

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_									=   logging.get_logger(__name__)
def      a      (			A__	:   Optional[int]     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =DPTConfig()

      if "large" in checkpoint_url:
            _lowercase	       =1024
            _lowercase	       =4096
            _lowercase	       =24
            _lowercase	       =16
            _lowercase	       =[5, 11, 17, 23]
            _lowercase	       =[256, 512, 1024, 1024]
            _lowercase	       =(1, 384, 384)

      if "ade" in checkpoint_url:
            _lowercase	       =True

            _lowercase	       =150
            _lowercase	       ='huggingface/label-files'
            _lowercase	       ='ade20k-id2label.json'
            _lowercase	       =json.load(open(cached_download(hf_hub_url(A__      ,   A__      ,   repo_type='dataset'     )     )      ,   'r'     )     )
            _lowercase	       ={int(A__     ): v for k, v in idalabel.items()}
            _lowercase	       =idalabel
            _lowercase	       ={v: k for k, v in idalabel.items()}
            _lowercase	       =[1, 150, 480, 480]

      return config, expected_shape
def      a      (			A__	:   Optional[Any]     )    ->      Tuple:





      """simple docstring"""



      _lowercase	       =['pretrained.model.head.weight', 'pretrained.model.head.bias']
      for k in ignore_keys:
            state_dict.pop(A__      ,   A__     )
def      a      (			A__	:   List[Any]     )    ->      Optional[Any]:





      """simple docstring"""



      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            _lowercase	       =name.replace('pretrained.model'      ,   'dpt.encoder'     )
      if "pretrained.model" in name:
            _lowercase	       =name.replace('pretrained.model'      ,   'dpt.embeddings'     )
      if "patch_embed" in name:
            _lowercase	       =name.replace('patch_embed'      ,   'patch_embeddings'     )
      if "pos_embed" in name:
            _lowercase	       =name.replace('pos_embed'      ,   'position_embeddings'     )
      if "attn.proj" in name:
            _lowercase	       =name.replace('attn.proj'      ,   'attention.output.dense'     )
      if "proj" in name and "project" not in name:
            _lowercase	       =name.replace('proj'      ,   'projection'     )
      if "blocks" in name:
            _lowercase	       =name.replace('blocks'      ,   'layer'     )
      if "mlp.fc1" in name:
            _lowercase	       =name.replace('mlp.fc1'      ,   'intermediate.dense'     )
      if "mlp.fc2" in name:
            _lowercase	       =name.replace('mlp.fc2'      ,   'output.dense'     )
      if "norm1" in name:
            _lowercase	       =name.replace('norm1'      ,   'layernorm_before'     )
      if "norm2" in name:
            _lowercase	       =name.replace('norm2'      ,   'layernorm_after'     )
      if "scratch.output_conv" in name:
            _lowercase	       =name.replace('scratch.output_conv'      ,   'head'     )
      if "scratch" in name:
            _lowercase	       =name.replace('scratch'      ,   'neck'     )
      if "layer1_rn" in name:
            _lowercase	       =name.replace('layer1_rn'      ,   'convs.0'     )
      if "layer2_rn" in name:
            _lowercase	       =name.replace('layer2_rn'      ,   'convs.1'     )
      if "layer3_rn" in name:
            _lowercase	       =name.replace('layer3_rn'      ,   'convs.2'     )
      if "layer4_rn" in name:
            _lowercase	       =name.replace('layer4_rn'      ,   'convs.3'     )
      if "refinenet" in name:
            _lowercase	       =int(name[len('neck.refinenet'     ) : len('neck.refinenet'     ) + 1]     )
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            _lowercase	       =name.replace(F'''refinenet{layer_idx}'''      ,   F'''fusion_stage.layers.{abs(layer_idx-4     )}'''     )
      if "out_conv" in name:
            _lowercase	       =name.replace('out_conv'      ,   'projection'     )
      if "resConfUnit1" in name:
            _lowercase	       =name.replace('resConfUnit1'      ,   'residual_layer1'     )
      if "resConfUnit2" in name:
            _lowercase	       =name.replace('resConfUnit2'      ,   'residual_layer2'     )
      if "conv1" in name:
            _lowercase	       =name.replace('conv1'      ,   'convolution1'     )
      if "conv2" in name:
            _lowercase	       =name.replace('conv2'      ,   'convolution2'     )
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess1.0.project.0'      ,   'neck.reassemble_stage.readout_projects.0.0'     )
      if "pretrained.act_postprocess2.0.project.0" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess2.0.project.0'      ,   'neck.reassemble_stage.readout_projects.1.0'     )
      if "pretrained.act_postprocess3.0.project.0" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess3.0.project.0'      ,   'neck.reassemble_stage.readout_projects.2.0'     )
      if "pretrained.act_postprocess4.0.project.0" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess4.0.project.0'      ,   'neck.reassemble_stage.readout_projects.3.0'     )
      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess1.3'      ,   'neck.reassemble_stage.layers.0.projection'     )
      if "pretrained.act_postprocess1.4" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess1.4'      ,   'neck.reassemble_stage.layers.0.resize'     )
      if "pretrained.act_postprocess2.3" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess2.3'      ,   'neck.reassemble_stage.layers.1.projection'     )
      if "pretrained.act_postprocess2.4" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess2.4'      ,   'neck.reassemble_stage.layers.1.resize'     )
      if "pretrained.act_postprocess3.3" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess3.3'      ,   'neck.reassemble_stage.layers.2.projection'     )
      if "pretrained.act_postprocess4.3" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess4.3'      ,   'neck.reassemble_stage.layers.3.projection'     )
      if "pretrained.act_postprocess4.4" in name:
            _lowercase	       =name.replace('pretrained.act_postprocess4.4'      ,   'neck.reassemble_stage.layers.3.resize'     )
      if "pretrained" in name:
            _lowercase	       =name.replace('pretrained'      ,   'dpt'     )
      if "bn" in name:
            _lowercase	       =name.replace('bn'      ,   'batch_norm'     )
      if "head" in name:
            _lowercase	       =name.replace('head'      ,   'head.head'     )
      if "encoder.norm" in name:
            _lowercase	       =name.replace('encoder.norm'      ,   'layernorm'     )
      if "auxlayer" in name:
            _lowercase	       =name.replace('auxlayer'      ,   'auxiliary_head.head'     )

      return name
def      a      (			A__	:   List[Any]      ,   A__	:   Tuple     )    ->      Optional[Any]:





      """simple docstring"""



      for i in range(config.num_hidden_layers     ):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowercase	       =state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''     )
            _lowercase	       =state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''     )
            # next, add query, keys and values (in that order) to the state dict
            _lowercase	       =in_proj_weight[: config.hidden_size, :]
            _lowercase	       =in_proj_bias[: config.hidden_size]
            _lowercase	       =in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowercase	       =in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowercase	       =in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowercase	       =in_proj_bias[-config.hidden_size :]
def      a      (			)    ->      str:





      """simple docstring"""



      _lowercase	       ='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase	       =Image.open(requests.get(A__      ,   stream=A__     ).raw     )
      return im
@torch.no_grad()
def      a      (			A__	:   List[str]      ,   A__	:   str      ,   A__	:   Tuple      ,   A__	:   Optional[int]     )    ->      Union[str, Any]:





      """simple docstring"""



      _lowercase       ,       _lowercase	       =get_dpt_config(A__     )
      # load original state_dict from URL
      _lowercase	       =torch.hub.load_state_dict_from_url(A__      ,   map_location='cpu'     )
      # remove certain keys
      remove_ignore_keys_(A__     )
      # rename keys
      for key in state_dict.copy().keys():
            _lowercase	       =state_dict.pop(A__     )
            _lowercase	       =val
      # read in qkv matrices
      read_in_q_k_v(A__      ,   A__     )

      # load HuggingFace model
      _lowercase	       =DPTForSemanticSegmentation(A__     ) if 'ade' in checkpoint_url else DPTForDepthEstimation(A__     )
      model.load_state_dict(A__     )
      model.eval()

      # Check outputs on an image
      _lowercase	       =480 if 'ade' in checkpoint_url else 384
      _lowercase	       =DPTImageProcessor(size=A__     )

      _lowercase	       =prepare_img()
      _lowercase	       =image_processor(A__      ,   return_tensors='pt'     )

      # forward pass
      _lowercase	       =model(**A__     ).logits if 'ade' in checkpoint_url else model(**A__     ).predicted_depth

      # Assert logits
      _lowercase	       =torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]     )
      if "ade" in checkpoint_url:
            _lowercase	       =torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]     )
      assert outputs.shape == torch.Size(A__     )
      assert (
          torch.allclose(outputs[0, 0, :3, :3]      ,   A__      ,   atol=1e-4     )
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]      ,   A__     )
      )

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

      if push_to_hub:
            print('Pushing model to hub...'     )
            model.push_to_hub(
                repo_path_or_name=Path(A__      ,   A__     )      ,   organization='nielsr'      ,   commit_message='Add model'      ,   use_temp_dir=A__      ,   )
            image_processor.push_to_hub(
                repo_path_or_name=Path(A__      ,   A__     )      ,   organization='nielsr'      ,   commit_message='Add image processor'      ,   use_temp_dir=A__      ,   )


if __name__ == "__main__":
  lowercase_									=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
      type=str,
      help='URL of the original DPT checkpoint you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=str,
      required=True,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
  )
  parser.add_argument(
      '--model_name',
      default='dpt-large',
      type=str,
      help='Name of the model, in case you\'re pushing to the hub.',
  )

  lowercase_									=   parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





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


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


set_seed(4_2)
lowercase_									=   'sshleifer/student_marian_en_ro_6_1'
lowercase_									=   'sshleifer/tiny-mbart'





@require_torch
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self							,   lowerCAmelCase=False							,   lowerCAmelCase=None							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   )  -> Dict:
        '''simple docstring'''






        _lowercase	       =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							,   )
        _lowercase	       =TrainerState.load_from_json(os.path.join(lowerCAmelCase							,   'trainer_state.json'					)					).log_history

        if not do_eval:
              return

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

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

              _lowercase	       =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					)  -> Optional[Any]:
        '''simple docstring'''






        self.run_seqaseq_quick()




  @require_torch_multi_gpu
  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        self.run_seqaseq_quick(distributed=lowerCAmelCase					)




  @require_torch_multi_gpu
  def   A__     (				self					)  -> Optional[Any]:
        '''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					)  -> Optional[Any]:
        '''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					)  -> Optional[int]:
        '''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					)  -> Dict:
        '''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					)  -> Optional[int]:
        '''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					)  -> List[Any]:
        '''simple docstring'''






        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					)  -> Any:
        '''simple docstring'''






        _lowercase	       ={
            # 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},
        }

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




  @slow
  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.run_trainer(
            eval_steps=2							,   max_len=128							,   model_name=lowerCAmelCase							,   learning_rate=3e-4							,   num_train_epochs=10							,   distributed=lowerCAmelCase							,   )

        # Check metrics
        _lowercase	       =TrainerState.load_from_json(os.path.join(lowerCAmelCase							,   'trainer_state.json'					)					).log_history
        _lowercase	       =[log for log in logs if 'eval_loss' in log.keys()]
        _lowercase	       =eval_metrics[0]
        _lowercase	       =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
        _lowercase	       =os.listdir(lowerCAmelCase					)
        _lowercase	       ={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					)  -> List[str]:
        '''simple docstring'''






        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(lowerCAmelCase					) -> Tuple[int, float]:
              _lowercase	       ='--skip_memory_metrics 0'

              _lowercase	       =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
              _lowercase	       =TrainerState.load_from_json(Path(lowerCAmelCase							,   'trainer_state.json'					)					).log_history
              _lowercase	       =int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20					)
              _lowercase	       =int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20					)

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

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

        _lowercase	       =gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        _lowercase	       =gpu_peak_mem_orig + gpu_alloc_mem_orig
        _lowercase	       =gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        _lowercase	       =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
        _lowercase	       =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   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							,   )  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
        _lowercase	       =self.get_auto_remove_tmp_dir()
        _lowercase	       =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()

        _lowercase	       =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()

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

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

        return output_dir


