


'''simple docstring'''






import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__A	:	Tuple     =     logging.getLogger(__name__)
__A	:	Optional[int]     =     list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__A	:	Any     =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 						__UpperCamelCase	:
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={
							        'help': (
							            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
							        )
							    }						,  )
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCAmelCase__							)}						,  )
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={
							        'help': (
							            'Override some existing default config settings when a model is trained from scratch. Example: '
							            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
							        )
							    }						,  )
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}							)
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,  )
							lowercase			:		bool   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}						,  )
							lowercase			:		str   		=  field(
							    default='main'						,  metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}						,  )
							lowercase			:		bool   		=  field(
							    default=lowerCAmelCase__						,  metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }						,  )





							def 		a__   (  self		:List[str]			):





									if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
											raise ValueError(
											    """--config_overrides can\'t be used in combination with --config_name or --model_name_or_path"""			)





@dataclass
class 						__UpperCamelCase	:
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'The name of the dataset to use (via the datasets library).'}							)
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							)
							lowercase			:		Optional[str]   		=  field(default=lowerCAmelCase__						,  metadata={'help': 'The input training data file (a text file).'}							)
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}						,  )
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}						,  )
							lowercase			:		Optional[str]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}						,  )
							lowercase			:		bool   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'Overwrite the cached training and evaluation sets'}							)
							lowercase			:		Optional[int]   		=  field(
							    default=5						,  metadata={
							        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
							    }						,  )
							lowercase			:		Optional[int]   		=  field(
							    default=lowerCAmelCase__						,  metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated. Default to the max input length of the model.'
							        )
							    }						,  )
							lowercase			:		Optional[int]   		=  field(
							    default=lowerCAmelCase__						,  metadata={'help': 'The number of processes to use for the preprocessing.'}						,  )
							lowercase			:		float   		=  field(
							    default=0.1_5						,  metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}							)
							lowercase			:		bool   		=  field(
							    default=lowerCAmelCase__						,  metadata={
							        'help': (
							            'Whether to pad all samples to `max_seq_length`. '
							            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
							        )
							    }						,  )





							def 		a__   (  self		:Dict			):





									if self.train_file is not None:
											snake_case_		:							Optional[Any]			       =						self.train_file.split("""."""			)[-1]
											assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
									if self.validation_file is not None:
											snake_case_		:							List[Any]			       =						self.validation_file.split("""."""			)[-1]
											assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





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







		'''simple docstring'''
		with open(lowerCamelCase_				,      """r"""				,      encoding="""utf-8""" ) as f:
				snake_case_		:							int			       =						[json.loads(lowerCamelCase_ ) for line in f.read().splitlines() if (len(lowerCamelCase_ ) > 0 and not line.isspace())]
		assert len(lowerCamelCase_ ) == len(lowerCamelCase_ )

		snake_case_		:							Any			       =						{c: dataset[c] for c in dataset.column_names}
		snake_case_		:							int			       =						refs
		return Dataset.from_dict(lowerCamelCase_ )





def      UpperCAmelCase      (		):







		'''simple docstring'''
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		snake_case_		:							List[Any]			       =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
		if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				snake_case_       ,      snake_case_       ,      snake_case_		:							int			       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
		else:
				snake_case_       ,      snake_case_       ,      snake_case_		:							Tuple			       =						parser.parse_args_into_dataclasses()

		# Detecting last checkpoint.
		snake_case_		:							str			       =						None
		if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
				snake_case_		:							str			       =						get_last_checkpoint(training_args.output_dir )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
						raise ValueError(
						    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
						    """Use --overwrite_output_dir to overcome.""" )
				elif last_checkpoint is not None:
						logger.info(
						    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
						    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,      datefmt="""%m/%d/%Y %H:%M:%S"""				,      handlers=[logging.StreamHandler(sys.stdout )]				,      )
		logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

		# Log on each process the small summary:
		logger.warning(
		    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
		    + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("""Training/evaluation parameters %s"""				,      lowerCamelCase_ )

		# Set seed before initializing model.
		set_seed(training_args.seed )

		# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
		# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
		# (the dataset will be downloaded automatically from the datasets Hub).
		#
		# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
		# 'text' is found. You can easily tweak this behavior (see below).
		#
		# In distributed training, the load_dataset function guarantee that only one local process can concurrently
		# download the dataset.
		if data_args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				snake_case_		:							Tuple			       =						load_dataset(data_args.dataset_name				,      data_args.dataset_config_name )
				if "validation" not in datasets.keys():
						snake_case_		:							Union[str, Any]			       =						load_dataset(
						    data_args.dataset_name				,      data_args.dataset_config_name				,      split=F'''train[:{data_args.validation_split_percentage}%]'''				,      )
						snake_case_		:							Union[str, Any]			       =						load_dataset(
						    data_args.dataset_name				,      data_args.dataset_config_name				,      split=F'''train[{data_args.validation_split_percentage}%:]'''				,      )
		else:
				snake_case_		:							Tuple			       =						{}
				if data_args.train_file is not None:
						snake_case_		:							Tuple			       =						data_args.train_file
				if data_args.validation_file is not None:
						snake_case_		:							Dict			       =						data_args.validation_file
				snake_case_		:							int			       =						data_args.train_file.split(""".""" )[-1]
				if extension == "txt":
						snake_case_		:							Optional[Any]			       =						"""text"""
				snake_case_		:							Union[str, Any]			       =						load_dataset(lowerCamelCase_				,      data_files=lowerCamelCase_ )
		# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
		# https://huggingface.co/docs/datasets/loading_datasets.html.

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		snake_case_		:							Dict			       =						{
		    """cache_dir""": model_args.cache_dir,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				snake_case_		:							str			       =						AutoConfig.from_pretrained(model_args.config_name				,      **lowerCamelCase_ )
		elif model_args.model_name_or_path:
				snake_case_		:							Optional[Any]			       =						AutoConfig.from_pretrained(model_args.model_name_or_path				,      **lowerCamelCase_ )
		else:
				snake_case_		:							List[str]			       =						CONFIG_MAPPING[model_args.model_type]()
				logger.warning("""You are instantiating a new config instance from scratch.""" )
				if model_args.config_overrides is not None:
						logger.info(F'''Overriding config: {model_args.config_overrides}''' )
						config.update_from_string(model_args.config_overrides )
						logger.info(F'''New config: {config}''' )

		snake_case_		:							int			       =						{
		    """cache_dir""": model_args.cache_dir,
		    """use_fast""": model_args.use_fast_tokenizer,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.tokenizer_name:
				snake_case_		:							str			       =						AutoTokenizer.from_pretrained(model_args.tokenizer_name				,      **lowerCamelCase_ )
		elif model_args.model_name_or_path:
				snake_case_		:							Optional[int]			       =						AutoTokenizer.from_pretrained(model_args.model_name_or_path				,      **lowerCamelCase_ )
		else:
				raise ValueError(
				    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
				    """You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )

		if model_args.model_name_or_path:
				snake_case_		:							Optional[Any]			       =						AutoModelForMaskedLM.from_pretrained(
				    model_args.model_name_or_path				,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path )				,      config=lowerCamelCase_				,      cache_dir=model_args.cache_dir				,      revision=model_args.model_revision				,      use_auth_token=True if model_args.use_auth_token else None				,      )
		else:
				logger.info("""Training new model from scratch""" )
				snake_case_		:							int			       =						AutoModelForMaskedLM.from_config(lowerCamelCase_ )

		model.resize_token_embeddings(len(lowerCamelCase_ ) )

		# Preprocessing the datasets.
		# First we tokenize all the texts.
		if training_args.do_train:
				snake_case_		:							str			       =						datasets["""train"""].column_names
		else:
				snake_case_		:							List[Any]			       =						datasets["""validation"""].column_names
		snake_case_		:							Any			       =						"""text""" if """text""" in column_names else column_names[0]

		snake_case_		:							Union[str, Any]			       =						"""max_length""" if data_args.pad_to_max_length else False

		def tokenize_function(lowerCamelCase_  :int ):
				# Remove empty lines
				snake_case_		:							Optional[Any]			       =						[line for line in examples["""text"""] if len(lowerCamelCase_ ) > 0 and not line.isspace()]
				return tokenizer(examples["""text"""]				,      padding=lowerCamelCase_				,      truncation=lowerCamelCase_				,      max_length=data_args.max_seq_length )

		snake_case_		:							Dict			       =						datasets.map(
		    lowerCamelCase_				,      batched=lowerCamelCase_				,      num_proc=data_args.preprocessing_num_workers				,      remove_columns=[text_column_name]				,      load_from_cache_file=not data_args.overwrite_cache				,      )

		# Add the chinese references if provided
		if data_args.train_ref_file is not None:
				snake_case_		:							Tuple			       =						add_chinese_references(tokenized_datasets["""train"""]				,      data_args.train_ref_file )
		if data_args.validation_ref_file is not None:
				snake_case_		:							Dict			       =						add_chinese_references(
				    tokenized_datasets["""validation"""]				,      data_args.validation_ref_file )
		# If we have ref files, need to avoid it removed by trainer
		snake_case_		:							Any			       =						data_args.train_ref_file or data_args.validation_ref_file
		if has_ref:
				snake_case_		:							Tuple			       =						False

		# Data collator
		# This one will take care of randomly masking the tokens.
		snake_case_		:							Union[str, Any]			       =						DataCollatorForWholeWordMask(tokenizer=lowerCamelCase_				,      mlm_probability=data_args.mlm_probability )

		# Initialize our Trainer
		snake_case_		:							Optional[int]			       =						Trainer(
		    model=lowerCamelCase_				,      args=lowerCamelCase_				,      train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None				,      eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None				,      tokenizer=lowerCamelCase_				,      data_collator=lowerCamelCase_				,      )

		# Training
		if training_args.do_train:
				if last_checkpoint is not None:
						snake_case_		:							Optional[Any]			       =						last_checkpoint
				elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
						snake_case_		:							Any			       =						model_args.model_name_or_path
				else:
						snake_case_		:							Union[str, Any]			       =						None
				snake_case_		:							Any			       =						trainer.train(resume_from_checkpoint=lowerCamelCase_ )
				trainer.save_model()  # Saves the tokenizer too for easy upload

				snake_case_		:							List[str]			       =						os.path.join(training_args.output_dir				,      """train_results.txt""" )
				if trainer.is_world_process_zero():
						with open(lowerCamelCase_				,      """w""" ) as writer:
								logger.info("""***** Train results *****""" )
								for key, value in sorted(train_result.metrics.items() ):
										logger.info(F'''  {key} = {value}''' )
										writer.write(F'''{key} = {value}\n''' )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
						trainer.state.save_to_json(os.path.join(training_args.output_dir				,      """trainer_state.json""" ) )

    # Evaluation
		snake_case_		:							Dict			       =						{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***""" )

				snake_case_		:							Any			       =						trainer.evaluate()

				snake_case_		:							Optional[int]			       =						math.exp(eval_output["""eval_loss"""] )
				snake_case_		:							Any			       =						perplexity

				snake_case_		:							Union[str, Any]			       =						os.path.join(training_args.output_dir				,      """eval_results_mlm_wwm.txt""" )
				if trainer.is_world_process_zero():
						with open(lowerCamelCase_				,      """w""" ) as writer:
								logger.info("""***** Eval results *****""" )
								for key, value in sorted(results.items() ):
										logger.info(F'''  {key} = {value}''' )
										writer.write(F'''{key} = {value}\n''' )

		return results





def      UpperCAmelCase      (		lowerCamelCase_  :Any ):







		'''simple docstring'''
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
							main()



'''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)