_layers(teacher.prophetnet.encoder.layers ,   student.prophetnet.encoder.layers ,   UpperCamelCase__      )
               copy_layers(teacher.prophetnet.decoder.layers ,   student.prophetnet.decoder.layers ,   UpperCamelCase__      )
          else:
               copy_layers(teacher.model.encoder.layers ,   student.model.encoder.layers ,   UpperCamelCase__      )
               copy_layers(teacher.model.decoder.layers ,   student.model.decoder.layers ,   UpperCamelCase__      )
     except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
          copy_layers(teacher.encoder.block ,   student.encoder.block ,   UpperCamelCase__      )
          copy_layers(teacher.decoder.block ,   student.decoder.block ,   UpperCamelCase__      )
     logger.info(
         f'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'      )
     UpperCamelCase__      :       Any	    =  {
         "teacher_type": teacher.config.model_type,
         "copied_encoder_layers": e_layers_to_copy,
         "copied_decoder_layers": d_layers_to_copy,
     }
     student.save_pretrained(UpperCamelCase__      )
     # Save information about copying for easier reproducibility

     return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
  fire.Fire(create_student_by_copying_alternating_layers)







import re







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     bool:
     UpperCamelCase__      :       Union[str, Any]	    =  re.compile(
         R"^(?:0|94|\+94|0{2}94)" R"7(0|1|2|4|5|6|7|8)" R"(-| |)" R"\d{7}$"      )

     return bool(re.search(__lowerCAmelCase ,   __lowerCAmelCase      )      )


if __name__ == "__main__":
 lowerCamelCase     :							Union[str, Any]    ='''0094702343221'''

 print(is_sri_lankan_phone_number(phone))