





'''simple docstring'''

def lowerCamelCase						(     __lowerCamelCase :    List[Any]    )						->list[int]:
    _SCREAMING_SNAKE_CASE									=			[0 for i in range(len(__lowerCamelCase    )    )]

    # initialize interval's left pointer and right pointer
    _SCREAMING_SNAKE_CASE									=			0, 0

    for i in range(1					,       len(__lowerCamelCase    )    ):
        # case when current index is inside the interval
        if i <= right_pointer:
            _SCREAMING_SNAKE_CASE									=			min(right_pointer - i + 1					,       z_result[i - left_pointer]    )
            _SCREAMING_SNAKE_CASE									=			min_edge

        while go_next(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    ):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            _SCREAMING_SNAKE_CASE									=			i, i + z_result[i] - 1

    return z_result





def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[str]    )						->bool:
    return i + z_result[i] < len(__lowerCamelCase    ) and s[z_result[i]] == s[i + z_result[i]]





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[int]    )						->int:
    _SCREAMING_SNAKE_CASE									=			0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    _SCREAMING_SNAKE_CASE									=			z_function(pattern + input_str    )

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(__lowerCamelCase    ):
            answer += 1

    return answer


if __name__ == "__main__":
      import doctest

      doctest.testmod()










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


UpperCAmelCase						:   Any			  =     logging.get_logger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      None:
					__A	:						int       =				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()  )


UpperCAmelCase						:   List[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))},
}
UpperCAmelCase						:   Optional[int]			  =     {
    # 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				(   a					,						a  )      ->      Dict:
					try:
										__A	:						int       =				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  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      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(a  )  )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def  _SCREAMING_SNAKE_CASE				(   a					,						a = "student"					,						a = None					,						a = None					,						a=False					,						a=None					,						a=None					,						**a					,						)      ->      Tuple[PreTrainedModel, List[int], List[int]]:
					__A	:						List[str]       =				'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
										__A	:						Optional[int]       =				AutoModelForSeqaSeqLM.from_pretrained(a  ).eval()
					else:
										assert isinstance(a					,						a  ), F"""teacher must be a model or string got type {type(a  )}"""
					__A	:						int       =				teacher.config.to_diff_dict()

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

					# Copy weights
					__A	:						Dict       =				teacher.config_class(**a  )
					__A	:						int       =				AutoModelForSeqaSeqLM.from_config(a  )
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					__A	:						Any       =				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
										__A    ,				__A	:						Optional[int]       =				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:
										__A	:						List[int]       =				pick_layers_to_copy(a					,						a  )
					if d_layers_to_copy is None:
										__A	:						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}"""  )
					__A	:						Optional[int]       =				{
					    '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)




