





"""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)

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     lowercase__			(    lowercase_						,lowercase_	)    ->						Any:




      """simple docstring"""

      _UpperCamelCase					:		Optional[Any]  =				"\n".join(lowercase_	)
      Path(lowercase_	).open("w"	).writelines(lowercase_	)


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     lowercase__			(    lowercase_	)    ->						int:




      """simple docstring"""

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



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  @classmethod
  def       __SCREAMING_SNAKE_CASE (						cls      :				Any		)						-> str:
        logging.disable(logging.CRITICAL		)  # remove noisy download output from tracebacks
        return cls

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

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Any:
        _UpperCamelCase					:		Dict  =				{"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
        self._test_distiller_cli(__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> int:
        _UpperCamelCase					:		List[str]  =				{
            "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",
        }
        _UpperCamelCase					:		Any  =				self._test_distiller_cli(__a    ,      check_contents=__a		)

        _UpperCamelCase					:		Tuple  =				list(Path(model.output_dir		).glob("*.ckpt"		)		)
        self.assertEqual(1    ,      len(__a		)		)
        _UpperCamelCase					:		Tuple  =				list(Path(model.output_dir		).glob("**/*.bin"		)		)
        self.assertEqual(len(__a		)    ,      2		)
        _UpperCamelCase					:		Optional[int]  =				lmap(str.strip    ,      Path(model.hparams.data_dir		).joinpath("test.source"		).open().readlines()		)
        _UpperCamelCase					:		Union[str, Any]  =				tempfile.mktemp()  # XXX: not being cleaned up
        generate_summaries_or_translations(__a    ,      __a    ,      str(model.output_dir / "best_tfmr"		)		)
        self.assertTrue(Path(__a		).exists()		)

        _UpperCamelCase					:		int  =				self.get_auto_remove_tmp_dir()
        convert_pl_to_hf(ckpts[0]    ,      transformer_ckpts[0].parent    ,      __a		)
        assert os.path.exists(os.path.join(__a    ,      "pytorch_model.bin"		)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> Dict:
        _UpperCamelCase					:		Any  =				AutoModelForSeqaSeqLM.from_pretrained(__a		)
        _UpperCamelCase,     _UpperCamelCase					:		str  =				model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
        _UpperCamelCase					:		Dict  =				torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,      dtype=torch.long    ,      device=model.device		)
        _UpperCamelCase					:		Optional[int]  =				target_ids[:, :-1].contiguous()  # Why this line?
        _UpperCamelCase					:		List[Any]  =				target_ids[:, 1:].clone()  # why clone?
        _UpperCamelCase					:		Union[str, Any]  =				model(
            __a    ,      attention_mask=__a    ,      decoder_input_ids=__a    ,      labels=__a    ,      use_cache=__a		).loss

        _UpperCamelCase					:		Any  =				model(__a    ,      attention_mask=__a    ,      decoder_input_ids=__a    ,      use_cache=__a		).logits

        _UpperCamelCase					:		Optional[int]  =				nn.functional.log_softmax(__a    ,      dim=-1		)
        _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				label_smoothed_nll_loss(
            __a    ,      __a    ,      0.1    ,      ignore_index=model.config.pad_token_id		)
        with self.assertRaises(__a		):
              # TODO: understand why this breaks
              self.assertEqual(__a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Dict:
        _UpperCamelCase					:		int  =				{
            "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",
        }
        _UpperCamelCase					:		Tuple  =				self._test_distiller_cli(__a    ,      check_contents=__a		)
        assert model.model.config.model_type == "mbart"

        _UpperCamelCase					:		Union[str, Any]  =				list(Path(model.output_dir		).glob("*.ckpt"		)		)
        self.assertEqual(1    ,      len(__a		)		)
        _UpperCamelCase					:		Optional[Any]  =				list(Path(model.output_dir		).glob("**/*.bin"		)		)
        _UpperCamelCase					:		Optional[int]  =				list(Path(model.output_dir		).glob("best_tfmr/*"		)		)
        assert len(__a		) > 2
        self.assertEqual(len(__a		)    ,      2		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> Dict:
        _UpperCamelCase					:		Any  =				{
            "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(__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> List[Any]:
        _UpperCamelCase					:		int  =				{
            "teacher": T5_TINY,
            "student": T5_TINIER,
            "model_name_or_path": T5_TINIER,
            "tokenizer_name": T5_TINIER,
        }
        self._test_distiller_cli(__a		)


  def       __SCREAMING_SNAKE_CASE (						self      :				int    ,      __a      :				Optional[Any]    ,      __a      :				Tuple=True		)						-> Optional[int]:
        _UpperCamelCase					:		str  =				{
            "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(__a		)
        _UpperCamelCase					:		dict  =				CHEAP_ARGS.copy()
        _UpperCamelCase					:		Tuple  =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        _UpperCamelCase					:		Optional[Any]  =				self.get_auto_remove_tmp_dir()

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

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

        _UpperCamelCase					:		int  =				load_json(model.metrics_save_path		)
        _UpperCamelCase					:		List[str]  =				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}''']    ,      __a		)
        _UpperCamelCase					:		str  =				int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1		)
        self.assertEqual(len(metrics["val"]		)    ,      __a		)
        self.assertEqual(len(metrics["test"]		)    ,      1		)
        return model





class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,      )
  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple    ,      __a      :				Optional[int]		)						-> List[Any]:
        _UpperCamelCase					:		dict  =				CHEAP_ARGS.copy()
        _UpperCamelCase					:		Dict  =				"translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
        _UpperCamelCase					:		Any  =				0.1 if task == "translation" else 0

        _UpperCamelCase					:		Tuple  =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        _UpperCamelCase					:		Dict  =				self.get_auto_remove_tmp_dir()
        args_d.update(
            data_dir=__a    ,      model_name_or_path=__a    ,      tokenizer_name=__a    ,      train_batch_size=2    ,      eval_batch_size=2    ,      output_dir=__a    ,      do_predict=__a    ,      task=__a    ,      src_lang="en_XX"    ,      tgt_lang="ro_RO"    ,      freeze_encoder=__a    ,      freeze_embeds=__a    ,      )
        assert "n_train" in args_d
        _UpperCamelCase					:		List[Any]  =				argparse.Namespace(**__a		)
        _UpperCamelCase					:		List[Any]  =				main(__a		)

        _UpperCamelCase					:		List[str]  =				module.model.get_input_embeddings()
        assert not input_embeds.weight.requires_grad
        if model == T5_TINY:
              _UpperCamelCase					:		Tuple  =				module.model.lm_head
              assert not lm_head.weight.requires_grad
              assert (lm_head.weight == input_embeds.weight).all().item()
        elif model == FSMT_TINY:
              _UpperCamelCase					:		Optional[Any]  =				module.model.model
              _UpperCamelCase					:		Any  =				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:
              _UpperCamelCase					:		int  =				module.model.model
              _UpperCamelCase					:		Dict  =				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

        _UpperCamelCase					:		Union[str, Any]  =				load_json(module.output_dir / "text_batch.json"		)
        assert isinstance(__a    ,      __a		)
        assert len(__a		) >= 4

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Optional[Any]:
        _UpperCamelCase					:		dict  =				CHEAP_ARGS.copy()

        _UpperCamelCase					:		Optional[int]  =				"summarization"
        _UpperCamelCase					:		Union[str, Any]  =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)

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

        # test models whose config includes the extra_model_args
        _UpperCamelCase					:		Any  =				BART_TINY
        _UpperCamelCase					:		Union[str, Any]  =				self.get_auto_remove_tmp_dir()
        _UpperCamelCase					:		Any  =				args_d.copy()
        args_da.update(
            model_name_or_path=__a    ,      output_dir=__a    ,      )
        _UpperCamelCase					:		Tuple  =				("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
        for p in extra_model_params:
              _UpperCamelCase					:		Optional[int]  =				0.5
        _UpperCamelCase					:		Any  =				argparse.Namespace(**__a		)
        _UpperCamelCase					:		Optional[Any]  =				main(__a		)
        for p in extra_model_params:
              assert getattr(model.config    ,      __a		) == 0.5, F'''failed to override the model config for param {p}'''

        # test models whose config doesn't include the extra_model_args
        _UpperCamelCase					:		Tuple  =				T5_TINY
        _UpperCamelCase					:		Dict  =				self.get_auto_remove_tmp_dir()
        _UpperCamelCase					:		int  =				args_d.copy()
        args_da.update(
            model_name_or_path=__a    ,      output_dir=__a    ,      )
        _UpperCamelCase					:		Tuple  =				"encoder_layerdrop"
        _UpperCamelCase					:		List[str]  =				0.5
        _UpperCamelCase					:		Any  =				argparse.Namespace(**__a		)
        with pytest.raises(__a		) as excinfo:
              _UpperCamelCase					:		Any  =				main(__a		)
        assert str(excinfo.value		) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''


  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Optional[int]:
        _UpperCamelCase					:		dict  =				CHEAP_ARGS.copy()

        _UpperCamelCase					:		int  =				"summarization"
        _UpperCamelCase					:		Dict  =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)

        _UpperCamelCase					:		Dict  =				BART_TINY
        _UpperCamelCase					:		Union[str, Any]  =				self.get_auto_remove_tmp_dir()

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

        # emulate finetune.py
        _UpperCamelCase					:		List[Any]  =				argparse.ArgumentParser()
        _UpperCamelCase					:		List[Any]  =				pl.Trainer.add_argparse_args(__a		)
        _UpperCamelCase					:		Dict  =				SummarizationModule.add_model_specific_args(__a    ,      os.getcwd()		)
        _UpperCamelCase					:		Union[str, Any]  =				{"--help": True}

        # --help test
        with pytest.raises(__a		) as excinfo:
              with CaptureStdout() as cs:
                    _UpperCamelCase					:		Optional[int]  =				parser.parse_args(__a		)
              assert False, "--help is expected to sys.exit"
        assert excinfo.type == SystemExit
        _UpperCamelCase					:		List[Any]  =				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
        _UpperCamelCase					:		Optional[int]  =				"non_existing_scheduler"
        _UpperCamelCase					:		Optional[int]  =				{F'''--lr_scheduler={unsupported_param}'''}
        with pytest.raises(__a		) as excinfo:
              with CaptureStderr() as cs:
                    _UpperCamelCase					:		Optional[int]  =				parser.parse_args(__a		)
              assert False, "invalid argument is expected to sys.exit"
        assert excinfo.type == SystemExit
        _UpperCamelCase					:		Union[str, Any]  =				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
        _UpperCamelCase					:		str  =				"cosine"
        _UpperCamelCase					:		Optional[Any]  =				args_d.copy()
        _UpperCamelCase					:		Optional[Any]  =				supported_param
        _UpperCamelCase					:		Dict  =				argparse.Namespace(**__a		)
        _UpperCamelCase					:		List[Any]  =				main(__a		)
        assert (
            getattr(model.hparams    ,      "lr_scheduler"		) == supported_param
        ), F'''lr_scheduler={supported_param} shouldn\'t fail'''







"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
  lowerCamelCase__      =      []

  lowerCamelCase__      =      os.path.join("tests", "models", "**", "test_modeling_*.py")
  lowerCamelCase__      =      glob.glob(pattern)
  # TODO: deal with TF/Flax too
  lowerCamelCase__      =      [
      x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
  ]

  for test_file in test_files:
    lowerCamelCase__      =      get_tester_classes(test_file)
    for tester_class in tester_classes:
      # A few tester classes don't have `parent` parameter in `__init__`.
      # TODO: deal this better
      try:
        lowerCamelCase__      =      tester_class(parent=None)
      except Exception:
        continue
      if hasattr(tester, "get_config"):
        lowerCamelCase__      =      tester.get_config()
        for k, v in config.to_dict().items():
          if isinstance(v, int):
            lowerCamelCase__      =      None
            if k in ["vocab_size"]:
              lowerCamelCase__      =      100
            elif k in ["max_position_embeddings"]:
              lowerCamelCase__      =      128
            elif k in ["hidden_size", "d_model"]:
              lowerCamelCase__      =      40
            elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
              lowerCamelCase__      =      5
            if target is not None and v > target:
              failures.append(
                  f"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
                  f""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
                  f""" than {target}."""
              )

  if len(failures) > 0:
    raise Exception(f"""There were {len(failures)} failures:\n""" + "\n".join(failures))
