


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_  :				int						= logging.get_logger(__name__)





def __snake_case					(      UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Tuple=False		):
 lowerCamelCase_      =  []
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ("cls_token", "vit.embeddings.cls_token"),
         ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
         ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
         ("pos_embed", "vit.embeddings.position_embeddings"),
     ]		)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
          ("pre_logits.fc.weight", "pooler.dense.weight"),
          ("pre_logits.fc.bias", "pooler.dense.bias"),
      ]		)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  lowerCamelCase_      =  [(pair[0], pair[1][4:]) if pair[1].startswith("vit"		) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("norm.weight", "vit.layernorm.weight"),
          ("norm.bias", "vit.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]		)

 return rename_keys





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Dict=False		):
 for i in range(config.num_hidden_layers		):
  if base_model:
   lowerCamelCase_      =  ""
  else:
   lowerCamelCase_      =  "vit."
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  lowerCamelCase_      =  state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''		)
  lowerCamelCase_      =  state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''		)
  # next, add query, keys and values (in that order) to the state dict
  lowerCamelCase_      =  in_proj_weight[
      : config.hidden_size, :
  ]
  lowerCamelCase_      =  in_proj_bias[: config.hidden_size]
  lowerCamelCase_      =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowerCamelCase_      =  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  lowerCamelCase_      =  in_proj_weight[
      -config.hidden_size :, :
  ]
  lowerCamelCase_      =  in_proj_bias[-config.hidden_size :]





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_					,				UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      ):
 lowerCamelCase_      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_      =  Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_		).raw		)
 return im





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  ViTConfig()
 lowerCamelCase_      =  False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  True
  lowerCamelCase_      =  int(vit_name[-12:-10]		)
  lowerCamelCase_      =  int(vit_name[-9:-6]		)
 else:
  lowerCamelCase_      =  1000
  lowerCamelCase_      =  "huggingface/label-files"
  lowerCamelCase_      =  "imagenet-1k-id2label.json"
  lowerCamelCase_      =  json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type="dataset"		)					,				"r"		)		)
  lowerCamelCase_      =  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  lowerCamelCase_      =  idalabel
  lowerCamelCase_      =  {v: k for k, v in idalabel.items()}
  lowerCamelCase_      =  int(vit_name[-6:-4]		)
  lowerCamelCase_      =  int(vit_name[-3:]		)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("tiny"		):
   lowerCamelCase_      =  192
   lowerCamelCase_      =  768
   lowerCamelCase_      =  12
   lowerCamelCase_      =  3
  elif vit_name[9:].startswith("small"		):
   lowerCamelCase_      =  384
   lowerCamelCase_      =  1536
   lowerCamelCase_      =  12
   lowerCamelCase_      =  6
  else:
   pass
 else:
  if vit_name[4:].startswith("small"		):
   lowerCamelCase_      =  768
   lowerCamelCase_      =  2304
   lowerCamelCase_      =  8
   lowerCamelCase_      =  8
  elif vit_name[4:].startswith("base"		):
   pass
  elif vit_name[4:].startswith("large"		):
   lowerCamelCase_      =  1024
   lowerCamelCase_      =  4096
   lowerCamelCase_      =  24
   lowerCamelCase_      =  16
  elif vit_name[4:].startswith("huge"		):
   lowerCamelCase_      =  1280
   lowerCamelCase_      =  5120
   lowerCamelCase_      =  32
   lowerCamelCase_      =  16

    # load original model from timm
 lowerCamelCase_      =  timm.create_model(UpperCAmelCase_					,				pretrained=UpperCAmelCase_		)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 lowerCamelCase_      =  timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCAmelCase_		)
 lowerCamelCase_      =  create_rename_keys(UpperCAmelCase_					,				UpperCAmelCase_		)
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)
 read_in_q_k_v(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  ViTModel(UpperCAmelCase_		).eval()
 else:
  lowerCamelCase_      =  ViTForImageClassification(UpperCAmelCase_		).eval()
 model.load_state_dict(UpperCAmelCase_		)

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  lowerCamelCase_      =  DeiTImageProcessor(size=config.image_size		)
 else:
  lowerCamelCase_      =  ViTImageProcessor(size=config.image_size		)
 lowerCamelCase_      =  image_processor(images=prepare_img()					,				return_tensors="pt"		)
 lowerCamelCase_      =  encoding["pixel_values"]
 lowerCamelCase_      =  model(UpperCAmelCase_		)

 if base_model:
  lowerCamelCase_      =  timm_model.forward_features(UpperCAmelCase_		)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.pooler_output					,				atol=1E-3		)
 else:
  lowerCamelCase_      =  timm_model(UpperCAmelCase_		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.logits					,				atol=1E-3		)

 Path(UpperCAmelCase_		).mkdir(exist_ok=UpperCAmelCase_		)
 print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''		)
 model.save_pretrained(UpperCAmelCase_		)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
 image_processor.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     a_  :				Union[str, Any]						= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--vit_name""",
         default="""vit_base_patch16_224""",
         type=str,
         help="""Name of the ViT timm model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
     )

     a_  :				List[str]						= parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''simple docstring'''

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


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


set_seed(42)
a_  :				int						= """sshleifer/student_marian_en_ro_6_1"""
a_  :				str						= """sshleifer/tiny-mbart"""





@require_torch
class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase=False			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.run_trainer(
       eval_steps=1			,  max_len=12			,  model_name=UpperCamelCase			,  num_train_epochs=1			,  distributed=UpperCamelCase			,  extra_args_str=UpperCamelCase			,  predict_with_generate=UpperCamelCase			,  do_train=UpperCamelCase			,  do_eval=UpperCamelCase			,  do_predict=UpperCamelCase			,  )
   lowerCamelCase_      =  TrainerState.load_from_json(os.path.join(UpperCamelCase			,  "trainer_state.json"     )     ).log_history

   if not do_eval:
    return

   lowerCamelCase_      =  [log for log in logs if "eval_loss" in log.keys()]

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

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






  @require_torch_non_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick()






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






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







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp simple"     )






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







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  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   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp zero_dp_2"			,  predict_with_generate=UpperCamelCase     )






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







   """simple docstring"""
   self.run_seqaseq_quick(
       distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp zero_dp_2 --fp16"			,  predict_with_generate=UpperCamelCase     )






  @require_apex
  @require_torch_gpu
  def   snake_case		(    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=UpperCamelCase			,  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=UpperCamelCase			,  extra_args_str="--fp16 --fp16_backend=apex"     )






  @parameterized.expand(["base", "low", "high", "mixed"]     )
  @require_torch_multi_gpu
  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
   lowerCamelCase_      =  {
       # 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},
   }

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






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.run_trainer(
       eval_steps=2			,  max_len=128			,  model_name=UpperCamelCase			,  learning_rate=3e-4			,  num_train_epochs=10			,  distributed=UpperCamelCase			,  )

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

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

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






  @slow
  @require_bitsandbytes
  def   snake_case		(    self     ):







   """simple docstring"""
   from transformers.training_args import OptimizerNames

   def train_and_return_metrics(UpperCamelCase     ) -> Tuple[int, float]:
    lowerCamelCase_      =  "--skip_memory_metrics 0"

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

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

    lowerCamelCase_      =  logs[0]["train_loss"]
    return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

   lowerCamelCase_      =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

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







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = 3e-3			,  UpperCamelCase = "adafactor"			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = 0			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = None			,  ):







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

   lowerCamelCase_      =  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(UpperCamelCase     )}
        '''.split()

   lowerCamelCase_      =  "\n            --do_predict\n        ".split()

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

   return output_dir



