

'''simple docstring'''


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


lowerCAmelCase__    :							Any					    =    """sshleifer/mar_enro_6_3_student"""




class      a      (					SCREAMING_SNAKE_CASE_							):






						"""simple docstring"""




						def    __magic_name__				(				self       :   int	):



								'''simple docstring'''



								super().setUp()

								snake_case__    :							Optional[int]							    =	cached_path(
								    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''      ,      extract_compressed_file=snake_case_      ,      )
								snake_case__    :							Optional[Any]							    =	F"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""







						@slow
						@require_torch_gpu
						def    __magic_name__				(				self       :   str	):



								'''simple docstring'''



								MarianMTModel.from_pretrained(snake_case_	)







						@slow
						@require_torch_gpu
						def    __magic_name__				(				self       :   Union[str, Any]	):



								'''simple docstring'''



								snake_case__    :							Optional[int]							    =	{
								    '$MAX_LEN': 6_4,
								    '$BS': 6_4,
								    '$GAS': 1,
								    '$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
								snake_case__    :							List[Any]							    =	(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('''finetune.py'''	)[1].strip()
								snake_case__    :							List[Any]							    =	bash_script.replace('''\\\n'''      ,      ''''''	).strip().replace('''"$@"'''      ,      ''''''	)
								for k, v in env_vars_to_replace.items():
										snake_case__    :							Tuple							    =	bash_script.replace(snake_case_      ,      str(snake_case_	)	)
								snake_case__    :							Union[str, Any]							    =	self.get_auto_remove_tmp_dir()

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

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

								# Check metrics
								snake_case__    :							int							    =	load_json(model.metrics_save_path	)
								snake_case__    :							int							    =	metrics['val'][0]
								snake_case__    :							Optional[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}"""]      ,      snake_case_	)

								self.assertGreater(last_step_stats['''val_avg_gen_time''']      ,      0.0_1	)
								# 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''']      ,      1_7	)

								# 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
								snake_case__    :							Union[str, Any]							    =	os.listdir(snake_case_	)
								snake_case__    :							Any							    =	[x for x in contents if x.endswith('''.ckpt'''	)][0]
								snake_case__    :							Dict							    =	os.path.join(args.output_dir      ,      snake_case_	)
								snake_case__    :							Optional[Any]							    =	torch.load(snake_case_      ,      map_location='''cpu'''	)
								snake_case__    :							List[str]							    =	'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:
										snake_case__    :							List[Any]							    =	{os.path.basename(snake_case_	) 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      a      (					SCREAMING_SNAKE_CASE_							):






						"""simple docstring"""




						@timeout_decorator.timeout(6_0_0	)
						@slow
						@require_torch_gpu
						def    __magic_name__				(				self       :   str	):



								'''simple docstring'''



								snake_case__    :							Union[str, Any]							    =	F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
								snake_case__    :							Dict							    =	{
								    '--fp16_opt_level=O1': '',
								    '$MAX_LEN': 1_2_8,
								    '$BS': 1_6,
								    '$GAS': 1,
								    '$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
								snake_case__    :							Any							    =	(
								    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('''distillation.py'''	)[1].strip()
								)
								snake_case__    :							Optional[Any]							    =	bash_script.replace('''\\\n'''      ,      ''''''	).strip().replace('''"$@"'''      ,      ''''''	)
								snake_case__    :							List[str]							    =	bash_script.replace('''--fp16 '''      ,      ''' '''	)

								for k, v in env_vars_to_replace.items():
										snake_case__    :							Tuple							    =	bash_script.replace(snake_case_      ,      str(snake_case_	)	)
								snake_case__    :							Dict							    =	self.get_auto_remove_tmp_dir()
								snake_case__    :							Optional[int]							    =	bash_script.replace('''--fp16'''      ,      ''''''	)
								snake_case__    :							List[Any]							    =	6
								snake_case__    :							Dict							    =	(
								    ['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(snake_case_      ,      '''argv'''      ,      snake_case_	):
										snake_case__    :							List[str]							    =	argparse.ArgumentParser()
										snake_case__    :							int							    =	pl.Trainer.add_argparse_args(snake_case_	)
										snake_case__    :							Optional[int]							    =	SummarizationDistiller.add_model_specific_args(snake_case_      ,      os.getcwd()	)
										snake_case__    :							List[Any]							    =	parser.parse_args()
										# assert args.gpus == gpus THIS BREAKS for multi_gpu

										snake_case__    :							Union[str, Any]							    =	distill_main(snake_case_	)

								# Check metrics
								snake_case__    :							List[Any]							    =	load_json(model.metrics_save_path	)
								snake_case__    :							List[str]							    =	metrics['val'][0]
								snake_case__    :							Tuple							    =	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"] >= 0.0_1

								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}"""]      ,      snake_case_	)

								# check lightning ckpt can be loaded and has a reasonable statedict
								snake_case__    :							int							    =	os.listdir(snake_case_	)
								snake_case__    :							str							    =	[x for x in contents if x.endswith('''.ckpt'''	)][0]
								snake_case__    :							int							    =	os.path.join(args.output_dir      ,      snake_case_	)
								snake_case__    :							List[str]							    =	torch.load(snake_case_      ,      map_location='''cpu'''	)
								snake_case__    :							Tuple							    =	'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:
										snake_case__    :							Dict							    =	{os.path.basename(snake_case_	) 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








"""simple docstring"""







# Lint as: python3
import itertools
import os
import re


a_							=    re.compile(r"""([A-Z]+)([A-Z][a-z])""")
a_							=    re.compile(r"""([a-z\d])([A-Z])""")

a_							=    re.compile(r"""(?<!_)_(?!_)""")
a_							=    re.compile(r"""(_{2,})""")

a_							=    r"""^\w+(\.\w+)*$"""

a_							=    r"""<>:/\|?*"""

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					str	       =    _uppercase_uppercase_re.sub(r'\1_\2'      ,							__a						)
			_lowerCamelCase					:					Tuple	       =    _lowercase_uppercase_re.sub(r'\1_\2'      ,							__a						)
			return name.lower()

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					Dict	       =    _single_underscore_re.split(__a						)
			_lowerCamelCase					:					Tuple	       =    [_multiple_underscores_re.split(__a						) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(__a						) if n != ''						)

def     UpperCAmelCase_					(				__a   :			List[Any]						):




			'''simple docstring'''



			if os.path.basename(__a						) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"						)
			return camelcase_to_snakecase(__a						)

def     UpperCAmelCase_					(				__a   :			Union[str, Any]      ,							__a   :			Optional[int]						):




			'''simple docstring'''



			if os.path.basename(__a						) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"						)
			if not re.match(_split_re      ,							__a						):
						raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."						)
			return f"{filename_prefix_for_name(__a						)}-{split}"

def     UpperCAmelCase_					(				__a   :			Any      ,							__a   :			Union[str, Any]      ,							__a   :			List[Any]      ,							__a   :			List[str]=None						):




			'''simple docstring'''



			_lowerCamelCase					:					List[Any]	       =    filename_prefix_for_split(__a      ,							__a						)
			if filetype_suffix:
						prefix += f".{filetype_suffix}"
			_lowerCamelCase					:					List[str]	       =    os.path.join(__a      ,							__a						)
			return f"{filepath}*"



def     UpperCAmelCase_					(				__a   :			str      ,							__a   :			List[Any]      ,							__a   :			List[str]      ,							__a   :			Tuple=None      ,							__a   :			Tuple=None						):




			'''simple docstring'''



			_lowerCamelCase					:					Tuple	       =    filename_prefix_for_split(__a      ,							__a						)
			_lowerCamelCase					:					List[str]	       =    os.path.join(__a      ,							__a						)

			if shard_lengths:
						_lowerCamelCase					:					Union[str, Any]	       =    len(__a						)
						_lowerCamelCase					:					str	       =    [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(__a						)]
						if filetype_suffix:
									_lowerCamelCase					:					int	       =    [filename + f".{filetype_suffix}" for filename in filenames]
						return filenames
			else:
						_lowerCamelCase					:					int	       =    prefix
						if filetype_suffix:
									filename += f".{filetype_suffix}"
						return [filename]





