




'''simple docstring'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_				):
      __magic_name__  :       Union[str, Any]   							=					1






      @register_to_config
      def __init__( self   :  Dict						,						lowercase__   :  int = 1000						,						lowercase__   :  Optional[Union[np.ndarray, List[float]]] = None       ):


           '''simple docstring'''

           self.set_timesteps(lowercase__       )

           # standard deviation of the initial noise distribution
           a_     :							Any     						=  1.0

           # For now we only support F-PNDM, i.e. the runge-kutta method
           # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
           # mainly at formula (9), (12), (13) and the Algorithm 2.
           a_     :							Any     						=  4

           # running values
           a_     :							str     						=  []






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  int						,						lowercase__   :  Union[str, torch.device] = None       ):


           '''simple docstring'''

           a_     :							Any     						=  num_inference_steps
           a_     :							Optional[int]     						=  torch.linspace(1						,						0						,						num_inference_steps + 1       )[:-1]
           a_     :							Tuple     						=  torch.cat([steps, torch.tensor([0.0]       )]       )

           if self.config.trained_betas is not None:
                a_     :							int     						=  torch.tensor(self.config.trained_betas						,						dtype=torch.floataa       )
           else:
                a_     :							Optional[int]     						=  torch.sin(steps * math.pi / 2       ) ** 2

           a_     :							str     						=  (1.0 - self.betas**2) ** 0.5

           a_     :							Optional[int]     						=  (torch.atana(self.betas						,						self.alphas       ) / math.pi * 2)[:-1]
           a_     :							Any     						=  timesteps.to(lowercase__       )

           a_     :							Dict     						=  []






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  torch.FloatTensor						,						lowercase__   :  int						,						lowercase__   :  torch.FloatTensor						,						lowercase__   :  bool = True						,						):


           '''simple docstring'''

           if self.num_inference_steps is None:
                raise ValueError(
                    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

           a_     :							Tuple     						=  (self.timesteps == timestep).nonzero().item()
           a_     :							Any     						=  timestep_index + 1

           a_     :							Optional[int]     						=  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
           self.ets.append(lowercase__       )

           if len(self.ets       ) == 1:
                a_     :							Tuple     						=  self.ets[-1]
           elif len(self.ets       ) == 2:
                a_     :							Union[str, Any]     						=  (3 * self.ets[-1] - self.ets[-2]) / 2
           elif len(self.ets       ) == 3:
                a_     :							Optional[int]     						=  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
           else:
                a_     :							Dict     						=  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

           a_     :							List[str]     						=  self._get_prev_sample(lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )

           if not return_dict:
                return (prev_sample,)

           return SchedulerOutput(prev_sample=lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  torch.FloatTensor						,						*lowercase__   :  Dict						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           return sample






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  str						,						lowercase__   :  int						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Any     						=  self.alphas[timestep_index]
           a_     :							int     						=  self.betas[timestep_index]

           a_     :							List[Any]     						=  self.alphas[prev_timestep_index]
           a_     :							Optional[Any]     						=  self.betas[prev_timestep_index]

           a_     :							Any     						=  (sample - sigma * ets) / max(lowercase__						,						1e-8       )
           a_     :							Any     						=  next_alpha * pred + ets * next_sigma

           return prev_sample






      def __len__( self   :  List[str]       ):


           '''simple docstring'''

           return self.config.num_train_timesteps






'''simple docstring'''


import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowerCAmelCase_      :  List[str]				     =  'sshleifer/mar_enro_6_3_student'







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

           a_     :							Optional[int]     						=  cached_path(
               """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,						extract_compressed_file=lowercase__						,						)
           a_     :							List[Any]     						=  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """$MAX_LEN""": 64,
               """$BS""": 64,
               """$GAS""": 1,
               """$ENRO_DIR""": self.data_dir,
               """facebook/mbart-large-cc25""": MARIAN_MODEL,
               # "val_check_interval=0.25": "val_check_interval=1.0",
               """--learning_rate=3e-5""": """--learning_rate 3e-4""",
               """--num_train_epochs 6""": """--num_train_epochs 1""",
           }

           # Clean up bash script
           a_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           a_     :							List[str]     						=  F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
           # XXX: args.gpus > 1 : handle multi_gpu in the future

           a_     :							Dict     						=  ["""finetune.py"""] + bash_script.split() + args
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							List[Any]     						=  argparse.ArgumentParser()
                a_     :							int     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							Dict     						=  SummarizationModule.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[str]     						=  parser.parse_args()
                a_     :							List[Any]     						=  main(lowercase__       )

           # Check metrics
           a_     :							List[str]     						=  load_json(model.metrics_save_path       )
           a_     :							Any     						=  metrics["""val"""][0]
           a_     :							int     						=  metrics["""val"""][-1]
           self.assertEqual(len(metrics["""val"""]       )						,						(args.max_epochs / args.val_check_interval)       )
           assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]						,						lowercase__       )

           self.assertGreater(last_step_stats["""val_avg_gen_time"""]						,						0.01       )
           # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
           self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]						,						1.0       )

           # test learning requirements:

           # 1. BLEU improves over the course of training by more than 2 pts
           self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]						,						2       )

           # 2. BLEU finishes above 17
           self.assertGreater(last_step_stats["""val_avg_bleu"""]						,						17       )

           # 3. test BLEU and val BLEU within ~1.1 pt.
           self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]       )						,						1.1       )

           # check lightning ckpt can be loaded and has a reasonable statedict
           a_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Union[str, Any]     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Dict     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							Tuple     						=  """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
           assert expected_key in ckpt["state_dict"]
           assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

           # TODO: turn on args.do_predict when PL bug fixed.
           if args.do_predict:
                a_     :							int     						=  {os.path.basename(lowercase__       ) for p in contents}
                assert "test_generations.txt" in contents
                assert "test_results.txt" in contents
                # assert len(metrics["val"]) ==  desired_n_evals
                assert len(metrics["""test"""]       ) == 1







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      @timeout_decorator.timeout(600       )
      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--fp16_opt_level=O1""": """""",
               """$MAX_LEN""": 128,
               """$BS""": 16,
               """$GAS""": 1,
               """$ENRO_DIR""": data_dir,
               """$m""": """sshleifer/student_marian_en_ro_6_1""",
               """val_check_interval=0.25""": """val_check_interval=1.0""",
           }

           # Clean up bash script
           a_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

           for k, v in env_vars_to_replace.items():
                a_     :							Union[str, Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							List[str]     						=  self.get_auto_remove_tmp_dir()
           a_     :							List[str]     						=  bash_script.replace("""--fp16"""						,						""""""       )
           a_     :							str     						=  6
           a_     :							Union[str, Any]     						=  (
               ["""distillation.py"""]
               + bash_script.split()
               + [
                   F"--output_dir={output_dir}",
                   """--gpus=1""",
                   """--learning_rate=1e-3""",
                   F"--num_train_epochs={epochs}",
                   """--warmup_steps=10""",
                   """--val_check_interval=1.0""",
                   """--do_predict""",
               ]
           )
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							Dict     						=  argparse.ArgumentParser()
                a_     :							Tuple     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							int     						=  SummarizationDistiller.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[Any]     						=  parser.parse_args()
                # assert args.gpus == gpus THIS BREAKS for multi_gpu

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

           # Check metrics
           a_     :							Union[str, Any]     						=  load_json(model.metrics_save_path       )
           a_     :							List[str]     						=  metrics["""val"""][0]
           a_     :							Optional[Any]     						=  metrics["""val"""][-1]
           assert len(metrics["""val"""]       ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

           assert last_step_stats["val_avg_gen_time"] >= 0.01

           assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
           assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
           assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]						,						lowercase__       )

           # check lightning ckpt can be loaded and has a reasonable statedict
           a_     :							Optional[int]     						=  os.listdir(lowercase__       )
           a_     :							Dict     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Optional[Any]     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							List[str]     						=  """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
           assert expected_key in ckpt["state_dict"]
           assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

           # TODO: turn on args.do_predict when PL bug fixed.
           if args.do_predict:
                a_     :							int     						=  {os.path.basename(lowercase__       ) for p in contents}
                assert "test_generations.txt" in contents
                assert "test_results.txt" in contents
                # assert len(metrics["val"]) ==  desired_n_evals
                assert len(metrics["""test"""]       ) == 1
