
'''simple docstring'''


import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__SCREAMING_SNAKE_CASE  =			os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__SCREAMING_SNAKE_CASE  =			os.path.join(hf_cache_home, "accelerate")
__SCREAMING_SNAKE_CASE  =			os.path.join(cache_dir, "default_config.yaml")
__SCREAMING_SNAKE_CASE  =			os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
	__SCREAMING_SNAKE_CASE  =			default_yaml_config_file
else:
	__SCREAMING_SNAKE_CASE  =			default_json_config_file
def 				__magic_name__(					UpperCAmelCase__   :					int  ):
						if config_file is not None:
												if not os.path.isfile(UpperCAmelCase__  ):
																		raise FileNotFoundError(
																		    f"""The passed configuration file `{config_file}` does not exist. """
																		    """Please pass an existing file to `accelerate launch`, or use the the default one """
																		    """created through `accelerate config` and run `accelerate launch` """
																		    """without the `--config_file` argument."""  )
						else:
												UpperCamelCase_    :  List[Any]				    =    default_config_file
						with open(UpperCAmelCase__       ,							"""r"""       ,							encoding="""utf-8"""  ) as f:
												if config_file.endswith(""".json"""  ):
																		if (
																		    json.load(UpperCAmelCase__  ).get("""compute_environment"""       ,							ComputeEnvironment.LOCAL_MACHINE  )
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								UpperCamelCase_    :  int				    =    ClusterConfig
																		else:
																								UpperCamelCase_    :  Optional[Any]				    =    SageMakerConfig
																		return config_class.from_json_file(json_file=UpperCAmelCase__  )
												else:
																		if (
																		    yaml.safe_load(UpperCAmelCase__  ).get("""compute_environment"""       ,							ComputeEnvironment.LOCAL_MACHINE  )
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								UpperCamelCase_    :  Union[str, Any]				    =    ClusterConfig
																		else:
																								UpperCamelCase_    :  Optional[int]				    =    SageMakerConfig
																		return config_class.from_yaml_file(yaml_file=UpperCAmelCase__  )




@dataclass
class 						__snake_case :
		a_        =				42
		a_        =				42
		a_        =				42
		a_        =				42




		def   _snake_case  (     self				:List[Any])				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    self.__dict__
								# For serialization, it's best to convert Enums to strings (or their underlying value type).
								for key, value in result.items():
														if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
																				UpperCamelCase_    :  str				    =    value.value
														if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE) and not bool(_SCREAMING_SNAKE_CASE):
																				UpperCamelCase_    :  Dict				    =    None
								UpperCamelCase_    :  Dict				    =    {k: v for k, v in result.items() if v is not None}
								return result




		@classmethod
		def   _snake_case  (     cls				:Dict ,						_SCREAMING_SNAKE_CASE				:Optional[int]=None)				-> int:






								'''simple docstring'''






								UpperCamelCase_    :  str				    =    default_json_config_file if json_file is None else json_file
								with open(_SCREAMING_SNAKE_CASE ,						"""r""" ,						encoding="""utf-8""") as f:
														UpperCamelCase_    :  List[str]				    =    json.load(_SCREAMING_SNAKE_CASE)
								if "compute_environment" not in config_dict:
														UpperCamelCase_    :  Union[str, Any]				    =    ComputeEnvironment.LOCAL_MACHINE
								if "mixed_precision" not in config_dict:
														UpperCamelCase_    :  List[Any]				    =    """fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
								if "fp16" in config_dict:  # Convert the config to the new format.
														del config_dict["fp16"]
								if "dynamo_backend" in config_dict:  # Convert the config to the new format.
														UpperCamelCase_    :  Optional[int]				    =    config_dict.pop("""dynamo_backend""")
														UpperCamelCase_    :  List[str]				    =    {} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
								if "use_cpu" not in config_dict:
														UpperCamelCase_    :  Dict				    =    False
								return cls(**_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:int ,						_SCREAMING_SNAKE_CASE				:List[Any])				-> List[str]:






								'''simple docstring'''






								with open(_SCREAMING_SNAKE_CASE ,						"""w""" ,						encoding="""utf-8""") as f:
														UpperCamelCase_    :  Union[str, Any]				    =    json.dumps(self.to_dict() ,						indent=2 ,						sort_keys=_SCREAMING_SNAKE_CASE) + """\n"""
														f.write(_SCREAMING_SNAKE_CASE)




		@classmethod
		def   _snake_case  (     cls				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:Any=None)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    default_yaml_config_file if yaml_file is None else yaml_file
								with open(_SCREAMING_SNAKE_CASE ,						"""r""" ,						encoding="""utf-8""") as f:
														UpperCamelCase_    :  Any				    =    yaml.safe_load(_SCREAMING_SNAKE_CASE)
								if "compute_environment" not in config_dict:
														UpperCamelCase_    :  Union[str, Any]				    =    ComputeEnvironment.LOCAL_MACHINE

								if "mixed_precision" not in config_dict:
														UpperCamelCase_    :  int				    =    """fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
								if isinstance(config_dict["""mixed_precision"""] ,						_SCREAMING_SNAKE_CASE) and not config_dict["mixed_precision"]:
														UpperCamelCase_    :  Union[str, Any]				    =    """no"""
								if "fp16" in config_dict:  # Convert the config to the new format.
														del config_dict["fp16"]
								if "dynamo_backend" in config_dict:  # Convert the config to the new format.
														UpperCamelCase_    :  Any				    =    config_dict.pop("""dynamo_backend""")
														UpperCamelCase_    :  Optional[Any]				    =    {} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
								if "use_cpu" not in config_dict:
														UpperCamelCase_    :  List[str]				    =    False
								return cls(**_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:str)				-> int:






								'''simple docstring'''






								with open(_SCREAMING_SNAKE_CASE ,						"""w""" ,						encoding="""utf-8""") as f:
														yaml.safe_dump(self.to_dict() ,						_SCREAMING_SNAKE_CASE)





		def   _snake_case  (     self				:Any)				-> int:






								'''simple docstring'''






								if isinstance(self.compute_environment ,						_SCREAMING_SNAKE_CASE):
														UpperCamelCase_    :  Dict				    =    ComputeEnvironment(self.compute_environment)
								if isinstance(self.distributed_type ,						_SCREAMING_SNAKE_CASE):
														if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																				UpperCamelCase_    :  Optional[Any]				    =    SageMakerDistributedType(self.distributed_type)
														else:
																				UpperCamelCase_    :  Optional[int]				    =    DistributedType(self.distributed_type)
								if self.dynamo_config is None:
														UpperCamelCase_    :  str				    =    {}







@dataclass
class 						__snake_case (						_a  ):
		a_        =				42
		a_        =				0
		a_        =				1
		a_        =				None
		a_        =				None
		a_        =				None
		a_        =				"static"
		a_        =				False
		a_        =				"main"

		# args for deepspeed_plugin
		a_        =				None
		# args for fsdp
		a_        =				None
		# args for megatron_lm
		a_        =				None
		# args for ipex
		a_        =				None
		# args for TPU
		a_        =				False

		# args for TPU pods
		a_        =				None
		a_        =				None
		a_        =				False
		a_        =				False
		a_        =				None
		a_        =				None
		a_        =				None
		a_        =				None

		# args for dynamo
		a_        =				None




		def   _snake_case  (     self				:List[str])				-> Tuple:






								'''simple docstring'''






								if self.deepspeed_config is None:
														UpperCamelCase_    :  Tuple				    =    {}
								if self.fsdp_config is None:
														UpperCamelCase_    :  Union[str, Any]				    =    {}
								if self.megatron_lm_config is None:
														UpperCamelCase_    :  int				    =    {}
								if self.ipex_config is None:
														UpperCamelCase_    :  Optional[int]				    =    {}
								return super().__post_init__()







@dataclass
class 						__snake_case (						_a  ):
		a_        =				42
		a_        =				42
		a_        =				None
		a_        =				None
		a_        =				"us-east-1"
		a_        =				1
		a_        =				"all"
		a_        =				F"""accelerate-sagemaker-{num_machines}"""
		a_        =				SAGEMAKER_PYTORCH_VERSION
		a_        =				SAGEMAKER_TRANSFORMERS_VERSION
		a_        =				SAGEMAKER_PYTHON_VERSION
		a_        =				None
		a_        =				None
		a_        =				None
		a_        =				None






'''simple docstring'''


import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa




class 						__snake_case (						_a  ):
		a_        =				["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]




		def __init__(     self				:int ,						_SCREAMING_SNAKE_CASE				:Dict)				-> Optional[int]:






								'''simple docstring'''






								assert Path(hparams.data_dir).exists()
								UpperCamelCase_    :  Union[str, Any]				    =    Path(hparams.output_dir)
								self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  List[Any]				    =    self.output_dir.joinpath("""student""")

								UpperCamelCase_    :  Any				    =    str(_SCREAMING_SNAKE_CASE)  # Tell lightning we are training the student
								UpperCamelCase_    :  Tuple				    =    AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher).eval()
								use_task_specific_params(_SCREAMING_SNAKE_CASE ,						hparams.task)  # We copy good generation parameters to student by default
								if hparams.student is not None:
														UpperCamelCase_    :  Dict				    =    AutoModelForSeqaSeqLM.from_pretrained(hparams.student)
														use_task_specific_params(_SCREAMING_SNAKE_CASE ,						hparams.task)
														UpperCamelCase_    ,UpperCamelCase_    :  Dict				    =    None, None
								else:
														UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  Union[str, Any]				    =    create_student_by_copying_alternating_layers(
														    _SCREAMING_SNAKE_CASE ,						e=hparams.student_encoder_layers ,						d=hparams.student_decoder_layers ,						save_path=_SCREAMING_SNAKE_CASE)

								if hparams.length_penalty != -1:
														UpperCamelCase_    :  int				    =    hparams.length_penalty
								UpperCamelCase_    :  int				    =    hparams.teacher  # Use teacher's tokenizer
								super().__init__(_SCREAMING_SNAKE_CASE ,						model=_SCREAMING_SNAKE_CASE ,						config=student.config)
								assert student.config.model_type == teacher.config.model_type, (
								    F"""teacher, student model types should be the same, got {student.config.model_type} !="""
								    F""" {teacher.config.model_type}"""
								)

								if student.config.model_type == "t5":
														UpperCamelCase_    :  List[str]				    =    len(student.get_encoder().block)
														UpperCamelCase_    :  Optional[Any]				    =    len(student.get_decoder().block)
														UpperCamelCase_    :  Dict				    =    len(teacher.get_encoder().block)
														UpperCamelCase_    :  Tuple				    =    len(teacher.get_decoder().block)
								else:
														UpperCamelCase_    :  List[str]				    =    student.config.encoder_layers
														UpperCamelCase_    :  Dict				    =    student.config.decoder_layers
														UpperCamelCase_    :  List[str]				    =    teacher.config.encoder_layers
														UpperCamelCase_    :  str				    =    teacher.config.decoder_layers

								UpperCamelCase_    :  Tuple				    =    not (hparams.student is None or hparams.teacher == hparams.student)
								UpperCamelCase_    :  Union[str, Any]				    =    (not self.different_base_models) and hparams.alpha_hid > 0
								UpperCamelCase_    :  List[str]				    =    self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
								# self.different_encoder determines whether we need to run the teacher encoder
								UpperCamelCase_    :  Dict				    =    teacher
								freeze_params(self.teacher)

								if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
														try:
																				del self.teacher.model.encoder
														except AttributeError:  # T5
																				del self.teacher.encoder

								if e_layer_ids is None:
														UpperCamelCase_    :  Dict				    =    list(range(_SCREAMING_SNAKE_CASE))
								if d_layer_ids is None:
														UpperCamelCase_    :  Dict				    =    list(range(_SCREAMING_SNAKE_CASE))

								UpperCamelCase_    ,UpperCamelCase_    :  Union[str, Any]				    =    e_layer_ids, d_layer_ids  # type: List[int], List[int]

								if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
														if hparams.supervise_forward:
																				UpperCamelCase_    :  Optional[int]				    =    get_layers_to_supervise(
																				    n_student=len(self.e_layer_ids) ,						n_teacher=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_    :  Optional[Any]				    =    get_layers_to_supervise(
																				    n_student=len(self.d_layer_ids) ,						n_teacher=_SCREAMING_SNAKE_CASE)
														else:  # student layer should emulate hidden states of the teacher layer it was copied from
																				UpperCamelCase_    :  Any				    =    self.e_layer_ids
																				UpperCamelCase_    :  Tuple				    =    self.d_layer_ids
								else:
														UpperCamelCase_    :  int				    =    None
														UpperCamelCase_    :  Optional[int]				    =    None

								UpperCamelCase_    :  Dict				    =    nn.KLDivLoss(reduction="""batchmean""")
								UpperCamelCase_    :  str				    =    2.0
								UpperCamelCase_    :  int				    =    hparams.alpha_mlm
								UpperCamelCase_    :  Dict				    =    hparams.alpha_ce
								UpperCamelCase_    :  Any				    =    hparams.alpha_hid
								gc.collect()
								torch.cuda.empty_cache()




		def   _snake_case  (     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Dict)				-> Dict:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Dict				    =    s_logits.size(-1)
								UpperCamelCase_    :  Optional[int]				    =    torch.masked_select(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCamelCase_    :  Optional[Any]				    =    torch.masked_select(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCamelCase_    :  Optional[int]				    =    s_logits_slct.view(-1 ,						_SCREAMING_SNAKE_CASE)  # (bs * seq_length, voc_size) modulo the 1s in mask
								UpperCamelCase_    :  str				    =    t_logits_slct.view(-1 ,						_SCREAMING_SNAKE_CASE)  # (bs * seq_length, voc_size) modulo the 1s in mask
								assert t_logits_slct.size() == s_logits_slct.size()
								UpperCamelCase_    :  Tuple				    =    (
								    self.ce_loss_fct(
								        nn.functional.log_softmax(s_logits_slct / self.temperature ,						dim=-1) ,						nn.functional.softmax(t_logits_slct / self.temperature ,						dim=-1) ,						)
								    * (self.temperature) ** 2
								)
								return loss_ce




		@staticmethod
		def   _snake_case  (     _SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Any)				-> Optional[Any]:






								'''simple docstring'''






								SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								add_distill_args(_SCREAMING_SNAKE_CASE)
								return parser




		def   _snake_case  (     self				:Tuple ,						_SCREAMING_SNAKE_CASE				:dict)				-> tuple:






								'''simple docstring'''






								UpperCamelCase_    :  Dict				    =    self.tokenizer.pad_token_id
								UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  Any				    =    batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
								if isinstance(self.model ,						_SCREAMING_SNAKE_CASE):
														UpperCamelCase_    :  int				    =    self.model._shift_right(_SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_    :  str				    =    shift_tokens_right(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

								# noinspection PyCallingNonCallable
								UpperCamelCase_    :  Union[str, Any]				    =    self(
								    _SCREAMING_SNAKE_CASE ,						attention_mask=_SCREAMING_SNAKE_CASE ,						decoder_input_ids=_SCREAMING_SNAKE_CASE ,						output_hidden_states=self.do_calc_hidden_loss ,						output_attentions=_SCREAMING_SNAKE_CASE ,						use_cache=_SCREAMING_SNAKE_CASE ,						)
								UpperCamelCase_    :  Union[str, Any]				    =    student_outputs["""logits"""]

								# Same cross entropy vs. label smoothing logic as finetune.py
								assert lm_logits.shape[-1] == self.model.config.vocab_size
								if self.hparams.label_smoothing == 0:
														# Same behavior as modeling_bart.py, besides ignoring pad_token_id
														UpperCamelCase_    :  Dict				    =    nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_    :  str				    =    loss_fct(lm_logits.view(-1 ,						lm_logits.shape[-1]) ,						labels.view(-1))
								else:
														UpperCamelCase_    :  List[str]				    =    nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,						dim=-1)
														UpperCamelCase_    ,UpperCamelCase_    :  Dict				    =    label_smoothed_nll_loss(
														    _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						self.hparams.label_smoothing ,						ignore_index=_SCREAMING_SNAKE_CASE)

								def zero_tensor():
														return torch.tensor(0.0).type_as(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  Tuple				    =    student_outputs[
								    """encoder_last_hidden_state"""
								]  # use this unless self.different_base_models
								UpperCamelCase_    ,UpperCamelCase_    :  int				    =    zero_tensor(), zero_tensor()
								if self.different_encoder:  # compute encoder hidden state loss
														UpperCamelCase_    :  List[str]				    =    self.teacher.get_encoder()(
														    _SCREAMING_SNAKE_CASE ,						attention_mask=_SCREAMING_SNAKE_CASE ,						output_hidden_states=self.do_calc_hidden_loss ,						)
														if self.different_base_models:
																				UpperCamelCase_    :  List[Any]				    =    all_teacher_encoder_outputs["""last_hidden_state"""]
														elif self.do_calc_hidden_loss:
																				UpperCamelCase_    :  Union[str, Any]				    =    self.calc_hidden_loss(
																				    _SCREAMING_SNAKE_CASE ,						student_outputs["""encoder_hidden_states"""] ,						all_teacher_encoder_outputs["""hidden_states"""] ,						self.e_matches ,						normalize_hidden=self.hparams.normalize_hidden ,						)

								UpperCamelCase_    :  Tuple				    =    self.teacher(
								    _SCREAMING_SNAKE_CASE ,						attention_mask=_SCREAMING_SNAKE_CASE ,						encoder_outputs=(teacher_enc_outputs,) ,						decoder_input_ids=_SCREAMING_SNAKE_CASE ,						output_hidden_states=self.do_calc_hidden_loss ,						use_cache=_SCREAMING_SNAKE_CASE ,						)
								UpperCamelCase_    :  Any				    =    decoder_input_ids.ne(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  int				    =    self.calc_ce_loss(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						teacher_outputs["""logits"""])
								if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
														UpperCamelCase_    :  str				    =    self.calc_hidden_loss(
														    _SCREAMING_SNAKE_CASE ,						student_outputs["""decoder_hidden_states"""] ,						teacher_outputs["""decoder_hidden_states"""] ,						self.d_matches ,						normalize_hidden=self.hparams.normalize_hidden ,						)

								UpperCamelCase_    :  Optional[int]				    =    (
								    self.alpha_ce * loss_ce
								    + self.alpha_mlm * student_lm_loss
								    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
								)
								return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec





		@staticmethod
		def   _snake_case  (     _SCREAMING_SNAKE_CASE				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Optional[Any])				-> Optional[int]:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    """expected list or tuple for hidden_states, got tensor of shape: """
								assert not isinstance(_SCREAMING_SNAKE_CASE ,						torch.Tensor), F"""{msg}{hidden_states.shape}"""
								assert not isinstance(_SCREAMING_SNAKE_CASE ,						torch.Tensor), F"""{msg}{hidden_states_T.shape}"""
								UpperCamelCase_    :  List[Any]				    =    attention_mask.to(hidden_states[0])
								UpperCamelCase_    :  Union[str, Any]				    =    mask.sum() * hidden_states[0].size(-1)
								UpperCamelCase_    :  List[Any]				    =    torch.stack([hidden_states[i] for i in range(len(_SCREAMING_SNAKE_CASE))])
								UpperCamelCase_    :  Union[str, Any]				    =    torch.stack([hidden_states_T[j] for j in matches])
								assert student_states.shape == teacher_states.shape, F"""{student_states.shape} != {teacher_states.shape}"""
								if normalize_hidden:
														UpperCamelCase_    :  Dict				    =    nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,						student_states.shape[1:])
														UpperCamelCase_    :  str				    =    nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,						teacher_states.shape[1:])
								UpperCamelCase_    :  Optional[Any]				    =    nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						reduction="""none""")
								UpperCamelCase_    :  Tuple				    =    (mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
								return masked_mse







def 				__magic_name__(					UpperCAmelCase__   :					List[str]  ):
						# NOTE: if --student argument was specified and the teacher and student base models
						# are different, the models still have to have the same tokenizer, specified by
						# --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
						# from bart to t5. This s because if the tokenizers are different, the output space
						# for the two models is also different and their logits are not comparable.
						parser.add_argument("""--teacher"""       ,							type=UpperCAmelCase__  )
						parser.add_argument("""--alpha_ce"""       ,							default=0.8       ,							type=UpperCAmelCase__  )
						parser.add_argument("""--alpha_mlm"""       ,							default=0.2       ,							type=UpperCAmelCase__  )
						parser.add_argument("""--alpha_hid"""       ,							default=0.0       ,							type=UpperCAmelCase__       ,							required=UpperCAmelCase__  )
						parser.add_argument("""--student"""       ,							type=UpperCAmelCase__       ,							required=UpperCAmelCase__  )
						parser.add_argument("""--student_decoder_layers"""       ,							default=12       ,							type=UpperCAmelCase__       ,							required=UpperCAmelCase__  )
						parser.add_argument("""--student_encoder_layers"""       ,							default=12       ,							type=UpperCAmelCase__       ,							required=UpperCAmelCase__  )
						parser.add_argument("""--no_teacher"""       ,							action="""store_true"""       ,							default=UpperCAmelCase__  )
						parser.add_argument("""--length_penalty"""       ,							type=UpperCAmelCase__       ,							default=-1  )
						parser.add_argument("""--supervise_forward"""       ,							action="""store_true"""       ,							default=UpperCAmelCase__  )
						parser.add_argument("""--normalize_hidden"""       ,							action="""store_true"""       ,							default=UpperCAmelCase__  )




class 						__snake_case (						_a  ):
		a_        =				"translation"
		a_        =				["bleu"]
		a_        =				"bleu"




		def __init__(     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Tuple ,						**_SCREAMING_SNAKE_CASE				:Tuple)				-> str:






								'''simple docstring'''






								super().__init__(_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)
								assert hparams.src_lang is not None
								assert hparams.tgt_lang is not None
								UpperCamelCase_    :  Dict				    =    hparams.src_lang
								UpperCamelCase_    :  List[Any]				    =    hparams.tgt_lang
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,						_SCREAMING_SNAKE_CASE):
														UpperCamelCase_    :  Tuple				    =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]




		def   _snake_case  (     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Tuple)				-> dict:






								'''simple docstring'''






								return calculate_bleu(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)





		@staticmethod
		def   _snake_case  (     _SCREAMING_SNAKE_CASE				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[str])				-> int:






								'''simple docstring'''






								TranslationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								add_distill_args(_SCREAMING_SNAKE_CASE)
								return parser







def 				__magic_name__(					UpperCAmelCase__   :					List[str]  ):
						if args.no_teacher:
												UpperCamelCase_    :  str				    =    TranslationModule if """translation""" in args.task else SummarizationModule
						else:  # DISTILL WITH TEACHER
												UpperCamelCase_    :  Tuple				    =    TranslationDistiller if """translation""" in args.task else SummarizationDistiller
						UpperCamelCase_    :  str				    =    module_cls.__name__
						print(f"""using module {args.setup_cls}"""  )
						UpperCamelCase_    :  Dict				    =    module_cls(UpperCAmelCase__  )
						return model
def 				__magic_name__(					UpperCAmelCase__   :					Any  ):
						Path(args.output_dir  ).mkdir(exist_ok=UpperCAmelCase__  )
						check_output_dir(UpperCAmelCase__       ,							expected_items=3  )

						UpperCamelCase_    :  int				    =    create_module(UpperCAmelCase__  )
						return ft_main(UpperCAmelCase__       ,							model=UpperCAmelCase__  )


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE  =			argparse.ArgumentParser()
	__SCREAMING_SNAKE_CASE  =			pl.Trainer.add_argparse_args(parser)
	__SCREAMING_SNAKE_CASE  =			SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
	__SCREAMING_SNAKE_CASE  =			parser.parse_args()

	distill_main(args)




