
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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		None:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 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()		)


__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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					try:
										SCREAMING_SNAKE_CASE__													= 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		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(UpperCamelCase_		)		)
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_ = "student"	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_=False	,	UpperCamelCase_=None	,	UpperCamelCase_=None	,	**UpperCamelCase_	,	)		->		Tuple[PreTrainedModel, List[int], List[int]]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= '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
										SCREAMING_SNAKE_CASE__													= AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_		).eval()
					else:
										assert isinstance(UpperCamelCase_	,	UpperCamelCase_		), F'teacher must be a model or string got type {type(UpperCamelCase_		)}'
					SCREAMING_SNAKE_CASE__													= teacher.config.to_diff_dict()

					try:
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= teacher.config.encoder_layers, teacher.config.decoder_layers
										if e is None:
															SCREAMING_SNAKE_CASE__													= teacher_e
										if d is None:
															SCREAMING_SNAKE_CASE__													= teacher_d
										init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}		)
					except AttributeError:  # T5
										if hasattr(teacher.config	,	'num_encoder_layers'		):
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
										else:
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= teacher.config.num_layers, teacher.config.num_decoder_layers
										if e is None:
															SCREAMING_SNAKE_CASE__													= teacher_e
										if d is None:
															SCREAMING_SNAKE_CASE__													= 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
					SCREAMING_SNAKE_CASE__													= teacher.config_class(**UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= AutoModelForSeqaSeqLM.from_config(UpperCamelCase_		)
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					SCREAMING_SNAKE_CASE__													= 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
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= 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:
										SCREAMING_SNAKE_CASE__													= pick_layers_to_copy(UpperCamelCase_	,	UpperCamelCase_		)
					if d_layers_to_copy is None:
										SCREAMING_SNAKE_CASE__													= 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}'		)
					SCREAMING_SNAKE_CASE__													= {
					    '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 random






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= [], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase_		)
										elif element > pivot:
															greater.append(UpperCamelCase_		)
										else:
															equal.append(UpperCamelCase_		)
					return less, equal, greater






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					if index >= len(UpperCamelCase_		) or index < 0:
										return None

					SCREAMING_SNAKE_CASE__													= items[random.randint(0	,	len(UpperCamelCase_		) - 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= _partition(UpperCamelCase_	,	UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase_	,	UpperCamelCase_		)
					# must be in larger
					else:
										return quick_select(UpperCamelCase_	,	index - (m + count)		)





