import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__SCREAMING_SNAKE_CASE						:   Any         =			logging.getLogger(__name__)



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'summarization'
							_lowerCamelCase						=		['loss']
							_lowerCamelCase						=		ROUGE_KEYS
							_lowerCamelCase						=		'rouge2'



							def __init__(						self				,    lowercase_				,    **lowercase_    ):
													if hparams.sortish_sampler and hparams.gpus > 1:
																			_snake_case						:					int    =   False
													elif hparams.max_tokens_per_batch is not None:
																			if hparams.gpus > 1:
																									raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training"    )
																			if hparams.sortish_sampler:
																									raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"    )

													super().__init__(lowercase_				,    num_labels=lowercase_				,    mode=self.mode				,    **lowercase_    )
													use_task_specific_params(self.model				,    "summarization"    )
													save_git_info(self.hparams.output_dir    )
													_snake_case						:					int    =   Path(self.output_dir    ) / "metrics.json"
													_snake_case						:					Optional[Any]    =   Path(self.output_dir    ) / "hparams.pkl"
													pickle_save(self.hparams				,    self.hparams_save_path    )
													_snake_case						:					Optional[int]    =   0
													_snake_case						:					Any    =   defaultdict(lowercase_    )
													_snake_case						:					Union[str, Any]    =   self.config.model_type
													_snake_case						:					str    =   self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

													_snake_case						:					dict    =   {
													    "data_dir": self.hparams.data_dir,
													    "max_source_length": self.hparams.max_source_length,
													    "prefix": self.model.config.prefix or "",
													}
													_snake_case						:					List[Any]    =   {
													    "train": self.hparams.n_train,
													    "val": self.hparams.n_val,
													    "test": self.hparams.n_test,
													}
													_snake_case						:					Optional[int]    =   {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

													_snake_case						:					str    =   {
													    "train": self.hparams.max_target_length,
													    "val": self.hparams.val_max_target_length,
													    "test": self.hparams.test_max_target_length,
													}
													assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
													assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
													if self.hparams.freeze_embeds:
																			freeze_embeds(self.model    )
													if self.hparams.freeze_encoder:
																			freeze_params(self.model.get_encoder()    )
																			assert_all_frozen(self.model.get_encoder()    )

													_snake_case						:					List[Any]    =   get_git_info()["repo_sha"]
													_snake_case						:					List[str]    =   hparams.num_workers
													_snake_case						:					Any    =   None  # default to config
													if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,    lowercase_    ):
																			_snake_case						:					Optional[Any]    =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																			_snake_case						:					Optional[Any]    =   self.decoder_start_token_id
													_snake_case						:					Optional[Any]    =   (
													    SeqaSeqDataset if hasattr(self.tokenizer				,    "prepare_seq2seq_batch"    ) else LegacySeqaSeqDataset
													)
													_snake_case						:					Any    =   False
													_snake_case						:					Any    =   self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
													if self.hparams.eval_max_gen_length is not None:
																			_snake_case						:					Optional[int]    =   self.hparams.eval_max_gen_length
													else:
																			_snake_case						:					List[Any]    =   self.model.config.max_length
													_snake_case						:					List[Any]    =   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Dict    =   {
													    k: self.tokenizer.batch_decode(v.tolist()    ) if "mask" not in k else v.shape for k, v in batch.items()
													}
													save_json(lowercase_				,    Path(self.output_dir    ) / "text_batch.json"    )
													save_json({k: v.tolist() for k, v in batch.items()}				,    Path(self.output_dir    ) / "tok_batch.json"    )

													_snake_case						:					List[str]    =   True
													return readable_batch



							def 	UpperCamelCase	(						self				,    lowercase_				,    **lowercase_    ):
													return self.model(lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					int    =   self.tokenizer.batch_decode(
													    lowercase_				,    skip_special_tokens=lowercase_				,    clean_up_tokenization_spaces=lowercase_    )
													return lmap(str.strip				,    lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					List[Any]    =   self.tokenizer.pad_token_id
													_snake_case    ,_snake_case						:					Optional[Any]    =   batch["input_ids"], batch["attention_mask"]
													_snake_case						:					Optional[int]    =   batch["labels"]
													if isinstance(self.model				,    lowercase_    ):
																			_snake_case						:					List[str]    =   self.model._shift_right(lowercase_    )
													else:
																			_snake_case						:					Optional[int]    =   shift_tokens_right(lowercase_				,    lowercase_    )
													if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																			_snake_case						:					int    =   decoder_input_ids
																			self.save_readable_batch(lowercase_    )

													_snake_case						:					int    =   self(lowercase_				,    attention_mask=lowercase_				,    decoder_input_ids=lowercase_				,    use_cache=lowercase_    )
													_snake_case						:					Dict    =   outputs["logits"]
													if self.hparams.label_smoothing == 0:
																			# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																			_snake_case						:					List[Any]    =   nn.CrossEntropyLoss(ignore_index=lowercase_    )

																			assert lm_logits.shape[-1] == self.vocab_size
																			_snake_case						:					int    =   ce_loss_fct(lm_logits.view(-1				,    lm_logits.shape[-1]    )				,    tgt_ids.view(-1    )    )
													else:
																			_snake_case						:					Dict    =   nn.functional.log_softmax(lowercase_				,    dim=-1    )
																			_snake_case    ,_snake_case						:					Union[str, Any]    =   label_smoothed_nll_loss(
																			    lowercase_				,    lowercase_				,    self.hparams.label_smoothing				,    ignore_index=lowercase_    )
													return (loss,)



							@property
							def 	UpperCamelCase	(						self    ):
													return self.tokenizer.pad_token_id



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[int]    =   self._step(lowercase_    )

													_snake_case						:					int    =   dict(zip(self.loss_names				,    lowercase_    )    )
													# tokens per batch
													_snake_case						:					List[Any]    =   batch["input_ids"].ne(self.pad    ).sum() + batch["labels"].ne(self.pad    ).sum()
													_snake_case						:					List[str]    =   batch["input_ids"].shape[0]
													_snake_case						:					List[Any]    =   batch["input_ids"].eq(self.pad    ).sum()
													_snake_case						:					Optional[int]    =   batch["input_ids"].eq(self.pad    ).float().mean()
													# TODO(SS): make a wandb summary metric for this
													return {"loss": loss_tensors[0], "log": logs}



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													return self._generative_step(lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_="val"    ):
													self.step_count += 1
													_snake_case						:					Any    =   {k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
													_snake_case						:					Optional[int]    =   losses["loss"]
													_snake_case						:					Optional[int]    =   {
													    k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
													}
													_snake_case						:					List[str]    =   (
													    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
													)
													_snake_case						:					torch.FloatTensor    =   torch.tensor(lowercase_    ).type_as(lowercase_    )
													generative_metrics.update({k: v.item() for k, v in losses.items()}    )
													losses.update(lowercase_    )
													_snake_case						:					Optional[int]    =   {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
													_snake_case						:					int    =   self.step_count
													self.metrics[prefix].append(lowercase_    )  # callback writes this to self.metrics_save_path
													_snake_case						:					str    =   flatten_list([x["preds"] for x in outputs]    )
													return {
													    "log": all_metrics,
													    "preds": preds,
													    f"""{prefix}_loss""": loss,
													    f"""{prefix}_{self.val_metric}""": metric_tensor,
													}



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													return calculate_rouge(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Dict    =   time.time()

													# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
													_snake_case						:					int    =   self.model.generate(
													    batch["input_ids"]				,    attention_mask=batch["attention_mask"]				,    use_cache=lowercase_				,    decoder_start_token_id=self.decoder_start_token_id				,    num_beams=self.eval_beams				,    max_length=self.eval_max_length				,    )
													_snake_case						:					Any    =   (time.time() - ta) / batch["input_ids"].shape[0]
													_snake_case						:					List[str]    =   self.ids_to_clean_text(lowercase_    )
													_snake_case						:					List[str]    =   self.ids_to_clean_text(batch["labels"]    )
													_snake_case						:					List[str]    =   self._step(lowercase_    )
													_snake_case						:					Any    =   dict(zip(self.loss_names				,    lowercase_    )    )
													_snake_case						:					Dict    =   self.calc_generative_metrics(lowercase_				,    lowercase_    )
													_snake_case						:					List[str]    =   np.mean(lmap(lowercase_				,    lowercase_    )    )
													base_metrics.update(gen_time=lowercase_				,    gen_len=lowercase_				,    preds=lowercase_				,    target=lowercase_				,    **lowercase_    )
													return base_metrics



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													return self._generative_step(lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													return self.validation_epoch_end(lowercase_				,    prefix="test"    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					List[Any]    =   self.n_obs[type_path]
													_snake_case						:					Any    =   self.target_lens[type_path]
													_snake_case						:					Optional[int]    =   self.dataset_class(
													    self.tokenizer				,    type_path=lowercase_				,    n_obs=lowercase_				,    max_target_length=lowercase_				,    **self.dataset_kwargs				,    )
													return dataset



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = False    ):
													_snake_case						:					str    =   self.get_dataset(lowercase_    )

													if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																			_snake_case						:					Dict    =   dataset.make_sortish_sampler(lowercase_				,    distributed=self.hparams.gpus > 1    )
																			return DataLoader(
																			    lowercase_				,    batch_size=lowercase_				,    collate_fn=dataset.collate_fn				,    shuffle=lowercase_				,    num_workers=self.num_workers				,    sampler=lowercase_				,    )

													elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
																			_snake_case						:					List[Any]    =   dataset.make_dynamic_sampler(
																			    self.hparams.max_tokens_per_batch				,    distributed=self.hparams.gpus > 1    )
																			return DataLoader(
																			    lowercase_				,    batch_sampler=lowercase_				,    collate_fn=dataset.collate_fn				,    num_workers=self.num_workers				,    )
													else:
																			return DataLoader(
																			    lowercase_				,    batch_size=lowercase_				,    collate_fn=dataset.collate_fn				,    shuffle=lowercase_				,    num_workers=self.num_workers				,    sampler=lowercase_				,    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.get_dataloader("train"				,    batch_size=self.hparams.train_batch_size				,    shuffle=lowercase_    )
													return dataloader



							def 	UpperCamelCase	(						self    ):
													return self.get_dataloader("val"				,    batch_size=self.hparams.eval_batch_size    )



							def 	UpperCamelCase	(						self    ):
													return self.get_dataloader("test"				,    batch_size=self.hparams.eval_batch_size    )



							@staticmethod
							def 	UpperCamelCase	(						lowercase_				,    lowercase_    ):
													BaseTransformer.add_model_specific_args(lowercase_				,    lowercase_    )
													add_generic_args(lowercase_				,    lowercase_    )
													parser.add_argument(
													    "--max_source_length"				,    default=1_024				,    type=lowercase_				,    help=(
													        "The maximum total input sequence length after tokenization. Sequences longer "
													        "than this will be truncated, sequences shorter will be padded."
													    )				,    )
													parser.add_argument(
													    "--max_target_length"				,    default=56				,    type=lowercase_				,    help=(
													        "The maximum total input sequence length after tokenization. Sequences longer "
													        "than this will be truncated, sequences shorter will be padded."
													    )				,    )
													parser.add_argument(
													    "--val_max_target_length"				,    default=142				,    type=lowercase_				,    help=(
													        "The maximum total input sequence length after tokenization. Sequences longer "
													        "than this will be truncated, sequences shorter will be padded."
													    )				,    )
													parser.add_argument(
													    "--test_max_target_length"				,    default=142				,    type=lowercase_				,    help=(
													        "The maximum total input sequence length after tokenization. Sequences longer "
													        "than this will be truncated, sequences shorter will be padded."
													    )				,    )
													parser.add_argument("--freeze_encoder"				,    action="store_true"    )
													parser.add_argument("--freeze_embeds"				,    action="store_true"    )
													parser.add_argument("--sortish_sampler"				,    action="store_true"				,    default=lowercase_    )
													parser.add_argument("--overwrite_output_dir"				,    action="store_true"				,    default=lowercase_    )
													parser.add_argument("--max_tokens_per_batch"				,    type=lowercase_				,    default=lowercase_    )
													parser.add_argument("--logger_name"				,    type=lowercase_				,    choices=["default", "wandb", "wandb_shared"]				,    default="default"    )
													parser.add_argument("--n_train"				,    type=lowercase_				,    default=-1				,    required=lowercase_				,    help="# examples. -1 means use all."    )
													parser.add_argument("--n_val"				,    type=lowercase_				,    default=500				,    required=lowercase_				,    help="# examples. -1 means use all."    )
													parser.add_argument("--n_test"				,    type=lowercase_				,    default=-1				,    required=lowercase_				,    help="# examples. -1 means use all."    )
													parser.add_argument(
													    "--task"				,    type=lowercase_				,    default="summarization"				,    required=lowercase_				,    help="# examples. -1 means use all."    )
													parser.add_argument("--label_smoothing"				,    type=lowercase_				,    default=0.0				,    required=lowercase_    )
													parser.add_argument("--src_lang"				,    type=lowercase_				,    default=""				,    required=lowercase_    )
													parser.add_argument("--tgt_lang"				,    type=lowercase_				,    default=""				,    required=lowercase_    )
													parser.add_argument("--eval_beams"				,    type=lowercase_				,    default=lowercase_				,    required=lowercase_    )
													parser.add_argument(
													    "--val_metric"				,    type=lowercase_				,    default=lowercase_				,    required=lowercase_				,    choices=["bleu", "rouge2", "loss", None]    )
													parser.add_argument("--eval_max_gen_length"				,    type=lowercase_				,    default=lowercase_				,    help="never generate more than n tokens"    )
													parser.add_argument("--save_top_k"				,    type=lowercase_				,    default=1				,    required=lowercase_				,    help="How many checkpoints to save"    )
													parser.add_argument(
													    "--early_stopping_patience"				,    type=lowercase_				,    default=-1				,    required=lowercase_				,    help=(
													        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
													        " val_check_interval will effect it."
													    )				,    )
													return parser




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'translation'
							_lowerCamelCase						=		['loss']
							_lowerCamelCase						=		['bleu']
							_lowerCamelCase						=		'bleu'



							def __init__(						self				,    lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    **lowercase_    )
													_snake_case						:					Optional[int]    =   hparams.src_lang
													_snake_case						:					str    =   hparams.tgt_lang



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													return calculate_bleu(lowercase_				,    lowercase_    )




def  snake_case						(__lowercase ,      __lowercase=None						)	->	SummarizationModule:

						'''simple docstring'''



						Path(args.output_dir						).mkdir(exist_ok=__lowercase						)
						check_output_dir(__lowercase ,      expected_items=3						)

						if model is None:
												if "summarization" in args.task:
																		_snake_case						:					SummarizationModule    =   SummarizationModule(__lowercase						)
												else:
																		_snake_case						:					SummarizationModule    =   TranslationModule(__lowercase						)
						_snake_case						:					Optional[int]    =   Path(args.data_dir						).name
						if (
						    args.logger_name == "default"
						    or args.fast_dev_run
						    or str(args.output_dir						).startswith("/tmp"						)
						    or str(args.output_dir						).startswith("/var"						)
						):
												_snake_case						:					Dict    =   True  # don't pollute wandb logs unnecessarily
						elif args.logger_name == "wandb":
												from pytorch_lightning.loggers import WandbLogger

												_snake_case						:					Dict    =   os.environ.get("WANDB_PROJECT" ,      __lowercase						)
												_snake_case						:					List[Any]    =   WandbLogger(name=model.output_dir.name ,      project=__lowercase						)

						elif args.logger_name == "wandb_shared":
												from pytorch_lightning.loggers import WandbLogger

												_snake_case						:					Optional[Any]    =   WandbLogger(name=model.output_dir.name ,      project=F"""hf_{dataset}"""						)

						if args.early_stopping_patience >= 0:
												_snake_case						:					Optional[int]    =   get_early_stopping_callback(model.val_metric ,      args.early_stopping_patience						)
						else:
												_snake_case						:					List[Any]    =   False

						_snake_case						:					List[str]    =   args.val_metric == "loss"
						_snake_case						:					pl.Trainer    =   generic_train(
						    __lowercase ,      __lowercase ,      logging_callback=SeqaSeqLoggingCallback() ,      checkpoint_callback=get_checkpoint_callback(
						        args.output_dir ,      model.val_metric ,      args.save_top_k ,      __lowercase						) ,      early_stopping_callback=__lowercase ,      logger=__lowercase ,      )
						pickle_save(model.hparams ,      model.output_dir / "hparams.pkl"						)
						if not args.do_predict:
												return model

						_snake_case						:					List[Any]    =   ""
						_snake_case						:					Union[str, Any]    =   sorted(glob.glob(os.path.join(args.output_dir ,      "*.ckpt"						) ,      recursive=__lowercase						)						)
						if checkpoints:
												_snake_case						:					Optional[int]    =   checkpoints[-1]
												_snake_case						:					Optional[Any]    =   checkpoints[-1]
						trainer.logger.log_hyperparams(model.hparams						)

						# test() without a model tests using the best checkpoint automatically
						trainer.test()
						return model


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   int         =			argparse.ArgumentParser()
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			pl.Trainer.add_argparse_args(parser)
				__SCREAMING_SNAKE_CASE						:   str         =			SummarizationModule.add_model_specific_args(parser, os.getcwd())

				__SCREAMING_SNAKE_CASE						:   List[Any]         =			parser.parse_args()

				main(args)
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
				import tensorflow as tf


__SCREAMING_SNAKE_CASE						:   str         =			logging.get_logger(__name__)



@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		[
							    'no_inference',
							    'no_cuda',
							    'no_tpu',
							    'no_speed',
							    'no_memory',
							    'no_env_print',
							    'no_multi_process',
							]



							def __init__(						self				,    **lowercase_    ):
													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									_snake_case						:					List[str]    =   deprecated_arg[3:]
																									_snake_case						:					Optional[int]    =   not kwargs.pop(lowercase_    )
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""    )
													_snake_case						:					Tuple    =   kwargs.pop("tpu_name"				,    self.tpu_name    )
													_snake_case						:					Any    =   kwargs.pop("device_idx"				,    self.device_idx    )
													_snake_case						:					List[str]    =   kwargs.pop("eager_mode"				,    self.eager_mode    )
													_snake_case						:					List[str]    =   kwargs.pop("use_xla"				,    self.use_xla    )
													super().__init__(**lowercase_    )

							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={'help': 'Name of TPU'}						,			)
							_lowerCamelCase						=		field(
							    default=0						,			metadata={'help': 'CPU / GPU device index. Defaults to 0.'}						,			)
							_lowerCamelCase						=		field(default=__snake_case						,			metadata={'help': 'Benchmark models in eager model.'}		)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
							    }						,			)



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													_snake_case						:					str    =   None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															_snake_case						:					Optional[Any]    =   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
																									else:
																															_snake_case						:					List[str]    =   tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									_snake_case						:					Union[str, Any]    =   None
													return tpu



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.is_tpu:
																			tf.config.experimental_connect_to_cluster(self._setup_tpu    )
																			tf.tpu.experimental.initialize_tpu_system(self._setup_tpu    )

																			_snake_case						:					List[str]    =   tf.distribute.TPUStrategy(self._setup_tpu    )
													else:
																			# currently no multi gpu is allowed
																			if self.is_gpu:
																									# TODO: Currently only single GPU is supported
																									tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,    "GPU"    )
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""    )
																			else:
																									tf.config.set_visible_devices([]				,    "GPU"    )  # disable GPU
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""    )

													return strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_tpu is not None



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return tf.config.list_physical_devices("GPU"    )



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.cuda:
																			return len(self.gpu_list    )
													return 0



							@property
							def 	UpperCamelCase	(						self    ):
													return self.n_gpu > 0