

import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_lowerCamelCase							=logging.getLogger()
_lowerCamelCase							=torch.cuda.is_available()
_lowerCamelCase							={
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1e-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		='\n'.join(lowerCAmelCase_     )
     Path(lowerCAmelCase_     ).open('w'     ).writelines(lowerCAmelCase_     )


_lowerCamelCase							=[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCamelCase							=["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCamelCase							="patrickvonplaten/t5-tiny-random"
_lowerCamelCase							="sshleifer/t5-tinier-random"
_lowerCamelCase							="sshleifer/bart-tiny-random"
_lowerCamelCase							="sshleifer/tiny-mbart"
_lowerCamelCase							="sshleifer/tiny-marian-en-de"
_lowerCamelCase							="stas/tiny-wmt19-en-de"


_lowerCamelCase							=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(lowerCAmelCase_,       F'{split}.source'     ),       lowerCAmelCase_     )
          _dump_articles(os.path.join(lowerCAmelCase_,       F'{split}.target'     ),       lowerCAmelCase_     )
     return tmp_dir



class 						a_			(				lowerCamelCase_	):



      """simple docstring"""







      @classmethod
      def 			_lowerCAmelCase	( cls	:				Optional[int]							):
           logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
           return cls
      @slow
      @require_torch_gpu
      def 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=list_models()
           SCREAMING_SNAKE_CASE 		='sshleifer'
           SCREAMING_SNAKE_CASE 		=[x.modelId for x in model_list if x.modelId.startswith(snake_case							)]
           SCREAMING_SNAKE_CASE 		=['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
           SCREAMING_SNAKE_CASE 		=[]
           for m in model_ids:
                if m in allowed_to_be_broken:
                     continue
                try:
                     AutoConfig.from_pretrained(snake_case							)
                except Exception:
                     failures.append(snake_case							)
           assert not failures, f'The following models could not be loaded through AutoConfig: {failures}'
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		={'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
           self._test_distiller_cli(snake_case							)
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		={
               'student_encoder_layers': 2,
               'student_decoder_layers': 1,
               'max_epochs': 4,
               'val_check_interval': 0.25,
               'alpha_hid': 2.0,
               'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
           }
           SCREAMING_SNAKE_CASE 		=self._test_distiller_cli(snake_case     ,check_contents=snake_case							)

           SCREAMING_SNAKE_CASE 		=list(Path(model.output_dir							).glob('*.ckpt'							)							)
           self.assertEqual(1     ,len(snake_case							)							)
           SCREAMING_SNAKE_CASE 		=list(Path(model.output_dir							).glob('**/*.bin'							)							)
           self.assertEqual(len(snake_case							)     ,2							)
           SCREAMING_SNAKE_CASE 		=lmap(str.strip     ,Path(model.hparams.data_dir							).joinpath('test.source'							).open().readlines()							)
           SCREAMING_SNAKE_CASE 		=tempfile.mktemp()  # XXX: not being cleaned up
           generate_summaries_or_translations(snake_case     ,snake_case     ,str(model.output_dir / 'best_tfmr'							)							)
           self.assertTrue(Path(snake_case							).exists()							)

           SCREAMING_SNAKE_CASE 		=self.get_auto_remove_tmp_dir()
           convert_pl_to_hf(ckpts[0]     ,transformer_ckpts[0].parent     ,snake_case							)
           assert os.path.exists(os.path.join(snake_case     ,'pytorch_model.bin'							)							)
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		=AutoModelForSeqaSeqLM.from_pretrained(snake_case							)
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
           SCREAMING_SNAKE_CASE 		=torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]     ,dtype=torch.long     ,device=model.device							)
           SCREAMING_SNAKE_CASE 		=target_ids[:, :-1].contiguous()  # Why this line?
           SCREAMING_SNAKE_CASE 		=target_ids[:, 1:].clone()  # why clone?
           SCREAMING_SNAKE_CASE 		=model(
               snake_case     ,attention_mask=snake_case     ,decoder_input_ids=snake_case     ,labels=snake_case     ,use_cache=snake_case							).loss

           SCREAMING_SNAKE_CASE 		=model(snake_case     ,attention_mask=snake_case     ,decoder_input_ids=snake_case     ,use_cache=snake_case							).logits

           SCREAMING_SNAKE_CASE 		=nn.functional.log_softmax(snake_case     ,dim=-1							)
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=label_smoothed_nll_loss(
               snake_case     ,snake_case     ,0.1     ,ignore_index=model.config.pad_token_id							)
           with self.assertRaises(snake_case							):
                # TODO: understand why this breaks
                self.assertEqual(snake_case     ,snake_case							)
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		={
               'student_encoder_layers': 2,
               'student_decoder_layers': 1,
               'num_train_epochs': 4,
               'val_check_interval': 0.25,
               'alpha_hid': 2.0,
               'task': 'translation',
               'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
               'tokenizer_name': MBART_TINY,
               'teacher': MBART_TINY,
               'src_lang': 'en_XX',
               'tgt_lang': 'ro_RO',
           }
           SCREAMING_SNAKE_CASE 		=self._test_distiller_cli(snake_case     ,check_contents=snake_case							)
           assert model.model.config.model_type == "mbart"

           SCREAMING_SNAKE_CASE 		=list(Path(model.output_dir							).glob('*.ckpt'							)							)
           self.assertEqual(1     ,len(snake_case							)							)
           SCREAMING_SNAKE_CASE 		=list(Path(model.output_dir							).glob('**/*.bin'							)							)
           SCREAMING_SNAKE_CASE 		=list(Path(model.output_dir							).glob('best_tfmr/*'							)							)
           assert len(snake_case							) > 2
           self.assertEqual(len(snake_case							)     ,2							)
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		={
               'student_encoder_layers': 1,
               'student_decoder_layers': 1,
               'alpha_hid': 2.0,
               'teacher': T5_TINY,
               'model_name_or_path': T5_TINY,
               'tokenizer_name': T5_TINY,
           }
           self._test_distiller_cli(snake_case							)
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		={
               'teacher': T5_TINY,
               'student': T5_TINIER,
               'model_name_or_path': T5_TINIER,
               'tokenizer_name': T5_TINIER,
           }
           self._test_distiller_cli(snake_case							)


      def 			_lowerCAmelCase	( self	:				str     ,snake_case	:				Tuple     ,snake_case	:				int=True							):
           SCREAMING_SNAKE_CASE 		={
               'label_smoothing': 0.0,
               'early_stopping_patience': -1,
               'train_batch_size': 1,
               'eval_batch_size': 2,
               'max_epochs': 2,
               'alpha_mlm': 0.2,
               'alpha_ce': 0.8,
               'do_predict': True,
               'model_name_or_path': 'sshleifer/tinier_bart',
               'teacher': CHEAP_ARGS['model_name_or_path'],
               'val_check_interval': 0.5,
           }
           default_updates.update(snake_case							)
           SCREAMING_SNAKE_CASE 		=CHEAP_ARGS.copy()
           SCREAMING_SNAKE_CASE 		=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
           SCREAMING_SNAKE_CASE 		=self.get_auto_remove_tmp_dir()

           args_d.update(data_dir=snake_case     ,output_dir=snake_case     ,**snake_case							)
           SCREAMING_SNAKE_CASE 		=distill_main(argparse.Namespace(**snake_case							)							)
           if not check_contents:
                return model
           SCREAMING_SNAKE_CASE 		=os.listdir(snake_case							)
           SCREAMING_SNAKE_CASE 		={os.path.basename(snake_case							) for p in contents}
           SCREAMING_SNAKE_CASE 		=[p for p in contents if p.endswith('ckpt'							)]
           assert len(snake_case							) > 0

           self.assertIn('test_generations.txt'     ,snake_case							)
           self.assertIn('test_results.txt'     ,snake_case							)

           SCREAMING_SNAKE_CASE 		=load_json(model.metrics_save_path							)
           SCREAMING_SNAKE_CASE 		=metrics['val'][-1]
           self.assertGreaterEqual(last_step_stats['val_avg_gen_time']     ,0.01							)
           self.assertGreaterEqual(1.0     ,last_step_stats['val_avg_gen_time']							)
           self.assertIsInstance(last_step_stats[f'val_avg_{model.val_metric}']     ,snake_case							)
           SCREAMING_SNAKE_CASE 		=int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1							)
           self.assertEqual(len(metrics['val']							)     ,snake_case							)
           self.assertEqual(len(metrics['test']							)     ,1							)
           return model







class 						a_			(				lowerCamelCase_	):



      """simple docstring"""







      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]     ,)
      def 			_lowerCAmelCase	( self	:				List[str]     ,snake_case	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		=CHEAP_ARGS.copy()
           SCREAMING_SNAKE_CASE 		='translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
           SCREAMING_SNAKE_CASE 		=0.1 if task == 'translation' else 0

           SCREAMING_SNAKE_CASE 		=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
           SCREAMING_SNAKE_CASE 		=self.get_auto_remove_tmp_dir()
           args_d.update(
               data_dir=snake_case     ,model_name_or_path=snake_case     ,tokenizer_name=snake_case     ,train_batch_size=2     ,eval_batch_size=2     ,output_dir=snake_case     ,do_predict=snake_case     ,task=snake_case     ,src_lang='en_XX'     ,tgt_lang='ro_RO'     ,freeze_encoder=snake_case     ,freeze_embeds=snake_case     ,)
           assert "n_train" in args_d
           SCREAMING_SNAKE_CASE 		=argparse.Namespace(**snake_case							)
           SCREAMING_SNAKE_CASE 		=main(snake_case							)

           SCREAMING_SNAKE_CASE 		=module.model.get_input_embeddings()
           assert not input_embeds.weight.requires_grad
           if model == T5_TINY:
                SCREAMING_SNAKE_CASE 		=module.model.lm_head
                assert not lm_head.weight.requires_grad
                assert (lm_head.weight == input_embeds.weight).all().item()
           elif model == FSMT_TINY:
                SCREAMING_SNAKE_CASE 		=module.model.model
                SCREAMING_SNAKE_CASE 		=fsmt.decoder.embed_positions
                assert not embed_pos.weight.requires_grad
                assert not fsmt.decoder.embed_tokens.weight.requires_grad
                # check that embeds are not the same
                assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
           else:
                SCREAMING_SNAKE_CASE 		=module.model.model
                SCREAMING_SNAKE_CASE 		=bart.decoder.embed_positions
                assert not embed_pos.weight.requires_grad
                assert not bart.shared.weight.requires_grad
                # check that embeds are the same
                assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
                assert bart.decoder.embed_tokens == bart.shared

           SCREAMING_SNAKE_CASE 		=load_json(module.output_dir / 'text_batch.json'							)
           assert isinstance(snake_case     ,snake_case							)
           assert len(snake_case							) >= 4
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE 		=CHEAP_ARGS.copy()

           SCREAMING_SNAKE_CASE 		='summarization'
           SCREAMING_SNAKE_CASE 		=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

           args_d.update(
               data_dir=snake_case     ,tokenizer_name=snake_case     ,train_batch_size=2     ,eval_batch_size=2     ,do_predict=snake_case     ,task=snake_case     ,src_lang='en_XX'     ,tgt_lang='ro_RO'     ,freeze_encoder=snake_case     ,freeze_embeds=snake_case     ,)

           # test models whose config includes the extra_model_args
           SCREAMING_SNAKE_CASE 		=BART_TINY
           SCREAMING_SNAKE_CASE 		=self.get_auto_remove_tmp_dir()
           SCREAMING_SNAKE_CASE 		=args_d.copy()
           args_da.update(
               model_name_or_path=snake_case     ,output_dir=snake_case     ,)
           SCREAMING_SNAKE_CASE 		=('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
           for p in extra_model_params:
                SCREAMING_SNAKE_CASE 		=0.5
           SCREAMING_SNAKE_CASE 		=argparse.Namespace(**snake_case							)
           SCREAMING_SNAKE_CASE 		=main(snake_case							)
           for p in extra_model_params:
                assert getattr(model.config     ,snake_case							) == 0.5, f'failed to override the model config for param {p}'

           # test models whose config doesn't include the extra_model_args
           SCREAMING_SNAKE_CASE 		=T5_TINY
           SCREAMING_SNAKE_CASE 		=self.get_auto_remove_tmp_dir()
           SCREAMING_SNAKE_CASE 		=args_d.copy()
           args_da.update(
               model_name_or_path=snake_case     ,output_dir=snake_case     ,)
           SCREAMING_SNAKE_CASE 		='encoder_layerdrop'
           SCREAMING_SNAKE_CASE 		=0.5
           SCREAMING_SNAKE_CASE 		=argparse.Namespace(**snake_case							)
           with pytest.raises(snake_case							) as excinfo:
                SCREAMING_SNAKE_CASE 		=main(snake_case							)
           assert str(excinfo.value							) == f'model config doesn\'t have a `{unsupported_param}` attribute'


      def 			_lowerCAmelCase	( self	:				Any							):
           SCREAMING_SNAKE_CASE 		=CHEAP_ARGS.copy()

           SCREAMING_SNAKE_CASE 		='summarization'
           SCREAMING_SNAKE_CASE 		=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

           SCREAMING_SNAKE_CASE 		=BART_TINY
           SCREAMING_SNAKE_CASE 		=self.get_auto_remove_tmp_dir()

           args_d.update(
               data_dir=snake_case     ,model_name_or_path=snake_case     ,output_dir=snake_case     ,tokenizer_name=snake_case     ,train_batch_size=2     ,eval_batch_size=2     ,do_predict=snake_case     ,task=snake_case     ,src_lang='en_XX'     ,tgt_lang='ro_RO'     ,freeze_encoder=snake_case     ,freeze_embeds=snake_case     ,)

           # emulate finetune.py
           SCREAMING_SNAKE_CASE 		=argparse.ArgumentParser()
           SCREAMING_SNAKE_CASE 		=pl.Trainer.add_argparse_args(snake_case							)
           SCREAMING_SNAKE_CASE 		=SummarizationModule.add_model_specific_args(snake_case     ,os.getcwd()							)
           SCREAMING_SNAKE_CASE 		={'--help': True}

           # --help test
           with pytest.raises(snake_case							) as excinfo:
                with CaptureStdout() as cs:
                     SCREAMING_SNAKE_CASE 		=parser.parse_args(snake_case							)
                assert False, "--help is expected to sys.exit"
           assert excinfo.type == SystemExit
           SCREAMING_SNAKE_CASE 		=lightning_base.arg_to_scheduler_metavar
           assert expected in cs.out, "--help is expected to list the supported schedulers"

           # --lr_scheduler=non_existing_scheduler test
           SCREAMING_SNAKE_CASE 		='non_existing_scheduler'
           SCREAMING_SNAKE_CASE 		={f'--lr_scheduler={unsupported_param}'}
           with pytest.raises(snake_case							) as excinfo:
                with CaptureStderr() as cs:
                     SCREAMING_SNAKE_CASE 		=parser.parse_args(snake_case							)
                assert False, "invalid argument is expected to sys.exit"
           assert excinfo.type == SystemExit
           SCREAMING_SNAKE_CASE 		=f'invalid choice: \'{unsupported_param}\''
           assert expected in cs.err, f'should have bailed on invalid choice of scheduler {unsupported_param}'

           # --lr_scheduler=existing_scheduler test
           SCREAMING_SNAKE_CASE 		='cosine'
           SCREAMING_SNAKE_CASE 		=args_d.copy()
           SCREAMING_SNAKE_CASE 		=supported_param
           SCREAMING_SNAKE_CASE 		=argparse.Namespace(**snake_case							)
           SCREAMING_SNAKE_CASE 		=main(snake_case							)
           assert (
               getattr(model.hparams     ,'lr_scheduler'							) == supported_param
           ), f'lr_scheduler={supported_param} shouldn\'t fail'




import math



class 						a_			:



      """simple docstring"""







      def __init__( self	:				str     ,snake_case	:				List[Any]     ,snake_case	:				Union[str, Any]     ,snake_case	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		=x
           SCREAMING_SNAKE_CASE 		=y
           SCREAMING_SNAKE_CASE 		=z


      def __repr__( self	:				Any							):
           return f'Point({self.x}, {self.y}, {self.z})'







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2     )     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

