

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


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


set_seed(42)
a		='sshleifer/student_marian_en_ro_6_1'
a		='sshleifer/tiny-mbart'






@require_torch
class        A_		(       SCREAMING_SNAKE_CASE				):
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Tuple=False   ,SCREAMING_SNAKE_CASE__		:			int=None   ,SCREAMING_SNAKE_CASE__		:			str=True   ,SCREAMING_SNAKE_CASE__		:			Dict=True   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=True   ,SCREAMING_SNAKE_CASE__		:			Any=True   ,):
								__lowerCamelCase				:				Any					       = self.run_trainer(
								    eval_steps=1   ,max_len=1_2   ,model_name=_snake_case   ,num_train_epochs=1   ,distributed=_snake_case   ,extra_args_str=_snake_case   ,predict_with_generate=_snake_case   ,do_train=_snake_case   ,do_eval=_snake_case   ,do_predict=_snake_case   ,)
								__lowerCamelCase				:				List[str]					       = TrainerState.load_from_json(os.path.join(_snake_case   ,'trainer_state.json')).log_history

								if not do_eval:
									return

								__lowerCamelCase				:				Tuple					       = [log for log in logs if 'eval_loss' in log.keys()]

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

									__lowerCamelCase				:				Union[str, Any]					       = eval_metrics[-1]
									assert isinstance(last_step_stats['eval_bleu']   ,_snake_case)
									assert not math.isnan(float(last_step_stats['eval_loss'])), "eval_loss must not be `nan`"
							@require_torch_non_multi_gpu
							def     lowerCAmelCase     (		self		:			Any):
								self.run_seqaseq_quick()
							@require_torch_multi_gpu
							def     lowerCAmelCase     (		self		:			str):
								self.run_seqaseq_quick(distributed=_snake_case)
							@require_torch_multi_gpu
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								self.run_seqaseq_quick(distributed=_snake_case)
							@unittest.skip('Requires an update of the env running those tests')
							@require_torch_multi_gpu
							@require_fairscale
							def     lowerCAmelCase     (		self		:			List[Any]):
								self.run_seqaseq_quick(distributed=_snake_case   ,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		:			Optional[Any]):
								self.run_seqaseq_quick(distributed=_snake_case   ,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		:			List[Any]):
								self.run_seqaseq_quick(distributed=_snake_case   ,extra_args_str='--sharded_ddp zero_dp_2'   ,predict_with_generate=_snake_case)
							@unittest.skip('Requires an update of the env running those tests')
							@require_torch_multi_gpu
							@require_fairscale
							def     lowerCAmelCase     (		self		:			Tuple):
								self.run_seqaseq_quick(
								    distributed=_snake_case   ,extra_args_str='--sharded_ddp zero_dp_2 --fp16'   ,predict_with_generate=_snake_case)
							@require_apex
							@require_torch_gpu
							def     lowerCAmelCase     (		self		:			str):
								self.run_seqaseq_quick(distributed=_snake_case   ,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=_snake_case   ,extra_args_str='--fp16 --fp16_backend=apex')
							@parameterized.expand(['base', 'low', 'high', 'mixed'])
							@require_torch_multi_gpu
							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								__lowerCamelCase				:				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},
								}

								__lowerCamelCase				:				str					       = experiments[experiment_id]
								__lowerCamelCase				:				int					       = {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
								__lowerCamelCase				:				Union[str, Any]					       = 'Running training'
								with CaptureStderr() as cl:
									self.run_seqaseq_quick(**_snake_case   ,extra_args_str=data['extra_args_str'])
								__lowerCamelCase				:				List[Any]					       = len(re.findall(_snake_case   ,cl.err))
								self.assertEqual(_snake_case   ,data['n_matches'])
							@slow
							def     lowerCAmelCase     (		self		:			List[Any]):
								__lowerCamelCase				:				str					       = self.run_trainer(
								    eval_steps=2   ,max_len=1_2_8   ,model_name=_snake_case   ,learning_rate=3E-4   ,num_train_epochs=1_0   ,distributed=_snake_case   ,)

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

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

								# test if do_predict saves generations and metrics
								__lowerCamelCase				:				Union[str, Any]					       = os.listdir(_snake_case)
								__lowerCamelCase				:				List[str]					       = {os.path.basename(_snake_case) for p in contents}
								assert "generated_predictions.txt" in contents
								assert "predict_results.json" in contents
							@slow
							@require_bitsandbytes
							def     lowerCAmelCase     (		self		:			List[Any]):
								from transformers.training_args import OptimizerNames

								def train_and_return_metrics(SCREAMING_SNAKE_CASE__		:			Any) -> Tuple[int, float]:
									__lowerCamelCase				:				List[str]					       = '--skip_memory_metrics 0'

									__lowerCamelCase				:				Dict					       = self.run_trainer(
									    max_len=1_2_8   ,model_name=_snake_case   ,learning_rate=3E-4   ,num_train_epochs=1   ,optim=_snake_case   ,distributed=_snake_case   ,extra_args_str=_snake_case   ,do_eval=_snake_case   ,do_predict=_snake_case   ,n_gpus_to_use=1   ,)

									# Check metrics
									__lowerCamelCase				:				Optional[Any]					       = TrainerState.load_from_json(Path(_snake_case   ,'trainer_state.json')).log_history
									__lowerCamelCase				:				str					       = int(logs[0]['train_mem_gpu_peaked_delta'] / 2**2_0)
									__lowerCamelCase				:				Any					       = int(logs[0]['train_mem_gpu_alloc_delta'] / 2**2_0)

									__lowerCamelCase				:				Union[str, Any]					       = logs[0]['train_loss']
									return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				str					       = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

								__lowerCamelCase				:				Optional[int]					       = gpu_alloc_mem_orig - gpu_alloc_mem_bnb

								__lowerCamelCase				:				List[str]					       = gpu_peak_mem_orig + gpu_alloc_mem_orig
								__lowerCamelCase				:				Tuple					       = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
								__lowerCamelCase				:				Tuple					       = 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
								__lowerCamelCase				:				Any					       = 1_2_0

								# 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(
								    _snake_case   ,_snake_case   ,'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(
								    _snake_case   ,_snake_case   ,'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(
								    _snake_case   ,_snake_case   ,F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}")


							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tuple = 3E-3   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = "adafactor"   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = False   ,SCREAMING_SNAKE_CASE__		:			str = None   ,SCREAMING_SNAKE_CASE__		:			Dict = 0   ,SCREAMING_SNAKE_CASE__		:			str = True   ,SCREAMING_SNAKE_CASE__		:			Tuple = True   ,SCREAMING_SNAKE_CASE__		:			Dict = True   ,SCREAMING_SNAKE_CASE__		:			List[str] = True   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,):
								__lowerCamelCase				:				Dict					       = self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
								__lowerCamelCase				:				Dict					       = self.get_auto_remove_tmp_dir()
								__lowerCamelCase				:				Union[str, Any]					       = F"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_snake_case)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_snake_case)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

								__lowerCamelCase				:				List[str]					       = F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_snake_case)}\n        ".split()

								__lowerCamelCase				:				int					       = '\n            --do_predict\n        '.split()

								__lowerCamelCase				:				Tuple					       = []
								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:
										__lowerCamelCase				:				Any					       = get_gpu_count()
									__lowerCamelCase				:				Tuple					       = get_torch_dist_unique_port()
									__lowerCamelCase				:				List[Any]					       = F"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
									__lowerCamelCase				:				Dict					       = [sys.executable] + distributed_args + args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(_snake_case   ,env=self.get_env())
								else:
									__lowerCamelCase				:				Union[str, Any]					       = ['run_translation.py'] + args
									with patch.object(_snake_case   ,'argv'   ,_snake_case):
										main()

								return output_dir






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
					import bsa
					from bsa import BeautifulSoup


A     :	str    = logging.get_logger(__name__)







class 			__A( a			):




				def __init__(     self		,     **_snake_case				)    ->      List[Any]:


								'''simple docstring'''




								requires_backends(self		,     ['''bs4''']				)
								super().__init__(**_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								__a										=    []
								__a										=    []
								__a										=    element if element.name else element.parent
								for parent in child.parents:  # type: bs4.element.Tag
												__a										=    parent.find_all(child.name		,     recursive=_snake_case				)
												xpath_tags.append(child.name				)
												xpath_subscripts.append(
												    0 if 1 == len(_snake_case				) else next(i for i, s in enumerate(_snake_case		,     1				) if s is child				)				)
												__a										=    parent
								xpath_tags.reverse()
								xpath_subscripts.reverse()
								return xpath_tags, xpath_subscripts




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    BeautifulSoup(_snake_case		,     '''html.parser'''				)

								__a										=    []
								__a										=    []
								__a										=    []

								for element in html_code.descendants:
												if type(_snake_case				) == bsa.element.NavigableString:
																if type(element.parent				) != bsa.element.Tag:
																				continue

																__a										=    html.unescape(_snake_case				).strip()
																if not text_in_this_tag:
																				continue

																all_doc_strings.append(_snake_case				)

																__a							,     __a										=    self.xpath_soup(_snake_case				)
																stringaxtag_seq.append(_snake_case				)
																stringaxsubs_seq.append(_snake_case				)

								if len(_snake_case				) != len(_snake_case				):
												raise ValueError('''Number of doc strings and xtags does not correspond'''				)
								if len(_snake_case				) != len(_snake_case				):
												raise ValueError('''Number of doc strings and xsubs does not correspond'''				)

								return all_doc_strings, stringaxtag_seq, stringaxsubs_seq




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    ''''''
								for tagname, subs in zip(_snake_case		,     _snake_case				):
												xpath += F"""/{tagname}"""
												if subs != 0:
																xpath += F"""[{subs}]"""
								return xpath




				def __call__(     self		,     _snake_case				)    ->      BatchFeature:


								'''simple docstring'''




								__a										=    False

								# Check that strings has a valid type
								if isinstance(_snake_case		,     _snake_case				):
												__a										=    True
								elif isinstance(_snake_case		,     (list, tuple)				):
												if len(_snake_case				) == 0 or isinstance(html_strings[0]		,     _snake_case				):
																__a										=    True

								if not valid_strings:
												raise ValueError(
												    '''HTML strings must of type `str`, `List[str]` (batch of examples), '''
												    F"""but is of type {type(_snake_case				)}."""				)

								__a										=    bool(isinstance(_snake_case		,     (list, tuple)				) and (isinstance(html_strings[0]		,     _snake_case				))				)

								if not is_batched:
												__a										=    [html_strings]

								# Get nodes + xpaths
								__a										=    []
								__a										=    []
								for html_string in html_strings:
												__a							,     __a							,     __a										=    self.get_three_from_single(_snake_case				)
												nodes.append(_snake_case				)
												__a										=    []
												for node, tag_list, sub_list in zip(_snake_case		,     _snake_case		,     _snake_case				):
																__a										=    self.construct_xpath(_snake_case		,     _snake_case				)
																xpath_strings.append(_snake_case				)
												xpaths.append(_snake_case				)

								# return as Dict
								__a										=    {'''nodes''': nodes, '''xpaths''': xpaths}
								__a										=    BatchFeature(data=_snake_case		,     tensor_type=_snake_case				)

								return encoded_inputs