


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__  :str             =							logging.get_logger(__name__)

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


							'''simple docstring'''





							_UpperCAmelCase					=      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()	)


lowerCAmelCase__  :Union[str, Any]             =							{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowerCAmelCase__  :Any             =							{
    # 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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}

def 	lowerCAmelCase__			(      a__:		Union[str, Any]      ,  a__:		Optional[Any]	)  ->		Tuple:


							'''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(a__	)	)

def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		Dict	)  ->		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 	lowerCAmelCase__			(      a__:		Union[str, PreTrainedModel]      ,  a__:		Union[str, Path] = "student"      ,  a__:		Union[int, None] = None      ,  a__:		Union[int, None] = None      ,  a__:		Tuple=False      ,  a__:		Optional[Any]=None      ,  a__:		Dict=None      ,  **a__:		List[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(a__      ,  a__	):
														AutoTokenizer.from_pretrained(a__	).save_pretrained(a__	)  # purely for convenience
														_UpperCAmelCase					=      AutoModelForSeqaSeqLM.from_pretrained(a__	).eval()
							else:
														assert isinstance(a__      ,  a__	), F'''teacher must be a model or string got type {type(a__	)}'''
							_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(a__	)

							# Copy weights
							_UpperCAmelCase					=      teacher.config_class(**a__	)
							_UpperCAmelCase					=      AutoModelForSeqaSeqLM.from_config(a__	)
							# 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=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					=      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					=      pick_layers_to_copy(a__      ,  a__	)
							if d_layers_to_copy is None:
														_UpperCAmelCase					=      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					=      {
							    '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)




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
