

"""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


snake_case_ : Tuple   =							"""sshleifer/mar_enro_6_3_student"""







class    snake_case__	(			__a  ):







			def       __lowerCAmelCase					(      self      :			Tuple	):





								'''simple docstring'''



								super().setUp()

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







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





								'''simple docstring'''



								MarianMTModel.from_pretrained(_lowercase	)







			@slow
			@require_torch_gpu
			def       __lowerCAmelCase					(      self      :			Tuple	):





								'''simple docstring'''



								UpperCAmelCase      : Tuple   =					{
								    '''$MAX_LEN''': 64,
								    '''$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
								UpperCAmelCase      : List[Any]   =					(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split("finetune.py"	)[1].strip()
								UpperCAmelCase      : str   =					bash_script.replace("\\\n"      ,	""	).strip().replace("\"$@\""      ,	""	)
								for k, v in env_vars_to_replace.items():
													UpperCAmelCase      : str   =					bash_script.replace(_lowercase      ,	str(_lowercase	)	)
								UpperCAmelCase      : Any   =					self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								UpperCAmelCase      : 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

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

								# Check metrics
								UpperCAmelCase      : List[Any]   =					load_json(model.metrics_save_path	)
								UpperCAmelCase      : Tuple   =					metrics['''val'''][0]
								UpperCAmelCase      : 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}"""]      ,	_lowercase	)

								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"]      ,	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
								UpperCAmelCase      : int   =					os.listdir(_lowercase	)
								UpperCAmelCase      : List[Any]   =					[x for x in contents if x.endswith(".ckpt"	)][0]
								UpperCAmelCase      : List[Any]   =					os.path.join(args.output_dir      ,	_lowercase	)
								UpperCAmelCase      : Any   =					torch.load(_lowercase      ,	map_location="cpu"	)
								UpperCAmelCase      : 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:
													UpperCAmelCase      : List[Any]   =					{os.path.basename(_lowercase	) 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    snake_case__	(			__a  ):







			@timeout_decorator.timeout(6_00	)
			@slow
			@require_torch_gpu
			def       __lowerCAmelCase					(      self      :			Optional[int]	):





								'''simple docstring'''



								UpperCAmelCase      : Dict   =					f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
								UpperCAmelCase      : Optional[int]   =					{
								    '''--fp16_opt_level=O1''': '''''',
								    '''$MAX_LEN''': 1_28,
								    '''$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
								UpperCAmelCase      : Union[str, Any]   =					(
								    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split("distillation.py"	)[1].strip()
								)
								UpperCAmelCase      : Dict   =					bash_script.replace("\\\n"      ,	""	).strip().replace("\"$@\""      ,	""	)
								UpperCAmelCase      : int   =					bash_script.replace("--fp16 "      ,	" "	)

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

													UpperCAmelCase      : Tuple   =					distill_main(_lowercase	)

								# Check metrics
								UpperCAmelCase      : Union[str, Any]   =					load_json(model.metrics_save_path	)
								UpperCAmelCase      : Any   =					metrics['''val'''][0]
								UpperCAmelCase      : Optional[int]   =					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}"""]      ,	_lowercase	)

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



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


UpperCAmelCase_           =				'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
  import torch

  print('''Torch version:''', torch.__version__)
  print('''Cuda available:''', torch.cuda.is_available())
  print('''Cuda version:''', torch.version.cuda)
  print('''CuDNN version:''', torch.backends.cudnn.version())
  print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
  print('''Torch version:''', None)

try:
  import transformers

  print('''transformers version:''', transformers.__version__)
except ImportError:
  print('''transformers version:''', None)