



'''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__       :	Optional[Any]    = '''sshleifer/mar_enro_6_3_student'''






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					super().setUp()

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

			@slow
			@require_torch_gpu
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					MarianMTModel.from_pretrained(lowerCAmelCase__						)



			@slow
			@require_torch_gpu
			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






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

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

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

					# Check metrics
					__SCREAMING_SNAKE_CASE       :		str           =		load_json(model.metrics_save_path						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		metrics["""val"""][0]
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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"""]							,     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
					__SCREAMING_SNAKE_CASE       :		str           =		os.listdir(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		[x for x in contents if x.endswith(""".ckpt"""						)][0]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		os.path.join(args.output_dir							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		torch.load(lowerCAmelCase__							,     map_location="""cpu"""						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		"""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       :		List[str]           =		{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 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			@timeout_decorator.timeout(6_0_0						)
			@slow
			@require_torch_gpu
			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		F"{self.test_file_dir_str}/test_data/wmt_en_ro"
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		{
					    """--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
					__SCREAMING_SNAKE_CASE       :		Dict           =		(
					    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""						)[1].strip()
					)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		bash_script.replace("""\\\n"""							,     """"""						).strip().replace("""\"$@\""""							,     """"""						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		bash_script.replace("""--fp16 """							,     """ """						)

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

							__SCREAMING_SNAKE_CASE       :		List[Any]           =		distill_main(lowerCAmelCase__						)

					# Check metrics
					__SCREAMING_SNAKE_CASE       :		str           =		load_json(model.metrics_save_path						)
					__SCREAMING_SNAKE_CASE       :		str           =		metrics["""val"""][0]
					__SCREAMING_SNAKE_CASE       :		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"] >= 0.01

					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
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		os.listdir(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		[x for x in contents if x.endswith(""".ckpt"""						)][0]
					__SCREAMING_SNAKE_CASE       :		Dict           =		os.path.join(args.output_dir							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		torch.load(lowerCAmelCase__							,     map_location="""cpu"""						)
					__SCREAMING_SNAKE_CASE       :		int           =		"""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       :		Optional[Any]           =		{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




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)
		if n_element < 1:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		ValueError("""a should be a positive number"""					)
				raise my_error

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		[1]
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(0, 0, 0)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		1
		while index < n_element:
				while hamming_list[i] * 2 <= hamming_list[-1]:
						i += 1
				while hamming_list[j] * 3 <= hamming_list[-1]:
						j += 1
				while hamming_list[k] * 5 <= hamming_list[-1]:
						k += 1
				hamming_list.append(
				    min(hamming_list[i] * 2       ,      hamming_list[j] * 3       ,      hamming_list[k] * 5					)					)
				index += 1
		return hamming_list


if __name__ == "__main__":
						UpperCamelCase__       :	Optional[Any]    = input('''Enter the last number (nth term) of the Hamming Number Series: ''')
						print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
						UpperCamelCase__       :	List[str]    = hamming(int(n))
						print('''-----------------------------------------------------''')
						print(f"The list with nth numbers is: {hamming_numbers}")
						print('''-----------------------------------------------------''')