

"""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)

SCREAMING_SNAKE_CASE_       :						int					=    logging.getLogger()
SCREAMING_SNAKE_CASE_       :						List[str]					=    torch.cuda.is_available()
SCREAMING_SNAKE_CASE_       :						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 			_snake_case  (     UpperCAmelCase_	:       Path       , UpperCAmelCase_	:       list      ):
     A__				      =	"""\n""".join(UpperCAmelCase_      )
     Path(UpperCAmelCase_      ).open("""w"""      ).writelines(UpperCAmelCase_      )


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


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





def 			_snake_case  (     UpperCAmelCase_	:       List[str]      ):
     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[Any]							):






         """simple docstring"""


         logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
         return cls

    @slow
    @require_torch_gpu
    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         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[Any]							):






         """simple docstring"""


         A__				      =	{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
         self._test_distiller_cli(UpperCamelCase							)

    def 					UpperCamelCase						(  self:			List[Any]							):






         """simple docstring"""


         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:			Any							):






         """simple docstring"""


         A__				      =	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase							)
         A__      ,					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__      ,					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:			Tuple							):






         """simple docstring"""


         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:			List[str]							):






         """simple docstring"""


         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:			int							):






         """simple docstring"""


         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:			Union[str, Any]     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			List[Any]=True							):






         """simple docstring"""


         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:			Dict     ,    UpperCamelCase:			Tuple							):






         """simple docstring"""


         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:			str							):






         """simple docstring"""


         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:			Any							):






         """simple docstring"""


         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_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 SCREAMING_SNAKE_CASE_       :						List[str]					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






