





import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

A__:  Union[str, Any]       					=					logging.getLogger()
A__:  Any       					=					torch.cuda.is_available()
A__:  Tuple       					=					{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}



def 							lowerCAmelCase_				(    A_  ,A_):
    UpperCamelCase__:					List[Any]												=      "\n".join(A_)
    Path(A_).open("w").writelines(A_)


A__:  Any       					=					[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
A__:  str       					=					['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
A__:  Union[str, Any]       					=					'''patrickvonplaten/t5-tiny-random'''
A__:  Optional[int]       					=					'''sshleifer/t5-tinier-random'''
A__:  Optional[Any]       					=					'''sshleifer/bart-tiny-random'''
A__:  str       					=					'''sshleifer/tiny-mbart'''
A__:  Any       					=					'''sshleifer/tiny-marian-en-de'''
A__:  Optional[int]       					=					'''stas/tiny-wmt19-en-de'''


A__:  List[Any]       					=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



def 							lowerCAmelCase_				(    A_):
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(A_  ,F"{split}.source")  ,A_)
        _dump_articles(os.path.join(A_  ,F"{split}.target")  ,A_)
    return tmp_dir




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      @classmethod
      def 	UpperCAmelCase_   (						cls:	str			):
          '''simple docstring'''


          logging.disable(logging.CRITICAL			)  # remove noisy download output from tracebacks
          return cls






      @slow
      @require_torch_gpu
      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      list_models()
          UpperCamelCase__:					Any												=      "sshleifer"
          UpperCamelCase__:					Dict												=      [x.modelId for x in model_list if x.modelId.startswith(__lowerCamelCase			)]
          UpperCamelCase__:					Union[str, Any]												=      ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
          UpperCamelCase__:					List[Any]												=      []
          for m in model_ids:
              if m in allowed_to_be_broken:
                  continue
              try:
                  AutoConfig.from_pretrained(__lowerCamelCase			)
              except Exception:
                  failures.append(__lowerCamelCase			)
          assert not failures, F"The following models could not be loaded through AutoConfig: {failures}"






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
          self._test_distiller_cli(__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      {
              "student_encoder_layers": 2,
              "student_decoder_layers": 1,
              "max_epochs": 4,
              "val_check_interval": 0.25,
              "alpha_hid": 2.0,
              "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
          }
          UpperCamelCase__:					List[str]												=      self._test_distiller_cli(__lowerCamelCase					,  check_contents=__lowerCamelCase			)

          UpperCamelCase__:					Tuple												=      list(Path(model.output_dir			).glob("*.ckpt"			)			)
          self.assertEqual(1					,  len(__lowerCamelCase			)			)
          UpperCamelCase__:					Optional[Any]												=      list(Path(model.output_dir			).glob("**/*.bin"			)			)
          self.assertEqual(len(__lowerCamelCase			)					,  2			)
          UpperCamelCase__:					str												=      lmap(str.strip					,  Path(model.hparams.data_dir			).joinpath("test.source"			).open().readlines()			)
          UpperCamelCase__:					Any												=      tempfile.mktemp()  # XXX: not being cleaned up
          generate_summaries_or_translations(__lowerCamelCase					,  __lowerCamelCase					,  str(model.output_dir / "best_tfmr"			)			)
          self.assertTrue(Path(__lowerCamelCase			).exists()			)

          UpperCamelCase__:					List[str]												=      self.get_auto_remove_tmp_dir()
          convert_pl_to_hf(ckpts[0]					,  transformer_ckpts[0].parent					,  __lowerCamelCase			)
          assert os.path.exists(os.path.join(__lowerCamelCase					,  "pytorch_model.bin"			)			)






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)
          UpperCamelCase__							,				UpperCamelCase__:					Tuple												=      model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
          UpperCamelCase__:					Tuple												=      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]					,  dtype=torch.long					,  device=model.device			)
          UpperCamelCase__:					Any												=      target_ids[:, :-1].contiguous()  # Why this line?
          UpperCamelCase__:					Optional[Any]												=      target_ids[:, 1:].clone()  # why clone?
          UpperCamelCase__:					Any												=      model(
              __lowerCamelCase					,  attention_mask=__lowerCamelCase					,  decoder_input_ids=__lowerCamelCase					,  labels=__lowerCamelCase					,  use_cache=__lowerCamelCase			).loss

          UpperCamelCase__:					Optional[int]												=      model(__lowerCamelCase					,  attention_mask=__lowerCamelCase					,  decoder_input_ids=__lowerCamelCase					,  use_cache=__lowerCamelCase			).logits

          UpperCamelCase__:					Optional[Any]												=      nn.functional.log_softmax(__lowerCamelCase					,  dim=-1			)
          UpperCamelCase__							,				UpperCamelCase__:					int												=      label_smoothed_nll_loss(
              __lowerCamelCase					,  __lowerCamelCase					,  0.1					,  ignore_index=model.config.pad_token_id			)
          with self.assertRaises(__lowerCamelCase			):
              # TODO: understand why this breaks
              self.assertEqual(__lowerCamelCase					,  __lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					List[Any]												=      {
              "student_encoder_layers": 2,
              "student_decoder_layers": 1,
              "num_train_epochs": 4,
              "val_check_interval": 0.25,
              "alpha_hid": 2.0,
              "task": "translation",
              "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
              "tokenizer_name": MBART_TINY,
              "teacher": MBART_TINY,
              "src_lang": "en_XX",
              "tgt_lang": "ro_RO",
          }
          UpperCamelCase__:					Optional[int]												=      self._test_distiller_cli(__lowerCamelCase					,  check_contents=__lowerCamelCase			)
          assert model.model.config.model_type == "mbart"

          UpperCamelCase__:					Tuple												=      list(Path(model.output_dir			).glob("*.ckpt"			)			)
          self.assertEqual(1					,  len(__lowerCamelCase			)			)
          UpperCamelCase__:					Tuple												=      list(Path(model.output_dir			).glob("**/*.bin"			)			)
          UpperCamelCase__:					Dict												=      list(Path(model.output_dir			).glob("best_tfmr/*"			)			)
          assert len(__lowerCamelCase			) > 2
          self.assertEqual(len(__lowerCamelCase			)					,  2			)






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      {
              "student_encoder_layers": 1,
              "student_decoder_layers": 1,
              "alpha_hid": 2.0,
              "teacher": T5_TINY,
              "model_name_or_path": T5_TINY,
              "tokenizer_name": T5_TINY,
          }
          self._test_distiller_cli(__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      {
              "teacher": T5_TINY,
              "student": T5_TINIER,
              "model_name_or_path": T5_TINIER,
              "tokenizer_name": T5_TINIER,
          }
          self._test_distiller_cli(__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Union[str, Any]					,  __lowerCamelCase:	Optional[Any]					,  __lowerCamelCase:	List[str]=True			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      {
              "label_smoothing": 0.0,
              "early_stopping_patience": -1,
              "train_batch_size": 1,
              "eval_batch_size": 2,
              "max_epochs": 2,
              "alpha_mlm": 0.2,
              "alpha_ce": 0.8,
              "do_predict": True,
              "model_name_or_path": "sshleifer/tinier_bart",
              "teacher": CHEAP_ARGS["model_name_or_path"],
              "val_check_interval": 0.5,
          }
          default_updates.update(__lowerCamelCase			)
          UpperCamelCase__:					dict												=      CHEAP_ARGS.copy()
          UpperCamelCase__:					int												=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
          UpperCamelCase__:					Any												=      self.get_auto_remove_tmp_dir()

          args_d.update(data_dir=__lowerCamelCase					,  output_dir=__lowerCamelCase					,  **__lowerCamelCase			)
          UpperCamelCase__:					Dict												=      distill_main(argparse.Namespace(**__lowerCamelCase			)			)
          if not check_contents:
              return model
          UpperCamelCase__:					Optional[int]												=      os.listdir(__lowerCamelCase			)
          UpperCamelCase__:					Any												=      {os.path.basename(__lowerCamelCase			) for p in contents}
          UpperCamelCase__:					Union[str, Any]												=      [p for p in contents if p.endswith("ckpt"			)]
          assert len(__lowerCamelCase			) > 0

          self.assertIn("test_generations.txt"					,  __lowerCamelCase			)
          self.assertIn("test_results.txt"					,  __lowerCamelCase			)

          UpperCamelCase__:					Any												=      load_json(model.metrics_save_path			)
          UpperCamelCase__:					List[Any]												=      metrics["val"][-1]
          self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]					,  0.01			)
          self.assertGreaterEqual(1.0					,  last_step_stats["val_avg_gen_time"]			)
          self.assertIsInstance(last_step_stats[F"val_avg_{model.val_metric}"]					,  __lowerCamelCase			)
          UpperCamelCase__:					Any												=      int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1			)
          self.assertEqual(len(metrics["val"]			)					,  __lowerCamelCase			)
          self.assertEqual(len(metrics["test"]			)					,  1			)
          return model




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,  )
      def 	UpperCAmelCase_   (						self:	List[str]					,  __lowerCamelCase:	Optional[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					dict												=      CHEAP_ARGS.copy()
          UpperCamelCase__:					List[Any]												=      "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
          UpperCamelCase__:					int												=      0.1 if task == "translation" else 0

          UpperCamelCase__:					Any												=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
          UpperCamelCase__:					Optional[int]												=      self.get_auto_remove_tmp_dir()
          args_d.update(
              data_dir=__lowerCamelCase					,  model_name_or_path=__lowerCamelCase					,  tokenizer_name=__lowerCamelCase					,  train_batch_size=2					,  eval_batch_size=2					,  output_dir=__lowerCamelCase					,  do_predict=__lowerCamelCase					,  task=__lowerCamelCase					,  src_lang="en_XX"					,  tgt_lang="ro_RO"					,  freeze_encoder=__lowerCamelCase					,  freeze_embeds=__lowerCamelCase					,  )
          assert "n_train" in args_d
          UpperCamelCase__:					Dict												=      argparse.Namespace(**__lowerCamelCase			)
          UpperCamelCase__:					List[Any]												=      main(__lowerCamelCase			)

          UpperCamelCase__:					Any												=      module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
              UpperCamelCase__:					Tuple												=      module.model.lm_head
              assert not lm_head.weight.requires_grad
              assert (lm_head.weight == input_embeds.weight).all().item()
          elif model == FSMT_TINY:
              UpperCamelCase__:					Tuple												=      module.model.model
              UpperCamelCase__:					Dict												=      fsmt.decoder.embed_positions
              assert not embed_pos.weight.requires_grad
              assert not fsmt.decoder.embed_tokens.weight.requires_grad
              # check that embeds are not the same
              assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
          else:
              UpperCamelCase__:					Any												=      module.model.model
              UpperCamelCase__:					Union[str, Any]												=      bart.decoder.embed_positions
              assert not embed_pos.weight.requires_grad
              assert not bart.shared.weight.requires_grad
              # check that embeds are the same
              assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
              assert bart.decoder.embed_tokens == bart.shared

          UpperCamelCase__:					Tuple												=      load_json(module.output_dir / "text_batch.json"			)
          assert isinstance(__lowerCamelCase					,  __lowerCamelCase			)
          assert len(__lowerCamelCase			) >= 4






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					dict												=      CHEAP_ARGS.copy()

          UpperCamelCase__:					Tuple												=      "summarization"
          UpperCamelCase__:					Tuple												=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

          args_d.update(
              data_dir=__lowerCamelCase					,  tokenizer_name=__lowerCamelCase					,  train_batch_size=2					,  eval_batch_size=2					,  do_predict=__lowerCamelCase					,  task=__lowerCamelCase					,  src_lang="en_XX"					,  tgt_lang="ro_RO"					,  freeze_encoder=__lowerCamelCase					,  freeze_embeds=__lowerCamelCase					,  )

          # test models whose config includes the extra_model_args
          UpperCamelCase__:					Any												=      BART_TINY
          UpperCamelCase__:					Dict												=      self.get_auto_remove_tmp_dir()
          UpperCamelCase__:					int												=      args_d.copy()
          args_da.update(
              model_name_or_path=__lowerCamelCase					,  output_dir=__lowerCamelCase					,  )
          UpperCamelCase__:					Union[str, Any]												=      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
              UpperCamelCase__:					Any												=      0.5
          UpperCamelCase__:					int												=      argparse.Namespace(**__lowerCamelCase			)
          UpperCamelCase__:					Optional[int]												=      main(__lowerCamelCase			)
          for p in extra_model_params:
              assert getattr(model.config					,  __lowerCamelCase			) == 0.5, F"failed to override the model config for param {p}"

          # test models whose config doesn't include the extra_model_args
          UpperCamelCase__:					Dict												=      T5_TINY
          UpperCamelCase__:					str												=      self.get_auto_remove_tmp_dir()
          UpperCamelCase__:					Dict												=      args_d.copy()
          args_da.update(
              model_name_or_path=__lowerCamelCase					,  output_dir=__lowerCamelCase					,  )
          UpperCamelCase__:					List[Any]												=      "encoder_layerdrop"
          UpperCamelCase__:					str												=      0.5
          UpperCamelCase__:					Union[str, Any]												=      argparse.Namespace(**__lowerCamelCase			)
          with pytest.raises(__lowerCamelCase			) as excinfo:
              UpperCamelCase__:					List[str]												=      main(__lowerCamelCase			)
          assert str(excinfo.value			) == F"model config doesn't have a `{unsupported_param}` attribute"






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					dict												=      CHEAP_ARGS.copy()

          UpperCamelCase__:					str												=      "summarization"
          UpperCamelCase__:					Any												=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

          UpperCamelCase__:					Dict												=      BART_TINY
          UpperCamelCase__:					Union[str, Any]												=      self.get_auto_remove_tmp_dir()

          args_d.update(
              data_dir=__lowerCamelCase					,  model_name_or_path=__lowerCamelCase					,  output_dir=__lowerCamelCase					,  tokenizer_name=__lowerCamelCase					,  train_batch_size=2					,  eval_batch_size=2					,  do_predict=__lowerCamelCase					,  task=__lowerCamelCase					,  src_lang="en_XX"					,  tgt_lang="ro_RO"					,  freeze_encoder=__lowerCamelCase					,  freeze_embeds=__lowerCamelCase					,  )

          # emulate finetune.py
          UpperCamelCase__:					Dict												=      argparse.ArgumentParser()
          UpperCamelCase__:					str												=      pl.Trainer.add_argparse_args(__lowerCamelCase			)
          UpperCamelCase__:					Dict												=      SummarizationModule.add_model_specific_args(__lowerCamelCase					,  os.getcwd()			)
          UpperCamelCase__:					List[Any]												=      {"--help": True}

          # --help test
          with pytest.raises(__lowerCamelCase			) as excinfo:
              with CaptureStdout() as cs:
                  UpperCamelCase__:					Tuple												=      parser.parse_args(__lowerCamelCase			)
              assert False, "--help is expected to sys.exit"
          assert excinfo.type == SystemExit
          UpperCamelCase__:					Optional[Any]												=      lightning_base.arg_to_scheduler_metavar
          assert expected in cs.out, "--help is expected to list the supported schedulers"

          # --lr_scheduler=non_existing_scheduler test
          UpperCamelCase__:					Any												=      "non_existing_scheduler"
          UpperCamelCase__:					str												=      {F"--lr_scheduler={unsupported_param}"}
          with pytest.raises(__lowerCamelCase			) as excinfo:
              with CaptureStderr() as cs:
                  UpperCamelCase__:					Dict												=      parser.parse_args(__lowerCamelCase			)
              assert False, "invalid argument is expected to sys.exit"
          assert excinfo.type == SystemExit
          UpperCamelCase__:					str												=      F"invalid choice: '{unsupported_param}'"
          assert expected in cs.err, F"should have bailed on invalid choice of scheduler {unsupported_param}"

          # --lr_scheduler=existing_scheduler test
          UpperCamelCase__:					int												=      "cosine"
          UpperCamelCase__:					Any												=      args_d.copy()
          UpperCamelCase__:					List[str]												=      supported_param
          UpperCamelCase__:					Dict												=      argparse.Namespace(**__lowerCamelCase			)
          UpperCamelCase__:					List[Any]												=      main(__lowerCamelCase			)
          assert (
              getattr(model.hparams					,  "lr_scheduler"			) == supported_param
          ), F"lr_scheduler={supported_param} shouldn't fail"








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


A__:  Union[str, Any]       					=					os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
A__:  int       					=					os.path.join(hf_cache_home, '''accelerate''')
A__:  Dict       					=					os.path.join(cache_dir, '''default_config.yaml''')
A__:  Union[str, Any]       					=					os.path.join(cache_dir, '''default_config.yaml''')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
   A__:  Tuple       					=					default_yaml_config_file
else:
   A__:  int       					=					default_json_config_file



def 							lowerCAmelCase_				(    A_):
    if config_file is not None:
        if not os.path.isfile(A_):
            raise FileNotFoundError(
                F"The passed configuration file `{config_file}` does not exist. "
                "Please pass an existing file to `accelerate launch`, or use the the default one "
                "created through `accelerate config` and run `accelerate launch` "
                "without the `--config_file` argument.")
    else:
        UpperCamelCase__:					Optional[int]												=      default_config_file
    with open(A_  ,"r"  ,encoding="utf-8") as f:
        if config_file.endswith(".json"):
            if (
                json.load(A_).get("compute_environment"  ,ComputeEnvironment.LOCAL_MACHINE)
                == ComputeEnvironment.LOCAL_MACHINE
            ):
                UpperCamelCase__:					Union[str, Any]												=      ClusterConfig
            else:
                UpperCamelCase__:					int												=      SageMakerConfig
            return config_class.from_json_file(json_file=A_)
        else:
            if (
                yaml.safe_load(A_).get("compute_environment"  ,ComputeEnvironment.LOCAL_MACHINE)
                == ComputeEnvironment.LOCAL_MACHINE
            ):
                UpperCamelCase__:					Union[str, Any]												=      ClusterConfig
            else:
                UpperCamelCase__:					int												=      SageMakerConfig
            return config_class.from_yaml_file(yaml_file=A_)




@dataclass
class    _a  :

      """simple docstring"""


      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42






      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Tuple												=      self.__dict__
          # For serialization, it's best to convert Enums to strings (or their underlying value type).
          for key, value in result.items():
              if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
                  UpperCamelCase__:					str												=      value.value
              if isinstance(__lowerCamelCase					,  __lowerCamelCase			) and not bool(__lowerCamelCase			):
                  UpperCamelCase__:					Tuple												=      None
          UpperCamelCase__:					List[Any]												=      {k: v for k, v in result.items() if v is not None}
          return result






      @classmethod
      def 	UpperCAmelCase_   (						cls:	str					,  __lowerCamelCase:	Any=None			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      default_json_config_file if json_file is None else json_file
          with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"			) as f:
              UpperCamelCase__:					str												=      json.load(__lowerCamelCase			)
          if "compute_environment" not in config_dict:
              UpperCamelCase__:					List[str]												=      ComputeEnvironment.LOCAL_MACHINE
          if "mixed_precision" not in config_dict:
              UpperCamelCase__:					Optional[Any]												=      "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
          if "fp16" in config_dict:  # Convert the config to the new format.
              del config_dict["fp16"]
          if "dynamo_backend" in config_dict:  # Convert the config to the new format.
              UpperCamelCase__:					int												=      config_dict.pop("dynamo_backend"			)
              UpperCamelCase__:					Dict												=      {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
          if "use_cpu" not in config_dict:
              UpperCamelCase__:					str												=      False
          return cls(**__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Tuple					,  __lowerCamelCase:	List[Any]			):
          '''simple docstring'''


          with open(__lowerCamelCase					,  "w"					,  encoding="utf-8"			) as f:
              UpperCamelCase__:					Any												=      json.dumps(self.to_dict()					,  indent=2					,  sort_keys=__lowerCamelCase			) + "\n"
              f.write(__lowerCamelCase			)






      @classmethod
      def 	UpperCAmelCase_   (						cls:	Tuple					,  __lowerCamelCase:	Tuple=None			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      default_yaml_config_file if yaml_file is None else yaml_file
          with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"			) as f:
              UpperCamelCase__:					Union[str, Any]												=      yaml.safe_load(__lowerCamelCase			)
          if "compute_environment" not in config_dict:
              UpperCamelCase__:					List[Any]												=      ComputeEnvironment.LOCAL_MACHINE

          if "mixed_precision" not in config_dict:
              UpperCamelCase__:					Optional[int]												=      "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
          if isinstance(config_dict["mixed_precision"]					,  __lowerCamelCase			) and not config_dict["mixed_precision"]:
              UpperCamelCase__:					List[Any]												=      "no"
          if "fp16" in config_dict:  # Convert the config to the new format.
              del config_dict["fp16"]
          if "dynamo_backend" in config_dict:  # Convert the config to the new format.
              UpperCamelCase__:					List[str]												=      config_dict.pop("dynamo_backend"			)
              UpperCamelCase__:					Optional[Any]												=      {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
          if "use_cpu" not in config_dict:
              UpperCamelCase__:					int												=      False
          return cls(**__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Optional[int]					,  __lowerCamelCase:	Tuple			):
          '''simple docstring'''


          with open(__lowerCamelCase					,  "w"					,  encoding="utf-8"			) as f:
              yaml.safe_dump(self.to_dict()					,  __lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          if isinstance(self.compute_environment					,  __lowerCamelCase			):
              UpperCamelCase__:					Optional[int]												=      ComputeEnvironment(self.compute_environment			)
          if isinstance(self.distributed_type					,  __lowerCamelCase			):
              if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                  UpperCamelCase__:					str												=      SageMakerDistributedType(self.distributed_type			)
              else:
                  UpperCamelCase__:					Optional[int]												=      DistributedType(self.distributed_type			)
          if self.dynamo_config is None:
              UpperCamelCase__:					List[Any]												=      {}




@dataclass
class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   0
      UpperCamelCase__       						=   1
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   "static"
      UpperCamelCase__       						=   False
      UpperCamelCase__       						=   "main"

      # args for deepspeed_plugin
      UpperCamelCase__       						=   None
      # args for fsdp
      UpperCamelCase__       						=   None
      # args for megatron_lm
      UpperCamelCase__       						=   None
      # args for ipex
      UpperCamelCase__       						=   None
      # args for TPU
      UpperCamelCase__       						=   False

      # args for TPU pods
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   False
      UpperCamelCase__       						=   False
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None

      # args for dynamo
      UpperCamelCase__       						=   None






      def 	UpperCAmelCase_   (						self:	Optional[Any]			):
          '''simple docstring'''


          if self.deepspeed_config is None:
              UpperCamelCase__:					int												=      {}
          if self.fsdp_config is None:
              UpperCamelCase__:					List[Any]												=      {}
          if self.megatron_lm_config is None:
              UpperCamelCase__:					Union[str, Any]												=      {}
          if self.ipex_config is None:
              UpperCamelCase__:					List[str]												=      {}
          return super().__post_init__()




@dataclass
class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   "us-east-1"
      UpperCamelCase__       						=   1
      UpperCamelCase__       						=   "all"
      UpperCamelCase__       						=   F'''accelerate-sagemaker-{num_machines}'''
      UpperCamelCase__       						=   SAGEMAKER_PYTORCH_VERSION
      UpperCamelCase__       						=   SAGEMAKER_TRANSFORMERS_VERSION
      UpperCamelCase__       						=   SAGEMAKER_PYTHON_VERSION
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None
      UpperCamelCase__       						=   None

