




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)

lowercase__      :			Optional[Any]           =					logging.getLogger()
lowercase__      :			Optional[int]           =					torch.cuda.is_available()
lowercase__      :			Dict           =					{
    "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 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Tuple:
     a      =      "\n".join(__UpperCamelCase)
     Path(__UpperCamelCase).open("w").writelines(__UpperCamelCase)


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


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

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     Dict:
     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__       (					UpperCamelCase__      ):







     @classmethod
     def 			lowerCAmelCase_				(	cls )  ->    Any:



          '''simple docstring'''



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







     @slow
     @require_torch_gpu
     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      list_models()
          a      =      "sshleifer"
          a      =      [x.modelId for x in model_list if x.modelId.startswith(A )]
          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(A )
               except Exception:
                    failures.append(A )
          assert not failures, F'''The following models could not be loaded through AutoConfig: {failures}'''







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      {
              "student_encoder_layers": 2,
              "student_decoder_layers": 1,
              "max_epochs": 4,
              "val_check_interval": 0.2_5,
              "alpha_hid": 2.0,
              "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
          }
          a      =      self._test_distiller_cli(A  ,		check_contents=A )

          a      =      list(Path(model.output_dir ).glob("*.ckpt" ) )
          self.assertEqual(1  ,		len(A ) )
          a      =      list(Path(model.output_dir ).glob("**/*.bin" ) )
          self.assertEqual(len(A )  ,		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(A  ,		A  ,		str(model.output_dir / "best_tfmr" ) )
          self.assertTrue(Path(A ).exists() )

          a      =      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 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      AutoModelForSeqaSeqLM.from_pretrained(A )
          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(
              A  ,		attention_mask=A  ,		decoder_input_ids=A  ,		labels=A  ,		use_cache=A ).loss

          a      =      model(A  ,		attention_mask=A  ,		decoder_input_ids=A  ,		use_cache=A ).logits

          a      =      nn.functional.log_softmax(A  ,		dim=-1 )
          a  ,      a      =      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 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          a      =      {
              "student_encoder_layers": 2,
              "student_decoder_layers": 1,
              "num_train_epochs": 4,
              "val_check_interval": 0.2_5,
              "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(A  ,		check_contents=A )
          assert model.model.config.model_type == "mbart"

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







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''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(A )







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      {
              "teacher": T5_TINY,
              "student": T5_TINIER,
              "model_name_or_path": T5_TINIER,
              "tokenizer_name": T5_TINIER,
          }
          self._test_distiller_cli(A )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A=True )  ->    List[str]:



          '''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(A )
          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=A  ,		output_dir=A  ,		**A )
          a      =      distill_main(argparse.Namespace(**A ) )
          if not check_contents:
               return model
          a      =      os.listdir(A )
          a      =      {os.path.basename(A ) for p in contents}
          a      =      [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 )

          a      =      load_json(model.metrics_save_path )
          a      =      metrics["val"][-1]
          self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,		0.0_1 )
          self.assertGreaterEqual(1.0  ,		last_step_stats["val_avg_gen_time"] )
          self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']  ,		A )
          a      =      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 			a__       (					UpperCamelCase__      ):







     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]  ,		)
     def 			lowerCAmelCase_				(	self  ,		A )  ->    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=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
          a      =      argparse.Namespace(**A )
          a      =      main(A )

          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(A  ,		A )
          assert len(A ) >= 4







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''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=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
          a      =      BART_TINY
          a      =      self.get_auto_remove_tmp_dir()
          a      =      args_d.copy()
          args_da.update(
              model_name_or_path=A  ,		output_dir=A  ,		)
          a      =      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
               a      =      0.5
          a      =      argparse.Namespace(**A )
          a      =      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
          a      =      T5_TINY
          a      =      self.get_auto_remove_tmp_dir()
          a      =      args_d.copy()
          args_da.update(
              model_name_or_path=A  ,		output_dir=A  ,		)
          a      =      "encoder_layerdrop"
          a      =      0.5
          a      =      argparse.Namespace(**A )
          with pytest.raises(A ) as excinfo:
               a      =      main(A )
          assert str(excinfo.value ) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''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=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
          a      =      argparse.ArgumentParser()
          a      =      pl.Trainer.add_argparse_args(A )
          a      =      SummarizationModule.add_model_specific_args(A  ,		os.getcwd() )
          a      =      {"--help": True}

          # --help test
          with pytest.raises(A ) as excinfo:
               with CaptureStdout() as cs:
                    a      =      parser.parse_args(A )
               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(A ) as excinfo:
               with CaptureStderr() as cs:
                    a      =      parser.parse_args(A )
               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(**A )
          a      =      main(A )
          assert (
              getattr(model.hparams  ,		"lr_scheduler" ) == supported_param
          ), F'''lr_scheduler={supported_param} shouldn\'t fail'''







import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

   # Generate a list of 10 random numbers
   lowercase__      :			List[str]           =					[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print("Random numbers", random_numbers_list)

