



import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]=None				,					lowercase__						:					List[str]=None	):



			'''simple docstring'''





			return field(default_factory=lambda: default				,					metadata=__lowerCamelCase	)
@dataclass
class lowerCAmelCase_       :







	'''simple docstring'''




	A_       :     Dict									=		field(
	    metadata={'help': 'The csv file to plot.'} ,)
	A_       :     Tuple									=		field(
	    default=__lowerCamelCase ,metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'} ,)
	A_       :     Union[str, Any]									=		field(
	    default=__lowerCamelCase ,metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'} ,)
	A_       :     Optional[Any]									=		field(
	    default=__lowerCamelCase ,metadata={'help': 'Disable logarithmic scale when plotting'} ,)
	A_       :     Union[str, Any]									=		field(
	    default=__lowerCamelCase ,metadata={
	        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
	    } ,)
	A_       :     Dict									=		field(
	    default=__lowerCamelCase ,metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'} ,)
	A_       :     int									=		list_field(
	    default=__lowerCamelCase ,metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}			)




def    UpperCAmelCase					(  lowercase__						:					int	):



			'''simple docstring'''





			try:
						int(__lowerCamelCase	)
						return True
			except ValueError:
						return False




def    UpperCAmelCase					(  lowercase__						:					Union[str, Any]	):



			'''simple docstring'''





			try:
						float(__lowerCamelCase	)
						return True
			except ValueError:
						return False
class lowerCAmelCase_       :







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  args
				a__					=  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

				with open(self.args.csv_file   ,       newline=""""""       ) as csv_file:
							a__					=  csv.DictReader(__lowerCAmelCase       )
							for row in reader:
										a__					=  row["""model"""]
										self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]       )       )
										self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]       )       )
										if can_convert_to_int(row["""result"""]       ):
													# value is not None
													a__					=  int(row["""result"""]       )
										elif can_convert_to_float(row["""result"""]       ):
													# value is not None
													a__					=  float(row["""result"""]       )







	def 				_A			(	self       ):



				'''simple docstring'''


				a__				,       a__					=  plt.subplots()
				a__					=  """Time usage""" if self.args.is_time else """Memory usage"""
				a__					=  title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

				if not self.args.no_log_scale:
							# set logarithm scales
							ax.set_xscale("""log"""       )
							ax.set_yscale("""log"""       )

				for axis in [ax.xaxis, ax.yaxis]:
							axis.set_major_formatter(ScalarFormatter()       )

				for model_name_idx, model_name in enumerate(self.result_dict.keys()       ):
							a__					=  sorted(set(self.result_dict[model_name]["""bsz"""]       )       )
							a__					=  sorted(set(self.result_dict[model_name]["""seq_len"""]       )       )
							a__					=  self.result_dict[model_name]["""result"""]

							((a__)				,       (a__))					=  (
							    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
							)

							a__					=  (
							    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
							)

							for inner_loop_value in inner_loop_array:
										if self.args.plot_along_batch:
													a__					=  np.asarray(
													    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,       dtype=__lowerCAmelCase   ,       )
										else:
													a__					=  np.asarray(
													    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,       dtype=np.floataa   ,       )

										((a__)				,       (a__))					=  (
										    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
										)

										a__					=  np.asarray(__lowerCAmelCase   ,       __lowerCAmelCase       )[: len(__lowerCAmelCase       )]
										plt.scatter(
										    __lowerCAmelCase   ,       __lowerCAmelCase   ,       label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'       )
										plt.plot(__lowerCAmelCase   ,       __lowerCAmelCase   ,       """--"""       )

							title_str += f' {label_model_name} vs.'

				a__					=  title_str[:-4]
				a__					=  """Time in s""" if self.args.is_time else """Memory in MB"""

				# plot
				plt.title(__lowerCAmelCase       )
				plt.xlabel(__lowerCAmelCase       )
				plt.ylabel(__lowerCAmelCase       )
				plt.legend()

				if self.args.figure_png_file is not None:
							plt.savefig(self.args.figure_png_file       )
				else:
							plt.show()




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  HfArgumentParser(__lowerCamelCase	)
			a__					=  parser.parse_args_into_dataclasses()[0]
			a__					=  Plot(args=__lowerCamelCase	)
			plot.plot()


if __name__ == "__main__":
	main()


'''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 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']


   def __init__( self					:						Any ,		__lowerCAmelCase					:						str)   ->       List[Any]:
         assert Path(hparams.data_dir).exists()
         lowercase_     			=   Path(hparams.output_dir)
         self.output_dir.mkdir(exist_ok=__lowerCAmelCase)

         lowercase_     			=   self.output_dir.joinpath("student")

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

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

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

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

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


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


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						dict)   ->       tuple:
         lowercase_     			=   self.tokenizer.pad_token_id
         lowercase_			,				lowercase_			,				lowercase_     			=   batch["input_ids"], batch["attention_mask"], batch["labels"]
         if isinstance(self.model ,		__lowerCAmelCase):
               lowercase_     			=   self.model._shift_right(__lowerCAmelCase)
         else:
               lowercase_     			=   shift_tokens_right(__lowerCAmelCase ,		__lowerCAmelCase)

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

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

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

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

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




def __a			(						__lowerCamelCase      :    Tuple					)						->				str:
      '''simple docstring'''


      parser.add_argument("--teacher"			,		type=__lowerCamelCase					)
      parser.add_argument("--alpha_ce"			,		default=0.8			,		type=__lowerCamelCase					)
      parser.add_argument("--alpha_mlm"			,		default=0.2			,		type=__lowerCamelCase					)
      parser.add_argument("--alpha_hid"			,		default=0.0			,		type=__lowerCamelCase			,		required=__lowerCamelCase					)
      parser.add_argument("--student"			,		type=__lowerCamelCase			,		required=__lowerCamelCase					)
      parser.add_argument("--student_decoder_layers"			,		default=12			,		type=__lowerCamelCase			,		required=__lowerCamelCase					)
      parser.add_argument("--student_encoder_layers"			,		default=12			,		type=__lowerCamelCase			,		required=__lowerCamelCase					)
      parser.add_argument("--no_teacher"			,		action="store_true"			,		default=__lowerCamelCase					)
      parser.add_argument("--length_penalty"			,		type=__lowerCamelCase			,		default=-1					)
      parser.add_argument("--supervise_forward"			,		action="store_true"			,		default=__lowerCamelCase					)
      parser.add_argument("--normalize_hidden"			,		action="store_true"			,		default=__lowerCamelCase					)





class 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            ='translation'
   lowerCamelCase_            =['bleu']
   lowerCamelCase_            ='bleu'


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						str ,		**__lowerCAmelCase					:						List[Any])   ->       Dict:
         super().__init__(__lowerCAmelCase ,		**__lowerCAmelCase)
         assert hparams.src_lang is not None
         assert hparams.tgt_lang is not None
         lowercase_     			=   hparams.src_lang
         lowercase_     			=   hparams.tgt_lang
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,		__lowerCAmelCase):
               lowercase_     			=   self.tokenizer.lang_code_to_id[hparams.tgt_lang]


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Tuple)   ->       dict:
         return calculate_bleu(__lowerCAmelCase ,		__lowerCAmelCase)


   @staticmethod
   def __UpperCAmelCase							( __lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Tuple)   ->       str:
         TranslationModule.add_model_specific_args(__lowerCAmelCase ,		__lowerCAmelCase)
         add_distill_args(__lowerCAmelCase)
         return parser




def __a			(						__lowerCamelCase      :    List[str]					)						->				Union[str, Any]:
      '''simple docstring'''


      if args.no_teacher:
            lowercase_     			=   TranslationModule if "translation" in args.task else SummarizationModule
      else:  # DISTILL WITH TEACHER
            lowercase_     			=   TranslationDistiller if "translation" in args.task else SummarizationDistiller
      lowercase_     			=   module_cls.__name__
      print(f'using module {args.setup_cls}'					)
      lowercase_     			=   module_cls(__lowerCamelCase					)
      return model


def __a			(						__lowerCamelCase      :    Union[str, Any]					)						->				int:
      '''simple docstring'''


      Path(args.output_dir					).mkdir(exist_ok=__lowerCamelCase					)
      check_output_dir(__lowerCamelCase			,		expected_items=3					)

      lowercase_     			=   create_module(__lowerCamelCase					)
      return ft_main(__lowerCamelCase			,		model=__lowerCamelCase					)


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

       distill_main(args)






