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__						:						Tuple		  =			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[int]		  =			'sshleifer/tiny-mbart'
@require_torch
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Tuple=False					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Union[str, Any]=True					,lowerCamelCase__     :			Optional[int]=True					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			int=True					,):
									UpperCAmelCase__    =							self.run_trainer(
									    eval_steps=1					,max_len=12					,model_name=lowerCamelCase__					,num_train_epochs=1					,distributed=lowerCamelCase__					,extra_args_str=lowerCamelCase__					,predict_with_generate=lowerCamelCase__					,do_train=lowerCamelCase__					,do_eval=lowerCamelCase__					,do_predict=lowerCamelCase__					,)
									UpperCAmelCase__    =							TrainerState.load_from_json(os.path.join(lowerCamelCase__					,'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']					,lowerCamelCase__    )
													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     :			Tuple    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__    )







					@require_torch_multi_gpu
					def 	__lowerCAmelCase		(       self     :			int    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__    )







					@unittest.skip('Requires an update of the env running those tests'    )
					@require_torch_multi_gpu
					@require_fairscale
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,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]    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,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     :			Tuple    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,extra_args_str='--sharded_ddp zero_dp_2'					,predict_with_generate=lowerCamelCase__    )







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







					@require_apex
					@require_torch_gpu
					def 	__lowerCAmelCase		(       self     :			int    ):
									# 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=lowerCamelCase__					,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=lowerCamelCase__					,extra_args_str='--fp16 --fp16_backend=apex'    )







					@parameterized.expand(['base', 'low', 'high', 'mixed']    )
					@require_torch_multi_gpu
					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Any    ):
									# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
									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(**lowerCamelCase__					,extra_args_str=data['extra_args_str']    )
									UpperCAmelCase__    =							len(re.findall(lowerCamelCase__					,cl.err    )    )
									self.assertEqual(lowerCamelCase__					,data['n_matches']    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.run_trainer(
									    eval_steps=2					,max_len=128					,model_name=lowerCamelCase__					,learning_rate=3e-4					,num_train_epochs=10					,distributed=lowerCamelCase__					,)

									# Check metrics
									UpperCAmelCase__    =							TrainerState.load_from_json(os.path.join(lowerCamelCase__					,'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']					,lowerCamelCase__    )

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







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

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

													UpperCAmelCase__    =							self.run_trainer(
													    max_len=128					,model_name=lowerCamelCase__					,learning_rate=3e-4					,num_train_epochs=1					,optim=lowerCamelCase__					,distributed=lowerCamelCase__					,extra_args_str=lowerCamelCase__					,do_eval=lowerCamelCase__					,do_predict=lowerCamelCase__					,n_gpus_to_use=1					,)

													# Check metrics
													UpperCAmelCase__    =							TrainerState.load_from_json(Path(lowerCamelCase__					,'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__    =							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(
									    lowerCamelCase__					,lowerCamelCase__					,'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(
									    lowerCamelCase__					,lowerCamelCase__					,'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(
									    lowerCamelCase__					,lowerCamelCase__					,f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''    )







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			float = 3e-3					,lowerCamelCase__     :			str = "adafactor"					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			str = None					,lowerCamelCase__     :			int = 0					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			int = None					,):
									UpperCAmelCase__    =							self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
									UpperCAmelCase__    =							self.get_auto_remove_tmp_dir()
									UpperCAmelCase__    =							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(lowerCamelCase__    )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCamelCase__    )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

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

									return output_dir




"""simple docstring"""






import socket



def 						a_		(      ):
				UpperCAmelCase__    =							socket.socket(socket.AF_INET      ,    socket.SOCK_STREAM )
				UpperCAmelCase__    =							socket.gethostname()
				UpperCAmelCase__    =							1_2_3_1_2

				sock.connect((host, port) )
				sock.send(b'Hello server!' )

				with open('Received_file'      ,    'wb' ) as out_file:
								print('File opened' )
								print('Receiving data...' )
								while True:
												UpperCAmelCase__    =							sock.recv(1_0_2_4 )
												if not data:
																break
												out_file.write(lowerCamelCase )

				print('Successfully received the file' )
				sock.close()
				print('Connection closed' )


if __name__ == "__main__":
						main()



