
"""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 __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]





     def __init__(      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      assert Path(hparams.data_dir ).exists()
      __UpperCamelCase							    =			Path(hparams.output_dir )
      self.output_dir.mkdir(exist_ok=__UpperCAmelCase )

      __UpperCamelCase							    =			self.output_dir.joinpath('student' )

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

      if hparams.length_penalty != -1:
       __UpperCamelCase							    =			hparams.length_penalty
      __UpperCamelCase							    =			hparams.teacher  # Use teacher's tokenizer
      super().__init__(__UpperCAmelCase      ,	model=__UpperCAmelCase      ,	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							    =			len(student.get_encoder().block )
       __UpperCamelCase							    =			len(student.get_decoder().block )
       __UpperCamelCase							    =			len(teacher.get_encoder().block )
       __UpperCamelCase							    =			len(teacher.get_decoder().block )
      else:
       __UpperCamelCase							    =			student.config.encoder_layers
       __UpperCamelCase							    =			student.config.decoder_layers
       __UpperCamelCase							    =			teacher.config.encoder_layers
       __UpperCamelCase							    =			teacher.config.decoder_layers

      __UpperCamelCase							    =			not (hparams.student is None or hparams.teacher == hparams.student)
      __UpperCamelCase							    =			(not self.different_base_models) and hparams.alpha_hid > 0
      __UpperCamelCase							    =			self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      __UpperCamelCase							    =			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							    =			list(range(__UpperCAmelCase ) )
      if d_layer_ids is None:
       __UpperCamelCase							    =			list(range(__UpperCAmelCase ) )

      __UpperCamelCase    ,      __UpperCamelCase							    =			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							    =			get_layers_to_supervise(
            n_student=len(self.e_layer_ids )      ,	n_teacher=__UpperCAmelCase )
        __UpperCamelCase							    =			get_layers_to_supervise(
            n_student=len(self.d_layer_ids )      ,	n_teacher=__UpperCAmelCase )
       else:  # student layer should emulate hidden states of the teacher layer it was copied from
        __UpperCamelCase							    =			self.e_layer_ids
        __UpperCamelCase							    =			self.d_layer_ids
      else:
       __UpperCamelCase							    =			None
       __UpperCamelCase							    =			None

      __UpperCamelCase							    =			nn.KLDivLoss(reduction='batchmean' )
      __UpperCamelCase							    =			2.0
      __UpperCamelCase							    =			hparams.alpha_mlm
      __UpperCamelCase							    =			hparams.alpha_ce
      __UpperCamelCase							    =			hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			mask[:, :, None].expand_as(__UpperCAmelCase )
      __UpperCamelCase							    =			s_logits.size(-1 )
      __UpperCamelCase							    =			torch.masked_select(__UpperCAmelCase      ,	__UpperCAmelCase )  # (bs * seq_length * voc_size) modulo the 1s in mask
      __UpperCamelCase							    =			torch.masked_select(__UpperCAmelCase      ,	__UpperCAmelCase )  # (bs * seq_length * voc_size) modulo the 1s in mask
      __UpperCamelCase							    =			s_logits_slct.view(-1      ,	__UpperCAmelCase )  # (bs * seq_length, voc_size) modulo the 1s in mask
      __UpperCamelCase							    =			t_logits_slct.view(-1      ,	__UpperCAmelCase )  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      __UpperCamelCase							    =			(
          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 		UpperCAmelCase   (      __UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.tokenizer.pad_token_id
      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase							    =			batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			self.model._shift_right(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			shift_tokens_right(__UpperCAmelCase      ,	__UpperCAmelCase )

      # noinspection PyCallingNonCallable
      __UpperCamelCase							    =			self(
          __UpperCAmelCase      ,	attention_mask=__UpperCAmelCase      ,	decoder_input_ids=__UpperCAmelCase      ,	output_hidden_states=self.do_calc_hidden_loss      ,	output_attentions=__UpperCAmelCase      ,	use_cache=__UpperCAmelCase      ,	)
      __UpperCamelCase							    =			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							    =			nn.CrossEntropyLoss(ignore_index=__UpperCAmelCase )
       __UpperCamelCase							    =			loss_fct(lm_logits.view(-1      ,	lm_logits.shape[-1] )      ,	labels.view(-1 ) )
      else:
       __UpperCamelCase							    =			nn.functional.log_softmax(__UpperCAmelCase      ,	dim=-1 )
       __UpperCamelCase    ,      __UpperCamelCase							    =			label_smoothed_nll_loss(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	self.hparams.label_smoothing      ,	ignore_index=__UpperCAmelCase )

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

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

      __UpperCamelCase							    =			self.teacher(
          __UpperCAmelCase      ,	attention_mask=__UpperCAmelCase      ,	encoder_outputs=(teacher_enc_outputs,)      ,	decoder_input_ids=__UpperCAmelCase      ,	output_hidden_states=self.do_calc_hidden_loss      ,	use_cache=__UpperCAmelCase      ,	)
      __UpperCamelCase							    =			decoder_input_ids.ne(__UpperCAmelCase )
      __UpperCamelCase							    =			self.calc_ce_loss(__UpperCAmelCase      ,	__UpperCAmelCase      ,	teacher_outputs['logits'] )
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       __UpperCamelCase							    =			self.calc_hidden_loss(
           __UpperCAmelCase      ,	student_outputs['decoder_hidden_states']      ,	teacher_outputs['decoder_hidden_states']      ,	self.d_matches      ,	normalize_hidden=self.hparams.normalize_hidden      ,	)

      __UpperCamelCase							    =			(
          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 		UpperCAmelCase   (      __UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			'expected list or tuple for hidden_states, got tensor of shape: '
      assert not isinstance(__UpperCAmelCase      ,	torch.Tensor ), F'{msg}{hidden_states.shape}'
      assert not isinstance(__UpperCAmelCase      ,	torch.Tensor ), F'{msg}{hidden_states_T.shape}'
      __UpperCamelCase							    =			attention_mask.to(hidden_states[0] )
      __UpperCamelCase							    =			mask.sum() * hidden_states[0].size(-1 )
      __UpperCamelCase							    =			torch.stack([hidden_states[i] for i in range(len(__UpperCAmelCase ) )] )
      __UpperCamelCase							    =			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							    =			nn.functional.layer_norm(__UpperCAmelCase      ,	student_states.shape[1:] )
       __UpperCamelCase							    =			nn.functional.layer_norm(__UpperCAmelCase      ,	teacher_states.shape[1:] )
      __UpperCamelCase							    =			nn.functional.mse_loss(__UpperCAmelCase      ,	__UpperCAmelCase      ,	reduction='none' )
      __UpperCamelCase							    =			(mse * mask.unsqueeze(0 ).unsqueeze(-1 )).sum() / valid_count
      return masked_mse






def 				A		(							snake_case :Any						) ->      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=snake_case						)
 parser.add_argument('--alpha_ce'							,   default=0.8							,   type=snake_case						)
 parser.add_argument('--alpha_mlm'							,   default=0.2							,   type=snake_case						)
 parser.add_argument('--alpha_hid'							,   default=0.0							,   type=snake_case							,   required=snake_case						)
 parser.add_argument('--student'							,   type=snake_case							,   required=snake_case						)
 parser.add_argument('--student_decoder_layers'							,   default=1_2							,   type=snake_case							,   required=snake_case						)
 parser.add_argument('--student_encoder_layers'							,   default=1_2							,   type=snake_case							,   required=snake_case						)
 parser.add_argument('--no_teacher'							,   action='store_true'							,   default=snake_case						)
 parser.add_argument('--length_penalty'							,   type=snake_case							,   default=-1						)
 parser.add_argument('--supervise_forward'							,   action='store_true'							,   default=snake_case						)
 parser.add_argument('--normalize_hidden'							,   action='store_true'							,   default=snake_case						)

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"translation"
     lowercase		  =		["bleu"]
     lowercase		  =		"bleu"





     def __init__(      self      ,	__UpperCAmelCase      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase      ,	**__UpperCAmelCase )
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      __UpperCamelCase							    =			hparams.src_lang
      __UpperCamelCase							    =			hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			self.tokenizer.lang_code_to_id[hparams.tgt_lang]





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      return calculate_bleu(__UpperCAmelCase      ,	__UpperCAmelCase )







     @staticmethod
     def 		UpperCAmelCase   (      __UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




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






def 				A		(							snake_case :str						) ->      Optional[Any]:
 if args.no_teacher:
  __UpperCamelCase							    =			TranslationModule if 'translation' in args.task else SummarizationModule
 else:  # DISTILL WITH TEACHER
  __UpperCamelCase							    =			TranslationDistiller if 'translation' in args.task else SummarizationDistiller
 __UpperCamelCase							    =			module_cls.__name__
 print(f'using module {args.setup_cls}'						)
 __UpperCamelCase							    =			module_cls(snake_case						)
 return model






def 				A		(							snake_case :Dict						) ->      str:
 Path(args.output_dir						).mkdir(exist_ok=snake_case						)
 check_output_dir(snake_case							,   expected_items=3						)

 __UpperCamelCase							    =			create_module(snake_case						)
 return ft_main(snake_case							,   model=snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							Tuple          =					argparse.ArgumentParser()
      UpperCamelCase      :							Optional[int]          =					pl.Trainer.add_argparse_args(parser)
      UpperCamelCase      :							Tuple          =					SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
      UpperCamelCase      :							Optional[Any]          =					parser.parse_args()

      distill_main(args)





"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,	)



