

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


UpperCAmelCase__       :   List[Any] 			=			"sshleifer/mar_enro_6_3_student"







class 				a__				(      A__				):







  """simple docstring"""




  def        _lowercase					(	self			:       Tuple						)    ->Tuple:
        """simple docstring"""







        super().setUp()

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


  @slow
  @require_torch_gpu
  def        _lowercase					(	self			:       List[Any]						)    ->str:
        """simple docstring"""







        MarianMTModel.from_pretrained(_a						)




  @slow
  @require_torch_gpu
  def        _lowercase					(	self			:       int						)    ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[str]        =      {
            """$MAX_LEN""": 6_4,
            """$BS""": 6_4,
            """$GAS""": 1,
            """$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
        SCREAMING_SNAKE_CASE       :					Any        =      (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""						)[1].strip()
        SCREAMING_SNAKE_CASE       :					List[Any]        =      bash_script.replace("""\\\n""" , """"""						).strip().replace("""\"$@\"""" , """"""						)
        for k, v in env_vars_to_replace.items():
              SCREAMING_SNAKE_CASE       :					List[Any]        =      bash_script.replace(_a , str(_a						)						)
        SCREAMING_SNAKE_CASE       :					int        =      self.get_auto_remove_tmp_dir()

        # bash_script = bash_script.replace("--fp16 ", "")
        SCREAMING_SNAKE_CASE       :					Optional[Any]        =      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

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

        # Check metrics
        SCREAMING_SNAKE_CASE       :					List[str]        =      load_json(model.metrics_save_path						)
        SCREAMING_SNAKE_CASE       :					Tuple        =      metrics["""val"""][0]
        SCREAMING_SNAKE_CASE       :					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}"] , _a						)

        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"""] , 1_7						)

        # 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
        SCREAMING_SNAKE_CASE       :					Dict        =      os.listdir(_a						)
        SCREAMING_SNAKE_CASE       :					Dict        =      [x for x in contents if x.endswith(""".ckpt"""						)][0]
        SCREAMING_SNAKE_CASE       :					Dict        =      os.path.join(args.output_dir , _a						)
        SCREAMING_SNAKE_CASE       :					Optional[Any]        =      torch.load(_a , map_location="""cpu"""						)
        SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      """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:
              SCREAMING_SNAKE_CASE       :					Optional[int]        =      {os.path.basename(_a						) 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 				a__				(      A__				):







  """simple docstring"""




  @timeout_decorator.timeout(6_0_0						)
  @slow
  @require_torch_gpu
  def        _lowercase					(	self			:       List[str]						)    ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					str        =      f"{self.test_file_dir_str}/test_data/wmt_en_ro"
        SCREAMING_SNAKE_CASE       :					Optional[Any]        =      {
            """--fp16_opt_level=O1""": """""",
            """$MAX_LEN""": 1_2_8,
            """$BS""": 1_6,
            """$GAS""": 1,
            """$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
        SCREAMING_SNAKE_CASE       :					str        =      (
            (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""						)[1].strip()
        )
        SCREAMING_SNAKE_CASE       :					Optional[int]        =      bash_script.replace("""\\\n""" , """"""						).strip().replace("""\"$@\"""" , """"""						)
        SCREAMING_SNAKE_CASE       :					List[str]        =      bash_script.replace("""--fp16 """ , """ """						)

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

              SCREAMING_SNAKE_CASE       :					int        =      distill_main(_a						)

        # Check metrics
        SCREAMING_SNAKE_CASE       :					Optional[int]        =      load_json(model.metrics_save_path						)
        SCREAMING_SNAKE_CASE       :					Dict        =      metrics["""val"""][0]
        SCREAMING_SNAKE_CASE       :					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}"] , _a						)

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







'''simple docstring'''
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
