





'''simple docstring'''

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


lowercase__						:      List[Any]	=    logging.get_logger(__name__)







def  a__    (    lowercase :					nn.ModuleList, lowercase :					nn.ModuleList, lowercase :					List[int]				)		->		None:





     """simple docstring"""

     _UpperCamelCase       =	nn.ModuleList([src_layers[i] for i in layers_to_copy]				)
     assert len(lowercase				) == len(lowercase				), F"""{len(lowercase				)} != {len(lowercase				)}"""
     dest_layers.load_state_dict(layers_to_copy.state_dict()				)


lowercase__						:      Any	=    {
    # 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))},
}
lowercase__						:      List[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  a__    (    lowercase :					List[Any], lowercase :					Optional[int]				)		->		Dict:





     """simple docstring"""

     try:
          _UpperCamelCase       =	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(lowercase				)				)







def  a__    (    lowercase :					Union[str, Any], lowercase :					Union[str, Any]				)		->		List[int]:





     """simple docstring"""

     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(lowercase				)				)
     elif n_student == 1:
          return [n_teacher - 1]
     else:
          return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def  a__    (    lowercase :					Union[str, PreTrainedModel], lowercase :					Union[str, Path] = "student", lowercase :					Union[int, None] = None, lowercase :					Union[int, None] = None, lowercase :					Optional[int]=False, lowercase :					List[str]=None, lowercase :					str=None, **lowercase :					Union[str, Any], )		->		Tuple[PreTrainedModel, List[int], List[int]]:





     """simple docstring"""

     _UpperCamelCase       =	'''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(lowercase, lowercase				):
          AutoTokenizer.from_pretrained(lowercase				).save_pretrained(lowercase				)  # purely for convenience
          _UpperCamelCase       =	AutoModelForSeqaSeqLM.from_pretrained(lowercase				).eval()
     else:
          assert isinstance(lowercase, lowercase				), F"""teacher must be a model or string got type {type(lowercase				)}"""
     _UpperCamelCase       =	teacher.config.to_diff_dict()

     try:
          _UpperCamelCase   ,    _UpperCamelCase       =	teacher.config.encoder_layers, teacher.config.decoder_layers
          if e is None:
               _UpperCamelCase       =	teacher_e
          if d is None:
               _UpperCamelCase       =	teacher_d
          init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}				)
     except AttributeError:  # T5
          if hasattr(teacher.config, '''num_encoder_layers'''				):
               _UpperCamelCase   ,    _UpperCamelCase       =	teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
          else:
               _UpperCamelCase   ,    _UpperCamelCase       =	teacher.config.num_layers, teacher.config.num_decoder_layers
          if e is None:
               _UpperCamelCase       =	teacher_e
          if d is None:
               _UpperCamelCase       =	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(lowercase				)

     # Copy weights
     _UpperCamelCase       =	teacher.config_class(**lowercase				)
     _UpperCamelCase       =	AutoModelForSeqaSeqLM.from_config(lowercase				)
     # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
     _UpperCamelCase       =	student.load_state_dict(teacher.state_dict(), strict=lowercase				)
     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       =	list(range(lowercase				)				), list(range(lowercase				)				)
          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(lowercase				)
          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       =	pick_layers_to_copy(lowercase, lowercase				)
     if d_layers_to_copy is None:
          _UpperCamelCase       =	pick_layers_to_copy(lowercase, lowercase				)

     try:
          if hasattr(
              lowercase, '''prophetnet'''				):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
               copy_layers(teacher.prophetnet.encoder.layers, student.prophetnet.encoder.layers, lowercase				)
               copy_layers(teacher.prophetnet.decoder.layers, student.prophetnet.decoder.layers, lowercase				)
          else:
               copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, lowercase				)
               copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, lowercase				)
     except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
          copy_layers(teacher.encoder.block, student.encoder.block, lowercase				)
          copy_layers(teacher.decoder.block, student.decoder.block, lowercase				)
     logger.info(
         F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""				)
     _UpperCamelCase       =	{
         '''teacher_type''': teacher.config.model_type,
         '''copied_encoder_layers''': e_layers_to_copy,
         '''copied_decoder_layers''': d_layers_to_copy,
     }
     student.save_pretrained(lowercase				)
     # 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)










'''simple docstring'''

import math







def  a__    (    lowercase :					float, lowercase :					float				)		->		float:





     """simple docstring"""

     if initial_intensity < 0:
          raise ValueError('''The value of intensity cannot be negative'''				)
          # handling of negative values of initial intensity
     if angle < 0 or angle > 360:
          raise ValueError('''In Malus Law, the angle is in the range 0-360 degrees'''				)
          # handling of values out of allowed range
     return initial_intensity * (math.cos(math.radians(lowercase				)				) ** 2)


if __name__ == "__main__":
   import doctest

   doctest.testmod(name='malus_law')



