
'''simple docstring'''



import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




       """simple docstring"""
       if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
              # expert layer
              _SCREAMING_SNAKE_CASE      =				flax_key_tuple[:-1] + ("""weight""",)
              _SCREAMING_SNAKE_CASE      =				torch.permute(SCREAMING_SNAKE_CASE_    ,      (0, 2, 1)						)
       elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_						):
              # linear layer
              _SCREAMING_SNAKE_CASE      =				flax_key_tuple[:-1] + ("""weight""",)
              _SCREAMING_SNAKE_CASE      =				flax_tensor.T
       elif flax_key_tuple[-1] in ["scale", "embedding"]:
              _SCREAMING_SNAKE_CASE      =				flax_key_tuple[:-1] + ("""weight""",)

       return flax_key_tuple, flax_tensor
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




       """simple docstring"""
       if "metadata" in layer:
              _SCREAMING_SNAKE_CASE      =				layer.split("""metadata"""						)
              _SCREAMING_SNAKE_CASE      =				"""""".join(split_layer[0]						)[:-1]
              _SCREAMING_SNAKE_CASE      =				[tuple(("""metadata""" + split_layer[1]).split("""/"""						)						)]
       elif "kvstore" in layer:
              _SCREAMING_SNAKE_CASE      =				layer.split("""kvstore"""						)
              _SCREAMING_SNAKE_CASE      =				"""""".join(split_layer[0]						)[:-1]
              _SCREAMING_SNAKE_CASE      =				[tuple(("""kvstore""" + split_layer[1]).split("""/"""						)						)]

       else:
              _SCREAMING_SNAKE_CASE      =				layer.split("""/"""						)
              _SCREAMING_SNAKE_CASE      =				"""/""".join(split_layer[:-1]						)
              _SCREAMING_SNAKE_CASE      =				(split_layer[-1],)

       if "kvstore/path" in layer:
              _SCREAMING_SNAKE_CASE      =				F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
       elif "kvstore/driver" in layer:
              _SCREAMING_SNAKE_CASE      =				"""file"""
       else:
              _SCREAMING_SNAKE_CASE      =				checkpoint_info[layer]

       return curr_real_layer_name, split_layer, content
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Any:




       """simple docstring"""
       _SCREAMING_SNAKE_CASE      =				rename_keys(SCREAMING_SNAKE_CASE_						)
       _SCREAMING_SNAKE_CASE      =				{}
       for k, v in current_block.items():
              _SCREAMING_SNAKE_CASE      =				v
       _SCREAMING_SNAKE_CASE      =				new_current_block
       torch.save(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME						)     ->     Any:




       """simple docstring"""
       _SCREAMING_SNAKE_CASE      =				convert_file_size_to_int(SCREAMING_SNAKE_CASE_						)
       _SCREAMING_SNAKE_CASE      =				[]
       _SCREAMING_SNAKE_CASE      =				{}
       _SCREAMING_SNAKE_CASE      =				0
       _SCREAMING_SNAKE_CASE      =				0

       os.makedirs(SCREAMING_SNAKE_CASE_    ,      exist_ok=SCREAMING_SNAKE_CASE_						)
       with gfile.GFile(switch_checkpoint_path + """/checkpoint"""    ,      """rb"""						) as fp:
              _SCREAMING_SNAKE_CASE      =				serialization.msgpack_restore(fp.read()						)["""optimizer"""]["""target"""]
              _SCREAMING_SNAKE_CASE      =				flatten_dict(SCREAMING_SNAKE_CASE_    ,      sep="""/"""						)

       _SCREAMING_SNAKE_CASE      =				{}
       for layer in checkpoint_info.keys():
              _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				get_key_and_tensorstore_dict(
                  SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
              if curr_real_layer_name in all_layers:
                     _SCREAMING_SNAKE_CASE      =				content
              else:
                     _SCREAMING_SNAKE_CASE      =				{split_layer[-1]: content}

       for key in all_layers.keys():
              # open tensorstore file
              _SCREAMING_SNAKE_CASE      =				ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
              _SCREAMING_SNAKE_CASE      =				torch.tensor(SCREAMING_SNAKE_CASE_						)
              _SCREAMING_SNAKE_CASE      =				raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

              # use the renaming pattern from the small conversion scripts
              _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				rename_base_flax_keys(tuple(key.split("""/"""						)						)    ,      SCREAMING_SNAKE_CASE_						)
              _SCREAMING_SNAKE_CASE      =				"""/""".join(SCREAMING_SNAKE_CASE_						)

              # If this weight is going to tip up over the maximal size, we split.
              if current_block_size + weight_size > max_shard_size:
                     _SCREAMING_SNAKE_CASE      =				os.path.join(
                         SCREAMING_SNAKE_CASE_    ,      weights_name.replace(""".bin"""    ,      F"-{len(SCREAMING_SNAKE_CASE_						)+1:05d}-of-???.bin"						)						)
                     rename_and_save_block(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
                     sharded_state_dicts.append(current_block.keys()						)
                     del current_block
                     _SCREAMING_SNAKE_CASE      =				{}
                     _SCREAMING_SNAKE_CASE      =				0

              _SCREAMING_SNAKE_CASE      =				raw_weights.to(getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)						)
              current_block_size += weight_size
              total_size += weight_size

       # Add the last block
       _SCREAMING_SNAKE_CASE      =				os.path.join(SCREAMING_SNAKE_CASE_    ,      weights_name.replace(""".bin"""    ,      F"-{len(SCREAMING_SNAKE_CASE_						)+1:05d}-of-???.bin"						)						)
       rename_and_save_block(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
       sharded_state_dicts.append(current_block.keys()						)

       # If we only have one shard, we return it
       if len(SCREAMING_SNAKE_CASE_						) == 1:
              return {weights_name: sharded_state_dicts[0]}, None

       # Otherwise, let's build the index
       _SCREAMING_SNAKE_CASE      =				{}
       _SCREAMING_SNAKE_CASE      =				{}
       for idx, shard in enumerate(SCREAMING_SNAKE_CASE_						):
              _SCREAMING_SNAKE_CASE      =				weights_name.replace(
                  """.bin"""    ,      F"-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_						):05d}.bin"						)  # len(sharded_state_dicts):05d}
              _SCREAMING_SNAKE_CASE      =				os.path.join(SCREAMING_SNAKE_CASE_    ,      weights_name.replace(""".bin"""    ,      F"-{idx+1:05d}-of-???.bin"						)						)
              os.rename(SCREAMING_SNAKE_CASE_    ,      os.path.join(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)						)
              _SCREAMING_SNAKE_CASE      =				shard
              for key in shard:
                     _SCREAMING_SNAKE_CASE      =				shard_file

    # Add the metadata
       _SCREAMING_SNAKE_CASE      =				{"""total_size""": total_size}
       _SCREAMING_SNAKE_CASE      =				{"""metadata""": metadata, """weight_map""": weight_map}

       with open(os.path.join(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)    ,      """w"""    ,      encoding="""utf-8"""						) as f:
              _SCREAMING_SNAKE_CASE      =				json.dumps(SCREAMING_SNAKE_CASE_    ,      indent=2    ,      sort_keys=SCREAMING_SNAKE_CASE_						) + """\n"""
              f.write(SCREAMING_SNAKE_CASE_						)

       return metadata, index


if __name__ == "__main__":
     UpperCamelCase__      :		Any       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--switch_t5x_checkpoint_path",
         default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
         type=str,
         required=False,
         help="Path to a directory containing a folder per layer. Follows the original Google format.",
     )
     parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
     parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
         type=str,
         required=False,
         help="Path to the output pytorch model.",
     )
     UpperCamelCase__      :		Optional[Any]       							=  parser.parse_args()
     shard_on_the_fly(
         args.switch_tax_checkpoint_path,
         args.pytorch_dump_folder_path,
         args.max_shard_size,
         args.dtype,
     )







def    lowerCAmelCase_	(				)     ->     Tuple:




       """simple docstring"""
       from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

       _SCREAMING_SNAKE_CASE      =				SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""						)
       config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""						)
       _SCREAMING_SNAKE_CASE      =				SwitchTransformersForConditionalGeneration.from_pretrained(
           """/home/arthur_huggingface_co/transformers/switch_converted"""    ,      device_map="""auto"""						)

       _SCREAMING_SNAKE_CASE      =				TaTokenizer.from_pretrained("""t5-small"""						)
       _SCREAMING_SNAKE_CASE      =				"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

       _SCREAMING_SNAKE_CASE      =				tokenizer(SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""						).input_ids
       _SCREAMING_SNAKE_CASE      =				model.generate(SCREAMING_SNAKE_CASE_    ,      decoder_start_token_id=0						)
       print(tokenizer.decode(out[0]						)						)








'''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


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


set_seed(42)
UpperCamelCase__      :		Union[str, Any]       							=  "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__      :		str       							=  "sshleifer/tiny-mbart"

@require_torch
class    _a				(_lowerCamelCase):





       """simple docstring"""




       def        UpperCamelCase				(							self       ,							A__=False       ,							A__=None       ,							A__=True       ,							A__=True       ,							A__=True       ,							A__=True       ,							)    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				self.run_trainer(
              eval_steps=1       ,							max_len=12       ,							model_name=A__       ,							num_train_epochs=1       ,							distributed=A__       ,							extra_args_str=A__       ,							predict_with_generate=A__       ,							do_train=A__       ,							do_eval=A__       ,							do_predict=A__       ,							)
          _SCREAMING_SNAKE_CASE      =				TrainerState.load_from_json(os.path.join(A__       ,							"""trainer_state.json"""      )      ).log_history

          if not do_eval:
             return

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

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

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






       @require_torch_non_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          self.run_seqaseq_quick()






       @require_torch_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          self.run_seqaseq_quick(distributed=A__      )






       @require_torch_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          self.run_seqaseq_quick(distributed=A__      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			Any:
          self.run_seqaseq_quick(distributed=A__       ,							extra_args_str="""--sharded_ddp simple"""      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			Tuple:
          self.run_seqaseq_quick(distributed=A__       ,							extra_args_str="""--sharded_ddp simple --fp16"""      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			str:
          self.run_seqaseq_quick(distributed=A__       ,							extra_args_str="""--sharded_ddp zero_dp_2"""       ,							predict_with_generate=A__      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			List[str]:
          self.run_seqaseq_quick(
              distributed=A__       ,							extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""       ,							predict_with_generate=A__      )






       @require_apex
       @require_torch_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          # 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=A__       ,							extra_args_str="""--fp16 --fp16_backend=apex"""      )
          # test 2nd time - was getting eval_loss': nan'
          # to reproduce the problem set distributed=False
          self.run_seqaseq_quick(distributed=A__       ,							extra_args_str="""--fp16 --fp16_backend=apex"""      )






       @parameterized.expand(["""base""", """low""", """high""", """mixed"""]      )
       @require_torch_multi_gpu
       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
          _SCREAMING_SNAKE_CASE      =				{
              # 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},
          }

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






       @slow
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.run_trainer(
              eval_steps=2       ,							max_len=1_28       ,							model_name=A__       ,							learning_rate=3E-4       ,							num_train_epochs=10       ,							distributed=A__       ,							)

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

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

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






       @slow
       @require_bitsandbytes
       def        UpperCamelCase				(							self      )    ->			Dict:
          from transformers.training_args import OptimizerNames

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

             _SCREAMING_SNAKE_CASE      =				self.run_trainer(
                 max_len=1_28       ,							model_name=A__       ,							learning_rate=3E-4       ,							num_train_epochs=1       ,							optim=A__       ,							distributed=A__       ,							extra_args_str=A__       ,							do_eval=A__       ,							do_predict=A__       ,							n_gpus_to_use=1       ,							)

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

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

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

          _SCREAMING_SNAKE_CASE      =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

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

          # uncomment the following if this test starts failing - requires py38 for a new print feature
          # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
          # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
          # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
          # print(f"{gpu_alloc_mem_diff=}MB")
          # print(f"{gpu_peak_mem_diff=}MB")
          # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
          # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

          self.assertGreater(
              A__       ,							A__       ,							"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
              F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
              F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"       ,							)

          self.assertGreater(
              A__       ,							A__       ,							"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
              F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
              F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"       ,							)

          self.assertEqual(
              A__       ,							A__       ,							F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__ = 3E-3       ,							A__ = "adafactor"       ,							A__ = False       ,							A__ = None       ,							A__ = 0       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = None       ,							)    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
          _SCREAMING_SNAKE_CASE      =				self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE      =				F"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(A__      )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(A__      )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

          _SCREAMING_SNAKE_CASE      =				F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(A__      )}\n        ".split()

          _SCREAMING_SNAKE_CASE      =				"""
            --do_predict
        """.split()

          _SCREAMING_SNAKE_CASE      =				[]
          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:
                _SCREAMING_SNAKE_CASE      =				get_gpu_count()
             _SCREAMING_SNAKE_CASE      =				get_torch_dist_unique_port()
             _SCREAMING_SNAKE_CASE      =				F"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
             _SCREAMING_SNAKE_CASE      =				[sys.executable] + distributed_args + args
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
             execute_subprocess_async(A__       ,							env=self.get_env()      )
          else:
             _SCREAMING_SNAKE_CASE      =				["""run_translation.py"""] + args
             with patch.object(A__       ,							"""argv"""       ,							A__      ):
                main()

          return output_dir






