






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 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']



   def __init__( self   ,   _A			)    ->   Any:
    assert Path(hparams.data_dir			).exists()
    __a    :	Dict									=						Path(hparams.output_dir			)
    self.output_dir.mkdir(exist_ok=_A			)

    __a    :	Any									=						self.output_dir.joinpath('student'			)

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

    if hparams.length_penalty != -1:
     __a    :	Union[str, Any]									=						hparams.length_penalty
    __a    :	List[str]									=						hparams.teacher  # Use teacher's tokenizer
    super().__init__(_A   ,   model=_A   ,   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":
     __a    :	Dict									=						len(student.get_encoder().block			)
     __a    :	str									=						len(student.get_decoder().block			)
     __a    :	Optional[int]									=						len(teacher.get_encoder().block			)
     __a    :	Dict									=						len(teacher.get_decoder().block			)
    else:
     __a    :	List[str]									=						student.config.encoder_layers
     __a    :	str									=						student.config.decoder_layers
     __a    :	Tuple									=						teacher.config.encoder_layers
     __a    :	List[str]									=						teacher.config.decoder_layers

    __a    :	Tuple									=						not (hparams.student is None or hparams.teacher == hparams.student)
    __a    :	str									=						(not self.different_base_models) and hparams.alpha_hid > 0
    __a    :	Optional[Any]									=						self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
    # self.different_encoder determines whether we need to run the teacher encoder
    __a    :	List[str]									=						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:
     __a    :	str									=						list(range(_A			)			)
    if d_layer_ids is None:
     __a    :	Union[str, Any]									=						list(range(_A			)			)

    __a    :	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:
      __a    :	str									=						get_layers_to_supervise(
          n_student=len(self.e_layer_ids			)   ,   n_teacher=_A			)
      __a    :	Union[str, Any]									=						get_layers_to_supervise(
          n_student=len(self.d_layer_ids			)   ,   n_teacher=_A			)
     else:  # student layer should emulate hidden states of the teacher layer it was copied from
      __a    :	str									=						self.e_layer_ids
      __a    :	Optional[Any]									=						self.d_layer_ids
    else:
     __a    :	Dict									=						None
     __a    :	Dict									=						None

    __a    :	Any									=						nn.KLDivLoss(reduction='batchmean'			)
    __a    :	Optional[Any]									=						2.0
    __a    :	str									=						hparams.alpha_mlm
    __a    :	int									=						hparams.alpha_ce
    __a    :	Dict									=						hparams.alpha_hid
    gc.collect()
    torch.cuda.empty_cache()



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A			)    ->   Tuple:
    __a    :	Optional[Any]									=						mask[:, :, None].expand_as(_A			)
    __a    :	str									=						s_logits.size(-1			)
    __a    :	List[str]									=						torch.masked_select(_A   ,   _A			)  # (bs * seq_length * voc_size) modulo the 1s in mask
    __a    :	Dict									=						torch.masked_select(_A   ,   _A			)  # (bs * seq_length * voc_size) modulo the 1s in mask
    __a    :	List[str]									=						s_logits_slct.view(-1   ,   _A			)  # (bs * seq_length, voc_size) modulo the 1s in mask
    __a    :	Tuple									=						t_logits_slct.view(-1   ,   _A			)  # (bs * seq_length, voc_size) modulo the 1s in mask
    assert t_logits_slct.size() == s_logits_slct.size()
    __a    :	str									=						(
        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    __magic_name__    ( _A   ,   _A			)    ->   Optional[int]:
    SummarizationModule.add_model_specific_args(_A   ,   _A			)
    add_distill_args(_A			)
    return parser



   def    __magic_name__    ( self   ,   _A			)    ->   tuple:
    __a    :	Optional[int]									=						self.tokenizer.pad_token_id
    __a    :	Optional[Any]									=						batch['input_ids'], batch['attention_mask'], batch['labels']
    if isinstance(self.model   ,   _A			):
     __a    :	Any									=						self.model._shift_right(_A			)
    else:
     __a    :	Optional[int]									=						shift_tokens_right(_A   ,   _A			)

    # noinspection PyCallingNonCallable
    __a    :	Optional[Any]									=						self(
        _A   ,   attention_mask=_A   ,   decoder_input_ids=_A   ,   output_hidden_states=self.do_calc_hidden_loss   ,   output_attentions=_A   ,   use_cache=_A   ,   )
    __a    :	List[str]									=						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
     __a    :	str									=						nn.CrossEntropyLoss(ignore_index=_A			)
     __a    :	Union[str, Any]									=						loss_fct(lm_logits.view(-1   ,   lm_logits.shape[-1]			)   ,   labels.view(-1			)			)
    else:
     __a    :	List[Any]									=						nn.functional.log_softmax(_A   ,   dim=-1			)
     __a    :	Union[str, Any]									=						label_smoothed_nll_loss(
         _A   ,   _A   ,   self.hparams.label_smoothing   ,   ignore_index=_A			)

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

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

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

    __a    :	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    __magic_name__    ( _A   ,   _A   ,   _A   ,   _A   ,   _A			)    ->   str:
    __a    :	List[str]									=						'expected list or tuple for hidden_states, got tensor of shape: '
    assert not isinstance(_A   ,   torch.Tensor			), f'''{msg}{hidden_states.shape}'''
    assert not isinstance(_A   ,   torch.Tensor			), f'''{msg}{hidden_states_T.shape}'''
    __a    :	Any									=						attention_mask.to(hidden_states[0]			)
    __a    :	str									=						mask.sum() * hidden_states[0].size(-1			)
    __a    :	Dict									=						torch.stack([hidden_states[i] for i in range(len(_A			)			)]			)
    __a    :	List[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:
     __a    :	List[Any]									=						nn.functional.layer_norm(_A   ,   student_states.shape[1:]			)
     __a    :	Optional[int]									=						nn.functional.layer_norm(_A   ,   teacher_states.shape[1:]			)
    __a    :	Tuple									=						nn.functional.mse_loss(_A   ,   _A   ,   reduction='none'			)
    __a    :	Dict									=						(mse * mask.unsqueeze(0			).unsqueeze(-1			)).sum() / valid_count
    return masked_mse






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 # 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=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--alpha_ce'       ,		default=0.8       ,		type=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--alpha_mlm'       ,		default=0.2       ,		type=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--alpha_hid'       ,		default=0.0       ,		type=SCREAMING_SNAKE_CASE__       ,		required=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--student'       ,		type=SCREAMING_SNAKE_CASE__       ,		required=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--student_decoder_layers'       ,		default=12       ,		type=SCREAMING_SNAKE_CASE__       ,		required=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--student_encoder_layers'       ,		default=12       ,		type=SCREAMING_SNAKE_CASE__       ,		required=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--no_teacher'       ,		action='store_true'       ,		default=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--length_penalty'       ,		type=SCREAMING_SNAKE_CASE__       ,		default=-1		)
 parser.add_argument('--supervise_forward'       ,		action='store_true'       ,		default=SCREAMING_SNAKE_CASE__		)
 parser.add_argument('--normalize_hidden'       ,		action='store_true'       ,		default=SCREAMING_SNAKE_CASE__		)




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   'translation'
   _A						=   ['bleu']
   _A						=   'bleu'



   def __init__( self   ,   _A   ,   **_A			)    ->   Tuple:
    super().__init__(_A   ,   **_A			)
    assert hparams.src_lang is not None
    assert hparams.tgt_lang is not None
    __a    :	Dict									=						hparams.src_lang
    __a    :	Optional[int]									=						hparams.tgt_lang
    if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer   ,   _A			):
     __a    :	Any									=						self.tokenizer.lang_code_to_id[hparams.tgt_lang]



   def    __magic_name__    ( self   ,   _A   ,   _A			)    ->   dict:
    return calculate_bleu(_A   ,   _A			)



   @staticmethod
   def    __magic_name__    ( _A   ,   _A			)    ->   List[str]:
    TranslationModule.add_model_specific_args(_A   ,   _A			)
    add_distill_args(_A			)
    return parser






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if args.no_teacher:
  __a    :	Any									=						TranslationModule if 'translation' in args.task else SummarizationModule
 else:  # DISTILL WITH TEACHER
  __a    :	Any									=						TranslationDistiller if 'translation' in args.task else SummarizationDistiller
 __a    :	str									=						module_cls.__name__
 print(f'''using module {args.setup_cls}'''		)
 __a    :	Dict									=						module_cls(SCREAMING_SNAKE_CASE__		)
 return model






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 Path(args.output_dir		).mkdir(exist_ok=SCREAMING_SNAKE_CASE__		)
 check_output_dir(SCREAMING_SNAKE_CASE__       ,		expected_items=3		)

 __a    :	Dict									=						create_module(SCREAMING_SNAKE_CASE__		)
 return ft_main(SCREAMING_SNAKE_CASE__       ,		model=SCREAMING_SNAKE_CASE__		)


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)












'''simple docstring'''


from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
SCREAMING_SNAKE_CASE_				      =							2_0

# Time between time steps in seconds
SCREAMING_SNAKE_CASE_				      =							INTERVAL / 1_0_0_0




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A   ,   _A   ,   _A   ,   _A = 1.0   ,   _A = 1.0   ,   _A = "blue"   ,   )    ->   None:
    __a    :	str									=						position_x
    __a    :	Optional[Any]									=						position_y
    __a    :	List[Any]									=						velocity_x
    __a    :	Tuple									=						velocity_y
    __a    :	Dict									=						mass
    __a    :	int									=						size
    __a    :	Tuple									=						color



   @property
   def    __magic_name__    ( self			)    ->   tuple[float, float]:
    return self.position_x, self.position_y



   @property
   def    __magic_name__    ( self			)    ->   tuple[float, float]:
    return self.velocity_x, self.velocity_y



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A			)    ->   None:
    self.velocity_x += force_x * delta_time
    self.velocity_y += force_y * delta_time



   def    __magic_name__    ( self   ,   _A			)    ->   None:
    self.position_x += self.velocity_x * delta_time
    self.position_y += self.velocity_y * delta_time




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A = 1.0   ,   _A = 1.0   ,   _A = 0.0   ,   )    ->   None:
    __a    :	List[str]									=						bodies
    __a    :	List[str]									=						gravitation_constant
    __a    :	int									=						time_factor
    __a    :	List[str]									=						softening_factor



   def __len__( self			)    ->   int:
    return len(self.bodies			)



   def    __magic_name__    ( self   ,   _A			)    ->   None:
    for bodya in self.bodies:
     __a    :	Any									=						0.0
     __a    :	str									=						0.0
     for bodya in self.bodies:
      if bodya != bodya:
       __a    :	Optional[Any]									=						bodya.position_x - bodya.position_x
       __a    :	Dict									=						bodya.position_y - bodya.position_y

       # Calculation of the distance using Pythagoras's theorem
       # Extra factor due to the softening technique
       __a    :	Optional[Any]									=						(dif_x**2 + dif_y**2 + self.softening_factor) ** (
           1 / 2
       )

       # Newton's law of universal gravitation.
       force_x += (
           self.gravitation_constant * bodya.mass * dif_x / distance**3
       )
       force_y += (
           self.gravitation_constant * bodya.mass * dif_y / distance**3
       )

            # Update the body's velocity once all the force components have been added
     bodya.update_velocity(_A   ,   _A   ,   delta_time * self.time_factor			)

    # Update the positions only after all the velocities have been updated
    for body in self.bodies:
     body.update_position(delta_time * self.time_factor			)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 body_system.update_system(SCREAMING_SNAKE_CASE__		)

 # Update the positions of the patches
 for patch, body in zip(SCREAMING_SNAKE_CASE__       ,		body_system.bodies		):
  __a    :	Dict									=						(body.position_x, body.position_y)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = -1       ,		SCREAMING_SNAKE_CASE__ = 1       ,		SCREAMING_SNAKE_CASE__ = -1       ,		SCREAMING_SNAKE_CASE__ = 1       ,		):
 __a    :	int									=						plt.figure()
 fig.canvas.set_window_title(SCREAMING_SNAKE_CASE__		)
 __a    :	int									=						plt.axes(
     xlim=(x_start, x_end)       ,		ylim=(y_start, y_end)		)  # Set section to be plotted
 plt.gca().set_aspect('equal'		)  # Fix aspect ratio

 # Each body is drawn as a patch by the plt-function
 __a    :	str									=						[
     plt.Circle((body.position_x, body.position_y)       ,		body.size       ,		fc=body.color		)
     for body in body_system.bodies
 ]

 for patch in patches:
  ax.add_patch(SCREAMING_SNAKE_CASE__		)

 # Function called at each step of the animation
 def update(SCREAMING_SNAKE_CASE__		) -> list[plt.Circle]:
  update_step(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
  return patches

 __a    :	Dict									=						animation.FuncAnimation(  # noqa: F841
     SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		interval=SCREAMING_SNAKE_CASE__       ,		blit=SCREAMING_SNAKE_CASE__		)

 plt.show()






def   lowerCAmelCase__			(					):
 __a    :	Dict									=						0.970_0436
 __a    :	List[Any]									=						-0.2430_8753
 __a    :	Union[str, Any]									=						0.4_6620_3685
 __a    :	Any									=						0.4323_6573

 __a    :	Optional[int]									=						[
     Body(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.2       ,		color='red'		),
     Body(-position_x       ,		-position_y       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.2       ,		color='green'		),
     Body(0       ,		0       ,		-2 * velocity_x       ,		-2 * velocity_y       ,		size=0.2       ,		color='blue'		),
 ]
 return BodySystem(SCREAMING_SNAKE_CASE__       ,		time_factor=3		)






def   lowerCAmelCase__			(					):
 __a    :	Optional[int]									=						7.34_76E22
 __a    :	List[Any]									=						5.9_72E24
 __a    :	List[Any]									=						1022
 __a    :	str									=						384399000
 __a    :	Optional[int]									=						6.6_74E-11

 # Calculation of the respective velocities so that total impulse is zero,
 # i.e. the two bodies together don't move
 __a    :	str									=						earth_mass * velocity_dif / (earth_mass + moon_mass)
 __a    :	Optional[int]									=						moon_velocity - velocity_dif

 __a    :	Union[str, Any]									=						Body(-earth_moon_distance       ,		0       ,		0       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		10000000       ,		'grey'		)
 __a    :	Union[str, Any]									=						Body(0       ,		0       ,		0       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		50000000       ,		'blue'		)
 return BodySystem([earth, moon]       ,		SCREAMING_SNAKE_CASE__       ,		time_factor=1000000		)






def   lowerCAmelCase__			(					):
 __a    :	Tuple									=						[]
 for _ in range(10		):
  __a    :	Dict									=						random.uniform(-0.5       ,		0.5		)
  __a    :	Optional[int]									=						random.uniform(-0.5       ,		0.5		)

  # Bodies are created pairwise with opposite velocities so that the
  # total impulse remains zero
  bodies.append(
      Body(
          random.uniform(-0.5       ,		0.5		)       ,		random.uniform(-0.5       ,		0.5		)       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.05       ,		)		)
  bodies.append(
      Body(
          random.uniform(-0.5       ,		0.5		)       ,		random.uniform(-0.5       ,		0.5		)       ,		-velocity_x       ,		-velocity_y       ,		size=0.05       ,		)		)
 return BodySystem(SCREAMING_SNAKE_CASE__       ,		0.01       ,		10       ,		0.1		)


if __name__ == "__main__":
  plot("Figure-8 solution to the 3-body-problem", example_a(), -2, 2, -2, 2)
  plot(
      "Moon's orbit around the earth",
      example_a(),
      -4_3_0_0_0_0_0_0_0,
      4_3_0_0_0_0_0_0_0,
      -4_3_0_0_0_0_0_0_0,
      4_3_0_0_0_0_0_0_0,
  )
  plot("Random system with many bodies", example_a(), -1.5, 1.5, -1.5, 1.5)




