

'''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)

UpperCAmelCase				   =  logging.getLogger()
UpperCAmelCase				   =  torch.cuda.is_available()
UpperCAmelCase				   =  {
    '''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 				__UpperCamelCase							(	lowercase__						:  Path,					lowercase__						:  list ):






     '''simple docstring'''





     __lowercase					='\n'.join(lowercase__ )
     Path(lowercase__ ).open('w' ).writelines(lowercase__ )


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


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

def 				__UpperCamelCase							(	lowercase__						:  Union[str, 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   lowerCAmelCase     (      A   ):







       @classmethod
       def 			snake_case   (							cls			: List[str]      ):







            """simple docstring"""







            logging.disable(logging.CRITICAL      )  # remove noisy download output from tracebacks
            return cls







       @slow
       @require_torch_gpu
       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=list_models()
            __lowercase					='sshleifer'
            __lowercase					=[x.modelId for x in model_list if x.modelId.startswith(__lowercase      )]
            __lowercase					=['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
            __lowercase					=[]
            for m in model_ids:
                 if m in allowed_to_be_broken:
                      continue
                 try:
                      AutoConfig.from_pretrained(__lowercase      )
                 except Exception:
                      failures.append(__lowercase      )
            assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					={'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
            self._test_distiller_cli(__lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					={
                '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',
            }
            __lowercase					=self._test_distiller_cli(__lowercase						,      check_contents=__lowercase      )

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

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







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







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

            __lowercase					=model(__lowercase						,      attention_mask=__lowercase						,      decoder_input_ids=__lowercase						,      use_cache=__lowercase      ).logits

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







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					={
                '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',
            }
            __lowercase					=self._test_distiller_cli(__lowercase						,      check_contents=__lowercase      )
            assert model.model.config.model_type == "mbart"

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







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					={
                '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(__lowercase      )







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					={
                'teacher': T5_TINY,
                'student': T5_TINIER,
                'model_name_or_path': T5_TINIER,
                'tokenizer_name': T5_TINIER,
            }
            self._test_distiller_cli(__lowercase      )







       def 			snake_case   (							self			: Any						,      __lowercase			: List[str]						,      __lowercase			: str=True      ):







            """simple docstring"""







            __lowercase					={
                '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(__lowercase      )
            __lowercase					=CHEAP_ARGS.copy()
            __lowercase					=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
            __lowercase					=self.get_auto_remove_tmp_dir()

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

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

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






class   lowerCAmelCase     (      A   ):







       @parameterized.expand(
           [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,      )
       def 			snake_case   (							self			: List[Any]						,      __lowercase			: Tuple      ):







            """simple docstring"""







            __lowercase					=CHEAP_ARGS.copy()
            __lowercase					='translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
            __lowercase					=0.1 if task == 'translation' else 0

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

            __lowercase					=module.model.get_input_embeddings()
            assert not input_embeds.weight.requires_grad
            if model == T5_TINY:
                 __lowercase					=module.model.lm_head
                 assert not lm_head.weight.requires_grad
                 assert (lm_head.weight == input_embeds.weight).all().item()
            elif model == FSMT_TINY:
                 __lowercase					=module.model.model
                 __lowercase					=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:
                 __lowercase					=module.model.model
                 __lowercase					=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

            __lowercase					=load_json(module.output_dir / 'text_batch.json'      )
            assert isinstance(__lowercase						,      __lowercase      )
            assert len(__lowercase      ) >= 4







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=CHEAP_ARGS.copy()

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

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

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

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







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=CHEAP_ARGS.copy()

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

            __lowercase					=BART_TINY
            __lowercase					=self.get_auto_remove_tmp_dir()

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

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

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





'''simple docstring'''

from __future__ import annotations

from pathlib import Path

def 				__UpperCamelCase							(	lowercase__						:  tuple[int, int],					lowercase__						:  tuple[int, int] ):






     '''simple docstring'''





     return pointa[0] * pointa[1] - pointa[1] * pointa[0]

def 				__UpperCamelCase							(	lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int ):






     '''simple docstring'''





     __lowercase					=(xa, ya)
     __lowercase					=(xa - xa, ya - ya)
     __lowercase					=(xa - xa, ya - ya)
     __lowercase					=-vector_product(lowercase__,					lowercase__ ) / vector_product(
         lowercase__,					lowercase__ )
     __lowercase					=+vector_product(lowercase__,					lowercase__ ) / vector_product(
         lowercase__,					lowercase__ )

     return a > 0 and b > 0 and a + b < 1

def 				__UpperCamelCase							(	lowercase__						:  str = "p102_triangles.txt" ):






     '''simple docstring'''





     __lowercase					=Path(lowercase__ ).parent.joinpath(lowercase__ ).read_text(encoding='utf-8' )

     __lowercase					=[]
     for line in data.strip().split('\n' ):
          triangles.append([int(lowercase__ ) for number in line.split(',' )] )

     __lowercase					=0
     __lowercase					=42

     for triangle in triangles:
          ret += contains_origin(*lowercase__ )

     return ret


if __name__ == "__main__":
       print(F'''{solution() = }''')


