
'''simple docstring'''


import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling
def 				__magic_name__(					UpperCAmelCase__   :					Union[str, Any]  ):
						UpperCamelCase_    :  str				    =    """https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

						with open(UpperCAmelCase__       ,							"""wb"""  ) as f:
												with requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__  ) as req:
																		for chunk in req.iter_content(chunk_size=2048  ):
																								f.write(UpperCAmelCase__  )
def 				__magic_name__(					):
						UpperCamelCase_    :  Tuple				    =    VivitConfig()

						UpperCamelCase_    :  Dict				    =    400
						UpperCamelCase_    :  Any				    =    """huggingface/label-files"""
						UpperCamelCase_    :  Any				    =    """kinetics400-id2label.json"""

						UpperCamelCase_    :  Dict				    =    json.load(open(hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__       ,							repo_type="""dataset"""  )       ,							"""r"""  )  )
						UpperCamelCase_    :  Union[str, Any]				    =    {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
						UpperCamelCase_    :  int				    =    idalabel
						UpperCamelCase_    :  str				    =    {v: k for k, v in idalabel.items()}
						return config
def 				__magic_name__(					):
						UpperCamelCase_    :  List[Any]				    =    hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""       ,							filename="""eating_spaghetti_32_frames.npy"""       ,							repo_type="""dataset"""  )
						UpperCamelCase_    :  Optional[Any]				    =    np.load(UpperCAmelCase__  )
						return list(UpperCAmelCase__  )
def 				__magic_name__(					UpperCAmelCase__   :					np.ndarray  ):
						if np.ndim(UpperCAmelCase__  ) == 2:
												return transform_attention_bias(UpperCAmelCase__  )

						elif np.ndim(UpperCAmelCase__  ) == 3:
												return transform_attention_kernel(UpperCAmelCase__  )

						else:
												raise Exception(f"""Invalid number of dimesions: {np.ndim(UpperCAmelCase__  )}"""  )
def 				__magic_name__(					UpperCAmelCase__   :					np.ndarray  ):
						return current.flatten()
def 				__magic_name__(					UpperCAmelCase__   :					np.ndarray  ):
						return np.reshape(UpperCAmelCase__       ,							(current.shape[0], current.shape[1] * current.shape[2])  ).T
def 				__magic_name__(					UpperCAmelCase__   :					np.ndarray  ):
						return np.reshape(UpperCAmelCase__       ,							(current.shape[0] * current.shape[1], current.shape[2])  ).T
def 				__magic_name__(					UpperCAmelCase__   :					List[Any]       ,							UpperCAmelCase__   :					Any  ):
						UpperCamelCase_    :  Tuple				    =    state_dict["""optimizer"""]["""target"""]["""Transformer"""][f"""encoderblock_{i}"""]

						UpperCamelCase_    :  List[Any]				    =    f"""encoder.layer.{i}."""
						UpperCamelCase_    :  Union[str, Any]				    =    {
						    prefix + """intermediate.dense.bias""": state["""MlpBlock_0"""]["""Dense_0"""]["""bias"""],
						    prefix + """intermediate.dense.weight""": np.transpose(state["""MlpBlock_0"""]["""Dense_0"""]["""kernel"""]  ),
						    prefix + """output.dense.bias""": state["""MlpBlock_0"""]["""Dense_1"""]["""bias"""],
						    prefix + """output.dense.weight""": np.transpose(state["""MlpBlock_0"""]["""Dense_1"""]["""kernel"""]  ),
						    prefix + """layernorm_before.bias""": state["""LayerNorm_0"""]["""bias"""],
						    prefix + """layernorm_before.weight""": state["""LayerNorm_0"""]["""scale"""],
						    prefix + """layernorm_after.bias""": state["""LayerNorm_1"""]["""bias"""],
						    prefix + """layernorm_after.weight""": state["""LayerNorm_1"""]["""scale"""],
						    prefix
						    + """attention.attention.query.bias""": transform_attention(
						        state["""MultiHeadDotProductAttention_0"""]["""query"""]["""bias"""]  ),
						    prefix
						    + """attention.attention.query.weight""": transform_attention(
						        state["""MultiHeadDotProductAttention_0"""]["""query"""]["""kernel"""]  ),
						    prefix
						    + """attention.attention.key.bias""": transform_attention(state["""MultiHeadDotProductAttention_0"""]["""key"""]["""bias"""]  ),
						    prefix
						    + """attention.attention.key.weight""": transform_attention(
						        state["""MultiHeadDotProductAttention_0"""]["""key"""]["""kernel"""]  ),
						    prefix
						    + """attention.attention.value.bias""": transform_attention(
						        state["""MultiHeadDotProductAttention_0"""]["""value"""]["""bias"""]  ),
						    prefix
						    + """attention.attention.value.weight""": transform_attention(
						        state["""MultiHeadDotProductAttention_0"""]["""value"""]["""kernel"""]  ),
						    prefix + """attention.output.dense.bias""": state["""MultiHeadDotProductAttention_0"""]["""out"""]["""bias"""],
						    prefix
						    + """attention.output.dense.weight""": transform_attention_output_weight(
						        state["""MultiHeadDotProductAttention_0"""]["""out"""]["""kernel"""]  ),
						}

						return new_state
def 				__magic_name__(					UpperCAmelCase__   :					str  ):
						return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]  )
def 				__magic_name__(					UpperCAmelCase__   :					Optional[int]       ,							UpperCAmelCase__   :					int=False  ):
						UpperCamelCase_    :  Union[str, Any]				    =    get_n_layers(UpperCAmelCase__  )

						UpperCamelCase_    :  int				    =    OrderedDict()

						UpperCamelCase_    :  Tuple				    =    state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
						UpperCamelCase_    :  str				    =    state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

						UpperCamelCase_    :  List[Any]				    =    np.transpose(
						    state_dict["""optimizer"""]["""target"""]["""embedding"""]["""kernel"""]       ,							(4, 3, 0, 1, 2)  )
						UpperCamelCase_    :  Optional[Any]				    =    state_dict["""optimizer"""]["""target"""]["""embedding"""]["""bias"""]

						UpperCamelCase_    :  Tuple				    =    state_dict["""optimizer"""]["""target"""]["""cls"""]
						UpperCamelCase_    :  Any				    =    state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
						    """pos_embedding"""
						]

						for i in range(UpperCAmelCase__  ):
												new_state.update(transform_state_encoder_block(UpperCAmelCase__       ,							UpperCAmelCase__  )  )

						if classification_head:
												UpperCamelCase_    :  List[str]				    =    {"""vivit.""" + k: v for k, v in new_state.items()}
												UpperCamelCase_    :  Union[str, Any]				    =    np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""kernel"""]  )
												UpperCamelCase_    :  Dict				    =    np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""bias"""]  )

						return {k: torch.tensor(UpperCAmelCase__  ) for k, v in new_state.items()}
def 				__magic_name__(					):
						UpperCamelCase_    :  Dict				    =    VivitImageProcessor()

						assert extractor.do_resize is True
						assert extractor.size == {"shortest_edge": 256}
						assert extractor.do_center_crop is True
						assert extractor.crop_size == {"width": 224, "height": 224}
						assert extractor.resample == PILImageResampling.BILINEAR

						# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
						# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
						# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
						assert extractor.do_normalize is False
						assert extractor.do_rescale is True
						assert extractor.rescale_factor == 1 / 255

						# zero-centering = True in original implementation
						assert extractor.do_zero_centering is True

						return extractor
def 				__magic_name__(					UpperCAmelCase__   :					str  ):
						UpperCamelCase_    :  Union[str, Any]				    =    """checkpoint"""

						if not os.path.exists(UpperCAmelCase__  ):
												download_checkpoint(UpperCAmelCase__  )

						UpperCamelCase_    :  Optional[Any]				    =    restore_checkpoint(UpperCAmelCase__       ,							UpperCAmelCase__  )
						UpperCamelCase_    :  int				    =    transform_state(UpperCAmelCase__       ,							classification_head=UpperCAmelCase__  )

						UpperCamelCase_    :  str				    =    get_vivit_config()

						assert config.image_size == 224
						assert config.num_frames == 32

						UpperCamelCase_    :  int				    =    VivitForVideoClassification(UpperCAmelCase__  )
						model.load_state_dict(UpperCAmelCase__  )
						model.eval()

						UpperCamelCase_    :  Optional[Any]				    =    get_processor()

						UpperCamelCase_    :  Any				    =    prepare_video()
						UpperCamelCase_    :  Union[str, Any]				    =    extractor(UpperCAmelCase__       ,							return_tensors="""pt"""  )

						UpperCamelCase_    :  str				    =    model(**UpperCAmelCase__  )

						UpperCamelCase_    :  int				    =    torch.Size([1, 400]  )
						UpperCamelCase_    :  Optional[int]				    =    torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]  )

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :5]       ,							UpperCAmelCase__       ,							atol=1E-4  ), outputs.logits[0, :5]

						model.save_pretrained(UpperCAmelCase__  )
						extractor.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE  =			argparse.ArgumentParser()

	parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

	__SCREAMING_SNAKE_CASE  =			parser.parse_args()
	convert(args.output_model_name)






'''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)




