




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       a_       (     SCREAMING_SNAKE_CASE__     ):





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				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=SCREAMING_SNAKE_CASE      ,     )
				SCREAMING_SNAKE_CASE_								=						f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'





	@slow
	@require_torch_gpu
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				MarianMTModel.from_pretrained(SCREAMING_SNAKE_CASE      )





	@slow
	@require_torch_gpu
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				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(SCREAMING_SNAKE_CASE      ,     str(SCREAMING_SNAKE_CASE      )      )
				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(SCREAMING_SNAKE_CASE      ,     'argv'      ,     SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						argparse.ArgumentParser()
							SCREAMING_SNAKE_CASE_								=						pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE      ,     os.getcwd()      )
							SCREAMING_SNAKE_CASE_								=						parser.parse_args()
							SCREAMING_SNAKE_CASE_								=						main(SCREAMING_SNAKE_CASE      )

				# 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}']      ,     SCREAMING_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']      ,     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(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						[x for x in contents if x.endswith('.ckpt'      )][0]
				SCREAMING_SNAKE_CASE_								=						os.path.join(args.output_dir      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						torch.load(SCREAMING_SNAKE_CASE      ,     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(SCREAMING_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__     ):





	@timeout_decorator.timeout(600      )
	@slow
	@require_torch_gpu
	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				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(SCREAMING_SNAKE_CASE      ,     str(SCREAMING_SNAKE_CASE      )      )
				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(SCREAMING_SNAKE_CASE      ,     'argv'      ,     SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						argparse.ArgumentParser()
							SCREAMING_SNAKE_CASE_								=						pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						SummarizationDistiller.add_model_specific_args(SCREAMING_SNAKE_CASE      ,     os.getcwd()      )
							SCREAMING_SNAKE_CASE_								=						parser.parse_args()
							# assert args.gpus == gpus THIS BREAKS for multi_gpu

							SCREAMING_SNAKE_CASE_								=						distill_main(SCREAMING_SNAKE_CASE      )

				# 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"] >= 0.0_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}']      ,     SCREAMING_SNAKE_CASE      )

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






from __future__ import annotations

from collections.abc import MutableSequence



class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if len(SCREAMING_SNAKE_CASE      ) != degree + 1:
							raise ValueError(
							    'The number of coefficients should be equal to the degree + 1.'      )

				SCREAMING_SNAKE_CASE_								=						list(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						degree





	def __add__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				if self.degree > polynomial_a.degree:
							SCREAMING_SNAKE_CASE_								=						self.coefficients[:]
							for i in range(polynomial_a.degree + 1      ):
										coefficients[i] += polynomial_a.coefficients[i]
							return Polynomial(self.degree      ,     SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						polynomial_a.coefficients[:]
							for i in range(self.degree + 1      ):
										coefficients[i] += self.coefficients[i]
							return Polynomial(polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def __sub__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				return self + polynomial_a * Polynomial(0      ,     [-1]      )





	def __neg__(							self      )  ->					Polynomial:




				"""simple docstring"""



				return Polynomial(self.degree      ,     [-c for c in self.coefficients]      )





	def __mul__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + polynomial_a.degree + 1)
				for i in range(self.degree + 1      ):
							for j in range(polynomial_a.degree + 1      ):
										coefficients[i + j] += (
										    self.coefficients[i] * polynomial_a.coefficients[j]
										)

				return Polynomial(self.degree + polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int | float:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for i in range(self.degree + 1      ):
							result += self.coefficients[i] * (substitution**i)
				return result





	def __str__(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				for i in range(self.degree      ,     -1      ,     -1      ):
							if self.coefficients[i] == 0:
										continue
							elif self.coefficients[i] > 0:
										if polynomial:
													polynomial += " + "
							else:
										polynomial += " - "

							if i == 0:
										polynomial += str(abs(self.coefficients[i]      )      )
							elif i == 1:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x"
							else:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x^" + str(SCREAMING_SNAKE_CASE      )

				return polynomial





	def __repr__(							self      )  ->					str:




				"""simple docstring"""



				return self.__str__()





	def 						A_(							self      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * self.degree
				for i in range(self.degree      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i + 1] * (i + 1)
				return Polynomial(self.degree - 1      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + 2)
				SCREAMING_SNAKE_CASE_								=						constant
				for i in range(self.degree + 1      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i] / (i + 1)
				return Polynomial(self.degree + 1      ,     SCREAMING_SNAKE_CASE      )





	def __eq__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							return False

				if self.degree != polynomial_a.degree:
							return False

				for i in range(self.degree + 1      ):
							if self.coefficients[i] != polynomial_a.coefficients[i]:
										return False

				return True





	def __ne__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				return not self.__eq__(SCREAMING_SNAKE_CASE      )
