

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					nn.ModuleList					, lowerCAmelCase:					nn.ModuleList					, lowerCAmelCase:					List[int]	)				->    None:
				_UpperCAmelCase    :						Tuple							=      nn.ModuleList([src_layers[i] for i in layers_to_copy]	)
				assert len(lowerCAmelCase	) == len(lowerCAmelCase	), F'{len(lowerCAmelCase	)} != {len(lowerCAmelCase	)}'
				dest_layers.load_state_dict(layers_to_copy.state_dict()	)


SCREAMING_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))},
}
SCREAMING_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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					Optional[Any]	)				->    Any:
				try:
								_UpperCAmelCase    :						Optional[Any]							=      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(lowerCAmelCase	)	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]					, lowerCAmelCase:					int	)				->    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(lowerCAmelCase	)	)
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]

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

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

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

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

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      data
										_UpperCAmelCase    :						Any							=      None







class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.head
										while temp is not None:
														print(temp.data  ,   end=" "		)
														_UpperCAmelCase    :						str							=      temp.next
										print()


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      Node(A_		)
										_UpperCAmelCase    :						Tuple							=      self.head
										_UpperCAmelCase    :						Tuple							=      new_node





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if node_data_a == node_data_a:
														return
										else:
														_UpperCAmelCase    :						int							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						Tuple							=      node_a.next

														_UpperCAmelCase    :						Dict							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						List[Any]							=      node_a.next

														if node_a is None or node_a is None:
																		return

														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      node_a.data, node_a.data


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = LinkedList()
					for i in range(5, 0, -1):
										ll.push(i)

					ll.print_list()

					ll.swap_nodes(1, 4)
					print('After swapping')
					ll.print_list()


