


'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__snake_case							   =  os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
		from run_translation import main  # noqa


set_seed(42)
__snake_case							   =  '''sshleifer/student_marian_en_ro_6_1'''
__snake_case							   =  '''sshleifer/tiny-mbart'''
@require_torch
class      lowercase			(							A__      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=False					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.run_trainer(
							    eval_steps=1					,     max_len=12					,     model_name=UpperCamelCase_					,     num_train_epochs=1					,     distributed=UpperCamelCase_					,     extra_args_str=UpperCamelCase_					,     predict_with_generate=UpperCamelCase_					,     do_train=UpperCamelCase_					,     do_eval=UpperCamelCase_					,     do_predict=UpperCamelCase_					,     )
							UpperCamelCase__     :Tuple							      =				TrainerState.load_from_json(os.path.join(UpperCamelCase_					,     '''trainer_state.json'''							)							).log_history

							if not do_eval:
												return

							UpperCamelCase__     :List[str]							      =				[log for log in logs if '''eval_loss''' in log.keys()]

							UpperCamelCase__     :Tuple							      =				eval_metrics[0]
							if predict_with_generate:
												assert "eval_bleu" in first_step_stats

												UpperCamelCase__     :Tuple							      =				eval_metrics[-1]
												assert isinstance(last_step_stats['''eval_bleu''']					,     UpperCamelCase_							)
												assert not math.isnan(float(last_step_stats['''eval_loss''']							)							), "eval_loss must not be `nan`"




		@require_torch_non_multi_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick()




		@require_torch_multi_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_							)




		@require_torch_multi_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp simple'''							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp simple --fp16'''							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp zero_dp_2'''					,     predict_with_generate=UpperCamelCase_							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(
							    distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''					,     predict_with_generate=UpperCamelCase_							)




		@require_apex
		@require_torch_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--fp16 --fp16_backend=apex'''							)
							# test 2nd time - was getting eval_loss': nan'
							# to reproduce the problem set distributed=False
							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--fp16 --fp16_backend=apex'''							)




		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']							)
		@require_torch_multi_gpu
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				{
							    # test with the default log_level - should be info and thus log info once
							    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
							    # test with low log_level and log_level_replica - should be noisy on all processes
							    # now the info string should appear twice on 2 processes
							    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
							    # test with high log_level and low log_level_replica
							    # now the info string should appear once only on the replica
							    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
							    # test with high log_level and log_level_replica - should be quiet on all processes
							    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
							}

							UpperCamelCase__     :Any							      =				experiments[experiment_id]
							UpperCamelCase__     :Any							      =				{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
							UpperCamelCase__     :Optional[Any]							      =				'''Running training'''
							with CaptureStderr() as cl:
												self.run_seqaseq_quick(**UpperCamelCase_					,     extra_args_str=data['''extra_args_str''']							)
							UpperCamelCase__     :Optional[Any]							      =				len(re.findall(UpperCamelCase_					,     cl.err							)							)
							self.assertEqual(UpperCamelCase_					,     data['''n_matches''']							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.run_trainer(
							    eval_steps=2					,     max_len=128					,     model_name=UpperCamelCase_					,     learning_rate=3e-4					,     num_train_epochs=10					,     distributed=UpperCamelCase_					,     )

							# Check metrics
							UpperCamelCase__     :Tuple							      =				TrainerState.load_from_json(os.path.join(UpperCamelCase_					,     '''trainer_state.json'''							)							).log_history
							UpperCamelCase__     :Union[str, Any]							      =				[log for log in logs if '''eval_loss''' in log.keys()]
							UpperCamelCase__     :Optional[Any]							      =				eval_metrics[0]
							UpperCamelCase__     :int							      =				eval_metrics[-1]

							assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
							assert isinstance(last_step_stats['''eval_bleu''']					,     UpperCamelCase_							)

							# test if do_predict saves generations and metrics
							UpperCamelCase__     :Union[str, Any]							      =				os.listdir(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				{os.path.basename(UpperCamelCase_							) for p in contents}
							assert "generated_predictions.txt" in contents
							assert "predict_results.json" in contents




		@slow
		@require_bitsandbytes
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							from transformers.training_args import OptimizerNames

							def train_and_return_metrics(UpperCamelCase_							) -> Tuple[int, float]:
												UpperCamelCase__     :Union[str, Any]							      =				'''--skip_memory_metrics 0'''

												UpperCamelCase__     :Optional[int]							      =				self.run_trainer(
												    max_len=128					,     model_name=UpperCamelCase_					,     learning_rate=3e-4					,     num_train_epochs=1					,     optim=UpperCamelCase_					,     distributed=UpperCamelCase_					,     extra_args_str=UpperCamelCase_					,     do_eval=UpperCamelCase_					,     do_predict=UpperCamelCase_					,     n_gpus_to_use=1					,     )

												# Check metrics
												UpperCamelCase__     :Optional[int]							      =				TrainerState.load_from_json(Path(UpperCamelCase_					,     '''trainer_state.json'''							)							).log_history
												UpperCamelCase__     :Optional[int]							      =				int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20							)
												UpperCamelCase__     :int							      =				int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20							)

												UpperCamelCase__     :List[Any]							      =				logs[0]['''train_loss''']
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Any							      =				train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

							UpperCamelCase__     :Dict							      =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

							UpperCamelCase__     :int							      =				gpu_peak_mem_orig + gpu_alloc_mem_orig
							UpperCamelCase__     :Dict							      =				gpu_peak_mem_bnb + gpu_alloc_mem_bnb
							UpperCamelCase__     :Any							      =				gpu_total_mem_orig - gpu_total_mem_bnb

							# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
							# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
							# in 2 bytes and the diff in optim memory usage is derived as so:
							#
							# - normal 25*8=~200MB (8 bytes per param)
							# - bnb    25*2= ~50MB (2 bytes per param)
							#
							# Thus we should expect ~150MB total memory saved.
							#
							# Peak memory should be the same - the total should be different by about that same margin
							#
							# After leaving a small margin to accommodate for differences between gpus let's check
							# that we have at least 120MB in savings
							UpperCamelCase__     :List[str]							      =				120

							# uncomment the following if this test starts failing - requires py38 for a new print feature
							# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
							# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
							# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
							# print(f"{gpu_alloc_mem_diff=}MB")
							# print(f"{gpu_peak_mem_diff=}MB")
							# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
							# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

							self.assertGreater(
							    UpperCamelCase_					,     UpperCamelCase_					,     '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
							    F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
							    F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''					,     )

							self.assertGreater(
							    UpperCamelCase_					,     UpperCamelCase_					,     '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
							    F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
							    F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''					,     )

							self.assertEqual(
							    UpperCamelCase_					,     UpperCamelCase_					,     F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = 3e-3					,     UpperCamelCase_ = "adafactor"					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     UpperCamelCase_ = 0					,     UpperCamelCase_ = True					,     UpperCamelCase_ = True					,     UpperCamelCase_ = True					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
							UpperCamelCase__     :Any							      =				self.get_auto_remove_tmp_dir()
							UpperCamelCase__     :Any							      =				F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCamelCase_							)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase_							)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

							UpperCamelCase__     :Any							      =				F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCamelCase_							)}
        '''.split()

							UpperCamelCase__     :Optional[Any]							      =				'''
            --do_predict
        '''.split()

							UpperCamelCase__     :int							      =				[]
							if do_train:
												args += args_train

							if do_eval:
												args += args_eval

							if do_predict:
												args += args_predict

							if predict_with_generate:
												args += "--predict_with_generate".split()

							if do_train:
												if optim == "adafactor":
																	args += "--adafactor".split()
												else:
																	args += F'''--optim {optim}'''.split()

							if extra_args_str is not None:
												args += extra_args_str.split()

							if distributed:
												if n_gpus_to_use is None:
																	UpperCamelCase__     :str							      =				get_gpu_count()
												UpperCamelCase__     :str							      =				get_torch_dist_unique_port()
												UpperCamelCase__     :Optional[int]							      =				F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
												UpperCamelCase__     :int							      =				[sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(UpperCamelCase_					,     env=self.get_env()							)
							else:
												UpperCamelCase__     :List[Any]							      =				['''run_translation.py'''] + args
												with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
																	main()

							return output_dir



'''simple docstring'''
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()