






"""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__       :			List[Any]=						logging.getLogger()
A__       :			List[Any]=						torch.cuda.is_available()
A__       :			Optional[int]=						{
    """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 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Optional[Any]:
    """simple docstring"""

    UpperCamelCase__								=     '\n'.join(lowerCAmelCase_       )
    Path(lowerCAmelCase_       ).open('w'       ).writelines(lowerCAmelCase_       )


A__       :			Optional[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__       :			Any=						"""patrickvonplaten/t5-tiny-random"""
A__       :			str=						"""sshleifer/t5-tinier-random"""
A__       :			Optional[Any]=						"""sshleifer/bart-tiny-random"""
A__       :			Union[str, Any]=						"""sshleifer/tiny-mbart"""
A__       :			Optional[Any]=						"""sshleifer/tiny-marian-en-de"""
A__       :			Optional[int]=						"""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       )       ->						int:
    """simple docstring"""

    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(lowerCAmelCase_			,						F'{split}.source'       )			,						lowerCAmelCase_       )
        _dump_articles(os.path.join(lowerCAmelCase_			,						F'{split}.target'       )			,						lowerCAmelCase_       )
    return tmp_dir
class  __lowerCamelCase     (	lowerCamelCase_	):

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

   @slow
   @require_torch_gpu
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       UpperCamelCase__								=     list_models()
       UpperCamelCase__								=     'sshleifer'
       UpperCamelCase__								=     [x.modelId for x in model_list if x.modelId.startswith(snake_case_   )]
       UpperCamelCase__								=     ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
       UpperCamelCase__								=     []
       for m in model_ids:
           if m in allowed_to_be_broken:
               continue
           try:
               AutoConfig.from_pretrained(snake_case_   )
           except Exception:
               failures.append(snake_case_   )
       assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__								=     {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
       self._test_distiller_cli(snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       UpperCamelCase__								=     {
           '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__								=     self._test_distiller_cli(snake_case_			,					check_contents=snake_case_   )

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

       UpperCamelCase__								=     self.get_auto_remove_tmp_dir()
       convert_pl_to_hf(ckpts[0]			,					transformer_ckpts[0].parent			,					snake_case_   )
       assert os.path.exists(os.path.join(snake_case_			,					'pytorch_model.bin'   )   )

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

       UpperCamelCase__								=     model(snake_case_			,					attention_mask=snake_case_			,					decoder_input_ids=snake_case_			,					use_cache=snake_case_   ).logits

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

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__								=     {
           '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__								=     self._test_distiller_cli(snake_case_			,					check_contents=snake_case_   )
       assert model.model.config.model_type == "mbart"

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

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       UpperCamelCase__								=     {
           '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(snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__								=     {
           'teacher': T5_TINY,
           'student': T5_TINIER,
           'model_name_or_path': T5_TINIER,
           'tokenizer_name': T5_TINIER,
       }
       self._test_distiller_cli(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=True   )		->							str:
       UpperCamelCase__								=     {
           '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(snake_case_   )
       UpperCamelCase__								=     CHEAP_ARGS.copy()
       UpperCamelCase__								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
       UpperCamelCase__								=     self.get_auto_remove_tmp_dir()

       args_d.update(data_dir=snake_case_			,					output_dir=snake_case_			,					**snake_case_   )
       UpperCamelCase__								=     distill_main(argparse.Namespace(**snake_case_   )   )
       if not check_contents:
           return model
       UpperCamelCase__								=     os.listdir(snake_case_   )
       UpperCamelCase__								=     {os.path.basename(snake_case_   ) for p in contents}
       UpperCamelCase__								=     [p for p in contents if p.endswith('ckpt'   )]
       assert len(snake_case_   ) > 0

       self.assertIn('test_generations.txt'			,					snake_case_   )
       self.assertIn('test_results.txt'			,					snake_case_   )

       UpperCamelCase__								=     load_json(model.metrics_save_path   )
       UpperCamelCase__								=     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}']			,					snake_case_   )
       UpperCamelCase__								=     int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1   )
       self.assertEqual(len(metrics['val']   )			,					snake_case_   )
       self.assertEqual(len(metrics['test']   )			,					1   )
       return model







class  __lowerCamelCase     (	lowerCamelCase_	):

   @parameterized.expand(
       [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,					)
   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       UpperCamelCase__								=     CHEAP_ARGS.copy()
       UpperCamelCase__								=     'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
       UpperCamelCase__								=     0.1 if task == 'translation' else 0

       UpperCamelCase__								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
       UpperCamelCase__								=     self.get_auto_remove_tmp_dir()
       args_d.update(
           data_dir=snake_case_			,					model_name_or_path=snake_case_			,					tokenizer_name=snake_case_			,					train_batch_size=2			,					eval_batch_size=2			,					output_dir=snake_case_			,					do_predict=snake_case_			,					task=snake_case_			,					src_lang='en_XX'			,					tgt_lang='ro_RO'			,					freeze_encoder=snake_case_			,					freeze_embeds=snake_case_			,					)
       assert "n_train" in args_d
       UpperCamelCase__								=     argparse.Namespace(**snake_case_   )
       UpperCamelCase__								=     main(snake_case_   )

       UpperCamelCase__								=     module.model.get_input_embeddings()
       assert not input_embeds.weight.requires_grad
       if model == T5_TINY:
           UpperCamelCase__								=     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__								=     module.model.model
           UpperCamelCase__								=     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__								=     module.model.model
           UpperCamelCase__								=     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__								=     load_json(module.output_dir / 'text_batch.json'   )
       assert isinstance(snake_case_			,					snake_case_   )
       assert len(snake_case_   ) >= 4

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       UpperCamelCase__								=     CHEAP_ARGS.copy()

       UpperCamelCase__								=     'summarization'
       UpperCamelCase__								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

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

       # test models whose config includes the extra_model_args
       UpperCamelCase__								=     BART_TINY
       UpperCamelCase__								=     self.get_auto_remove_tmp_dir()
       UpperCamelCase__								=     args_d.copy()
       args_da.update(
           model_name_or_path=snake_case_			,					output_dir=snake_case_			,					)
       UpperCamelCase__								=     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
       for p in extra_model_params:
           UpperCamelCase__								=     0.5
       UpperCamelCase__								=     argparse.Namespace(**snake_case_   )
       UpperCamelCase__								=     main(snake_case_   )
       for p in extra_model_params:
           assert getattr(model.config			,					snake_case_   ) == 0.5, F'failed to override the model config for param {p}'

       # test models whose config doesn't include the extra_model_args
       UpperCamelCase__								=     T5_TINY
       UpperCamelCase__								=     self.get_auto_remove_tmp_dir()
       UpperCamelCase__								=     args_d.copy()
       args_da.update(
           model_name_or_path=snake_case_			,					output_dir=snake_case_			,					)
       UpperCamelCase__								=     'encoder_layerdrop'
       UpperCamelCase__								=     0.5
       UpperCamelCase__								=     argparse.Namespace(**snake_case_   )
       with pytest.raises(snake_case_   ) as excinfo:
           UpperCamelCase__								=     main(snake_case_   )
       assert str(excinfo.value   ) == F'model config doesn\'t have a `{unsupported_param}` attribute'







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__								=     CHEAP_ARGS.copy()

       UpperCamelCase__								=     'summarization'
       UpperCamelCase__								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

       UpperCamelCase__								=     BART_TINY
       UpperCamelCase__								=     self.get_auto_remove_tmp_dir()

       args_d.update(
           data_dir=snake_case_			,					model_name_or_path=snake_case_			,					output_dir=snake_case_			,					tokenizer_name=snake_case_			,					train_batch_size=2			,					eval_batch_size=2			,					do_predict=snake_case_			,					task=snake_case_			,					src_lang='en_XX'			,					tgt_lang='ro_RO'			,					freeze_encoder=snake_case_			,					freeze_embeds=snake_case_			,					)

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

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







from math import cos, sin







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=float(lowerCAmelCase_     )
     return [[scaling_factor * int(x == y     ) for x in range(2     )] for y in range(2     )]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     return [[c, -s], [s, c]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[c * c, cs], [cs, s * s]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f'    {scaling(5) = }')
print(f'  {rotation(45) = }')
print(f'{projection(45) = }')



print(f'{reflection(45) = }')

