
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)

_UpperCAmelCase    			=					logging.getLogger()
_UpperCAmelCase    			=					torch.cuda.is_available()
_UpperCAmelCase    			=					{
    "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 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						'''\n'''.join(_a  )
  Path(_a  ).open('''w'''  ).writelines(_a  )


_UpperCAmelCase    			=					[" Sam ate lunch today.", "Sams lunch ingredients."]
_UpperCAmelCase    			=					["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_UpperCAmelCase    			=					"patrickvonplaten/t5-tiny-random"
_UpperCAmelCase    			=					"sshleifer/t5-tinier-random"
_UpperCAmelCase    			=					"sshleifer/bart-tiny-random"
_UpperCAmelCase    			=					"sshleifer/tiny-mbart"
_UpperCAmelCase    			=					"sshleifer/tiny-marian-en-de"
_UpperCAmelCase    			=					"stas/tiny-wmt19-en-de"


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


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




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

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  @classmethod
  def     _UpperCAmelCase  (					cls		):
    logging.disable(logging.CRITICAL		)  # remove noisy download output from tracebacks
    return cls


  @slow
  @require_torch_gpu
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						list_models()
    _lowerCamelCase      =						'''sshleifer'''
    _lowerCamelCase      =						[x.modelId for x in model_list if x.modelId.startswith(a__		)]
    _lowerCamelCase      =						['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
    _lowerCamelCase      =						[]
    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     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
    self._test_distiller_cli(a__		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{
        '''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''',
    }
    _lowerCamelCase      =						self._test_distiller_cli(a__	,		check_contents=a__		)

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

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

    _lowerCamelCase      =						model(a__	,		attention_mask=a__	,		decoder_input_ids=a__	,		use_cache=a__		).logits

    _lowerCamelCase      =						nn.functional.log_softmax(a__	,		dim=-1		)
    _lowerCamelCase    ,    _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{
        '''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''',
    }
    _lowerCamelCase      =						self._test_distiller_cli(a__	,		check_contents=a__		)
    assert model.model.config.model_type == "mbart"

    _lowerCamelCase      =						list(Path(model.output_dir		).glob('''*.ckpt'''		)		)
    self.assertEqual(1	,		len(a__		)		)
    _lowerCamelCase      =						list(Path(model.output_dir		).glob('''**/*.bin'''		)		)
    _lowerCamelCase      =						list(Path(model.output_dir		).glob('''best_tfmr/*'''		)		)
    assert len(a__		) > 2
    self.assertEqual(len(a__		)	,		2		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{
        '''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     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{
        '''teacher''': T5_TINY,
        '''student''': T5_TINIER,
        '''model_name_or_path''': T5_TINIER,
        '''tokenizer_name''': T5_TINIER,
    }
    self._test_distiller_cli(a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__=True		):
    _lowerCamelCase      =						{
        '''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__		)
    _lowerCamelCase      =						CHEAP_ARGS.copy()
    _lowerCamelCase      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
    _lowerCamelCase      =						self.get_auto_remove_tmp_dir()

    args_d.update(data_dir=a__	,		output_dir=a__	,		**a__		)
    _lowerCamelCase      =						distill_main(argparse.Namespace(**a__		)		)
    if not check_contents:
      return model
    _lowerCamelCase      =						os.listdir(a__		)
    _lowerCamelCase      =						{os.path.basename(a__		) for p in contents}
    _lowerCamelCase      =						[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__		)

    _lowerCamelCase      =						load_json(model.metrics_save_path		)
    _lowerCamelCase      =						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__		)
    _lowerCamelCase      =						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 		__magic_name__				( lowercase_					):


  """simple docstring"""





  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]	,		)
  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						CHEAP_ARGS.copy()
    _lowerCamelCase      =						'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
    _lowerCamelCase      =						0.1 if task == '''translation''' else 0

    _lowerCamelCase      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
    _lowerCamelCase      =						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
    _lowerCamelCase      =						argparse.Namespace(**a__		)
    _lowerCamelCase      =						main(a__		)

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

    _lowerCamelCase      =						load_json(module.output_dir / '''text_batch.json'''		)
    assert isinstance(a__	,		a__		)
    assert len(a__		) >= 4


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						CHEAP_ARGS.copy()

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						CHEAP_ARGS.copy()

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

    _lowerCamelCase      =						BART_TINY
    _lowerCamelCase      =						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
    _lowerCamelCase      =						argparse.ArgumentParser()
    _lowerCamelCase      =						pl.Trainer.add_argparse_args(a__		)
    _lowerCamelCase      =						SummarizationModule.add_model_specific_args(a__	,		os.getcwd()		)
    _lowerCamelCase      =						{'''--help''': True}

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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["image_processor", "tokenizer"]
  _UpperCamelCase						 = "AutoImageProcessor"
  _UpperCamelCase						 = "AutoTokenizer"


  def __init__(					self	,		a__=None	,		a__=None	,		**a__		):
    _lowerCamelCase      =						None
    if "feature_extractor" in kwargs:
      warnings.warn(
          '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
          ''' instead.'''	,		a__	,		)
      _lowerCamelCase      =						kwargs.pop('''feature_extractor'''		)

    _lowerCamelCase      =						image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('''You need to specify an `image_processor`.'''		)
    if tokenizer is None:
      raise ValueError('''You need to specify a `tokenizer`.'''		)

    super().__init__(a__	,		a__		)
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  def __call__(					self	,		*a__	,		**a__		):
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor(*a__	,		**a__		)

    _lowerCamelCase      =						kwargs.pop('''images'''	,		a__		)
    _lowerCamelCase      =						kwargs.pop('''text'''	,		a__		)
    if len(a__		) > 0:
      _lowerCamelCase      =						args[0]
      _lowerCamelCase      =						args[1:]

    if images is None and text is None:
      raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

    if images is not None:
      _lowerCamelCase      =						self.image_processor(a__	,		*a__	,		**a__		)
    if text is not None:
      _lowerCamelCase      =						self.tokenizer(a__	,		**a__		)

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      _lowerCamelCase      =						encodings['''input_ids''']
      return inputs


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.batch_decode(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.decode(*a__	,		**a__		)


  @contextmanager
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
        '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
        '''your images inputs, or in a separate call.'''		)
    _lowerCamelCase      =						True
    _lowerCamelCase      =						self.tokenizer
    yield
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,		a__	,		)
    return self.image_processor_class


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,		a__	,		)
    return self.image_processor




