


'''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


__snake_case							      =     logging.get_logger(__name__)


def 			a      (			__a ,					__a ,					__a   )  ->	None:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= nn.ModuleList([src_layers[i] for i in layers_to_copy]   )
					assert len(__a   ) == len(__a   ), f'''{len(__a   )} != {len(__a   )}'''
					dest_layers.load_state_dict(layers_to_copy.state_dict()   )


__snake_case							      =     {
    # 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))},
}
__snake_case							      =     {
    # 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      (			__a ,					__a   )  ->	Any:



					'''simple docstring'''






					try:
										UpperCamelCase__   :List[str]												= 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(__a   )   )


def 			a      (			__a ,					__a   )  ->	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(__a   )   )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def 			a      (			__a ,					__a = "student" ,					__a = None ,					__a = None ,					__a=False ,					__a=None ,					__a=None ,					**__a ,					)  ->	Tuple[PreTrainedModel, List[int], List[int]]:



					'''simple docstring'''






					UpperCamelCase__   :Optional[int]												= '''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(__a ,					__a   ):
										AutoTokenizer.from_pretrained(__a   ).save_pretrained(__a   )  # purely for convenience
										UpperCamelCase__   :Union[str, Any]												= AutoModelForSeqaSeqLM.from_pretrained(__a   ).eval()
					else:
										assert isinstance(__a ,					__a   ), f'''teacher must be a model or string got type {type(__a   )}'''
					UpperCamelCase__   :Optional[int]												= teacher.config.to_diff_dict()

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

					# Copy weights
					UpperCamelCase__   :Union[str, Any]												= teacher.config_class(**__a   )
					UpperCamelCase__   :str												= AutoModelForSeqaSeqLM.from_config(__a   )
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					UpperCamelCase__   :Tuple												= student.load_state_dict(teacher.state_dict() ,					strict=__a   )
					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__   :Any												= list(range(__a   )   ), list(range(__a   )   )
										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(__a   )
										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[int]												= pick_layers_to_copy(__a ,					__a   )
					if d_layers_to_copy is None:
										UpperCamelCase__   :List[int]												= pick_layers_to_copy(__a ,					__a   )

					try:
										if hasattr(
										    __a ,					'''prophetnet'''   ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers ,					student.prophetnet.encoder.layers ,					__a   )
															copy_layers(teacher.prophetnet.decoder.layers ,					student.prophetnet.decoder.layers ,					__a   )
										else:
															copy_layers(teacher.model.encoder.layers ,					student.model.encoder.layers ,					__a   )
															copy_layers(teacher.model.decoder.layers ,					student.model.decoder.layers ,					__a   )
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block ,					student.encoder.block ,					__a   )
										copy_layers(teacher.decoder.block ,					student.decoder.block ,					__a   )
					logger.info(
					    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''   )
					UpperCamelCase__   :Union[str, Any]												= {
					    '''teacher_type''': teacher.config.model_type,
					    '''copied_encoder_layers''': e_layers_to_copy,
					    '''copied_decoder_layers''': d_layers_to_copy,
					}
					student.save_pretrained(__a   )
					# 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'''





from __future__ import annotations

from typing import TypedDict






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str
 __lowerCamelCase    :       int





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter s type must be str.''')

     return [s[i:] + s[:i] for i in range(len(__A))]




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter s type must be str.''')
     if not s:
          raise ValueError('''The parameter s must not be empty.''')

     _a      =  all_rotations(__A)
     rotations.sort()  # sort the list of rotations in alphabetically order
     # make a string composed of the last char of each rotation
     _a      =  {
         "bwt_string": "".join([word[-1] for word in rotations]),
         "idx_original_string": rotations.index(__A),
     }
     return response




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter bwt_string type must be str.''')
     if not bwt_string:
          raise ValueError('''The parameter bwt_string must not be empty.''')
     try:
          _a      =  int(__A)
     except ValueError:
          raise TypeError(
              '''The parameter idx_original_string type must be int or passive'''
              ''' of cast to int.''')
     if idx_original_string < 0:
          raise ValueError('''The parameter idx_original_string must not be lower than 0.''')
     if idx_original_string >= len(__A):
          raise ValueError(
              '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).''')

     _a      =  [''''''] * len(__A)
     for _ in range(len(__A)):
          for i in range(len(__A)):
               _a      =  bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


if __name__ == "__main__":
  lowercase_		       =  "Provide a string that I will generate its BWT transform: "
  lowercase_		       =  input(entry_msg).strip()
  lowercase_		       =  bwt_transform(s)
  print(
      F"""Burrows Wheeler transform for string '{s}' results """
      F"""in '{result['bwt_string']}'"""
  )
  lowercase_		       =  reverse_bwt(result["bwt_string"], result["idx_original_string"])
  print(
      F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
      F"""we get original string '{original_string}'"""
  )



