


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
A      = logging.getLogger(__name__)





def __UpperCAmelCase       (		__A ,						__A			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     np.argmax(_SCREAMING_SNAKE_CASE ,						axis=1			)
       return np.sum(outputs == labels			)





def __UpperCAmelCase       (		__A			)						->					Dict:



       '''simple docstring'''





       with open(_SCREAMING_SNAKE_CASE ,						encoding="utf_8"			) as f:
              UpperCAmelCase__										=     csv.reader(_SCREAMING_SNAKE_CASE			)
              UpperCAmelCase__										=     []
              next(_SCREAMING_SNAKE_CASE			)  # skip the first line
              for line in tqdm(_SCREAMING_SNAKE_CASE			):
                     output.append((" ".join(line[1:5]			), line[5], line[6], int(line[-1]			) - 1)			)
       return output





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A ,						__A ,						__A			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []
       for dataset in encoded_datasets:
              UpperCAmelCase__										=     len(_SCREAMING_SNAKE_CASE			)
              UpperCAmelCase__										=     np.zeros((n_batch, 2, input_len) ,						dtype=np.intaa			)
              UpperCAmelCase__										=     np.zeros((n_batch, 2) ,						dtype=np.intaa			)
              UpperCAmelCase__										=     np.full((n_batch, 2, input_len) ,						fill_value=-1_0_0 ,						dtype=np.intaa			)
              UpperCAmelCase__										=     np.zeros((n_batch,) ,						dtype=np.intaa			)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(_SCREAMING_SNAKE_CASE			):
                     UpperCAmelCase__										=     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     UpperCAmelCase__										=     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     UpperCAmelCase__										=     with_conta
                     UpperCAmelCase__										=     with_conta
                     UpperCAmelCase__										=     len(_SCREAMING_SNAKE_CASE			) - 1
                     UpperCAmelCase__										=     len(_SCREAMING_SNAKE_CASE			) - 1
                     UpperCAmelCase__										=     with_conta
                     UpperCAmelCase__										=     with_conta
                     UpperCAmelCase__										=     mc_label
              UpperCAmelCase__										=     (input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(_SCREAMING_SNAKE_CASE			) for t in all_inputs			)			)
       return tensor_datasets





def __UpperCAmelCase       (		)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser()
       parser.add_argument("--model_name" ,						type=_SCREAMING_SNAKE_CASE ,						default="openai-gpt" ,						help="pretrained model name"			)
       parser.add_argument("--do_train" ,						action="store_true" ,						help="Whether to run training."			)
       parser.add_argument("--do_eval" ,						action="store_true" ,						help="Whether to run eval on the dev set."			)
       parser.add_argument(
           "--output_dir" ,						default=_SCREAMING_SNAKE_CASE ,						type=_SCREAMING_SNAKE_CASE ,						required=_SCREAMING_SNAKE_CASE ,						help="The output directory where the model predictions and checkpoints will be written." ,						)
       parser.add_argument("--train_dataset" ,						type=_SCREAMING_SNAKE_CASE ,						default=""			)
       parser.add_argument("--eval_dataset" ,						type=_SCREAMING_SNAKE_CASE ,						default=""			)
       parser.add_argument("--seed" ,						type=_SCREAMING_SNAKE_CASE ,						default=4_2			)
       parser.add_argument("--num_train_epochs" ,						type=_SCREAMING_SNAKE_CASE ,						default=3			)
       parser.add_argument("--train_batch_size" ,						type=_SCREAMING_SNAKE_CASE ,						default=8			)
       parser.add_argument("--eval_batch_size" ,						type=_SCREAMING_SNAKE_CASE ,						default=1_6			)
       parser.add_argument("--adam_epsilon" ,						default=1E-8 ,						type=_SCREAMING_SNAKE_CASE ,						help="Epsilon for Adam optimizer."			)
       parser.add_argument("--max_grad_norm" ,						type=_SCREAMING_SNAKE_CASE ,						default=1			)
       parser.add_argument(
           "--max_steps" ,						default=-1 ,						type=_SCREAMING_SNAKE_CASE ,						help=(
               "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
           ) ,						)
       parser.add_argument(
           "--gradient_accumulation_steps" ,						type=_SCREAMING_SNAKE_CASE ,						default=1 ,						help="Number of updates steps to accumulate before                        performing a backward/update pass." ,						)
       parser.add_argument("--learning_rate" ,						type=_SCREAMING_SNAKE_CASE ,						default=6.25E-5			)
       parser.add_argument("--warmup_steps" ,						default=0 ,						type=_SCREAMING_SNAKE_CASE ,						help="Linear warmup over warmup_steps."			)
       parser.add_argument("--lr_schedule" ,						type=_SCREAMING_SNAKE_CASE ,						default="warmup_linear"			)
       parser.add_argument("--weight_decay" ,						type=_SCREAMING_SNAKE_CASE ,						default=0.01			)
       parser.add_argument("--lm_coef" ,						type=_SCREAMING_SNAKE_CASE ,						default=0.9			)
       parser.add_argument("--n_valid" ,						type=_SCREAMING_SNAKE_CASE ,						default=3_7_4			)

       parser.add_argument("--server_ip" ,						type=_SCREAMING_SNAKE_CASE ,						default="" ,						help="Can be used for distant debugging."			)
       parser.add_argument("--server_port" ,						type=_SCREAMING_SNAKE_CASE ,						default="" ,						help="Can be used for distant debugging."			)
       UpperCAmelCase__										=     parser.parse_args()
       print(_SCREAMING_SNAKE_CASE			)

       if args.server_ip and args.server_port:
              # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
              import ptvsd

              print("Waiting for debugger attach"			)
              ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,						redirect_output=_SCREAMING_SNAKE_CASE			)
              ptvsd.wait_for_attach()

       random.seed(args.seed			)
       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       torch.cuda.manual_seed_all(args.seed			)

       UpperCAmelCase__										=     torch.device("cuda" if torch.cuda.is_available() else "cpu"			)
       UpperCAmelCase__										=     torch.cuda.device_count()
       logger.info("device: {}, n_gpu {}".format(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE			)			)

       if not args.do_train and not args.do_eval:
              raise ValueError("At least one of `do_train` or `do_eval` must be True."			)

       if not os.path.exists(args.output_dir			):
              os.makedirs(args.output_dir			)

       # Load tokenizer and model
       # This loading functions also add new tokens and embeddings called `special tokens`
       # These new embeddings will be fine-tuned on the RocStories dataset
       UpperCAmelCase__										=     ["_start_", "_delimiter_", "_classify_"]
       UpperCAmelCase__										=     OpenAIGPTTokenizer.from_pretrained(args.model_name			)
       tokenizer.add_tokens(_SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name			)
       model.resize_token_embeddings(len(_SCREAMING_SNAKE_CASE			)			)
       model.to(_SCREAMING_SNAKE_CASE			)

       # Load and encode the datasets
       def tokenize_and_encode(__A			):
              if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE			):
                     return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_SCREAMING_SNAKE_CASE			)			)
              elif isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE			):
                     return obj
              return [tokenize_and_encode(_SCREAMING_SNAKE_CASE			) for o in obj]

       logger.info("Encoding dataset..."			)
       UpperCAmelCase__										=     load_rocstories_dataset(args.train_dataset			)
       UpperCAmelCase__										=     load_rocstories_dataset(args.eval_dataset			)
       UpperCAmelCase__										=     (train_dataset, eval_dataset)
       UpperCAmelCase__										=     tokenize_and_encode(_SCREAMING_SNAKE_CASE			)

       # Compute the max input length for the Transformer
       UpperCAmelCase__										=     model.config.n_positions // 2 - 2
       UpperCAmelCase__										=     max(
           len(story[:max_length]			) + max(len(conta[:max_length]			) ,						len(conta[:max_length]			)			) + 3
           for dataset in encoded_datasets
           for story, conta, conta, _ in dataset			)
       UpperCAmelCase__										=     min(_SCREAMING_SNAKE_CASE ,						model.config.n_positions			)  # Max size of input for the pre-trained model

       # Prepare inputs tensors and dataloaders
       UpperCAmelCase__										=     pre_process_datasets(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						*_SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__						,			UpperCAmelCase__										=     tensor_datasets[0], tensor_datasets[1]

       UpperCAmelCase__										=     TensorDataset(*_SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     RandomSampler(_SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     DataLoader(_SCREAMING_SNAKE_CASE ,						sampler=_SCREAMING_SNAKE_CASE ,						batch_size=args.train_batch_size			)

       UpperCAmelCase__										=     TensorDataset(*_SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     SequentialSampler(_SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     DataLoader(_SCREAMING_SNAKE_CASE ,						sampler=_SCREAMING_SNAKE_CASE ,						batch_size=args.eval_batch_size			)

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     UpperCAmelCase__										=     args.max_steps
                     UpperCAmelCase__										=     args.max_steps // (len(_SCREAMING_SNAKE_CASE			) // args.gradient_accumulation_steps) + 1
              else:
                     UpperCAmelCase__										=     len(_SCREAMING_SNAKE_CASE			) // args.gradient_accumulation_steps * args.num_train_epochs

              UpperCAmelCase__										=     list(model.named_parameters()			)
              UpperCAmelCase__										=     ["bias", "LayerNorm.bias", "LayerNorm.weight"]
              UpperCAmelCase__										=     [
                  {
                      "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay			)],
                      "weight_decay": args.weight_decay,
                  },
                  {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay			)], "weight_decay": 0.0},
              ]
              UpperCAmelCase__										=     AdamW(_SCREAMING_SNAKE_CASE ,						lr=args.learning_rate ,						eps=args.adam_epsilon			)
              UpperCAmelCase__										=     get_linear_schedule_with_warmup(
                  _SCREAMING_SNAKE_CASE ,						num_warmup_steps=args.warmup_steps ,						num_training_steps=_SCREAMING_SNAKE_CASE			)

       if args.do_train:
              UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs			) ,						desc="Epoch"			):
                     UpperCAmelCase__										=     0
                     UpperCAmelCase__										=     0
                     UpperCAmelCase__										=     tqdm(_SCREAMING_SNAKE_CASE ,						desc="Training"			)
                     for step, batch in enumerate(_SCREAMING_SNAKE_CASE			):
                            UpperCAmelCase__										=     tuple(t.to(_SCREAMING_SNAKE_CASE			) for t in batch			)
                            UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     batch
                            UpperCAmelCase__										=     model(_SCREAMING_SNAKE_CASE ,						mc_token_ids=_SCREAMING_SNAKE_CASE ,						lm_labels=_SCREAMING_SNAKE_CASE ,						mc_labels=_SCREAMING_SNAKE_CASE			)
                            UpperCAmelCase__										=     args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            UpperCAmelCase__										=     (
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            UpperCAmelCase__										=     "Training loss: {:.2e} lr: {:.2e}".format(_SCREAMING_SNAKE_CASE ,						scheduler.get_lr()[0]			)

    # Save a trained model
       if args.do_train:
              # Save a trained model, configuration and tokenizer
              UpperCAmelCase__										=     model.module if hasattr(_SCREAMING_SNAKE_CASE ,						"module"			) else model  # Only save the model itself

              # If we save using the predefined names, we can load using `from_pretrained`
              UpperCAmelCase__										=     os.path.join(args.output_dir ,						_SCREAMING_SNAKE_CASE			)
              UpperCAmelCase__										=     os.path.join(args.output_dir ,						_SCREAMING_SNAKE_CASE			)

              torch.save(model_to_save.state_dict() ,						_SCREAMING_SNAKE_CASE			)
              model_to_save.config.to_json_file(_SCREAMING_SNAKE_CASE			)
              tokenizer.save_vocabulary(args.output_dir			)

              # Load a trained model and vocabulary that you have fine-tuned
              UpperCAmelCase__										=     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir			)
              UpperCAmelCase__										=     OpenAIGPTTokenizer.from_pretrained(args.output_dir			)
              model.to(_SCREAMING_SNAKE_CASE			)

       if args.do_eval:
              model.eval()
              UpperCAmelCase__						,			UpperCAmelCase__										=     0, 0
              UpperCAmelCase__						,			UpperCAmelCase__										=     0, 0
              for batch in tqdm(_SCREAMING_SNAKE_CASE ,						desc="Evaluating"			):
                     UpperCAmelCase__										=     tuple(t.to(_SCREAMING_SNAKE_CASE			) for t in batch			)
                     UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     batch
                     with torch.no_grad():
                            UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     model(
                                _SCREAMING_SNAKE_CASE ,						mc_token_ids=_SCREAMING_SNAKE_CASE ,						lm_labels=_SCREAMING_SNAKE_CASE ,						mc_labels=_SCREAMING_SNAKE_CASE			)

                     UpperCAmelCase__										=     mc_logits.detach().cpu().numpy()
                     UpperCAmelCase__										=     mc_labels.to("cpu"			).numpy()
                     UpperCAmelCase__										=     accuracy(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE			)

                     eval_loss += mc_loss.mean().item()
                     eval_accuracy += tmp_eval_accuracy

                     nb_eval_examples += input_ids.size(0			)
                     nb_eval_steps += 1

              UpperCAmelCase__										=     eval_loss / nb_eval_steps
              UpperCAmelCase__										=     eval_accuracy / nb_eval_examples
              UpperCAmelCase__										=     tr_loss / nb_tr_steps if args.do_train else None
              UpperCAmelCase__										=     {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

              UpperCAmelCase__										=     os.path.join(args.output_dir ,						"eval_results.txt"			)
              with open(_SCREAMING_SNAKE_CASE ,						"w"			) as writer:
                     logger.info("***** Eval results *****"			)
                     for key in sorted(result.keys()			):
                            logger.info("  %s = %s" ,						_SCREAMING_SNAKE_CASE ,						str(result[key]			)			)
                            writer.write("%s = %s\n" % (key, str(result[key]			))			)


if __name__ == "__main__":
    main()









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      = logging.get_logger(__name__)





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					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()			)


A      = {
    # 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      = {
    # 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       (		__A ,						__A			)						->					int:



       '''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 __UpperCAmelCase       (		__A ,						__A			)						->					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 __UpperCAmelCase       (		__A ,						__A = "student" ,						__A = None ,						__A = None ,						__A=False ,						__A=None ,						__A=None ,						**__A ,						)						->					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)





