




"""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": 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 							lowercase   (snake_case__				:	Path ,    snake_case__				:	list	)       ->					Tuple:


				'''simple docstring'''






				lowerCAmelCase		=			"""\n""".join(__lowercase	)
				Path(__lowercase	).open("""w"""	).writelines(__lowercase	)


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 							lowercase   (snake_case__				:	Optional[int]	)       ->					Optional[Any]:


				'''simple docstring'''






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






class  SCREAMING_SNAKE_CASE__   (   lowercase_   ):






						"""simple docstring"""




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


						@slow
						@require_torch_gpu
						def 					__lowercase	(							self			:     Optional[int]   ):
										lowerCAmelCase		=			list_models()
										lowerCAmelCase		=			"""sshleifer"""
										lowerCAmelCase		=			[x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase_   )]
										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(UpperCamelCase_   )
														except Exception:
																		failures.append(UpperCamelCase_   )
										assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''


						def 					__lowercase	(							self			:     Tuple   ):
										lowerCAmelCase		=			{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
										self._test_distiller_cli(UpperCamelCase_   )


						def 					__lowercase	(							self			:     Any   ):
										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(UpperCamelCase_  ,  check_contents=UpperCamelCase_   )

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

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


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_   )
										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(
										    UpperCamelCase_  ,  attention_mask=UpperCamelCase_  ,  decoder_input_ids=UpperCamelCase_  ,  labels=UpperCamelCase_  ,  use_cache=UpperCamelCase_   ).loss

										lowerCAmelCase		=			model(UpperCamelCase_  ,  attention_mask=UpperCamelCase_  ,  decoder_input_ids=UpperCamelCase_  ,  use_cache=UpperCamelCase_   ).logits

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


						def 					__lowercase	(							self			:     Dict   ):
										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(UpperCamelCase_  ,  check_contents=UpperCamelCase_   )
										assert model.model.config.model_type == "mbart"

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


						def 					__lowercase	(							self			:     Any   ):
										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(UpperCamelCase_   )


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			{
										    """teacher""": T5_TINY,
										    """student""": T5_TINIER,
										    """model_name_or_path""": T5_TINIER,
										    """tokenizer_name""": T5_TINIER,
										}
										self._test_distiller_cli(UpperCamelCase_   )




						def 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     str=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(UpperCamelCase_   )
										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=UpperCamelCase_  ,  output_dir=UpperCamelCase_  ,  **UpperCamelCase_   )
										lowerCAmelCase		=			distill_main(argparse.Namespace(**UpperCamelCase_   )   )
										if not check_contents:
														return model
										lowerCAmelCase		=			os.listdir(UpperCamelCase_   )
										lowerCAmelCase		=			{os.path.basename(UpperCamelCase_   ) for p in contents}
										lowerCAmelCase		=			[p for p in contents if p.endswith("""ckpt"""   )]
										assert len(UpperCamelCase_   ) > 0

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

										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}''']  ,  UpperCamelCase_   )
										lowerCAmelCase		=			int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1   )
										self.assertEqual(len(metrics["""val"""]   )  ,  UpperCamelCase_   )
										self.assertEqual(len(metrics["""test"""]   )  ,  1   )
										return model






class  SCREAMING_SNAKE_CASE__   (   lowercase_   ):






						"""simple docstring"""




						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]  ,  )
						def 					__lowercase	(							self			:     Dict  ,  lowerCAmelCase			:     Optional[int]   ):
										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=UpperCamelCase_  ,  model_name_or_path=UpperCamelCase_  ,  tokenizer_name=UpperCamelCase_  ,  train_batch_size=2  ,  eval_batch_size=2  ,  output_dir=UpperCamelCase_  ,  do_predict=UpperCamelCase_  ,  task=UpperCamelCase_  ,  src_lang="""en_XX"""  ,  tgt_lang="""ro_RO"""  ,  freeze_encoder=UpperCamelCase_  ,  freeze_embeds=UpperCamelCase_  ,  )
										assert "n_train" in args_d
										lowerCAmelCase		=			argparse.Namespace(**UpperCamelCase_   )
										lowerCAmelCase		=			main(UpperCamelCase_   )

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


						def 					__lowercase	(							self			:     Any   ):
										lowerCAmelCase		=			CHEAP_ARGS.copy()

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

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

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




						def 					__lowercase	(							self			:     List[str]   ):
										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=UpperCamelCase_  ,  model_name_or_path=UpperCamelCase_  ,  output_dir=UpperCamelCase_  ,  tokenizer_name=UpperCamelCase_  ,  train_batch_size=2  ,  eval_batch_size=2  ,  do_predict=UpperCamelCase_  ,  task=UpperCamelCase_  ,  src_lang="""en_XX"""  ,  tgt_lang="""ro_RO"""  ,  freeze_encoder=UpperCamelCase_  ,  freeze_embeds=UpperCamelCase_  ,  )

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

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











"""simple docstring"""







def 							lowercase   (snake_case__				:	list	)       ->					bool:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				if len(snake_case__	) == 1 and series[0] != 0:
								return True
				lowerCAmelCase		=			[]
				lowerCAmelCase		=			len(snake_case__	)
				for i in range(0 ,    snake_case__	):
								if series[i] == 0:
												raise ValueError("""Input series cannot have 0 as an element"""	)
								rec_series.append(1 / series[i]	)
				lowerCAmelCase		=			rec_series[1] - rec_series[0]
				for index in range(2 ,    snake_case__	):
								if rec_series[index] - rec_series[index - 1] != common_diff:
												return False
				return True





def 							lowercase   (snake_case__				:	list	)       ->					float:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				lowerCAmelCase		=			0
				for val in series:
								answer += 1 / val
				return len(snake_case__	) / answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()





