






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


_UpperCamelCase			:	Optional[int]								=os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
	from run_translation import main  # noqa


set_seed(42)
_UpperCamelCase			:	Any								='sshleifer/student_marian_en_ro_6_1'
_UpperCamelCase			:	Tuple								='sshleifer/tiny-mbart'






@require_torch
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self       ,A__=False       ,A__=None       ,A__=True       ,A__=True       ,A__=True       ,A__=True       ,):
						_A	:				List[str]   =      self.run_trainer(
						    eval_steps=1       ,max_len=12       ,model_name=A__       ,num_train_epochs=1       ,distributed=A__       ,extra_args_str=A__       ,predict_with_generate=A__       ,do_train=A__       ,do_eval=A__       ,do_predict=A__       ,)
						_A	:				Dict   =      TrainerState.load_from_json(os.path.join(A__       ,'''trainer_state.json'''		)		).log_history

						if not do_eval:
										return

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

						_A	:				Optional[int]   =      eval_metrics[0]
						if predict_with_generate:
										assert "eval_bleu" in first_step_stats

										_A	:				Optional[Any]   =      eval_metrics[-1]
										assert isinstance(last_step_stats['''eval_bleu''']       ,A__		)
										assert not math.isnan(float(last_step_stats['''eval_loss''']		)		), "eval_loss must not be `nan`"







		@require_torch_non_multi_gpu
		def  A__      (					self		):
						self.run_seqaseq_quick()







		@require_torch_multi_gpu
		def  A__      (					self		):
						self.run_seqaseq_quick(distributed=A__		)







		@require_torch_multi_gpu
		def  A__      (					self		):
						self.run_seqaseq_quick(distributed=A__		)







		@unittest.skip('''Requires an update of the env running those tests'''		)
		@require_torch_multi_gpu
		@require_fairscale
		def  A__      (					self		):
						self.run_seqaseq_quick(distributed=A__       ,extra_args_str='''--sharded_ddp simple'''		)







		@unittest.skip('''Requires an update of the env running those tests'''		)
		@require_torch_multi_gpu
		@require_fairscale
		def  A__      (					self		):
						self.run_seqaseq_quick(distributed=A__       ,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  A__      (					self		):
						self.run_seqaseq_quick(distributed=A__       ,extra_args_str='''--sharded_ddp zero_dp_2'''       ,predict_with_generate=A__		)







		@unittest.skip('''Requires an update of the env running those tests'''		)
		@require_torch_multi_gpu
		@require_fairscale
		def  A__      (					self		):
						self.run_seqaseq_quick(
						    distributed=A__       ,extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''       ,predict_with_generate=A__		)







		@require_apex
		@require_torch_gpu
		def  A__      (					self		):
						# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
						# program and it breaks other tests that run from the same pytest worker, therefore until this is
						# sorted out it must be run only in an external program, that is distributed=True in this
						# test and only under one or more gpus - if we want cpu will need to make a special test
						#
						# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
						# 2nd main() call it botches the future eval.
						#
						self.run_seqaseq_quick(distributed=A__       ,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=A__       ,extra_args_str='''--fp16 --fp16_backend=apex'''		)







		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']		)
		@require_torch_multi_gpu
		def  A__      (					self       ,A__		):
						# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
						_A	:				Dict   =      {
						    # 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},
						}

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







		@slow
		def  A__      (					self		):
						_A	:				Any   =      self.run_trainer(
						    eval_steps=2       ,max_len=128       ,model_name=A__       ,learning_rate=3E-4       ,num_train_epochs=10       ,distributed=A__       ,)

						# Check metrics
						_A	:				Any   =      TrainerState.load_from_json(os.path.join(A__       ,'''trainer_state.json'''		)		).log_history
						_A	:				str   =      [log for log in logs if '''eval_loss''' in log.keys()]
						_A	:				Dict   =      eval_metrics[0]
						_A	:				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''']       ,A__		)

						# test if do_predict saves generations and metrics
						_A	:				Tuple   =      os.listdir(A__		)
						_A	:				List[Any]   =      {os.path.basename(A__		) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents







		@slow
		@require_bitsandbytes
		def  A__      (					self		):
						from transformers.training_args import OptimizerNames

						def train_and_return_metrics(A__		) -> Tuple[int, float]:
										_A	:				Dict   =      '''--skip_memory_metrics 0'''

										_A	:				Tuple   =      self.run_trainer(
										    max_len=128       ,model_name=A__       ,learning_rate=3E-4       ,num_train_epochs=1       ,optim=A__       ,distributed=A__       ,extra_args_str=A__       ,do_eval=A__       ,do_predict=A__       ,n_gpus_to_use=1       ,)

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

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

						_A  ,				_A  ,				_A	:				str   =      train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value		)
						_A  ,				_A  ,				_A	:				str   =      train_and_return_metrics(OptimizerNames.ADAMW_BNB.value		)

						_A	:				str   =      gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						_A	:				Tuple   =      gpu_peak_mem_orig + gpu_alloc_mem_orig
						_A	:				Tuple   =      gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						_A	:				Union[str, 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
						_A	:				int   =      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(
						    A__       ,A__       ,'''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(
						    A__       ,A__       ,'''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(
						    A__       ,A__       ,f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = 3E-3       ,A__ = "adafactor"       ,A__ = False       ,A__ = None       ,A__ = 0       ,A__ = True       ,A__ = True       ,A__ = True       ,A__ = True       ,A__ = None       ,):
						_A	:				int   =      self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
						_A	:				Optional[Any]   =      self.get_auto_remove_tmp_dir()
						_A	:				Dict   =      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(A__		)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(A__		)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

						_A	:				List[str]   =      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(A__		)}
        """.split()

						_A	:				List[str]   =      '''
            --do_predict
        '''.split()

						_A	:				Optional[Any]   =      []
						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:
														_A	:				Optional[int]   =      get_gpu_count()
										_A	:				int   =      get_torch_dist_unique_port()
										_A	:				Dict   =      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()
										_A	:				List[str]   =      [sys.executable] + distributed_args + args
										# keep for quick debug
										# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
										execute_subprocess_async(A__       ,env=self.get_env()		)
						else:
										_A	:				Union[str, Any]   =      ['''run_translation.py'''] + args
										with patch.object(A__       ,'''argv'''       ,A__		):
														main()

						return output_dir











def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> float:

				def get_matched_characters(__lowercase							:str					,  __lowercase							:str   ) -> str:
								_A	:				Union[str, Any]   =      []
								_A	:				Dict   =      min(len(_stra   )					,  len(_stra   )   ) // 2
								for i, l in enumerate(_stra   ):
												_A	:				Tuple   =      int(max(0					,  i - limit   )   )
												_A	:				Union[str, Any]   =      int(min(i + limit + 1					,  len(_stra   )   )   )
												if l in _stra[left:right]:
																matched.append(__lowercase   )
																_A	:				List[Any]   =      f"""{_stra[0:_stra.index(__lowercase   )]} {_stra[_stra.index(__lowercase   ) + 1:]}"""

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

				# transposition
				_A	:				str   =      (
				    len([(ca, ca) for ca, ca in zip(__lowercase					,  __lowercase   ) if ca != ca]   ) // 2
				)

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				Any   =      0
				for ca, ca in zip(stra[:4]					,  stra[:4]   ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler('hello', 'world'))



