
'''simple docstring'''


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE  =			logging.get_logger(__name__)




@dataclass
class 						__snake_case :
		a_        =				42
		a_        =				field(default_factory=_a  )
		a_        =				field(default_factory=_a  )




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






								'''simple docstring'''






								UpperCamelCase_    :  Union[str, Any]				    =    len(list(m.modules())) == 1 or isinstance(_SCREAMING_SNAKE_CASE ,						nn.Convad) or isinstance(_SCREAMING_SNAKE_CASE ,						nn.BatchNormad)
								if has_not_submodules:
														if not isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
																				self.traced.append(_SCREAMING_SNAKE_CASE)




		def __call__(     self				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:Tensor)				-> List[Any]:






								'''simple docstring'''






								for m in self.module.modules():
														self.handles.append(m.register_forward_hook(self._forward_hook))
								self.module(_SCREAMING_SNAKE_CASE)
								[x.remove() for x in self.handles]
								return self





		@property
		def   _snake_case  (     self				:Tuple)				-> Optional[int]:






								'''simple docstring'''






								return list(filter(lambda _SCREAMING_SNAKE_CASE: len(list(x.state_dict().keys())) > 0 ,						self.traced))







@dataclass
class 						__snake_case :
		a_        =				42
		a_        =				42
		a_        =				0
		a_        =				field(default_factory=_a  )
		a_        =				field(default_factory=_a  )




		def __call__(     self				:List[str] ,						_SCREAMING_SNAKE_CASE				:Tensor)				-> Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_    :  Dict				    =    Tracker(self.dest)(_SCREAMING_SNAKE_CASE).parametrized
								UpperCamelCase_    :  Union[str, Any]				    =    Tracker(self.src)(_SCREAMING_SNAKE_CASE).parametrized

								UpperCamelCase_    :  Union[str, Any]				    =    list(filter(lambda _SCREAMING_SNAKE_CASE: type(_SCREAMING_SNAKE_CASE) not in self.src_skip ,						_SCREAMING_SNAKE_CASE))
								UpperCamelCase_    :  Optional[Any]				    =    list(filter(lambda _SCREAMING_SNAKE_CASE: type(_SCREAMING_SNAKE_CASE) not in self.dest_skip ,						_SCREAMING_SNAKE_CASE))

								if len(_SCREAMING_SNAKE_CASE) != len(_SCREAMING_SNAKE_CASE):
														raise Exception(
														    F"""Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE)} operations while"""
														    F""" destination module has {len(_SCREAMING_SNAKE_CASE)}.""")

								for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
														dest_m.load_state_dict(src_m.state_dict())
														if self.verbose == 1:
																				print(F"""Transfered from={src_m} to={dest_m}""")







def 				__magic_name__(					UpperCAmelCase__   :					nn.Module       ,							UpperCAmelCase__   :					nn.Module  ):
						# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
						UpperCamelCase_    :  Optional[int]				    =    from_model.state_dict()
						UpperCamelCase_    :  Union[str, Any]				    =    our_model.state_dict()
						UpperCamelCase_    :  List[str]				    =    our_model.config
						UpperCamelCase_    :  str				    =    []
						for stage_idx in range(len(config.hidden_sizes  )  ):
												for block_id in range(config.depths[stage_idx]  ):
																		UpperCamelCase_    :  Union[str, Any]				    =    f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																		UpperCamelCase_    :  List[Any]				    =    f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																		all_keys.append((from_key, to_key)  )
																		UpperCamelCase_    :  str				    =    f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		UpperCamelCase_    :  Dict				    =    f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

																		all_keys.append((from_key, to_key)  )

						for from_key, to_key in all_keys:
												UpperCamelCase_    :  int				    =    from_state_dict.pop(UpperCAmelCase__  )

						our_model.load_state_dict(UpperCAmelCase__  )
						return our_model
def 				__magic_name__(					UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					VanConfig       ,							UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					nn.Module       ,							UpperCAmelCase__   :					Path       ,							UpperCAmelCase__   :					bool = True       ,							):
						print(f"""Downloading weights for {name}..."""  )
						UpperCamelCase_    :  List[str]				    =    cached_download(UpperCAmelCase__  )
						print(f"""Converting {name}..."""  )
						UpperCamelCase_    :  List[Any]				    =    torch.load(UpperCAmelCase__  )["""state_dict"""]
						from_model.load_state_dict(UpperCAmelCase__  )
						from_model.eval()
						with torch.no_grad():
												UpperCamelCase_    :  int				    =    VanForImageClassification(UpperCAmelCase__  ).eval()
												UpperCamelCase_    :  int				    =    ModuleTransfer(src=UpperCAmelCase__       ,							dest=UpperCAmelCase__  )
												UpperCamelCase_    :  Optional[int]				    =    torch.randn((1, 3, 224, 224)  )
												module_transfer(UpperCAmelCase__  )
												UpperCamelCase_    :  Any				    =    copy_parameters(UpperCAmelCase__       ,							UpperCAmelCase__  )

						if not torch.allclose(from_model(UpperCAmelCase__  )       ,							our_model(UpperCAmelCase__  ).logits  ):
												raise ValueError("""The model logits don't match the original one."""  )

						UpperCamelCase_    :  Tuple				    =    name
						print(UpperCAmelCase__  )

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name       ,							commit_message="""Add model"""       ,							use_temp_dir=UpperCAmelCase__       ,							)

												# we can use the convnext one
												UpperCamelCase_    :  Tuple				    =    AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""  )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name       ,							commit_message="""Add image processor"""       ,							use_temp_dir=UpperCAmelCase__       ,							)

												print(f"""Pushed {checkpoint_name}"""  )
def 				__magic_name__(					UpperCAmelCase__   :					Path       ,							UpperCAmelCase__   :					str = None       ,							UpperCAmelCase__   :					bool = True  ):
						UpperCamelCase_    :  int				    =    """imagenet-1k-id2label.json"""
						UpperCamelCase_    :  Optional[Any]				    =    1000

						UpperCamelCase_    :  Dict				    =    """huggingface/label-files"""
						UpperCamelCase_    :  Optional[int]				    =    num_labels
						UpperCamelCase_    :  int				    =    json.load(open(hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__       ,							repo_type="""dataset"""  )       ,							"""r"""  )  )
						UpperCamelCase_    :  int				    =    {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}

						UpperCamelCase_    :  Union[str, Any]				    =    idalabel
						UpperCamelCase_    :  Any				    =    {v: k for k, v in idalabel.items()}

						UpperCamelCase_    :  Optional[Any]				    =    partial(UpperCAmelCase__       ,							num_labels=UpperCAmelCase__       ,							idalabel=UpperCAmelCase__       ,							labelaid=UpperCAmelCase__  )

						UpperCamelCase_    :  int				    =    {
						    """van-tiny""": ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 160, 256]       ,							depths=[3, 3, 5, 2]       ,							mlp_ratios=[8, 8, 4, 4]       ,							),
						    """van-small""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]       ,							depths=[2, 2, 4, 2]       ,							mlp_ratios=[8, 8, 4, 4]       ,							),
						    """van-base""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]       ,							depths=[3, 3, 12, 3]       ,							mlp_ratios=[8, 8, 4, 4]       ,							),
						    """van-large""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]       ,							depths=[3, 5, 27, 3]       ,							mlp_ratios=[8, 8, 4, 4]       ,							),
						}

						UpperCamelCase_    :  str				    =    {
						    """van-tiny""": van_tiny,
						    """van-small""": van_small,
						    """van-base""": van_base,
						    """van-large""": van_large,
						}

						UpperCamelCase_    :  int				    =    {
						    """van-tiny""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
						    ),
						    """van-small""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
						    ),
						    """van-base""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
						    ),
						    """van-large""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    UpperCAmelCase__       ,							names_to_config[model_name]       ,							checkpoint=names_to_original_checkpoints[model_name]       ,							from_model=names_to_original_models[model_name]()       ,							save_directory=UpperCAmelCase__       ,							push_to_hub=UpperCAmelCase__       ,							)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    UpperCAmelCase__       ,							UpperCAmelCase__       ,							checkpoint=names_to_original_checkpoints[model_name]       ,							from_model=names_to_original_models[model_name]()       ,							save_directory=UpperCAmelCase__       ,							push_to_hub=UpperCAmelCase__       ,							)


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE  =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model-name",
	    default=None,
	    type=str,
	    help=(
	        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
	        " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
	    ),
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default=None,
	    type=Path,
	    required=True,
	    help="Path to the output PyTorch model directory.",
	)
	parser.add_argument(
	    "--van_dir",
	    required=True,
	    type=Path,
	    help=(
	        "A path to VAN's original implementation directory. You can download from here:"
	        " https://github.com/Visual-Attention-Network/VAN-Classification"
	    ),
	)
	parser.add_argument(
	    "--push_to_hub",
	    default=True,
	    type=bool,
	    required=False,
	    help="If True, push model and image processor to the hub.",
	)

	__SCREAMING_SNAKE_CASE  =			parser.parse_args()
	__SCREAMING_SNAKE_CASE  =			args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	__SCREAMING_SNAKE_CASE  =			args.van_dir
	# append the path to the parents to maskformer dir
	sys.path.append(str(van_dir.parent))
	from van.models.van import van_base, van_large, van_small, van_tiny

	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






'''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)




