






'''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)

__lowerCAmelCase							=logging.getLogger()
__lowerCAmelCase							=torch.cuda.is_available()
__lowerCAmelCase							={
    "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-0_8,
    "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 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







					a_									=   '\n'.join(_UpperCAmelCase )
					Path(_UpperCAmelCase ).open('w' ).writelines(_UpperCAmelCase )


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


__lowerCAmelCase							=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

def 							a	(   _UpperCAmelCase )				->				Union[str, Any]:






					"""simple docstring"""







					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   (				snake_case					):

					"""simple docstring"""





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




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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										a_									=   {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
										self._test_distiller_cli(UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:
										a_									=   {
										    'student_encoder_layers': 2,
										    'student_decoder_layers': 1,
										    'max_epochs': 4,
										    'val_check_interval': 0.2_5,
										    'alpha_hid': 2.0,
										    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
										}
										a_									=   self._test_distiller_cli(UpperCAmelCase__   , check_contents=UpperCAmelCase__     )

										a_									=   list(Path(model.output_dir     ).glob('*.ckpt'     )     )
										self.assertEqual(1   , len(UpperCAmelCase__     )     )
										a_									=   list(Path(model.output_dir     ).glob('**/*.bin'     )     )
										self.assertEqual(len(UpperCAmelCase__     )   , 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(UpperCAmelCase__   , UpperCAmelCase__   , str(model.output_dir / 'best_tfmr'     )     )
										self.assertTrue(Path(UpperCAmelCase__     ).exists()     )

										a_									=   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  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__     )
										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(
										    UpperCAmelCase__   , attention_mask=UpperCAmelCase__   , decoder_input_ids=UpperCAmelCase__   , labels=UpperCAmelCase__   , use_cache=UpperCAmelCase__     ).loss

										a_									=   model(UpperCAmelCase__   , attention_mask=UpperCAmelCase__   , decoder_input_ids=UpperCAmelCase__   , use_cache=UpperCAmelCase__     ).logits

										a_									=   nn.functional.log_softmax(UpperCAmelCase__   , dim=-1     )
										a_							,					a_									=   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  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:
										a_									=   {
										    'student_encoder_layers': 2,
										    'student_decoder_layers': 1,
										    'num_train_epochs': 4,
										    'val_check_interval': 0.2_5,
										    '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(UpperCAmelCase__   , check_contents=UpperCAmelCase__     )
										assert model.model.config.model_type == "mbart"

										a_									=   list(Path(model.output_dir     ).glob('*.ckpt'     )     )
										self.assertEqual(1   , len(UpperCAmelCase__     )     )
										a_									=   list(Path(model.output_dir     ).glob('**/*.bin'     )     )
										a_									=   list(Path(model.output_dir     ).glob('best_tfmr/*'     )     )
										assert len(UpperCAmelCase__     ) > 2
										self.assertEqual(len(UpperCAmelCase__     )   , 2     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	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(UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										a_									=   {
										    'teacher': T5_TINY,
										    'student': T5_TINIER,
										    'model_name_or_path': T5_TINIER,
										    'tokenizer_name': T5_TINIER,
										}
										self._test_distiller_cli(UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__=True     )      ->	Union[str, 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(UpperCAmelCase__     )
										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=UpperCAmelCase__   , output_dir=UpperCAmelCase__   , **UpperCAmelCase__     )
										a_									=   distill_main(argparse.Namespace(**UpperCAmelCase__     )     )
										if not check_contents:
															return model
										a_									=   os.listdir(UpperCAmelCase__     )
										a_									=   {os.path.basename(UpperCAmelCase__     ) for p in contents}
										a_									=   [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__     )

										a_									=   load_json(model.metrics_save_path     )
										a_									=   metrics['val'][-1]
										self.assertGreaterEqual(last_step_stats['val_avg_gen_time']   , 0.0_1     )
										self.assertGreaterEqual(1.0   , last_step_stats['val_avg_gen_time']     )
										self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']   , UpperCAmelCase__     )
										a_									=   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    _snake_case   (				snake_case					):

					"""simple docstring"""





					@parameterized.expand(
					    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]   , )
					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	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=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
										a_									=   argparse.Namespace(**UpperCAmelCase__     )
										a_									=   main(UpperCAmelCase__     )

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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:
										a_									=   CHEAP_ARGS.copy()

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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										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=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
										a_									=   argparse.ArgumentParser()
										a_									=   pl.Trainer.add_argparse_args(UpperCAmelCase__     )
										a_									=   SummarizationModule.add_model_specific_args(UpperCAmelCase__   , os.getcwd()     )
										a_									=   {'--help': True}

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









'''simple docstring'''
def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				list[int]:






					"""simple docstring"""







					if hue < 0 or hue > 3_6_0:
										raise Exception('hue should be between 0 and 360' )

					if saturation < 0 or saturation > 1:
										raise Exception('saturation should be between 0 and 1' )

					if value < 0 or value > 1:
										raise Exception('value should be between 0 and 1' )

					a_									=   value * saturation
					a_									=   hue / 6_0
					a_									=   chroma * (1 - abs(hue_section % 2 - 1 ))
					a_									=   value - chroma

					if hue_section >= 0 and hue_section <= 1:
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (match_value) )
					elif hue_section > 1 and hue_section <= 2:
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (match_value) )
					elif hue_section > 2 and hue_section <= 3:
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
					elif hue_section > 3 and hue_section <= 4:
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
					elif hue_section > 4 and hue_section <= 5:
										a_									=   round(2_5_5 * (second_largest_component + match_value) )
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (chroma + match_value) )
					else:
										a_									=   round(2_5_5 * (chroma + match_value) )
										a_									=   round(2_5_5 * (match_value) )
										a_									=   round(2_5_5 * (second_largest_component + match_value) )

					return [red, green, blue]

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				list[float]:






					"""simple docstring"""







					if red < 0 or red > 2_5_5:
										raise Exception('red should be between 0 and 255' )

					if green < 0 or green > 2_5_5:
										raise Exception('green should be between 0 and 255' )

					if blue < 0 or blue > 2_5_5:
										raise Exception('blue should be between 0 and 255' )

					a_									=   red / 2_5_5
					a_									=   green / 2_5_5
					a_									=   blue / 2_5_5
					a_									=   max(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )
					a_									=   value - min(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )
					a_									=   0 if value == 0 else chroma / value

					if chroma == 0:
										a_									=   0.0
					elif value == float_red:
										a_									=   6_0 * (0 + (float_green - float_blue) / chroma)
					elif value == float_green:
										a_									=   6_0 * (2 + (float_blue - float_red) / chroma)
					else:
										a_									=   6_0 * (4 + (float_red - float_green) / chroma)

					a_									=   (hue + 3_6_0) % 3_6_0

					return [hue, saturation, value]





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				bool:






					"""simple docstring"""







					a_									=   abs(hsv_a[0] - hsv_a[0] ) < 0.2
					a_									=   abs(hsv_a[1] - hsv_a[1] ) < 0.0_0_2
					a_									=   abs(hsv_a[2] - hsv_a[2] ) < 0.0_0_2

					return check_hue and check_saturation and check_value

