


'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCAmelCase    :List[Any]				       =     logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

__UpperCAmelCase    :int				       =     list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__UpperCAmelCase    :Optional[int]				       =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def 			_a			(		_lowercase      :			str						):





     '''simple docstring'''






     with open(_lowercase    ,      '''rb'''						) as f:
          __UpperCAmelCase			:				Tuple													=    Image.open(_lowercase						)
          return im.convert('''RGB'''						)



@dataclass
class 			a       :


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={
             "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
         }			,						)
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(default=_a			,						metadata={"help": "A folder containing the training data."} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(default=_a			,						metadata={"help": "A folder containing the validation data."} )
     SCREAMING_SNAKE_CASE :				Optional[float]							     =     field(
         default=0.15			,						metadata={"help": "Percent to split off of train for validation."} )
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     field(
         default=_a			,						metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }			,						)
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     field(
         default=_a			,						metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }			,						)

     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[Any]:
          if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
               raise ValueError(
                   '''You must specify either a dataset name from the hub or a train and/or validation directory.'''    )



@dataclass
class 			a       :


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				str							     =     field(
         default="google/vit-base-patch16-224-in21k"			,						metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			,						)
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_a )}			,						)
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "Pretrained config name or path if not the same as model_name"} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
     SCREAMING_SNAKE_CASE :				str							     =     field(
         default="main"			,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,						)
     SCREAMING_SNAKE_CASE :				str							     =     field(default=_a			,						metadata={"help": "Name or path of preprocessor config."} )
     SCREAMING_SNAKE_CASE :				bool							     =     field(
         default=_a			,						metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }			,						)
     SCREAMING_SNAKE_CASE :				bool							     =     field(
         default=_a			,						metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}			,						)




def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    torch.stack([example['''pixel_values'''] for example in examples]						)
     __UpperCAmelCase			:				Union[str, Any]													=    torch.tensor([example['''labels'''] for example in examples]						)
     return {"pixel_values": pixel_values, "labels": labels}




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     if len(sys.argv						) == 2 and sys.argv[1].endswith('''.json'''						):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('''run_image_classification'''    ,      _lowercase    ,      _lowercase						)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,      datefmt='''%m/%d/%Y %H:%M:%S'''    ,      handlers=[logging.StreamHandler(sys.stdout						)]    ,      )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     __UpperCAmelCase			:				Tuple													=    training_args.get_process_log_level()
     logger.setLevel(_lowercase						)
     transformers.utils.logging.set_verbosity(_lowercase						)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + F'distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}'						)
     logger.info(F'Training/evaluation parameters {training_args}'						)

     # Detecting last checkpoint.
     __UpperCAmelCase			:				Any													=    None
     if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
          __UpperCAmelCase			:				Optional[int]													=    get_last_checkpoint(training_args.output_dir						)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
               raise ValueError(
                   F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   '''Use --overwrite_output_dir to overcome.'''						)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''						)

    # Set seed before initializing model.
     set_seed(training_args.seed						)

     # Initialize our dataset and prepare it for the 'image-classification' task.
     if data_args.dataset_name is not None:
          __UpperCAmelCase			:				Union[str, Any]													=    load_dataset(
              data_args.dataset_name    ,      data_args.dataset_config_name    ,      cache_dir=model_args.cache_dir    ,      task='''image-classification'''    ,      use_auth_token=True if model_args.use_auth_token else None    ,      )
     else:
          __UpperCAmelCase			:				List[str]													=    {}
          if data_args.train_dir is not None:
               __UpperCAmelCase			:				Optional[Any]													=    os.path.join(data_args.train_dir    ,      '''**'''						)
          if data_args.validation_dir is not None:
               __UpperCAmelCase			:				Tuple													=    os.path.join(data_args.validation_dir    ,      '''**'''						)
          __UpperCAmelCase			:				Tuple													=    load_dataset(
              '''imagefolder'''    ,      data_files=_lowercase    ,      cache_dir=model_args.cache_dir    ,      task='''image-classification'''    ,      )

     # If we don't have a validation split, split off a percentage of train as validation.
     __UpperCAmelCase			:				str													=    None if '''validation''' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split    ,      _lowercase						) and data_args.train_val_split > 0.0:
          __UpperCAmelCase			:				str													=    dataset['''train'''].train_test_split(data_args.train_val_split						)
          __UpperCAmelCase			:				Union[str, Any]													=    split['''train''']
          __UpperCAmelCase			:				Union[str, Any]													=    split['''test''']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     __UpperCAmelCase			:				Optional[int]													=    dataset['''train'''].features['''labels'''].names
     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    {}, {}
     for i, label in enumerate(_lowercase						):
          __UpperCAmelCase			:				Tuple													=    str(_lowercase						)
          __UpperCAmelCase			:				List[str]													=    label

     # Load the accuracy metric from the datasets package
     __UpperCAmelCase			:				Dict													=    evaluate.load('''accuracy'''						)

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(_lowercase      :			List[Any]						):
          return metric.compute(predictions=np.argmax(p.predictions    ,      axis=1						)    ,      references=p.label_ids						)

     __UpperCAmelCase			:				Union[str, Any]													=    AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path    ,      num_labels=len(_lowercase						)    ,      labelaid=_lowercase    ,      idalabel=_lowercase    ,      finetuning_task='''image-classification'''    ,      cache_dir=model_args.cache_dir    ,      revision=model_args.model_revision    ,      use_auth_token=True if model_args.use_auth_token else None    ,      )
     __UpperCAmelCase			:				Tuple													=    AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path    ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)    ,      config=_lowercase    ,      cache_dir=model_args.cache_dir    ,      revision=model_args.model_revision    ,      use_auth_token=True if model_args.use_auth_token else None    ,      ignore_mismatched_sizes=model_args.ignore_mismatched_sizes    ,      )
     __UpperCAmelCase			:				Any													=    AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir    ,      revision=model_args.model_revision    ,      use_auth_token=True if model_args.use_auth_token else None    ,      )

     # Define torchvision transforms to be applied to each image.
     if "shortest_edge" in image_processor.size:
          __UpperCAmelCase			:				int													=    image_processor.size['''shortest_edge''']
     else:
          __UpperCAmelCase			:				Dict													=    (image_processor.size['''height'''], image_processor.size['''width'''])
     __UpperCAmelCase			:				Tuple													=    Normalize(mean=image_processor.image_mean    ,      std=image_processor.image_std						)
     __UpperCAmelCase			:				List[Any]													=    Compose(
         [
             RandomResizedCrop(_lowercase						),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]						)
     __UpperCAmelCase			:				str													=    Compose(
         [
             Resize(_lowercase						),
             CenterCrop(_lowercase						),
             ToTensor(),
             normalize,
         ]						)

     def train_transforms(_lowercase      :			Optional[int]						):
          __UpperCAmelCase			:				int													=    [
              _train_transforms(pil_img.convert('''RGB'''						)						) for pil_img in example_batch['''image''']
          ]
          return example_batch

     def val_transforms(_lowercase      :			Dict						):
          __UpperCAmelCase			:				str													=    [_val_transforms(pil_img.convert('''RGB'''						)						) for pil_img in example_batch['''image''']]
          return example_batch

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('''--do_train requires a train dataset'''						)
          if data_args.max_train_samples is not None:
               __UpperCAmelCase			:				List[Any]													=    (
                   dataset['''train'''].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
               )
          # Set the training transforms
          dataset["train"].set_transform(_lowercase						)

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('''--do_eval requires a validation dataset'''						)
          if data_args.max_eval_samples is not None:
               __UpperCAmelCase			:				Tuple													=    (
                   dataset['''validation'''].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(_lowercase						)

     # Initalize our trainer
     __UpperCAmelCase			:				List[Any]													=    Trainer(
         model=_lowercase    ,      args=_lowercase    ,      train_dataset=dataset['''train'''] if training_args.do_train else None    ,      eval_dataset=dataset['''validation'''] if training_args.do_eval else None    ,      compute_metrics=_lowercase    ,      tokenizer=_lowercase    ,      data_collator=_lowercase    ,      )

     # Training
     if training_args.do_train:
          __UpperCAmelCase			:				Tuple													=    None
          if training_args.resume_from_checkpoint is not None:
               __UpperCAmelCase			:				Dict													=    training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               __UpperCAmelCase			:				Union[str, Any]													=    last_checkpoint
          __UpperCAmelCase			:				Optional[int]													=    trainer.train(resume_from_checkpoint=_lowercase						)
          trainer.save_model()
          trainer.log_metrics('''train'''    ,      train_result.metrics						)
          trainer.save_metrics('''train'''    ,      train_result.metrics						)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          __UpperCAmelCase			:				Union[str, Any]													=    trainer.evaluate()
          trainer.log_metrics('''eval'''    ,      _lowercase						)
          trainer.save_metrics('''eval'''    ,      _lowercase						)

     # Write model card and (optionally) push to hub
     __UpperCAmelCase			:				List[Any]													=    {
         '''finetuned_from''': model_args.model_name_or_path,
         '''tasks''': '''image-classification''',
         '''dataset''': data_args.dataset_name,
         '''tags''': ['''image-classification''', '''vision'''],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_lowercase						)
     else:
          trainer.create_model_card(**_lowercase						)


if __name__ == "__main__":
      main()



'''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


__UpperCAmelCase    :Any				       =     "sshleifer/mar_enro_6_3_student"



class 			a       (	_a ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          super().setUp()

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

     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Optional[Any]:
          MarianMTModel.from_pretrained(snake_case    )






     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							int:
          __UpperCAmelCase			:				Optional[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
          __UpperCAmelCase			:				Tuple													=    (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''    )[1].strip()
          __UpperCAmelCase			:				List[Any]													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          for k, v in env_vars_to_replace.items():
               __UpperCAmelCase			:				Any													=    bash_script.replace(snake_case							,				str(snake_case    )    )
          __UpperCAmelCase			:				str													=    self.get_auto_remove_tmp_dir()

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

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

          # Check metrics
          __UpperCAmelCase			:				Optional[int]													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				int													=    metrics['''val'''][0]
          __UpperCAmelCase			:				List[Any]													=    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}']							,				snake_case    )

          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
          __UpperCAmelCase			:				Any													=    os.listdir(snake_case    )
          __UpperCAmelCase			:				Tuple													=    [x for x in contents if x.endswith('''.ckpt'''    )][0]
          __UpperCAmelCase			:				Optional[int]													=    os.path.join(args.output_dir							,				snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    torch.load(snake_case							,				map_location='''cpu'''    )
          __UpperCAmelCase			:				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:
               __UpperCAmelCase			:				Union[str, Any]													=    {os.path.basename(snake_case    ) 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(600    )
     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Any													=    f'{self.test_file_dir_str}/test_data/wmt_en_ro'
          __UpperCAmelCase			:				List[str]													=    {
              '''--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
          __UpperCAmelCase			:				str													=    (
              (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''    )[1].strip()
          )
          __UpperCAmelCase			:				Dict													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          __UpperCAmelCase			:				Tuple													=    bash_script.replace('''--fp16 '''							,				''' '''    )

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

               __UpperCAmelCase			:				str													=    distill_main(snake_case    )

          # Check metrics
          __UpperCAmelCase			:				int													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				List[str]													=    metrics['''val'''][0]
          __UpperCAmelCase			:				Tuple													=    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}']							,				snake_case    )

          # check lightning ckpt can be loaded and has a reasonable statedict
          __UpperCAmelCase			:				Tuple													=    os.listdir(snake_case    )
          __UpperCAmelCase			:				Tuple													=    [x for x in contents if x.endswith('''.ckpt'''    )][0]
          __UpperCAmelCase			:				int													=    os.path.join(args.output_dir							,				snake_case    )
          __UpperCAmelCase			:				Dict													=    torch.load(snake_case							,				map_location='''cpu'''    )
          __UpperCAmelCase			:				Dict													=    '''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:
               __UpperCAmelCase			:				int													=    {os.path.basename(snake_case    ) 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