


'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		if not head:
				return True
		# split the list to two parts
		snake_case_       ,      snake_case_		:							List[str]			       =						head.next, head
		while fast and fast.next:
				snake_case_		:							List[str]			       =						fast.next.next
				snake_case_		:							Optional[Any]			       =						slow.next
		snake_case_		:							Dict			       =						slow.next
		snake_case_		:							Optional[int]			       =						None  # Don't forget here! But forget still works!
		# reverse the second part
		snake_case_		:							Tuple			       =						None
		while second:
				snake_case_		:							Optional[int]			       =						second.next
				snake_case_		:							Optional[Any]			       =						node
				snake_case_		:							List[str]			       =						second
				snake_case_		:							Dict			       =						nxt
		# compare two parts
		# second part has the same or one less node
		while node:
				if node.val != head.val:
						return False
				snake_case_		:							str			       =						node.next
				snake_case_		:							Optional[Any]			       =						head.next
		return True





def      UpperCAmelCase      (		lowerCamelCase_  :Any ):







		'''simple docstring'''
		if not head or not head.next:
				return True

		# 1. Get the midpoint (slow)
		snake_case_		:							Any			       =						head
		while fast and fast.next:
				snake_case_       ,      snake_case_		:							Tuple			       =						fast.next.next, slow.next

		# 2. Push the second half into the stack
		snake_case_		:							List[Any]			       =						[slow.val]
		while slow.next:
				snake_case_		:							str			       =						slow.next
				stack.append(slow.val )

		# 3. Comparison
		while stack:
				if stack.pop() != cur.val:
						return False
				snake_case_		:							int			       =						cur.next

		return True






def      UpperCAmelCase      (		lowerCamelCase_  :Dict ):







		'''simple docstring'''
		if not head or not head.next:
				return True
		snake_case_		:							Tuple			       =						{}
		snake_case_		:							Union[str, Any]			       =						0
		while head:
				if head.val in d:
						d[head.val].append(lowerCamelCase_ )
				else:
						snake_case_		:							Any			       =						[pos]
				snake_case_		:							Union[str, Any]			       =						head.next
				pos += 1
		snake_case_		:							str			       =						pos - 1
		snake_case_		:							Union[str, Any]			       =						0
		for v in d.values():
				if len(lowerCamelCase_ ) % 2 != 0:
						middle += 1
				else:
						snake_case_		:							Dict			       =						0
						for i in range(0				,      len(lowerCamelCase_ ) ):
								if v[i] + v[len(lowerCamelCase_ ) - 1 - step] != checksum:
										return False
								step += 1
				if middle > 1:
						return False
		return True



'''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


__A	:	List[Any]     =     logging.get_logger(__name__)





def      UpperCAmelCase      (		lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :List[int] ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						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() )


__A	:	Optional[int]     =     {
    # 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))},
}
__A	:	Dict     =     {
    # 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      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		try:
				snake_case_		:							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(lowerCamelCase_ ) )





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[Any] ):







		'''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(lowerCamelCase_ ) )
		elif n_student == 1:
				return [n_teacher - 1]
		else:
				return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, PreTrainedModel]				,      lowerCamelCase_  :Union[str, Path] = "student"				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :List[str]=False				,      lowerCamelCase_  :Optional[int]=None				,      lowerCamelCase_  :Optional[Any]=None				,      **lowerCamelCase_  :Dict				,      ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						"""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
				snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ ).eval()
		else:
				assert isinstance(lowerCamelCase_				,      lowerCamelCase_ ), F'''teacher must be a model or string got type {type(lowerCamelCase_ )}'''
		snake_case_		:							Any			       =						teacher.config.to_diff_dict()

		try:
				snake_case_       ,      snake_case_		:							List[str]			       =						teacher.config.encoder_layers, teacher.config.decoder_layers
				if e is None:
						snake_case_		:							Dict			       =						teacher_e
				if d is None:
						snake_case_		:							List[Any]			       =						teacher_d
				init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
		except AttributeError:  # T5
				if hasattr(teacher.config				,      """num_encoder_layers""" ):
						snake_case_       ,      snake_case_		:							Tuple			       =						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
				else:
						snake_case_       ,      snake_case_		:							Union[str, Any]			       =						teacher.config.num_layers, teacher.config.num_decoder_layers
				if e is None:
						snake_case_		:							Optional[int]			       =						teacher_e
				if d is None:
						snake_case_		:							List[str]			       =						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
		snake_case_		:							List[Any]			       =						teacher.config_class(**lowerCamelCase_ )
		snake_case_		:							Tuple			       =						AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ )
		# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
		snake_case_		:							Tuple			       =						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
				snake_case_       ,      snake_case_		:							List[str]			       =						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:
				snake_case_		:							List[int]			       =						pick_layers_to_copy(lowerCamelCase_				,      lowerCamelCase_ )
		if d_layers_to_copy is None:
				snake_case_		:							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}''' )
		snake_case_		:							Any			       =						{
		    """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)