




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    :				Tuple    						=	logging.getLogger()
__A    :				Tuple    						=	torch.cuda.is_available()
__A    :				Tuple    						=	{
    """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""": 4_2,
    """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""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """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_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       """\n""".join(SCREAMING_SNAKE_CASE )
			Path(SCREAMING_SNAKE_CASE ).open("""w""" ).writelines(SCREAMING_SNAKE_CASE )


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


__A    :				Tuple    						=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(SCREAMING_SNAKE_CASE       ,						f"""{split}.source""" )       ,						SCREAMING_SNAKE_CASE )
						_dump_articles(os.path.join(SCREAMING_SNAKE_CASE       ,						f"""{split}.target""" )       ,						SCREAMING_SNAKE_CASE )
			return tmp_dir




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@classmethod
							def  _UpperCAmelCase  (       cls							:						Tuple      )      ->       List[str]:
										logging.disable(logging.CRITICAL      )  # remove noisy download output from tracebacks
										return cls





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





							def  _UpperCAmelCase  (       self							:						int      )      ->       Dict:
										SCREAMING_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""",
										}
										SCREAMING_SNAKE_CASE     =       self._test_distiller_cli(a							,  check_contents=a      )

										SCREAMING_SNAKE_CASE     =       list(Path(model.output_dir      ).glob("""*.ckpt"""      )      )
										self.assertEqual(1							,  len(a      )      )
										SCREAMING_SNAKE_CASE     =       list(Path(model.output_dir      ).glob("""**/*.bin"""      )      )
										self.assertEqual(len(a      )							,  2      )
										SCREAMING_SNAKE_CASE     =       lmap(str.strip							,  Path(model.hparams.data_dir      ).joinpath("""test.source"""      ).open().readlines()      )
										SCREAMING_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()      )

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

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

										SCREAMING_SNAKE_CASE     =       nn.functional.log_softmax(a							,  dim=-1      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_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							:						Dict      )      ->       List[str]:
										SCREAMING_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""",
										}
										SCREAMING_SNAKE_CASE     =       self._test_distiller_cli(a							,  check_contents=a      )
										assert model.model.config.model_type == "mbart"

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





							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       List[Any]:
										SCREAMING_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      )      ->       Union[str, Any]:
										SCREAMING_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							:						Tuple							,  a							:						int							,  a							:						Optional[Any]=True      )      ->       Dict:
										SCREAMING_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      )
										SCREAMING_SNAKE_CASE     =       CHEAP_ARGS.copy()
										SCREAMING_SNAKE_CASE     =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()

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

										SCREAMING_SNAKE_CASE     =       load_json(model.metrics_save_path      )
										SCREAMING_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      )
										SCREAMING_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       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@parameterized.expand(
							    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]							,  )
							def  _UpperCAmelCase  (       self							:						str							,  a							:						Tuple      )      ->       str:
										SCREAMING_SNAKE_CASE     =       CHEAP_ARGS.copy()
										SCREAMING_SNAKE_CASE     =       """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
										SCREAMING_SNAKE_CASE     =       0.1 if task == """translation""" else 0

										SCREAMING_SNAKE_CASE     =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
										SCREAMING_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
										SCREAMING_SNAKE_CASE     =       argparse.Namespace(**a      )
										SCREAMING_SNAKE_CASE     =       main(a      )

										SCREAMING_SNAKE_CASE     =       module.model.get_input_embeddings()
										assert not input_embeds.weight.requires_grad
										if model == T5_TINY:
													SCREAMING_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:
													SCREAMING_SNAKE_CASE     =       module.model.model
													SCREAMING_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:
													SCREAMING_SNAKE_CASE     =       module.model.model
													SCREAMING_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

										SCREAMING_SNAKE_CASE     =       load_json(module.output_dir / """text_batch.json"""      )
										assert isinstance(a							,  a      )
										assert len(a      ) >= 4





							def  _UpperCAmelCase  (       self							:						Any      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       CHEAP_ARGS.copy()

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







							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       CHEAP_ARGS.copy()

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

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

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












def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       len(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       [0] * length
			SCREAMING_SNAKE_CASE     =       [[False for i in range(SCREAMING_SNAKE_CASE )] for j in range(SCREAMING_SNAKE_CASE )]
			for i, c in enumerate(SCREAMING_SNAKE_CASE ):
						SCREAMING_SNAKE_CASE     =       i
						for j in range(i + 1 ):
									if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
												SCREAMING_SNAKE_CASE     =       True
												SCREAMING_SNAKE_CASE     =       min(SCREAMING_SNAKE_CASE       ,						0 if j == 0 else (cut[j - 1] + 1) )
						SCREAMING_SNAKE_CASE     =       mincut
			return cut[length - 1]


if __name__ == "__main__":
		__A    :				Optional[int]    						=	input("""Enter the string: """).strip()
		__A    :				List[Any]    						=	find_minimum_partitions(s)
		print(f'''Minimum number of partitions required for the \'{s}\' is {ans}''')






