





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase						:   Dict			  =     logging.getLogger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Union[str, Any]:
					return (preds == labels).mean()





@dataclass
class 						_A:


				"""simple docstring"""


				UpperCamelCase    :  str					    =	field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
				UpperCamelCase    :  Optional[str]					    =	field(
				    default=snake_case__				, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
				UpperCamelCase    :  Optional[str]					    =	field(
				    default=snake_case__				, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
				UpperCamelCase    :  Optional[str]					    =	field(
				    default=snake_case__				, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				, )





@dataclass
class 						_A:


				"""simple docstring"""


				UpperCamelCase    :  str					    =	field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()   )}   )
				UpperCamelCase    :  str					    =	field(metadata={'''help''': '''Should contain the data files for the task.'''}   )
				UpperCamelCase    :  int					    =	field(
				    default=128				, metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    }				, )
				UpperCamelCase    :  bool					    =	field(
				    default=snake_case__				, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







def  _SCREAMING_SNAKE_CASE				(   )      ->      Optional[Any]:
					# 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.

					__A	:						Optional[int]       =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
					__A    ,				__A    ,				__A	:						Optional[Any]       =				parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir  )
					    and os.listdir(training_args.output_dir  )
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    ' --overwrite_output_dir to overcome.'  )

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,						datefmt='%m/%d/%Y %H:%M:%S'					,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,						)
					logger.warning(
					    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,						training_args.local_rank					,						training_args.device					,						training_args.n_gpu					,						bool(training_args.local_rank != -1  )					,						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'					,						a  )

					# Set seed
					set_seed(training_args.seed  )

					try:
										__A	:						Any       =				processors[data_args.task_name]()
										__A	:						List[str]       =				processor.get_labels()
										__A	:						Union[str, Any]       =				len(a  )
					except KeyError:
										raise ValueError('Task not found: %s' % (data_args.task_name)  )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__A	:						Dict       =				AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						num_labels=a					,						finetuning_task=data_args.task_name					,						cache_dir=model_args.cache_dir					,						)
					__A	:						Dict       =				AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						)
					__A	:						Any       =				AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path					,						from_tf=bool('.ckpt' in model_args.model_name_or_path  )					,						config=a					,						cache_dir=model_args.cache_dir					,						)

					# Get datasets
					__A	:						str       =				(
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir					,						tokenizer=a					,						task=data_args.task_name					,						max_seq_length=data_args.max_seq_length					,						overwrite_cache=data_args.overwrite_cache					,						mode=Split.train					,						)
					    if training_args.do_train
					    else None
					)
					__A	:						Union[str, Any]       =				(
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir					,						tokenizer=a					,						task=data_args.task_name					,						max_seq_length=data_args.max_seq_length					,						overwrite_cache=data_args.overwrite_cache					,						mode=Split.dev					,						)
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(a  ) -> Dict:
										__A	:						Optional[Any]       =				np.argmax(p.predictions					,						axis=1  )
										return {"acc": simple_accuracy(a					,						p.label_ids  )}

					# Data collator
					__A	:						Any       =				DataCollatorWithPadding(a					,						pad_to_multiple_of=8  ) if training_args.fpaa else None

					# Initialize our Trainer
					__A	:						str       =				Trainer(
					    model=a					,						args=a					,						train_dataset=a					,						eval_dataset=a					,						compute_metrics=a					,						data_collator=a					,						)

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir  )

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

										__A	:						Union[str, Any]       =				trainer.evaluate()

										__A	:						Any       =				os.path.join(training_args.output_dir					,						'eval_results.txt'  )
										if trainer.is_world_master():
															with open(a					,						'w'  ) as writer:
																				logger.info('***** Eval results *****'  )
																				for key, value in result.items():
																									logger.info('  %s = %s'					,						a					,						a  )
																									writer.write('%s = %s\n' % (key, value)  )

																				results.update(a  )

					return results







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      List[str]:
					# For xla_spawn (TPUs)
					main()


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


UpperCAmelCase						:   Any			  =     logging.get_logger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      None:
					__A	:						int       =				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()  )


UpperCAmelCase						:   List[Any]			  =     {
    # 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))},
}
UpperCAmelCase						:   Optional[int]			  =     {
    # 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				(   a					,						a  )      ->      Dict:
					try:
										__A	:						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(a  )  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      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(a  )  )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def  _SCREAMING_SNAKE_CASE				(   a					,						a = "student"					,						a = None					,						a = None					,						a=False					,						a=None					,						a=None					,						**a					,						)      ->      Tuple[PreTrainedModel, List[int], List[int]]:
					__A	:						List[str]       =				'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
										__A	:						Optional[int]       =				AutoModelForSeqaSeqLM.from_pretrained(a  ).eval()
					else:
										assert isinstance(a					,						a  ), F"""teacher must be a model or string got type {type(a  )}"""
					__A	:						int       =				teacher.config.to_diff_dict()

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




