






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_					:		int           = logging.getLogger()
UpperCAmelCase_					:		Union[str, Any]           = torch.cuda.is_available()
UpperCAmelCase_					:		str           = {
    '''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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Path      ,     __magic_name__   :    list   )						->     str:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      """\n""".join(__magic_name__   )
	Path(__magic_name__   ).open("""w"""   ).writelines(__magic_name__   )


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


UpperCAmelCase_					:		Optional[Any]           = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[Any]   )						->     Optional[Any]:






	"""simple docstring"""



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

class        _SCREAMING_SNAKE_CASE			(     _a ):





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





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





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Tuple		=      {
		    """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""",
		}
		UpperCamelCase	:Optional[int]		=      self._test_distiller_cli(__lowerCamelCase					,    check_contents=__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      list(Path(model.output_dir      ).glob("""*.ckpt"""      )      )
		self.assertEqual(1					,    len(__lowerCamelCase      )      )
		UpperCamelCase	:Optional[int]		=      list(Path(model.output_dir      ).glob("""**/*.bin"""      )      )
		self.assertEqual(len(__lowerCamelCase      )					,    2      )
		UpperCamelCase	:int		=      lmap(str.strip					,    Path(model.hparams.data_dir      ).joinpath("""test.source"""      ).open().readlines()      )
		UpperCamelCase	:Optional[int]		=      tempfile.mktemp()  # XXX: not being cleaned up
		generate_summaries_or_translations(__lowerCamelCase					,    __lowerCamelCase					,    str(model.output_dir / """best_tfmr"""      )      )
		self.assertTrue(Path(__lowerCamelCase      ).exists()      )

		UpperCamelCase	:List[Any]		=      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 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:Dict		=      AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase      )
		UpperCamelCase	, UpperCamelCase	:str		=      model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
		UpperCamelCase	:int		=      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]					,    dtype=torch.long					,    device=model.device      )
		UpperCamelCase	:Any		=      target_ids[:, :-1].contiguous()  # Why this line?
		UpperCamelCase	:Union[str, Any]		=      target_ids[:, 1:].clone()  # why clone?
		UpperCamelCase	:Any		=      model(
		    __lowerCamelCase					,    attention_mask=__lowerCamelCase					,    decoder_input_ids=__lowerCamelCase					,    labels=__lowerCamelCase					,    use_cache=__lowerCamelCase      ).loss

		UpperCamelCase	:List[str]		=      model(__lowerCamelCase					,    attention_mask=__lowerCamelCase					,    decoder_input_ids=__lowerCamelCase					,    use_cache=__lowerCamelCase      ).logits

		UpperCamelCase	:str		=      nn.functional.log_softmax(__lowerCamelCase					,    dim=-1      )
		UpperCamelCase	, UpperCamelCase	:Optional[Any]		=      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 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Any		=      {
		    """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""",
		}
		UpperCamelCase	:Any		=      self._test_distiller_cli(__lowerCamelCase					,    check_contents=__lowerCamelCase      )
		assert model.model.config.model_type == "mbart"

		UpperCamelCase	:str		=      list(Path(model.output_dir      ).glob("""*.ckpt"""      )      )
		self.assertEqual(1					,    len(__lowerCamelCase      )      )
		UpperCamelCase	:Optional[Any]		=      list(Path(model.output_dir      ).glob("""**/*.bin"""      )      )
		UpperCamelCase	:int		=      list(Path(model.output_dir      ).glob("""best_tfmr/*"""      )      )
		assert len(__lowerCamelCase      ) > 2
		self.assertEqual(len(__lowerCamelCase      )					,    2      )





	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      {
		    """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 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:List[str]		=      {
		    """teacher""": T5_TINY,
		    """student""": T5_TINIER,
		    """model_name_or_path""": T5_TINIER,
		    """tokenizer_name""": T5_TINIER,
		}
		self._test_distiller_cli(__lowerCamelCase      )





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   Union[str, Any]=True      ):
		UpperCamelCase	:List[str]		=      {
		    """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      )
		UpperCamelCase	:dict		=      CHEAP_ARGS.copy()
		UpperCamelCase	:str		=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
		UpperCamelCase	:Any		=      self.get_auto_remove_tmp_dir()

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

		UpperCamelCase	:int		=      load_json(model.metrics_save_path      )
		UpperCamelCase	:Tuple		=      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      )
		UpperCamelCase	:Union[str, Any]		=      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        _SCREAMING_SNAKE_CASE			(     _a ):





	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,    )
	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   List[str]      ):
		UpperCamelCase	:dict		=      CHEAP_ARGS.copy()
		UpperCamelCase	:int		=      """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
		UpperCamelCase	:Optional[Any]		=      0.1 if task == """translation""" else 0

		UpperCamelCase	:Tuple		=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
		UpperCamelCase	:Tuple		=      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
		UpperCamelCase	:List[str]		=      argparse.Namespace(**__lowerCamelCase      )
		UpperCamelCase	:List[str]		=      main(__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      module.model.get_input_embeddings()
		assert not input_embeds.weight.requires_grad
		if model == T5_TINY:
			UpperCamelCase	:Optional[Any]		=      module.model.lm_head
			assert not lm_head.weight.requires_grad
			assert (lm_head.weight == input_embeds.weight).all().item()
		elif model == FSMT_TINY:
			UpperCamelCase	:Optional[int]		=      module.model.model
			UpperCamelCase	:Dict		=      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:
			UpperCamelCase	:str		=      module.model.model
			UpperCamelCase	:Optional[Any]		=      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

		UpperCamelCase	:Optional[Any]		=      load_json(module.output_dir / """text_batch.json"""      )
		assert isinstance(__lowerCamelCase					,    __lowerCamelCase      )
		assert len(__lowerCamelCase      ) >= 4





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:dict		=      CHEAP_ARGS.copy()

		UpperCamelCase	:Dict		=      """summarization"""
		UpperCamelCase	:int		=      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
		UpperCamelCase	:List[str]		=      BART_TINY
		UpperCamelCase	:Tuple		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:List[Any]		=      args_d.copy()
		args_da.update(
		    model_name_or_path=__lowerCamelCase					,    output_dir=__lowerCamelCase					,    )
		UpperCamelCase	:str		=      ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
		for p in extra_model_params:
			UpperCamelCase	:int		=      0.5
		UpperCamelCase	:Any		=      argparse.Namespace(**__lowerCamelCase      )
		UpperCamelCase	:Tuple		=      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
		UpperCamelCase	:List[Any]		=      T5_TINY
		UpperCamelCase	:Tuple		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:int		=      args_d.copy()
		args_da.update(
		    model_name_or_path=__lowerCamelCase					,    output_dir=__lowerCamelCase					,    )
		UpperCamelCase	:Any		=      """encoder_layerdrop"""
		UpperCamelCase	:Optional[int]		=      0.5
		UpperCamelCase	:str		=      argparse.Namespace(**__lowerCamelCase      )
		with pytest.raises(__lowerCamelCase      ) as excinfo:
			UpperCamelCase	:str		=      main(__lowerCamelCase      )
		assert str(excinfo.value      ) == F"""model config doesn't have a `{unsupported_param}` attribute"""





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:dict		=      CHEAP_ARGS.copy()

		UpperCamelCase	:Tuple		=      """summarization"""
		UpperCamelCase	:int		=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )

		UpperCamelCase	:Optional[Any]		=      BART_TINY
		UpperCamelCase	:Tuple		=      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
		UpperCamelCase	:Tuple		=      argparse.ArgumentParser()
		UpperCamelCase	:int		=      pl.Trainer.add_argparse_args(__lowerCamelCase      )
		UpperCamelCase	:Optional[Any]		=      SummarizationModule.add_model_specific_args(__lowerCamelCase					,    os.getcwd()      )
		UpperCamelCase	:List[Any]		=      {"""--help""": True}

		# --help test
		with pytest.raises(__lowerCamelCase      ) as excinfo:
			with CaptureStdout() as cs:
				UpperCamelCase	:str		=      parser.parse_args(__lowerCamelCase      )
			assert False, "--help is expected to sys.exit"
		assert excinfo.type == SystemExit
		UpperCamelCase	:Optional[int]		=      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
		UpperCamelCase	:str		=      """non_existing_scheduler"""
		UpperCamelCase	:Tuple		=      {F"""--lr_scheduler={unsupported_param}"""}
		with pytest.raises(__lowerCamelCase      ) as excinfo:
			with CaptureStderr() as cs:
				UpperCamelCase	:Optional[Any]		=      parser.parse_args(__lowerCamelCase      )
			assert False, "invalid argument is expected to sys.exit"
		assert excinfo.type == SystemExit
		UpperCamelCase	:Union[str, Any]		=      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
		UpperCamelCase	:Optional[int]		=      """cosine"""
		UpperCamelCase	:List[str]		=      args_d.copy()
		UpperCamelCase	:str		=      supported_param
		UpperCamelCase	:Tuple		=      argparse.Namespace(**__lowerCamelCase      )
		UpperCamelCase	:Union[str, Any]		=      main(__lowerCamelCase      )
		assert (
		    getattr(model.hparams					,    """lr_scheduler"""      ) == supported_param
		), F"""lr_scheduler={supported_param} shouldn't fail"""








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      [0, 1]





	def 		_A					(						self							:   str					,    __lowerCamelCase							:   int      ):
		if (difference := index - (len(self.sequence      ) - 2)) >= 1:
			for _ in range(__lowerCamelCase      ):
				self.sequence.append(self.sequence[-1] + self.sequence[-2]      )
		return self.sequence[:index]

def   SCREAMING_SNAKE_CASE_				(				)						->     None:






	"""simple docstring"""



	print(
	    """Fibonacci Series Using Dynamic Programming\n"""      ,     """Enter the index of the Fibonacci number you want to calculate """      ,     """in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,     sep=""""""      ,     )
	UpperCamelCase	:Any		=      Fibonacci()

	while True:
		UpperCamelCase	:str		=      input(""">> """   )
		if prompt in {"exit", "quit"}:
			break

		try:
			UpperCamelCase	:int		=      int(__magic_name__   )
		except ValueError:
			print("""Enter a number or 'exit'"""   )
			continue

		print(fibonacci.get(__magic_name__   )   )


if __name__ == "__main__":
	main()
