



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__  :  List[str]       = logging.getLogger()
A__  :  Any       = torch.cuda.is_available()
A__  :  Dict       = {
    '''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(  __UpperCamelCase							:		Path					,__UpperCamelCase							:		list			):
		lowerCAmelCase_      :					Tuple     =			'''\n'''.join(__UpperCamelCase			)
		Path(__UpperCamelCase			).open('''w'''			).writelines(__UpperCamelCase			)


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


A__  :  List[str]       = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def       UpperCamelCase(  __UpperCamelCase							:		Dict			):
		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     __snake_case						(					UpperCamelCase_						):




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




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




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					List[str]     =			{
									    '''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_      :					List[Any]     =			self._test_distiller_cli(A_    ,    check_contents=A_)

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

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

									lowerCAmelCase_      :					List[Any]     =			model(A_    ,    attention_mask=A_    ,    decoder_input_ids=A_    ,    use_cache=A_).logits

									lowerCAmelCase_      :					Tuple     =			nn.functional.log_softmax(A_    ,    dim=-1)
									lowerCAmelCase_ ,  lowerCAmelCase_      :					Tuple     =			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					:	Any):
									lowerCAmelCase_      :					List[str]     =			{
									    '''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_      :					Tuple     =			self._test_distiller_cli(A_    ,    check_contents=A_)
									assert model.model.config.model_type == "mbart"

									lowerCAmelCase_      :					List[Any]     =			list(Path(model.output_dir).glob('''*.ckpt'''))
									self.assertEqual(1    ,    len(A_))
									lowerCAmelCase_      :					Dict     =			list(Path(model.output_dir).glob('''**/*.bin'''))
									lowerCAmelCase_      :					Optional[Any]     =			list(Path(model.output_dir).glob('''best_tfmr/*'''))
									assert len(A_) > 2
									self.assertEqual(len(A_)    ,    2)




							def        UpperCAmelCase__      (	self					:	List[str]):
									lowerCAmelCase_      :					Dict     =			{
									    '''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					:	str):
									lowerCAmelCase_      :					List[Any]     =			{
									    '''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_					:	Dict    ,    A_					:	Any=True):
									lowerCAmelCase_      :					Any     =			{
									    '''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_)
									lowerCAmelCase_      :					dict     =			CHEAP_ARGS.copy()
									lowerCAmelCase_      :					Optional[int]     =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
									lowerCAmelCase_      :					Union[str, Any]     =			self.get_auto_remove_tmp_dir()

									args_d.update(data_dir=A_    ,    output_dir=A_    ,    **A_)
									lowerCAmelCase_      :					str     =			distill_main(argparse.Namespace(**A_))
									if not check_contents:
											return model
									lowerCAmelCase_      :					Tuple     =			os.listdir(A_)
									lowerCAmelCase_      :					Dict     =			{os.path.basename(A_) for p in contents}
									lowerCAmelCase_      :					str     =			[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_)

									lowerCAmelCase_      :					Tuple     =			load_json(model.metrics_save_path)
									lowerCAmelCase_      :					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}"""]    ,    A_)
									lowerCAmelCase_      :					Dict     =			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     __snake_case						(					UpperCamelCase_						):




							@parameterized.expand(
							    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,    )
							def        UpperCAmelCase__      (	self					:	List[Any]    ,    A_					:	Any):
									lowerCAmelCase_      :					dict     =			CHEAP_ARGS.copy()
									lowerCAmelCase_      :					Optional[int]     =			'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
									lowerCAmelCase_      :					int     =			0.1 if task == '''translation''' else 0

									lowerCAmelCase_      :					Optional[int]     =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
									lowerCAmelCase_      :					List[Any]     =			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
									lowerCAmelCase_      :					Optional[Any]     =			argparse.Namespace(**A_)
									lowerCAmelCase_      :					Union[str, Any]     =			main(A_)

									lowerCAmelCase_      :					List[Any]     =			module.model.get_input_embeddings()
									assert not input_embeds.weight.requires_grad
									if model == T5_TINY:
											lowerCAmelCase_      :					int     =			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_      :					List[Any]     =			module.model.model
											lowerCAmelCase_      :					List[Any]     =			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_      :					str     =			module.model.model
											lowerCAmelCase_      :					Optional[int]     =			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_      :					Tuple     =			load_json(module.output_dir / '''text_batch.json''')
									assert isinstance(A_    ,    A_)
									assert len(A_) >= 4




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									lowerCAmelCase_      :					dict     =			CHEAP_ARGS.copy()

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







							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					dict     =			CHEAP_ARGS.copy()

									lowerCAmelCase_      :					Any     =			'''summarization'''
									lowerCAmelCase_      :					List[str]     =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

									lowerCAmelCase_      :					Dict     =			BART_TINY
									lowerCAmelCase_      :					Dict     =			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
									lowerCAmelCase_      :					Tuple     =			argparse.ArgumentParser()
									lowerCAmelCase_      :					Dict     =			pl.Trainer.add_argparse_args(A_)
									lowerCAmelCase_      :					Optional[Any]     =			SummarizationModule.add_model_specific_args(A_    ,    os.getcwd())
									lowerCAmelCase_      :					List[Any]     =			{'''--help''': True}

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










def       UpperCamelCase(  __UpperCamelCase							:		list			):
		for i, _ in enumerate(__UpperCamelCase			):
				if (i % 2 == 1) == (nums[i - 1] > nums[i]):
						lowerCAmelCase_ ,  lowerCAmelCase_      :					Union[str, Any]     =			nums[i], nums[i - 1]

		return nums


if __name__ == "__main__":
						print('''Enter the array elements:''')
						A__  :  str       = list(map(int, input().split()))
						print('''The unsorted array is:''')
						print(array)
						print('''Array after Wiggle sort:''')
						print(wiggle_sort(array))





