
"""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__   :						Union[str, Any] 							=     logging.getLogger()
A__   :						List[str] 							=     torch.cuda.is_available()
A__   :						Optional[int] 							=     {
    """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 				a__			(					lowerCAmelCase						:							Path							,     lowerCAmelCase						:							list		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    "\n".join(lowerCAmelCase		)
     Path(lowerCAmelCase		).open("w"		).writelines(lowerCAmelCase		)


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


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



def 				a__			(					lowerCAmelCase						:							Optional[int]		):






     '''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 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





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



     @slow
     @require_torch_gpu
     def 			lowerCAmelCase__							(   self	)->       str:
          UpperCAmelCase__				:			Dict							=    list_models()
          UpperCAmelCase__				:			int							=    "sshleifer"
          UpperCAmelCase__				:			List[str]							=    [x.modelId for x in model_list if x.modelId.startswith(__UpperCamelCase	)]
          UpperCAmelCase__				:			Union[str, Any]							=    ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
          UpperCAmelCase__				:			Any							=    []
          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 			lowerCAmelCase__							(   self	)->       Tuple:
          UpperCAmelCase__				:			Optional[Any]							=    {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
          self._test_distiller_cli(__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			Dict							=    {
              "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__				:			Tuple							=    self._test_distiller_cli(__UpperCamelCase			,      check_contents=__UpperCamelCase	)

          UpperCAmelCase__				:			Dict							=    list(Path(model.output_dir	).glob("*.ckpt"	)	)
          self.assertEqual(1			,      len(__UpperCamelCase	)	)
          UpperCAmelCase__				:			Dict							=    list(Path(model.output_dir	).glob("**/*.bin"	)	)
          self.assertEqual(len(__UpperCamelCase	)			,      2	)
          UpperCAmelCase__				:			Dict							=    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(__UpperCamelCase			,      __UpperCamelCase			,      str(model.output_dir / "best_tfmr"	)	)
          self.assertTrue(Path(__UpperCamelCase	).exists()	)

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

          UpperCAmelCase__				:			List[Any]							=    model(__UpperCamelCase			,      attention_mask=__UpperCamelCase			,      decoder_input_ids=__UpperCamelCase			,      use_cache=__UpperCamelCase	).logits

          UpperCAmelCase__				:			Optional[Any]							=    nn.functional.log_softmax(__UpperCamelCase			,      dim=-1	)
          UpperCAmelCase__				,						UpperCAmelCase__				:			Union[str, Any]							=    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 			lowerCAmelCase__							(   self	)->       int:
          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__				:			Union[str, Any]							=    self._test_distiller_cli(__UpperCamelCase			,      check_contents=__UpperCamelCase	)
          assert model.model.config.model_type == "mbart"

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



     def 			lowerCAmelCase__							(   self	)->       List[str]:
          UpperCAmelCase__				:			List[str]							=    {
              "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 			lowerCAmelCase__							(   self	)->       Any:
          UpperCAmelCase__				:			str							=    {
              "teacher": T5_TINY,
              "student": T5_TINIER,
              "model_name_or_path": T5_TINIER,
              "tokenizer_name": T5_TINIER,
          }
          self._test_distiller_cli(__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase=True	)->       str:
          UpperCAmelCase__				:			Optional[Any]							=    {
              "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	)
          UpperCAmelCase__				:			dict							=    CHEAP_ARGS.copy()
          UpperCAmelCase__				:			Optional[Any]							=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
          UpperCAmelCase__				:			int							=    self.get_auto_remove_tmp_dir()

          args_d.update(data_dir=__UpperCamelCase			,      output_dir=__UpperCamelCase			,      **__UpperCamelCase	)
          UpperCAmelCase__				:			int							=    distill_main(argparse.Namespace(**__UpperCamelCase	)	)
          if not check_contents:
               return model
          UpperCAmelCase__				:			Optional[int]							=    os.listdir(__UpperCamelCase	)
          UpperCAmelCase__				:			Tuple							=    {os.path.basename(__UpperCamelCase	) for p in contents}
          UpperCAmelCase__				:			List[Any]							=    [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	)

          UpperCAmelCase__				:			Any							=    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}"]			,      __UpperCamelCase	)
          UpperCAmelCase__				:			str							=    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 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,      )
     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			dict							=    CHEAP_ARGS.copy()
          UpperCAmelCase__				:			int							=    "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
          UpperCAmelCase__				:			Union[str, Any]							=    0.1 if task == "translation" else 0

          UpperCAmelCase__				:			Tuple							=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
          UpperCAmelCase__				:			List[str]							=    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
          UpperCAmelCase__				:			Tuple							=    argparse.Namespace(**__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    main(__UpperCamelCase	)

          UpperCAmelCase__				:			List[Any]							=    module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
               UpperCAmelCase__				:			Optional[int]							=    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__				:			List[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__				:			str							=    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__				:			Dict							=    load_json(module.output_dir / "text_batch.json"	)
          assert isinstance(__UpperCamelCase			,      __UpperCamelCase	)
          assert len(__UpperCamelCase	) >= 4



     def 			lowerCAmelCase__							(   self	)->       List[Any]:
          UpperCAmelCase__				:			dict							=    CHEAP_ARGS.copy()

          UpperCAmelCase__				:			Union[str, Any]							=    "summarization"
          UpperCAmelCase__				:			Optional[int]							=    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
          UpperCAmelCase__				:			Any							=    BART_TINY
          UpperCAmelCase__				:			Tuple							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			Tuple							=    args_d.copy()
          args_da.update(
              model_name_or_path=__UpperCamelCase			,      output_dir=__UpperCamelCase			,      )
          UpperCAmelCase__				:			Optional[Any]							=    ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
               UpperCAmelCase__				:			int							=    0.5
          UpperCAmelCase__				:			int							=    argparse.Namespace(**__UpperCamelCase	)
          UpperCAmelCase__				:			Union[str, Any]							=    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
          UpperCAmelCase__				:			List[str]							=    T5_TINY
          UpperCAmelCase__				:			Dict							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			List[str]							=    args_d.copy()
          args_da.update(
              model_name_or_path=__UpperCamelCase			,      output_dir=__UpperCamelCase			,      )
          UpperCAmelCase__				:			Tuple							=    "encoder_layerdrop"
          UpperCAmelCase__				:			Union[str, Any]							=    0.5
          UpperCAmelCase__				:			Dict							=    argparse.Namespace(**__UpperCamelCase	)
          with pytest.raises(__UpperCamelCase	) as excinfo:
               UpperCAmelCase__				:			List[Any]							=    main(__UpperCamelCase	)
          assert str(excinfo.value	) == F"model config doesn't have a `{unsupported_param}` attribute"



     def 			lowerCAmelCase__							(   self	)->       List[Any]:
          UpperCAmelCase__				:			dict							=    CHEAP_ARGS.copy()

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

          UpperCAmelCase__				:			List[Any]							=    BART_TINY
          UpperCAmelCase__				:			Dict							=    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
          UpperCAmelCase__				:			Optional[Any]							=    argparse.ArgumentParser()
          UpperCAmelCase__				:			Optional[int]							=    pl.Trainer.add_argparse_args(__UpperCamelCase	)
          UpperCAmelCase__				:			List[Any]							=    SummarizationModule.add_model_specific_args(__UpperCamelCase			,      os.getcwd()	)
          UpperCAmelCase__				:			List[str]							=    {"--help": True}

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





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str							,     lowerCAmelCase						:							int = 13		):






     '''simple docstring'''
     UpperCAmelCase__				:			Any							=    ""
     for c in s:
          if "A" <= c <= "Z":
               out += chr(ord("A"		) + (ord(lowerCAmelCase		) - ord("A"		) + n) % 26		)
          elif "a" <= c <= "z":
               out += chr(ord("a"		) + (ord(lowerCAmelCase		) - ord("a"		) + n) % 26		)
          else:
               out += c
     return out



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    input("Enter message: "		)

     UpperCAmelCase__				:			Optional[Any]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Encryption:"							,     lowerCAmelCase		)

     UpperCAmelCase__				:			List[str]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Decryption: "							,     lowerCAmelCase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



