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-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			(       lowercase_:		Path						,  lowercase_:		list			):
       _UpperCamelCase       :    Tuple					  =					'\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 SCREAMING_SNAKE_CASE			(       lowercase_:		List[str]			):






       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					(    snake_case__  ):



 '''simple docstring'''

 @classmethod
 def    __snake_case   (     cls    :						Dict					)	->							Any:
        logging.disable(logging.CRITICAL					)  # remove noisy download output from tracebacks
        return cls

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

 def    __snake_case   (     self    :						List[Any]					)	->							List[str]:
        _UpperCamelCase       :    Tuple					  =					{
            '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',
        }
        _UpperCamelCase       :    Optional[int]					  =					self._test_distiller_cli(lowerCamelCase    ,     check_contents=lowerCamelCase					)

        _UpperCamelCase       :    Optional[Any]					  =					list(Path(model.output_dir					).glob('*.ckpt'					)					)
        self.assertEqual(1    ,     len(lowerCamelCase					)					)
        _UpperCamelCase       :    Dict					  =					list(Path(model.output_dir					).glob('**/*.bin'					)					)
        self.assertEqual(len(lowerCamelCase					)    ,     2					)
        _UpperCamelCase       :    Tuple					  =					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[str]					  =					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    __snake_case   (     self    :						Dict					)	->							Optional[Any]:
        _UpperCamelCase       :    str					  =					AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase					)
        _UpperCamelCase       :    str					  =					model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
        _UpperCamelCase       :    Dict					  =					torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,     dtype=torch.long    ,     device=model.device					)
        _UpperCamelCase       :    int					  =					target_ids[:, :-1].contiguous()  # Why this line?
        _UpperCamelCase       :    Optional[Any]					  =					target_ids[:, 1:].clone()  # why clone?
        _UpperCamelCase       :    List[Any]					  =					model(
            lowerCamelCase    ,     attention_mask=lowerCamelCase    ,     decoder_input_ids=lowerCamelCase    ,     labels=lowerCamelCase    ,     use_cache=lowerCamelCase					).loss

        _UpperCamelCase       :    List[Any]					  =					model(lowerCamelCase    ,     attention_mask=lowerCamelCase    ,     decoder_input_ids=lowerCamelCase    ,     use_cache=lowerCamelCase					).logits

        _UpperCamelCase       :    Union[str, Any]					  =					nn.functional.log_softmax(lowerCamelCase    ,     dim=-1					)
        _UpperCamelCase       :    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    __snake_case   (     self    :						int					)	->							Union[str, Any]:
        _UpperCamelCase       :    List[str]					  =					{
            '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',
        }
        _UpperCamelCase       :    Union[str, Any]					  =					self._test_distiller_cli(lowerCamelCase    ,     check_contents=lowerCamelCase					)
        assert model.model.config.model_type == "mbart"

        _UpperCamelCase       :    Tuple					  =					list(Path(model.output_dir					).glob('*.ckpt'					)					)
        self.assertEqual(1    ,     len(lowerCamelCase					)					)
        _UpperCamelCase       :    int					  =					list(Path(model.output_dir					).glob('**/*.bin'					)					)
        _UpperCamelCase       :    List[str]					  =					list(Path(model.output_dir					).glob('best_tfmr/*'					)					)
        assert len(lowerCamelCase					) > 2
        self.assertEqual(len(lowerCamelCase					)    ,     2					)

 def    __snake_case   (     self    :						Any					)	->							Any:
        _UpperCamelCase       :    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    __snake_case   (     self    :						Optional[int]					)	->							List[Any]:
        _UpperCamelCase       :    Tuple					  =					{
            'teacher': T5_TINY,
            'student': T5_TINIER,
            'model_name_or_path': T5_TINIER,
            'tokenizer_name': T5_TINIER,
        }
        self._test_distiller_cli(lowerCamelCase					)

 def    __snake_case   (     self    :						str    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						Optional[int]=True					)	->							Any:
        _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       :    Any					  =					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       :    List[str]					  =					os.listdir(lowerCamelCase					)
        _UpperCamelCase       :    int					  =					{os.path.basename(lowerCamelCase					) for p in contents}
        _UpperCamelCase       :    List[str]					  =					[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       :    Dict					  =					load_json(model.metrics_save_path					)
        _UpperCamelCase       :    int					  =					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}"]    ,     lowerCamelCase					)
        _UpperCamelCase       :    Tuple					  =					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 				__lowerCAmelCase					(    snake_case__  ):



 '''simple docstring'''

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

        _UpperCamelCase       :    Tuple					  =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
        _UpperCamelCase       :    str					  =					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       :    int					  =					argparse.Namespace(**lowerCamelCase					)
        _UpperCamelCase       :    Tuple					  =					main(lowerCamelCase					)

        _UpperCamelCase       :    Dict					  =					module.model.get_input_embeddings()
        assert not input_embeds.weight.requires_grad
        if model == T5_TINY:
               _UpperCamelCase       :    Optional[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:
               _UpperCamelCase       :    List[Any]					  =					module.model.model
               _UpperCamelCase       :    Optional[int]					  =					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       :    Optional[int]					  =					module.model.model
               _UpperCamelCase       :    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

        _UpperCamelCase       :    Any					  =					load_json(module.output_dir / 'text_batch.json'					)
        assert isinstance(lowerCamelCase    ,     lowerCamelCase					)
        assert len(lowerCamelCase					) >= 4

 def    __snake_case   (     self    :						Union[str, Any]					)	->							Dict:
        _UpperCamelCase       :    dict					  =					CHEAP_ARGS.copy()

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

 def    __snake_case   (     self    :						int					)	->							Optional[int]:
        _UpperCamelCase       :    dict					  =					CHEAP_ARGS.copy()

        _UpperCamelCase       :    Dict					  =					'summarization'
        _UpperCamelCase       :    Optional[Any]					  =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

        _UpperCamelCase       :    int					  =					BART_TINY
        _UpperCamelCase       :    Dict					  =					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       :    Any					  =					argparse.ArgumentParser()
        _UpperCamelCase       :    int					  =					pl.Trainer.add_argparse_args(lowerCamelCase					)
        _UpperCamelCase       :    Dict					  =					SummarizationModule.add_model_specific_args(lowerCamelCase    ,     os.getcwd()					)
        _UpperCamelCase       :    Any					  =					{'--help': True}

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




def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):

       # validate:
       if not isinstance(lowercase_						,  lowercase_			) or not all(
           isinstance(lowercase_						,  lowercase_			) and item >= 0 for item in citations			):
              raise ValueError('The citations should be a list of non negative integers.'			)

       citations.sort()
       _UpperCamelCase       :    List[str]					  =					len(lowercase_			)

       for i in range(lowercase_			):
              if citations[len_citations - 1 - i] <= i:
                     return i

       return len_citations


if __name__ == "__main__":
 import doctest

 doctest.testmod()



