





from typing import Any

class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     data
														UpperCamelCase__			       =     None






class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					):
														UpperCamelCase__			       =     None






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.head
														while temp is not None:
																					print(temp.data					,	end=""" """					)
																					UpperCamelCase__			       =     temp.next
														print()






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     Node(__lowerCAmelCase					)
														UpperCamelCase__			       =     self.head
														UpperCamelCase__			       =     new_node







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														if node_data_a == node_data_a:
																					return
														else:
																					UpperCamelCase__			       =     self.head
																					while node_a is not None and node_a.data != node_data_a:
																												UpperCamelCase__			       =     node_a.next

																					UpperCamelCase__			       =     self.head
																					while node_a is not None and node_a.data != node_data_a:
																												UpperCamelCase__			       =     node_a.next

																					if node_a is None or node_a is None:
																												return

																					UpperCamelCase__       ,       UpperCamelCase__			       =     node_a.data, node_a.data


if __name__ == "__main__":
					UpperCamelCase__     =	LinkedList()
					for i in range(5, 0, -1):
										ll.push(i)

					ll.print_list()

					ll.swap_nodes(1, 4)
					print("After swapping")
					ll.print_list()











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     __SCREAMING_SNAKE_CASE   (       _a    ):






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

														UpperCamelCase__			       =     cached_path(
														    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""					,	extract_compressed_file=__lowerCAmelCase					,	)
														UpperCamelCase__			       =     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					):
														UpperCamelCase__			       =     {
														    """$MAX_LEN""": 64,
														    """$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__			       =     (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""					)[1].strip()
														UpperCamelCase__			       =     bash_script.replace("""\\\n"""					,	""""""					).strip().replace("""\"$@\""""					,	""""""					)
														for k, v in env_vars_to_replace.items():
																					UpperCamelCase__			       =     bash_script.replace(__lowerCAmelCase					,	str(__lowerCAmelCase					)					)
														UpperCamelCase__			       =     self.get_auto_remove_tmp_dir()

														# bash_script = bash_script.replace("--fp16 ", "")
														UpperCamelCase__			       =     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__			       =     ["""finetune.py"""] + bash_script.split() + args
														with patch.object(__lowerCAmelCase					,	"""argv"""					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     argparse.ArgumentParser()
																					UpperCamelCase__			       =     pl.Trainer.add_argparse_args(__lowerCAmelCase					)
																					UpperCamelCase__			       =     SummarizationModule.add_model_specific_args(__lowerCAmelCase					,	os.getcwd()					)
																					UpperCamelCase__			       =     parser.parse_args()
																					UpperCamelCase__			       =     main(__lowerCAmelCase					)

														# Check metrics
														UpperCamelCase__			       =     load_json(model.metrics_save_path					)
														UpperCamelCase__			       =     metrics["""val"""][0]
														UpperCamelCase__			       =     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
														UpperCamelCase__			       =     os.listdir(__lowerCAmelCase					)
														UpperCamelCase__			       =     [x for x in contents if x.endswith(""".ckpt"""					)][0]
														UpperCamelCase__			       =     os.path.join(args.output_dir					,	__lowerCAmelCase					)
														UpperCamelCase__			       =     torch.load(__lowerCAmelCase					,	map_location="""cpu"""					)
														UpperCamelCase__			       =     """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__			       =     {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   (       _a    ):






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

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

																					UpperCamelCase__			       =     distill_main(__lowerCAmelCase					)

														# Check metrics
														UpperCamelCase__			       =     load_json(model.metrics_save_path					)
														UpperCamelCase__			       =     metrics["""val"""][0]
														UpperCamelCase__			       =     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
														UpperCamelCase__			       =     os.listdir(__lowerCAmelCase					)
														UpperCamelCase__			       =     [x for x in contents if x.endswith(""".ckpt"""					)][0]
														UpperCamelCase__			       =     os.path.join(args.output_dir					,	__lowerCAmelCase					)
														UpperCamelCase__			       =     torch.load(__lowerCAmelCase					,	map_location="""cpu"""					)
														UpperCamelCase__			       =     """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__			       =     {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




