






'''simple docstring'''







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)

__A      :       Dict      =	logging.getLogger()
__A      :       str      =	torch.cuda.is_available()
__A      :       Union[str, Any]      =	{
    '''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''': 4_2,
    '''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-0_8,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''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 						__UpperCAmelCase							(     snake_case__							,			snake_case__							):
      __A		   =      '\n'.join(_UpperCAmelCase							)
      Path(_UpperCAmelCase							).open("w"							).writelines(_UpperCAmelCase							)


__A      :       Optional[int]      =	[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__A      :       Dict      =	['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__A      :       str      =	'''patrickvonplaten/t5-tiny-random'''
__A      :       int      =	'''sshleifer/t5-tinier-random'''
__A      :       Optional[int]      =	'''sshleifer/bart-tiny-random'''
__A      :       List[str]      =	'''sshleifer/tiny-mbart'''
__A      :       int      =	'''sshleifer/tiny-marian-en-de'''
__A      :       Tuple      =	'''stas/tiny-wmt19-en-de'''


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


def 						__UpperCAmelCase							(     snake_case__							):






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






class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     @classmethod
     def     _UpperCamelCase   (cls    :	Optional[int]					)  ->       Any:
           logging.disable(logging.CRITICAL					)  # remove noisy download output from tracebacks
           return cls






     @slow
     @require_torch_gpu
     def     _UpperCamelCase   (self    :	int					)  ->       List[Any]:
           __A		   =      list_models()
           __A		   =      'sshleifer'
           __A		   =      [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_					)]
           __A		   =      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
           __A		   =      []
           for m in model_ids:
                 if m in allowed_to_be_broken:
                       continue
                 try:
                       AutoConfig.from_pretrained(UpperCAmelCase_					)
                 except Exception:
                       failures.append(UpperCAmelCase_					)
           assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       str:
           __A		   =      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
           self._test_distiller_cli(UpperCAmelCase_					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       Dict:
           __A		   =      {
               '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',
           }
           __A		   =      self._test_distiller_cli(UpperCAmelCase_				,				check_contents=UpperCAmelCase_					)

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

           __A		   =      self.get_auto_remove_tmp_dir()
           convert_pl_to_hf(ckpts[0]				,				transformer_ckpts[0].parent				,				UpperCAmelCase_					)
           assert os.path.exists(os.path.join(UpperCAmelCase_				,				"pytorch_model.bin"					)					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       int:
           __A		   =      AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_					)
           __A		   =      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
           __A		   =      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				,				dtype=torch.long				,				device=model.device					)
           __A		   =      target_ids[:, :-1].contiguous()  # Why this line?
           __A		   =      target_ids[:, 1:].clone()  # why clone?
           __A		   =      model(
               UpperCAmelCase_				,				attention_mask=UpperCAmelCase_				,				decoder_input_ids=UpperCAmelCase_				,				labels=UpperCAmelCase_				,				use_cache=UpperCAmelCase_					).loss

           __A		   =      model(UpperCAmelCase_				,				attention_mask=UpperCAmelCase_				,				decoder_input_ids=UpperCAmelCase_				,				use_cache=UpperCAmelCase_					).logits

           __A		   =      nn.functional.log_softmax(UpperCAmelCase_				,				dim=-1					)
           __A		   =      label_smoothed_nll_loss(
               UpperCAmelCase_				,				UpperCAmelCase_				,				0.1				,				ignore_index=model.config.pad_token_id					)
           with self.assertRaises(UpperCAmelCase_					):
                 # TODO: understand why this breaks
                 self.assertEqual(UpperCAmelCase_				,				UpperCAmelCase_					)






     def     _UpperCamelCase   (self    :	int					)  ->       Tuple:
           __A		   =      {
               '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',
           }
           __A		   =      self._test_distiller_cli(UpperCAmelCase_				,				check_contents=UpperCAmelCase_					)
           assert model.model.config.model_type == "mbart"

           __A		   =      list(Path(model.output_dir					).glob("*.ckpt"					)					)
           self.assertEqual(1				,				len(UpperCAmelCase_					)					)
           __A		   =      list(Path(model.output_dir					).glob("**/*.bin"					)					)
           __A		   =      list(Path(model.output_dir					).glob("best_tfmr/*"					)					)
           assert len(UpperCAmelCase_					) > 2
           self.assertEqual(len(UpperCAmelCase_					)				,				2					)






     def     _UpperCamelCase   (self    :	Any					)  ->       List[Any]:
           __A		   =      {
               '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(UpperCAmelCase_					)






     def     _UpperCamelCase   (self    :	str					)  ->       Tuple:
           __A		   =      {
               'teacher': T5_TINY,
               'student': T5_TINIER,
               'model_name_or_path': T5_TINIER,
               'tokenizer_name': T5_TINIER,
           }
           self._test_distiller_cli(UpperCAmelCase_					)






     def     _UpperCamelCase   (self    :	Tuple				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Dict=True					)  ->       str:
           __A		   =      {
               '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(UpperCAmelCase_					)
           __A		   =      CHEAP_ARGS.copy()
           __A		   =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
           __A		   =      self.get_auto_remove_tmp_dir()

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

           self.assertIn("test_generations.txt"				,				UpperCAmelCase_					)
           self.assertIn("test_results.txt"				,				UpperCAmelCase_					)

           __A		   =      load_json(model.metrics_save_path					)
           __A		   =      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}"]				,				UpperCAmelCase_					)
           __A		   =      int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1					)
           self.assertEqual(len(metrics["val"]					)				,				UpperCAmelCase_					)
           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     _UpperCamelCase   (self    :	int				,				lowercase__    :	int					)  ->       Tuple:
           __A		   =      CHEAP_ARGS.copy()
           __A		   =      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
           __A		   =      0.1 if task == 'translation' else 0

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

           __A		   =      module.model.get_input_embeddings()
           assert not input_embeds.weight.requires_grad
           if model == T5_TINY:
                 __A		   =      module.model.lm_head
                 assert not lm_head.weight.requires_grad
                 assert (lm_head.weight == input_embeds.weight).all().item()
           elif model == FSMT_TINY:
                 __A		   =      module.model.model
                 __A		   =      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:
                 __A		   =      module.model.model
                 __A		   =      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

           __A		   =      load_json(module.output_dir / "text_batch.json"					)
           assert isinstance(UpperCAmelCase_				,				UpperCAmelCase_					)
           assert len(UpperCAmelCase_					) >= 4






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Optional[Any]:
           __A		   =      CHEAP_ARGS.copy()

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

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

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

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






     def     _UpperCamelCase   (self    :	Tuple					)  ->       Any:
           __A		   =      CHEAP_ARGS.copy()

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

           __A		   =      BART_TINY
           __A		   =      self.get_auto_remove_tmp_dir()

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

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

           # --help test
           with pytest.raises(UpperCAmelCase_					) as excinfo:
                 with CaptureStdout() as cs:
                       __A		   =      parser.parse_args(UpperCAmelCase_					)
                 assert False, "--help is expected to sys.exit"
           assert excinfo.type == SystemExit
           __A		   =      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
           __A		   =      'non_existing_scheduler'
           __A		   =      {f"--lr_scheduler={unsupported_param}"}
           with pytest.raises(UpperCAmelCase_					) as excinfo:
                 with CaptureStderr() as cs:
                       __A		   =      parser.parse_args(UpperCAmelCase_					)
                 assert False, "invalid argument is expected to sys.exit"
           assert excinfo.type == SystemExit
           __A		   =      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
           __A		   =      'cosine'
           __A		   =      args_d.copy()
           __A		   =      supported_param
           __A		   =      argparse.Namespace(**UpperCAmelCase_					)
           __A		   =      main(UpperCAmelCase_					)
           assert (
               getattr(model.hparams				,				"lr_scheduler"					) == supported_param
           ), f"lr_scheduler={supported_param} shouldn't fail"










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A      :       str      =	{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A      :       str      =	[
           'UMT5EncoderModel',
           'UMT5ForConditionalGeneration',
           'UMT5ForQuestionAnswering',
           'UMT5Model',
           'UMT5PreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

       __A      :       Optional[int]      =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


