


import logging
import os

from .state import PartialState






class        __snake_case	(       logging.LoggerAdapter):







    """simple docstring"""



    @staticmethod
    def __lowercase       (       lowerCamelCase   :  str		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Tuple      =       PartialState()
      return not main_process_only or (main_process_only and state.is_main_process)





    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  List[Any]   ,						*lowerCamelCase   :  Optional[Any]   ,						**lowerCamelCase   :  Optional[Any]		)       ->  List[Any]:




      if PartialState._shared_state == {}:
        raise RuntimeError(
            """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""		)
      lowerCAmelCase_      :				List[Any]      =       kwargs.pop("""main_process_only"""   ,						a__		)
      lowerCAmelCase_      :				Any      =       kwargs.pop("""in_order"""   ,						a__		)

      if self.isEnabledFor(a__		):
        if self._should_log(a__		):
          lowerCAmelCase_,							lowerCAmelCase_      :				Union[str, Any]      =       self.process(a__   ,						a__		)
          self.logger.log(a__   ,						a__   ,						*a__   ,						**a__		)

        elif in_order:
          lowerCAmelCase_      :				List[str]      =       PartialState()
          for i in range(state.num_processes		):
            if i == state.process_index:
              lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       self.process(a__   ,						a__		)
              self.logger.log(a__   ,						a__   ,						*a__   ,						**a__		)
            state.wait_for_everyone()







def     UpperCamelCase_      (   A__ :       str							,   A__ :       str = None ):
  '''simple docstring'''





  if log_level is None:
    lowerCAmelCase_      :				Tuple      =       os.environ.get("""ACCELERATE_LOG_LEVEL"""							,   A__ )
  lowerCAmelCase_      :				Any      =       logging.getLogger(A__ )
  if log_level is not None:
    logger.setLevel(log_level.upper() )
    logger.root.setLevel(log_level.upper() )
  return MultiProcessAdapter(A__							,   {} )







'''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


__A :  Tuple    							=				"sshleifer/mar_enro_6_3_student"






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      super().setUp()

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

    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  str		)       ->  str:
      MarianMTModel.from_pretrained(lowerCamelCase		)





    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       {
          """$MAX_LEN""": 64,
          """$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
      lowerCAmelCase_      :				Dict      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
      lowerCAmelCase_      :				Optional[int]      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				Optional[Any]      =       self.get_auto_remove_tmp_dir()

      # bash_script = bash_script.replace("--fp16 ", "")
      lowerCAmelCase_      :				Tuple      =       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

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

      # Check metrics
      lowerCAmelCase_      :				int      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Optional[Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Tuple      =       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}']   ,						lowerCamelCase		)

      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
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				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:
        lowerCAmelCase_      :				List[str]      =       {os.path.basename(lowerCamelCase		) 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        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @timeout_decorator.timeout(6_00		)
    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       F'{self.test_file_dir_str}/test_data/wmt_en_ro'
      lowerCAmelCase_      :				Dict      =       {
          """--fp16_opt_level=O1""": """""",
          """$MAX_LEN""": 1_28,
          """$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
      lowerCAmelCase_      :				int      =       (
          (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
      )
      lowerCAmelCase_      :				str      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      lowerCAmelCase_      :				Tuple      =       bash_script.replace("""--fp16 """   ,						""" """		)

      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				int      =       self.get_auto_remove_tmp_dir()
      lowerCAmelCase_      :				Optional[Any]      =       bash_script.replace("""--fp16"""   ,						""""""		)
      lowerCAmelCase_      :				Dict      =       6
      lowerCAmelCase_      :				List[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(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Dict      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				int      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationDistiller.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				List[Any]      =       parser.parse_args()
        # assert args.gpus == gpus THIS BREAKS for multi_gpu

        lowerCAmelCase_      :				str      =       distill_main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				Union[str, Any]      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Union[str, 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}']   ,						lowerCamelCase		)

      # check lightning ckpt can be loaded and has a reasonable statedict
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Optional[int]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				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:
        lowerCAmelCase_      :				Union[str, Any]      =       {os.path.basename(lowerCamelCase		) 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



