



import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


SCREAMING_SNAKE_CASE_:Optional[int]					=						"""sshleifer/mar_enro_6_3_student"""






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        super().setUp()

        A			:	Union[str, Any]										=       cached_path(
            """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""",		extract_compressed_file=lowerCamelCase__,		)
        A			:	str										=       f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






    @slow
    @require_torch_gpu
    def       _lowerCAmelCase (	self		):
        MarianMTModel.from_pretrained(lowerCamelCase__		)






    @slow
    @require_torch_gpu
    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       {
            """$MAX_LEN""": 64,
            """$BS""": 64,
            """$GAS""": 1,
            """$ENRO_DIR""": self.data_dir,
            """facebook/mbart-large-cc25""": MARIAN_MODEL,
            # "val_check_interval=0.25": "val_check_interval=1.0",
            """--learning_rate=3e-5""": """--learning_rate 3e-4""",
            """--num_train_epochs 6""": """--num_train_epochs 1""",
        }

        # Clean up bash script
        A			:	List[str]										=       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
        A			:	Any										=       bash_script.replace("""\\\n""",		""""""		).strip().replace("""\"$@\"""",		""""""		)
        for k, v in env_vars_to_replace.items():
            A			:	int										=       bash_script.replace(lowerCamelCase__,		str(lowerCamelCase__		)		)
        A			:	Optional[int]										=       self.get_auto_remove_tmp_dir()

        # bash_script = bash_script.replace("--fp16 ", "")
        A			:	str										=       f'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
        # XXX: args.gpus > 1 : handle multi_gpu in the future

        A			:	Optional[Any]										=       ["""finetune.py"""] + bash_script.split() + args
        with patch.object(lowerCamelCase__,		"""argv""",		lowerCamelCase__		):
            A			:	Any										=       argparse.ArgumentParser()
            A			:	Union[str, Any]										=       pl.Trainer.add_argparse_args(lowerCamelCase__		)
            A			:	Union[str, Any]										=       SummarizationModule.add_model_specific_args(lowerCamelCase__,		os.getcwd()		)
            A			:	List[Any]										=       parser.parse_args()
            A			:	List[str]										=       main(lowerCamelCase__		)

        # Check metrics
        A			:	str										=       load_json(model.metrics_save_path		)
        A			:	Tuple										=       metrics["""val"""][0]
        A			:	int										=       metrics["""val"""][-1]
        self.assertEqual(len(metrics["""val"""]		),		(args.max_epochs / args.val_check_interval)		)
        assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}'''],		lowerCamelCase__		)

        self.assertGreater(last_step_stats["""val_avg_gen_time"""],		0.01		)
        # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
        self.assertLessEqual(last_step_stats["""val_avg_gen_time"""],		1.0		)

        # test learning requirements:

        # 1. BLEU improves over the course of training by more than 2 pts
        self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""],		2		)

        # 2. BLEU finishes above 17
        self.assertGreater(last_step_stats["""val_avg_bleu"""],		17		)

        # 3. test BLEU and val BLEU within ~1.1 pt.
        self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]		),		1.1		)

        # check lightning ckpt can be loaded and has a reasonable statedict
        A			:	Dict										=       os.listdir(lowerCamelCase__		)
        A			:	List[str]										=       [x for x in contents if x.endswith(""".ckpt"""		)][0]
        A			:	List[str]										=       os.path.join(args.output_dir,		lowerCamelCase__		)
        A			:	Tuple										=       torch.load(lowerCamelCase__,		map_location="""cpu"""		)
        A			:	Any										=       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
        assert expected_key in ckpt["state_dict"]
        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

        # TODO: turn on args.do_predict when PL bug fixed.
        if args.do_predict:
            A			:	Tuple										=       {os.path.basename(lowerCamelCase__		) for p in contents}
            assert "test_generations.txt" in contents
            assert "test_results.txt" in contents
            # assert len(metrics["val"]) ==  desired_n_evals
            assert len(metrics["""test"""]		) == 1






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @timeout_decorator.timeout(600		)
    @slow
    @require_torch_gpu
    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
        A			:	str										=       {
            """--fp16_opt_level=O1""": """""",
            """$MAX_LEN""": 128,
            """$BS""": 16,
            """$GAS""": 1,
            """$ENRO_DIR""": data_dir,
            """$m""": """sshleifer/student_marian_en_ro_6_1""",
            """val_check_interval=0.25""": """val_check_interval=1.0""",
        }

        # Clean up bash script
        A			:	int										=       (
            (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
        )
        A			:	Tuple										=       bash_script.replace("""\\\n""",		""""""		).strip().replace("""\"$@\"""",		""""""		)
        A			:	Optional[Any]										=       bash_script.replace("""--fp16 """,		""" """		)

        for k, v in env_vars_to_replace.items():
            A			:	int										=       bash_script.replace(lowerCamelCase__,		str(lowerCamelCase__		)		)
        A			:	Union[str, Any]										=       self.get_auto_remove_tmp_dir()
        A			:	Optional[int]										=       bash_script.replace("""--fp16""",		""""""		)
        A			:	Any										=       6
        A			:	int										=       (
            ["""distillation.py"""]
            + bash_script.split()
            + [
                f'''--output_dir={output_dir}''',
                """--gpus=1""",
                """--learning_rate=1e-3""",
                f'''--num_train_epochs={epochs}''',
                """--warmup_steps=10""",
                """--val_check_interval=1.0""",
                """--do_predict""",
            ]
        )
        with patch.object(lowerCamelCase__,		"""argv""",		lowerCamelCase__		):
            A			:	Union[str, Any]										=       argparse.ArgumentParser()
            A			:	List[str]										=       pl.Trainer.add_argparse_args(lowerCamelCase__		)
            A			:	Any										=       SummarizationDistiller.add_model_specific_args(lowerCamelCase__,		os.getcwd()		)
            A			:	Union[str, Any]										=       parser.parse_args()
            # assert args.gpus == gpus THIS BREAKS for multi_gpu

            A			:	List[str]										=       distill_main(lowerCamelCase__		)

        # Check metrics
        A			:	Optional[int]										=       load_json(model.metrics_save_path		)
        A			:	str										=       metrics["""val"""][0]
        A			:	Any										=       metrics["""val"""][-1]
        assert len(metrics["""val"""]		) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

        assert last_step_stats["val_avg_gen_time"] >= 0.01

        assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
        assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
        assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}'''],		lowerCamelCase__		)

        # check lightning ckpt can be loaded and has a reasonable statedict
        A			:	Tuple										=       os.listdir(lowerCamelCase__		)
        A			:	Any										=       [x for x in contents if x.endswith(""".ckpt"""		)][0]
        A			:	List[str]										=       os.path.join(args.output_dir,		lowerCamelCase__		)
        A			:	List[Any]										=       torch.load(lowerCamelCase__,		map_location="""cpu"""		)
        A			:	Optional[Any]										=       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
        assert expected_key in ckpt["state_dict"]
        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

        # TODO: turn on args.do_predict when PL bug fixed.
        if args.do_predict:
            A			:	Dict										=       {os.path.basename(lowerCamelCase__		) for p in contents}
            assert "test_generations.txt" in contents
            assert "test_results.txt" in contents
            # assert len(metrics["val"]) ==  desired_n_evals
            assert len(metrics["""test"""]		) == 1







from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


