


"""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


__UpperCamelCase		= "sshleifer/mar_enro_6_3_student"
class    lowerCAmelCase      (   lowercase_       ):




		'''simple docstring'''







		def 				__A		(      self							)	->				List[str]:
								super().setUp()

								SCREAMING_SNAKE_CASE						=   cached_path(
								    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'		,			extract_compressed_file=_lowercase		,			)
								SCREAMING_SNAKE_CASE						=   F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'







		@slow
		@require_torch_gpu
		def 				__A		(      self							)	->				int:
								MarianMTModel.from_pretrained(_lowercase							)







		@slow
		@require_torch_gpu
		def 				__A		(      self							)	->				Optional[int]:
								SCREAMING_SNAKE_CASE						=   {
								    '$MAX_LEN': 64,
								    '$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
								SCREAMING_SNAKE_CASE						=   (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'							)[1].strip()
								SCREAMING_SNAKE_CASE						=   bash_script.replace('\\\n'		,			''							).strip().replace('"$@"'		,			''							)
								for k, v in env_vars_to_replace.items():
														SCREAMING_SNAKE_CASE						=   bash_script.replace(_lowercase		,			str(_lowercase							)							)
								SCREAMING_SNAKE_CASE						=   self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								SCREAMING_SNAKE_CASE						=   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

								SCREAMING_SNAKE_CASE						=   ['finetune.py'] + bash_script.split() + args
								with patch.object(_lowercase		,			'argv'		,			_lowercase							):
														SCREAMING_SNAKE_CASE						=   argparse.ArgumentParser()
														SCREAMING_SNAKE_CASE						=   pl.Trainer.add_argparse_args(_lowercase							)
														SCREAMING_SNAKE_CASE						=   SummarizationModule.add_model_specific_args(_lowercase		,			os.getcwd()							)
														SCREAMING_SNAKE_CASE						=   parser.parse_args()
														SCREAMING_SNAKE_CASE						=   main(_lowercase							)

								# Check metrics
								SCREAMING_SNAKE_CASE						=   load_json(model.metrics_save_path							)
								SCREAMING_SNAKE_CASE						=   metrics['val'][0]
								SCREAMING_SNAKE_CASE						=   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']		,			0.01							)
								# 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
								SCREAMING_SNAKE_CASE						=   os.listdir(_lowercase							)
								SCREAMING_SNAKE_CASE						=   [x for x in contents if x.endswith('.ckpt'							)][0]
								SCREAMING_SNAKE_CASE						=   os.path.join(args.output_dir		,			_lowercase							)
								SCREAMING_SNAKE_CASE						=   torch.load(_lowercase		,			map_location='cpu'							)
								SCREAMING_SNAKE_CASE						=   '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:
														SCREAMING_SNAKE_CASE						=   {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    lowerCAmelCase      (   lowercase_       ):




		'''simple docstring'''







		@timeout_decorator.timeout(600							)
		@slow
		@require_torch_gpu
		def 				__A		(      self							)	->				Dict:
								SCREAMING_SNAKE_CASE						=   F'{self.test_file_dir_str}/test_data/wmt_en_ro'
								SCREAMING_SNAKE_CASE						=   {
								    '--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
								SCREAMING_SNAKE_CASE						=   (
								    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'							)[1].strip()
								)
								SCREAMING_SNAKE_CASE						=   bash_script.replace('\\\n'		,			''							).strip().replace('"$@"'		,			''							)
								SCREAMING_SNAKE_CASE						=   bash_script.replace('--fp16 '		,			' '							)

								for k, v in env_vars_to_replace.items():
														SCREAMING_SNAKE_CASE						=   bash_script.replace(_lowercase		,			str(_lowercase							)							)
								SCREAMING_SNAKE_CASE						=   self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE						=   bash_script.replace('--fp16'		,			''							)
								SCREAMING_SNAKE_CASE						=   6
								SCREAMING_SNAKE_CASE						=   (
								    ['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							):
														SCREAMING_SNAKE_CASE						=   argparse.ArgumentParser()
														SCREAMING_SNAKE_CASE						=   pl.Trainer.add_argparse_args(_lowercase							)
														SCREAMING_SNAKE_CASE						=   SummarizationDistiller.add_model_specific_args(_lowercase		,			os.getcwd()							)
														SCREAMING_SNAKE_CASE						=   parser.parse_args()
														# assert args.gpus == gpus THIS BREAKS for multi_gpu

														SCREAMING_SNAKE_CASE						=   distill_main(_lowercase							)

								# Check metrics
								SCREAMING_SNAKE_CASE						=   load_json(model.metrics_save_path							)
								SCREAMING_SNAKE_CASE						=   metrics['val'][0]
								SCREAMING_SNAKE_CASE						=   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}']		,			_lowercase							)

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





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				vocab_size
											SCREAMING_SNAKE_CASE_				=				hidden_size
											SCREAMING_SNAKE_CASE_				=				num_hidden_layers
											SCREAMING_SNAKE_CASE_				=				num_attention_heads
											SCREAMING_SNAKE_CASE_				=				hidden_act
											SCREAMING_SNAKE_CASE_				=				intermediate_size
											SCREAMING_SNAKE_CASE_				=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE_				=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_				=				max_position_embeddings
											SCREAMING_SNAKE_CASE_				=				type_vocab_size
											SCREAMING_SNAKE_CASE_				=				initializer_range
											SCREAMING_SNAKE_CASE_				=				layer_norm_eps
											SCREAMING_SNAKE_CASE_				=				position_embedding_type
											SCREAMING_SNAKE_CASE_				=				use_cache
											SCREAMING_SNAKE_CASE_				=				classifier_dropout






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )





