

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     						=      logging.getLogger()
__A     						=      torch.cuda.is_available()
__A     						=      {
    '''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  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				'''\n'''.join(_UpperCamelCase	)
  Path(_UpperCamelCase	).open('''w'''	).writelines(_UpperCamelCase	)


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


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



def  snake_case_(_UpperCamelCase	)				->      List[str]:



  """simple docstring"""






  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     lowercase_       (    __lowercase	):



      @classmethod
      def 		UpperCamelCase_						(     cls :							int						)       ->	Union[str, Any]:
        logging.disable(logging.CRITICAL						)  # remove noisy download output from tracebacks
        return cls



      @slow
      @require_torch_gpu
      def 		UpperCamelCase_						(     self :							int						)       ->	Any:
        _snake_case		=				list_models()
        _snake_case		=				'''sshleifer'''
        _snake_case		=				[x.modelId for x in model_list if x.modelId.startswith(A__						)]
        _snake_case		=				['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
        _snake_case		=				[]
        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 :							Optional[Any]						)       ->	Dict:
        _snake_case		=				{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
        self._test_distiller_cli(A__						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[int]:
        _snake_case		=				{
            '''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''',
        }
        _snake_case		=				self._test_distiller_cli(A__     ,   check_contents=A__						)

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

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

        _snake_case		=				model(A__     ,   attention_mask=A__     ,   decoder_input_ids=A__     ,   use_cache=A__						).logits

        _snake_case		=				nn.functional.log_softmax(A__     ,   dim=-1						)
        _snake_case,	_snake_case		=				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 :							Optional[Any]						)       ->	Dict:
        _snake_case		=				{
            '''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''',
        }
        _snake_case		=				self._test_distiller_cli(A__     ,   check_contents=A__						)
        assert model.model.config.model_type == "mbart"

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



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[Any]:
        _snake_case		=				{
            '''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 :							Tuple						)       ->	int:
        _snake_case		=				{
            '''teacher''': T5_TINY,
            '''student''': T5_TINIER,
            '''model_name_or_path''': T5_TINIER,
            '''tokenizer_name''': T5_TINIER,
        }
        self._test_distiller_cli(A__						)







      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							Union[str, Any]     ,   A__ :							List[Any]=True						)       ->	Tuple:
        _snake_case		=				{
            '''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__						)
        _snake_case		=				CHEAP_ARGS.copy()
        _snake_case		=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
        _snake_case		=				self.get_auto_remove_tmp_dir()

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

        _snake_case		=				load_json(model.metrics_save_path						)
        _snake_case		=				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__						)
        _snake_case		=				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     lowercase_       (    __lowercase	):



      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]     ,   )
      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							List[str]						)       ->	Any:
        _snake_case		=				CHEAP_ARGS.copy()
        _snake_case		=				'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
        _snake_case		=				0.1 if task == '''translation''' else 0

        _snake_case		=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
        _snake_case		=				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
        _snake_case		=				argparse.Namespace(**A__						)
        _snake_case		=				main(A__						)

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

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



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Union[str, Any]:
        _snake_case		=				CHEAP_ARGS.copy()

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







      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[Any]:
        _snake_case		=				CHEAP_ARGS.copy()

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

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

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




import functools
from math import sqrt
from time import time



def  snake_case_(_UpperCamelCase					,			*_UpperCamelCase					,			**_UpperCamelCase	)				->      str:



  """simple docstring"""






  _snake_case		=				time()
  _snake_case		=				func(*_UpperCamelCase					,			**_UpperCamelCase	)
  _snake_case		=				time()
  if int(end - start	) > 0:
    print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""	)
  else:
    print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""	)
  return output



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n == 0:
    return [0]
  _snake_case		=				[0, 1]
  for _ in range(n - 1	):
    fib.append(fib[-1] + fib[-2]	)
  return fib



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  @functools.cache
  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  # Cache must be outside recursuive function
  # other it will reset every time it calls itself.
  _snake_case		=				{0: 0, 1: 1, 2: 1}  # Prefilled cache

  def rec_fn_memoized(_UpperCamelCase	) -> int:
    if num in cache:
      return cache[num]

    _snake_case		=				rec_fn_memoized(num - 1	) + rec_fn_memoized(num - 2	)
    _snake_case		=				value
    return value

  return [rec_fn_memoized(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n >= 1_475:
    raise Exception('''n is too large'''	)
  _snake_case		=				sqrt(5	)
  _snake_case		=				(1 + sqrt_a) / 2
  return [round(phi**i / sqrt_a	) for i in range(n + 1	)]


if __name__ == "__main__":
 __A     						=      30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)

