



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


set_seed(42)
UpperCAmelCase__     = "sshleifer/student_marian_en_ro_6_1"
UpperCAmelCase__     = "sshleifer/tiny-mbart"







@require_torch
class     __lowerCAmelCase					(    A		):



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Dict=False				,	A     :						str=None				,	A     :						Dict=True				,	A     :						Tuple=True				,	A     :						Tuple=True				,	A     :						List[Any]=True				,	)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     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				,	)
						_UpperCAmelCase	 =     TrainerState.load_from_json(os.path.join(A				,	'trainer_state.json')).log_history

						if not do_eval:
									return

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

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

									_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






						self.run_seqaseq_quick()



			@require_torch_multi_gpu
			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						self.run_seqaseq_quick(distributed=A)



			@require_torch_multi_gpu
			def 					_lowerCamelCase (		self     :						Dict)     ->					str:



						"""simple docstring"""






						self.run_seqaseq_quick(distributed=A)



			@unittest.skip('Requires an update of the env running those tests')
			@require_torch_multi_gpu
			@require_fairscale
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Union[str, Any]:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						List[Any])     ->					Tuple:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[Any]:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[str]:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Optional[Any])     ->					Tuple:



						"""simple docstring"""






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



			@slow
			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.run_trainer(
						    eval_steps=2				,	max_len=1_28				,	model_name=A				,	learning_rate=3E-4				,	num_train_epochs=10				,	distributed=A				,	)

						# Check metrics
						_UpperCAmelCase	 =     TrainerState.load_from_json(os.path.join(A				,	'trainer_state.json')).log_history
						_UpperCAmelCase	 =     [log for log in logs if 'eval_loss' in log.keys()]
						_UpperCAmelCase	 =     eval_metrics[0]
						_UpperCAmelCase	 =     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
						_UpperCAmelCase	 =     os.listdir(A)
						_UpperCAmelCase	 =     {os.path.basename(A) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents



			@slow
			@require_bitsandbytes
			def 					_lowerCamelCase (		self     :						Any)     ->					List[str]:



						"""simple docstring"""






						from transformers.training_args import OptimizerNames

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

									_UpperCAmelCase	 =     self.run_trainer(
									    max_len=1_28				,	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
									_UpperCAmelCase	 =     TrainerState.load_from_json(Path(A				,	'trainer_state.json')).log_history
									_UpperCAmelCase	 =     int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20)
									_UpperCAmelCase	 =     int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20)

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

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

						_UpperCAmelCase	 =     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						_UpperCAmelCase	 =     gpu_peak_mem_orig + gpu_alloc_mem_orig
						_UpperCAmelCase	 =     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						_UpperCAmelCase	 =     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	 =     1_20

						# 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 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						int				,	A     :						str				,	A     :						int				,	A     :						float = 3E-3				,	A     :						str = "adafactor"				,	A     :						bool = False				,	A     :						str = None				,	A     :						int = 0				,	A     :						bool = True				,	A     :						bool = True				,	A     :						bool = True				,	A     :						bool = True				,	A     :						int = None				,	)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
						_UpperCAmelCase	 =     self.get_auto_remove_tmp_dir()
						_UpperCAmelCase	 =     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(A)}\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(A)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

						_UpperCAmelCase	 =     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(A)}\n        ".split()

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

						_UpperCAmelCase	 =     []
						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	 =     get_gpu_count()
									_UpperCAmelCase	 =     get_torch_dist_unique_port()
									_UpperCAmelCase	 =     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()
									_UpperCAmelCase	 =     [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:
									_UpperCAmelCase	 =     ['run_translation.py'] + args
									with patch.object(A				,	'argv'				,	A):
												main()

						return output_dir





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__     = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = ["YolosFeatureExtractor"]
							UpperCAmelCase__     = ["YolosImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "YolosForObjectDetection",
							    "YolosModel",
							    "YolosPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
