


'''simple docstring'''




def       __UpperCamelCase     (     _A     :					Dict       , _A     :					Tuple					)     ->   Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase       :     Tuple   				=			len(_lowerCAmelCase					)
	# Create a stack and push index of fist element to it
	lowerCAmelCase       :     Optional[Any]   				=			[]
	st.append(0					)

	# Span value of first element is always 1
	lowerCAmelCase       :     Dict   				=			1

	# Calculate span values for rest of the elements
	for i in range(1       , _lowerCAmelCase					):
		# Pop elements from stack while stack is not
		# empty and top of stack is smaller than price[i]
		while len(_lowerCAmelCase					) > 0 and price[st[0]] <= price[i]:
			st.pop()

		# If stack becomes empty, then price[i] is greater
		# than all elements on left of it, i.e. price[0],
		# price[1], ..price[i-1]. Else the price[i]  is
		# greater than elements after top of stack
		lowerCAmelCase       :     Dict   				=			i + 1 if len(_lowerCAmelCase					) <= 0 else (i - st[0])

		# Push this element to stack
		st.append(_lowerCAmelCase					)

def       __UpperCamelCase     (     _A     :					Dict       , _A     :					Optional[Any]					)     ->   str:




	"""simple docstring"""





	for i in range(0       , _lowerCAmelCase					):
		print(arr[i]       , end=' '					)


# Driver program to test above function
_lowerCAmelCase	:		Tuple		  =						[10, 4, 5, 90, 120, 80]
_lowerCAmelCase	:		Optional[int]		  =						[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)






print_array(S, len(price))

"""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       			=      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 						UpperCamelCase				(	_lowerCAmelCase   :							Path				, _lowerCAmelCase   :							list						):
    __a			      =						"""\n""".join(_lowerCAmelCase						)
    Path(_lowerCAmelCase						).open("""w"""						).writelines(_lowerCAmelCase						)


__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 						UpperCamelCase				(	_lowerCAmelCase   :							List[Any]						):
    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 					a					(				A_							):



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



  @slow
  @require_torch_gpu
  def        lowerCAmelCase_				(		self   :    List[str]     )      -> str:
      __a			      =						list_models()
      __a			      =						"""sshleifer"""
      __a			      =						[x.modelId for x in model_list if x.modelId.startswith(lowerCamelCase_     )]
      __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(lowerCamelCase_     )
          except Exception:
              failures.append(lowerCamelCase_     )
      assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""



  def        lowerCAmelCase_				(		self   :    Any     )      -> Dict:
      __a			      =						{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
      self._test_distiller_cli(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Any:
      __a			      =						{
          """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""",
      }
      __a			      =						self._test_distiller_cli(lowerCamelCase_						,  check_contents=lowerCamelCase_     )

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

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



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Any:
      __a			      =						AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_     )
      __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(
          lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  decoder_input_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  use_cache=lowerCamelCase_     ).loss

      __a			      =						model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  decoder_input_ids=lowerCamelCase_						,  use_cache=lowerCamelCase_     ).logits

      __a			      =						nn.functional.log_softmax(lowerCamelCase_						,  dim=-1     )
      __a      ,  __a			      =						label_smoothed_nll_loss(
          lowerCamelCase_						,  lowerCamelCase_						,  0.1						,  ignore_index=model.config.pad_token_id     )
      with self.assertRaises(lowerCamelCase_     ):
          # TODO: understand why this breaks
          self.assertEqual(lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str     )      -> int:
      __a			      =						{
          """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""",
      }
      __a			      =						self._test_distiller_cli(lowerCamelCase_						,  check_contents=lowerCamelCase_     )
      assert model.model.config.model_type == "mbart"

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



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



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> List[str]:
      __a			      =						{
          """teacher""": T5_TINY,
          """student""": T5_TINIER,
          """model_name_or_path""": T5_TINIER,
          """tokenizer_name""": T5_TINIER,
      }
      self._test_distiller_cli(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Union[str, Any]=True     )      -> Optional[Any]:
      __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(lowerCamelCase_     )
      __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=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  **lowerCamelCase_     )
      __a			      =						distill_main(argparse.Namespace(**lowerCamelCase_     )     )
      if not check_contents:
          return model
      __a			      =						os.listdir(lowerCamelCase_     )
      __a			      =						{os.path.basename(lowerCamelCase_     ) for p in contents}
      __a			      =						[p for p in contents if p.endswith("""ckpt"""     )]
      assert len(lowerCamelCase_     ) > 0

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

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






class 					a					(				A_							):



  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,  )
  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Optional[Any]     )      -> Dict:
      __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=lowerCamelCase_						,  model_name_or_path=lowerCamelCase_						,  tokenizer_name=lowerCamelCase_						,  train_batch_size=2						,  eval_batch_size=2						,  output_dir=lowerCamelCase_						,  do_predict=lowerCamelCase_						,  task=lowerCamelCase_						,  src_lang="""en_XX"""						,  tgt_lang="""ro_RO"""						,  freeze_encoder=lowerCamelCase_						,  freeze_embeds=lowerCamelCase_						,  )
      assert "n_train" in args_d
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )

      __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(lowerCamelCase_						,  lowerCamelCase_     )
      assert len(lowerCamelCase_     ) >= 4



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Tuple:
      __a			      =						CHEAP_ARGS.copy()

      __a			      =						"""summarization"""
      __a			      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )

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

      # 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=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  )
      __a			      =						("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
          __a			      =						0.5
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )
      for p in extra_model_params:
          assert getattr(model.config						,  lowerCamelCase_     ) == 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=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  )
      __a			      =						"""encoder_layerdrop"""
      __a			      =						0.5
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      with pytest.raises(lowerCamelCase_     ) as excinfo:
          __a			      =						main(lowerCamelCase_     )
      assert str(excinfo.value     ) == F"""model config doesn't have a `{unsupported_param}` attribute"""



  def        lowerCAmelCase_				(		self   :    int     )      -> int:
      __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=lowerCamelCase_						,  model_name_or_path=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  tokenizer_name=lowerCamelCase_						,  train_batch_size=2						,  eval_batch_size=2						,  do_predict=lowerCamelCase_						,  task=lowerCamelCase_						,  src_lang="""en_XX"""						,  tgt_lang="""ro_RO"""						,  freeze_encoder=lowerCamelCase_						,  freeze_embeds=lowerCamelCase_						,  )

      # emulate finetune.py
      __a			      =						argparse.ArgumentParser()
      __a			      =						pl.Trainer.add_argparse_args(lowerCamelCase_     )
      __a			      =						SummarizationModule.add_model_specific_args(lowerCamelCase_						,  os.getcwd()     )
      __a			      =						{"""--help""": True}

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