






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case="attention"			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
				__magic_name__		:Union[str, Any]   =   params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
				__magic_name__		:int   =   params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
				__magic_name__		:List[Any]   =   params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
				return k, o, q, v

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case=False			):







				"""simple docstring"""






				if split_mlp_wi:
								__magic_name__		:List[str]   =   params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
								__magic_name__		:Union[str, Any]   =   params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
								__magic_name__		:List[Any]   =   (wi_a, wi_a)
				else:
								__magic_name__		:List[Any]   =   params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

				__magic_name__		:Dict   =   params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
				return wi, wo

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']

def   __lowercase       (			snake_case,	*, snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   traverse_util.flatten_dict(variables['''target''']			)
				__magic_name__		:Dict   =   {'''/'''.join(snake_case			): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				__magic_name__		:Optional[int]   =   '''encoder/layers_0/mlp/wi_0/kernel''' in old
				print('''Split MLP:''',	snake_case			)

				__magic_name__		:Optional[int]   =   collections.OrderedDict()

				# Shared embeddings.
				__magic_name__		:Union[str, Any]   =   old['''token_embedder/embedding''']

				# Encoder.
				for i in range(snake_case			):
								# Block i, layer 0 (Self Attention).
								__magic_name__		:int   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''encoder''',	'''pre_attention_layer_norm'''			)
								__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:Optional[int]   =   tax_attention_lookup(snake_case,	snake_case,	'''encoder''',	'''attention'''			)
								__magic_name__		:List[Any]   =   layer_norm
								__magic_name__		:List[Any]   =   k.T
								__magic_name__		:Dict   =   o.T
								__magic_name__		:Union[str, Any]   =   q.T
								__magic_name__		:int   =   v.T

								# Block i, layer 1 (MLP).
								__magic_name__		:Union[str, Any]   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''encoder''',	'''pre_mlp_layer_norm'''			)
								__magic_name__  ,					__magic_name__		:List[Any]   =   tax_mlp_lookup(snake_case,	snake_case,	'''encoder''',	snake_case			)
								__magic_name__		:Optional[int]   =   layer_norm
								if split_mlp_wi:
												__magic_name__		:Optional[int]   =   wi[0].T
												__magic_name__		:Any   =   wi[1].T
								else:
												__magic_name__		:str   =   wi.T
								__magic_name__		:Tuple   =   wo.T

				__magic_name__		:List[str]   =   old[
				    '''encoder/relpos_bias/rel_embedding'''
				].T
				__magic_name__		:str   =   old['''encoder/encoder_norm/scale''']

				if not is_encoder_only:
								# Decoder.
								for i in range(snake_case			):
												# Block i, layer 0 (Self Attention).
												__magic_name__		:List[Any]   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''decoder''',	'''pre_self_attention_layer_norm'''			)
												__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:List[str]   =   tax_attention_lookup(snake_case,	snake_case,	'''decoder''',	'''self_attention'''			)
												__magic_name__		:Any   =   layer_norm
												__magic_name__		:List[Any]   =   k.T
												__magic_name__		:Any   =   o.T
												__magic_name__		:List[str]   =   q.T
												__magic_name__		:Optional[int]   =   v.T

												# Block i, layer 1 (Cross Attention).
												__magic_name__		:int   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''decoder''',	'''pre_cross_attention_layer_norm'''			)
												__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:Any   =   tax_attention_lookup(snake_case,	snake_case,	'''decoder''',	'''encoder_decoder_attention'''			)
												__magic_name__		:Union[str, Any]   =   layer_norm
												__magic_name__		:int   =   k.T
												__magic_name__		:Tuple   =   o.T
												__magic_name__		:int   =   q.T
												__magic_name__		:Union[str, Any]   =   v.T

												# Block i, layer 2 (MLP).
												__magic_name__		:Any   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''decoder''',	'''pre_mlp_layer_norm'''			)
												__magic_name__  ,					__magic_name__		:int   =   tax_mlp_lookup(snake_case,	snake_case,	'''decoder''',	snake_case			)
												__magic_name__		:Union[str, Any]   =   layer_norm
												if split_mlp_wi:
																__magic_name__		:List[str]   =   wi[0].T
																__magic_name__		:Dict   =   wi[1].T
												else:
																__magic_name__		:Tuple   =   wi.T
												__magic_name__		:Dict   =   wo.T

								__magic_name__		:Dict   =   old['''decoder/decoder_norm/scale''']
								__magic_name__		:List[str]   =   old[
								    '''decoder/relpos_bias/rel_embedding'''
								].T

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												__magic_name__		:Optional[int]   =   old['''decoder/logits_dense/kernel'''].T

				return new

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								__magic_name__		:int   =   state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												__magic_name__		:Optional[int]   =   state_dict['''shared.weight''']

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print('''Using shared word embeddings as lm_head.'''			)
												__magic_name__		:Any   =   state_dict['''shared.weight''']

				return state_dict

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   checkpoints.load_tax_checkpoint(snake_case			)
				__magic_name__		:Dict   =   convert_tax_to_pytorch(snake_case,	num_layers=config.num_layers,	is_encoder_only=snake_case			)
				__magic_name__		:str   =   make_state_dict(snake_case,	snake_case			)
				model.load_state_dict(snake_case,	strict=snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case = False			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   TaConfig.from_json_file(snake_case			)
				print(f'''Building PyTorch model from configuration: {config}'''			)
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								__magic_name__		:Optional[Any]   =   TaEncoderModel(snake_case			)
				else:
								__magic_name__		:Union[str, Any]   =   TaForConditionalGeneration(snake_case			)

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(snake_case,	snake_case,	snake_case,	snake_case			)

				# Save pytorch-model
				print(f'''Save PyTorch model to {pytorch_dump_path}'''			)
				model.save_pretrained(snake_case			)

				# Verify that we can load the checkpoint.
				model.from_pretrained(snake_case			)
				print('''Done'''			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      List[str]				=		argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
					# Required parameters
					parser.add_argument(
					    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
					)
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
					)














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


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


set_seed(42)
SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""sshleifer/student_marian_en_ro_6_1"""
SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""sshleifer/tiny-mbart"""
@require_torch
class     lowerCamelCase_					(   lowerCamelCase			):







				def       A						(    self							,       __lowerCAmelCase=False							,       __lowerCAmelCase=None							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.run_trainer(
								    eval_steps=1							,       max_len=1_2							,       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							,       )
								__magic_name__		:Any   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history

								if not do_eval:
												return

								__magic_name__		:Any   =   [log for log in logs if '''eval_loss''' in log.keys()]

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

												__magic_name__		:Tuple   =   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       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick()







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase  )







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								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       A						(    self  ):
								"""simple docstring"""
								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       A						(    self  ):
								"""simple docstring"""
								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       A						(    self  ):
								"""simple docstring"""
								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       A						(    self  ):
								"""simple docstring"""
								# 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       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
								__magic_name__		:Any   =   {
								    # 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},
								}

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







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.run_trainer(
								    eval_steps=2							,       max_len=1_2_8							,       model_name=__lowerCAmelCase							,       learning_rate=3E-4							,       num_train_epochs=1_0							,       distributed=__lowerCAmelCase							,       )

								# Check metrics
								__magic_name__		:Optional[int]   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
								__magic_name__		:List[str]   =   [log for log in logs if '''eval_loss''' in log.keys()]
								__magic_name__		:Any   =   eval_metrics[0]
								__magic_name__		: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''']							,       __lowerCAmelCase  )

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







				@slow
				@require_bitsandbytes
				def       A						(    self  ):
								"""simple docstring"""
								from transformers.training_args import OptimizerNames

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

												__magic_name__		:Dict   =   self.run_trainer(
												    max_len=1_2_8							,       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
												__magic_name__		:Optional[Any]   =   TrainerState.load_from_json(Path(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
												__magic_name__		:int   =   int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0  )
												__magic_name__		:Optional[Any]   =   int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0  )

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

								__magic_name__  ,					__magic_name__  ,					__magic_name__		:int   =   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

								__magic_name__		:Tuple   =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

								__magic_name__		:Tuple   =   gpu_peak_mem_orig + gpu_alloc_mem_orig
								__magic_name__		:List[Any]   =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
								__magic_name__		:Optional[int]   =   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
								__magic_name__		:Optional[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(
								    __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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = 3E-3							,       __lowerCAmelCase = "adafactor"							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 0							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
								__magic_name__		:Dict   =   self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple   =   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()

								__magic_name__		: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(__lowerCAmelCase  )}
        '''.split()

								__magic_name__		:Dict   =   '''
            --do_predict
        '''.split()

								__magic_name__		:Optional[int]   =   []
								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:
																__magic_name__		:List[Any]   =   get_gpu_count()
												__magic_name__		:Tuple   =   get_torch_dist_unique_port()
												__magic_name__		:Union[str, Any]   =   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()
												__magic_name__		:Any   =   [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:
												__magic_name__		:List[Any]   =   ['''run_translation.py'''] + args
												with patch.object(__lowerCAmelCase							,       '''argv'''							,       __lowerCAmelCase  ):
																main()

								return output_dir






