
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


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


set_seed(42)
_lowerCamelCase       :				List[str]       =			'''sshleifer/student_marian_en_ro_6_1'''
_lowerCamelCase       :				Optional[Any]       =			'''sshleifer/tiny-mbart'''




@require_torch
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def  A							(  self	:    str			,  lowercase	:    Optional[int]=False			,  lowercase	:    Union[str, Any]=None			,  lowercase	:    Optional[int]=True			,  lowercase	:    List[str]=True			,  lowercase	:    Tuple=True			,  lowercase	:    List[str]=True			,  ):


				'''simple docstring'''


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

				if not do_eval:
							return

				_snake_case		=			[log for log in logs if 'eval_loss' in log.keys()]

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

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



	@require_torch_non_multi_gpu
	def  A							(  self	:    Any	):


				'''simple docstring'''


				self.run_seqaseq_quick()



	@require_torch_multi_gpu
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				self.run_seqaseq_quick(distributed=lowercase	)



	@require_torch_multi_gpu
	def  A							(  self	:    Any	):


				'''simple docstring'''


				self.run_seqaseq_quick(distributed=lowercase	)



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


				'''simple docstring'''


				self.run_seqaseq_quick(distributed=lowercase			,  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	:    Optional[int]	):


				'''simple docstring'''


				self.run_seqaseq_quick(distributed=lowercase			,  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	:    List[str]	):


				'''simple docstring'''


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



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


				'''simple docstring'''


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



	@require_apex
	@require_torch_gpu
	def  A							(  self	:    Any	):


				'''simple docstring'''


				self.run_seqaseq_quick(distributed=lowercase			,  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=lowercase			,  extra_args_str='--fp16 --fp16_backend=apex'	)



	@parameterized.expand(['base', 'low', 'high', 'mixed']	)
	@require_torch_multi_gpu
	def  A							(  self	:    Tuple			,  lowercase	:    Any	):


				'''simple docstring'''


				_snake_case		=			{
				    # 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},
				}

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



	@slow
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.run_trainer(
				    eval_steps=2			,  max_len=128			,  model_name=lowercase			,  learning_rate=3E-4			,  num_train_epochs=10			,  distributed=lowercase			,  )

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

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

				# test if do_predict saves generations and metrics
				_snake_case		=			os.listdir(lowercase	)
				_snake_case		=			{os.path.basename(lowercase	) for p in contents}
				assert "generated_predictions.txt" in contents
				assert "predict_results.json" in contents



	@slow
	@require_bitsandbytes
	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				from transformers.training_args import OptimizerNames

				def train_and_return_metrics(lowercase	:    str	) -> Tuple[int, float]:
							_snake_case		=			'--skip_memory_metrics 0'

							_snake_case		=			self.run_trainer(
							    max_len=128			,  model_name=lowercase			,  learning_rate=3E-4			,  num_train_epochs=1			,  optim=lowercase			,  distributed=lowercase			,  extra_args_str=lowercase			,  do_eval=lowercase			,  do_predict=lowercase			,  n_gpus_to_use=1			,  )

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

							_snake_case		=			logs[0]['train_loss']
							return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

				_snake_case	,					_snake_case	,					_snake_case		=			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value	)
				_snake_case	,					_snake_case	,					_snake_case		=			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value	)

				_snake_case		=			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

				_snake_case		=			gpu_peak_mem_orig + gpu_alloc_mem_orig
				_snake_case		=			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
				_snake_case		=			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
				_snake_case		=			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(
				    lowercase			,  lowercase			,  '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(
				    lowercase			,  lowercase			,  '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(
				    lowercase			,  lowercase			,  f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''	)





	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    str			,  lowercase	:    int			,  lowercase	:    float = 3E-3			,  lowercase	:    str = "adafactor"			,  lowercase	:    bool = False			,  lowercase	:    str = None			,  lowercase	:    int = 0			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  lowercase	:    int = None			,  ):


				'''simple docstring'''


				_snake_case		=			self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
				_snake_case		=			self.get_auto_remove_tmp_dir()
				_snake_case		=			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(lowercase	)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowercase	)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

				_snake_case		=			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(lowercase	)}
        '''.split()

				_snake_case		=			'\n            --do_predict\n        '.split()

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

				return output_dir

class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    List[str]			,  lowercase	:    list[int]	):


				'''simple docstring'''


				_snake_case		=			len(lowercase	)
				_snake_case		=			[0] * len_array

				if len_array > 0:
							_snake_case		=			array[0]

				for i in range(1			,  lowercase	):
							_snake_case		=			self.prefix_sum[i - 1] + array[i]



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if start == 0:
							return self.prefix_sum[end]

				return self.prefix_sum[end] - self.prefix_sum[start - 1]





	def  A							(  self	:    Union[str, Any]			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			{0}
				for sum_item in self.prefix_sum:
							if sum_item - target_sum in sums:
										return True

							sums.add(lowercase	)

				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()