
import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase_				:							Any       =    logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		Tuple    ,__snake_case				:		str=0					)       ->   Optional[Any]:
       __snake_case						       =   (size, size) if isinstance(__snake_case    ,__snake_case					) else size
       __snake_case       ,       __snake_case						       =   img.size
       __snake_case						       =   size[1] - original_height if original_height < size[1] else 0
       __snake_case						       =   size[0] - original_width if original_width < size[0] else 0
       __snake_case						       =   functional.pad(__snake_case    ,(0, 0, pad_width, pad_height)    ,fill=__snake_case					)
       return img







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   transforms

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            for t in self.transforms:
                   __snake_case       ,       __snake_case						       =   t(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            return image, target







class  _lowerCamelCase			:

     def __init__(  self    ):
            pass

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            return image, target







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   size

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   functional.resize(SCREAMING_SNAKE_CASE_							,       self.size    )
            __snake_case						       =   functional.resize(SCREAMING_SNAKE_CASE_							,       self.size							,       interpolation=transforms.InterpolationMode.NEAREST    )
            return image, target







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=None    ):
            __snake_case						       =   min_size
            if max_size is None:
                   __snake_case						       =   min_size
            __snake_case						       =   max_size

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   random.randint(self.min_size							,       self.max_size    )
            __snake_case						       =   functional.resize(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   functional.resize(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       interpolation=transforms.InterpolationMode.NEAREST    )
            return image, target







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   size if isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) else (size, size)

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   pad_if_smaller(SCREAMING_SNAKE_CASE_							,       self.size    )
            __snake_case						       =   pad_if_smaller(SCREAMING_SNAKE_CASE_							,       self.size							,       fill=255    )
            __snake_case						       =   transforms.RandomCrop.get_params(SCREAMING_SNAKE_CASE_							,       self.size    )
            __snake_case						       =   functional.crop(SCREAMING_SNAKE_CASE_							,       *SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   functional.crop(SCREAMING_SNAKE_CASE_							,       *SCREAMING_SNAKE_CASE_    )
            return image, target







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   flip_prob

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            if random.random() < self.flip_prob:
                   __snake_case						       =   functional.hflip(SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   functional.hflip(SCREAMING_SNAKE_CASE_    )
            return image, target







class  _lowerCamelCase			:

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   functional.pil_to_tensor(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   torch.as_tensor(np.array(SCREAMING_SNAKE_CASE_    )							,       dtype=torch.intaa    )
            return image, target







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   dtype

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   functional.convert_image_dtype(SCREAMING_SNAKE_CASE_							,       self.dtype    )
            return image, target







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   mean
            __snake_case						       =   std

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   functional.normalize(SCREAMING_SNAKE_CASE_							,       mean=self.mean							,       std=self.std    )
            return image, target







class  _lowerCamelCase			:

     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            if not isinstance(SCREAMING_SNAKE_CASE_							,       np.ndarray    ):
                   __snake_case						       =   np.array(SCREAMING_SNAKE_CASE_    ).astype(np.uinta    )
            # avoid using underflow conversion
            __snake_case						       =   255
            __snake_case						       =   target - 1
            __snake_case						       =   255

            __snake_case						       =   Image.fromarray(SCREAMING_SNAKE_CASE_    )
            return image, target







@dataclass
class  _lowerCamelCase			:
     lowercase__				  =			field(
         default="""segments/sidewalk-semantic"""							,					metadata={
             """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
         }							,					)
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}					)
     lowercase__				  =			field(
         default=0.15							,					metadata={"""help""": """Percent to split off of train for validation."""}					)
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of training examples to this """
                 """value if set."""
             )
         }							,					)
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                 """value if set."""
             )
         }							,					)
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Whether or not to reduce all labels by 1 and replace background by 255."""}							,					)

     def 						__lowerCamelCase				(  self    ):
            if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
                   raise ValueError(
                       'You must specify either a dataset name from the hub or a train and/or validation directory.'    )







@dataclass
class  _lowerCamelCase			:
     lowercase__				  =			field(
         default="""nvidia/mit-b0"""							,					metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							,					)
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}					)
     lowercase__				  =			field(
         default="""main"""							,					metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,					)
     lowercase__				  =			field(default=lowerCamelCase							,					metadata={"""help""": """Name or path of preprocessor config."""}					)
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={
             """help""": (
                 """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                 """with private models)."""
             )
         }							,					)





def        __lowercase(							)       ->   Any:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       __snake_case						       =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __snake_case       ,       __snake_case       ,       __snake_case						       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
       else:
              __snake_case       ,       __snake_case       ,       __snake_case						       =   parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry('run_semantic_segmentation'    ,__snake_case    ,__snake_case					)

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    ,datefmt='%m/%d/%Y %H:%M:%S'    ,handlers=[logging.StreamHandler(sys.stdout					)]    ,)

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       __snake_case						       =   training_args.get_process_log_level()
       logger.setLevel(__snake_case					)
       transformers.utils.logging.set_verbosity(__snake_case					)
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # Log on each process the small summary:
       logger.warning(
           f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
           + f'''distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'''					)
       logger.info(f'''Training/evaluation parameters {training_args}'''					)

       # Detecting last checkpoint.
       __snake_case						       =   None
       if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
              __snake_case						       =   get_last_checkpoint(training_args.output_dir					)
              if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
                     raise ValueError(
                         f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                         'Use --overwrite_output_dir to overcome.'					)
              elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                     logger.info(
                         f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
       __snake_case						       =   load_dataset(data_args.dataset_name    ,cache_dir=model_args.cache_dir					)

       # Rename column names to standardized names (only "image" and "label" need to be present)
       if "pixel_values" in dataset["train"].column_names:
              __snake_case						       =   dataset.rename_columns({'pixel_values': 'image'}					)
       if "annotation" in dataset["train"].column_names:
              __snake_case						       =   dataset.rename_columns({'annotation': 'label'}					)

       # If we don't have a validation split, split off a percentage of train as validation.
       __snake_case						       =   None if 'validation' in dataset.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split    ,__snake_case					) and data_args.train_val_split > 0.0:
              __snake_case						       =   dataset['train'].train_test_split(data_args.train_val_split					)
              __snake_case						       =   split['train']
              __snake_case						       =   split['test']

       # Prepare label mappings.
       # We'll include these in the model's config to get human readable labels in the Inference API.
       if data_args.dataset_name == "scene_parse_150":
              __snake_case						       =   'huggingface/label-files'
              __snake_case						       =   'ade20k-id2label.json'
       else:
              __snake_case						       =   data_args.dataset_name
              __snake_case						       =   'id2label.json'
       __snake_case						       =   json.load(open(hf_hub_download(__snake_case    ,__snake_case    ,repo_type='dataset'					)    ,'r'					)					)
       __snake_case						       =   {int(__snake_case					): v for k, v in idalabel.items()}
       __snake_case						       =   {v: str(__snake_case					) for k, v in idalabel.items()}

       # Load the mean IoU metric from the datasets package
       __snake_case						       =   evaluate.load('mean_iou'					)

       # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
       # predictions and label_ids field) and has to return a dictionary string to float.
       @torch.no_grad()
       def compute_metrics(__snake_case				:		str					):
              __snake_case       ,       __snake_case						       =   eval_pred
              __snake_case						       =   torch.from_numpy(__snake_case					)
              # scale the logits to the size of the label
              __snake_case						       =   nn.functional.interpolate(
                  __snake_case    ,size=labels.shape[-2:]    ,mode='bilinear'    ,align_corners=__snake_case    ,).argmax(dim=1					)

              __snake_case						       =   logits_tensor.detach().cpu().numpy()
              __snake_case						       =   metric.compute(
                  predictions=__snake_case    ,references=__snake_case    ,num_labels=len(__snake_case					)    ,ignore_index=0    ,reduce_labels=image_processor.do_reduce_labels    ,)
              # add per category metrics as individual key-value pairs
              __snake_case						       =   metrics.pop('per_category_accuracy'					).tolist()
              __snake_case						       =   metrics.pop('per_category_iou'					).tolist()

              metrics.update({f'''accuracy_{idalabel[i]}''': v for i, v in enumerate(__snake_case					)}					)
              metrics.update({f'''iou_{idalabel[i]}''': v for i, v in enumerate(__snake_case					)}					)

              return metrics

       __snake_case						       =   AutoConfig.from_pretrained(
           model_args.config_name or model_args.model_name_or_path    ,labelaid=__snake_case    ,idalabel=__snake_case    ,cache_dir=model_args.cache_dir    ,revision=model_args.model_revision    ,use_auth_token=True if model_args.use_auth_token else None    ,)
       __snake_case						       =   AutoModelForSemanticSegmentation.from_pretrained(
           model_args.model_name_or_path    ,from_tf=bool('.ckpt' in model_args.model_name_or_path					)    ,config=__snake_case    ,cache_dir=model_args.cache_dir    ,revision=model_args.model_revision    ,use_auth_token=True if model_args.use_auth_token else None    ,)
       __snake_case						       =   AutoImageProcessor.from_pretrained(
           model_args.image_processor_name or model_args.model_name_or_path    ,cache_dir=model_args.cache_dir    ,revision=model_args.model_revision    ,use_auth_token=True if model_args.use_auth_token else None    ,)

       # Define torchvision transforms to be applied to each image + target.
       # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
       # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
       if "shortest_edge" in image_processor.size:
              # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
              __snake_case						       =   (image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
       else:
              __snake_case						       =   (image_processor.size['height'], image_processor.size['width'])
       __snake_case						       =   Compose(
           [
               ReduceLabels() if data_args.reduce_labels else Identity(),
               RandomCrop(size=__snake_case					),
               RandomHorizontalFlip(flip_prob=0.5					),
               PILToTensor(),
               ConvertImageDtype(torch.float					),
               Normalize(mean=image_processor.image_mean    ,std=image_processor.image_std					),
           ]					)
       # Define torchvision transform to be applied to each image.
       # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
       __snake_case						       =   Compose(
           [
               ReduceLabels() if data_args.reduce_labels else Identity(),
               Resize(size=__snake_case					),
               PILToTensor(),
               ConvertImageDtype(torch.float					),
               Normalize(mean=image_processor.image_mean    ,std=image_processor.image_std					),
           ]					)

       def preprocess_train(__snake_case				:		Tuple					):
              __snake_case						       =   []
              __snake_case						       =   []
              for image, target in zip(example_batch['image']    ,example_batch['label']					):
                     __snake_case       ,       __snake_case						       =   train_transforms(image.convert('RGB'					)    ,__snake_case					)
                     pixel_values.append(__snake_case					)
                     labels.append(__snake_case					)

              __snake_case						       =   {}
              __snake_case						       =   torch.stack(__snake_case					)
              __snake_case						       =   torch.stack(__snake_case					)

              return encoding

       def preprocess_val(__snake_case				:		Dict					):
              __snake_case						       =   []
              __snake_case						       =   []
              for image, target in zip(example_batch['image']    ,example_batch['label']					):
                     __snake_case       ,       __snake_case						       =   val_transforms(image.convert('RGB'					)    ,__snake_case					)
                     pixel_values.append(__snake_case					)
                     labels.append(__snake_case					)

              __snake_case						       =   {}
              __snake_case						       =   torch.stack(__snake_case					)
              __snake_case						       =   torch.stack(__snake_case					)

              return encoding

       if training_args.do_train:
              if "train" not in dataset:
                     raise ValueError('--do_train requires a train dataset'					)
              if data_args.max_train_samples is not None:
                     __snake_case						       =   (
                         dataset['train'].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
                     )
              # Set the training transforms
              dataset["train"].set_transform(__snake_case					)

       if training_args.do_eval:
              if "validation" not in dataset:
                     raise ValueError('--do_eval requires a validation dataset'					)
              if data_args.max_eval_samples is not None:
                     __snake_case						       =   (
                         dataset['validation'].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
                     )
              # Set the validation transforms
              dataset["validation"].set_transform(__snake_case					)

       # Initalize our trainer
       __snake_case						       =   Trainer(
           model=__snake_case    ,args=__snake_case    ,train_dataset=dataset['train'] if training_args.do_train else None    ,eval_dataset=dataset['validation'] if training_args.do_eval else None    ,compute_metrics=__snake_case    ,tokenizer=__snake_case    ,data_collator=__snake_case    ,)

       # Training
       if training_args.do_train:
              __snake_case						       =   None
              if training_args.resume_from_checkpoint is not None:
                     __snake_case						       =   training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     __snake_case						       =   last_checkpoint
              __snake_case						       =   trainer.train(resume_from_checkpoint=__snake_case					)
              trainer.save_model()
              trainer.log_metrics('train'    ,train_result.metrics					)
              trainer.save_metrics('train'    ,train_result.metrics					)
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              __snake_case						       =   trainer.evaluate()
              trainer.log_metrics('eval'    ,__snake_case					)
              trainer.save_metrics('eval'    ,__snake_case					)

       # Write model card and (optionally) push to hub
       __snake_case						       =   {
           'finetuned_from': model_args.model_name_or_path,
           'dataset': data_args.dataset_name,
           'tags': ['image-segmentation', 'vision'],
       }
       if training_args.push_to_hub:
              trainer.push_to_hub(**__snake_case					)
       else:
              trainer.create_model_card(**__snake_case					)


if __name__ == "__main__":
 main()


import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


lowerCamelCase_				:							Optional[Any]       =    logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""tf"""
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Zone of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of Cloud TPU-enabled project"""}							,					)

     lowercase__				  =			field(
         default=1.0							,					metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}							,					)

     lowercase__				  =			field(default=lowerCamelCase							,					metadata={"""help""": """Whether to activate the XLA compilation or not"""}					)

     @cached_property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            logger.info('Tensorflow: setting up strategy'    )

            __snake_case						       =   tf.config.list_physical_devices('GPU'    )

            # Set to float16 at first
            if self.fpaa:
                   tf.keras.mixed_precision.set_global_policy('mixed_float16'    )

            if self.no_cuda:
                   __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
            else:
                   try:
                          if self.tpu_name:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver(
                                     self.tpu_name							,       zone=self.tpu_zone							,       project=self.gcp_project    )
                          else:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver()
                   except ValueError:
                          if self.tpu_name:
                                 raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''    )
                          else:
                                 __snake_case						       =   None

                   if tpu:
                          # Set to bfloat16 in case of TPU
                          if self.fpaa:
                                 tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'    )

                          tf.config.experimental_connect_to_cluster(SCREAMING_SNAKE_CASE_    )
                          tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE_    )

                          __snake_case						       =   tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_    )

                   elif len(SCREAMING_SNAKE_CASE_    ) == 0:
                          __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
                   elif len(SCREAMING_SNAKE_CASE_    ) == 1:
                          __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/gpu:0'    )
                   elif len(SCREAMING_SNAKE_CASE_    ) > 1:
                          # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                          __snake_case						       =   tf.distribute.MirroredStrategy()
                   else:
                          raise ValueError('Cannot find the proper strategy, please check your environment properties.'    )

            return strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy.num_replicas_in_sync

     @property
     def 						__lowerCamelCase				(  self    ):
            return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.per_gpu_train_batch_size:
                   logger.warning(
                       'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
                       'version. Using `--per_device_train_batch_size` is preferred.'    )
            __snake_case						       =   self.per_gpu_train_batch_size or self.per_device_train_batch_size
            return per_device_batch_size * self.n_replicas

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.per_gpu_eval_batch_size:
                   logger.warning(
                       'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
                       'version. Using `--per_device_eval_batch_size` is preferred.'    )
            __snake_case						       =   self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
            return per_device_batch_size * self.n_replicas

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            warnings.warn(
                'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'							,       SCREAMING_SNAKE_CASE_							,       )
            return self._setup_strategy.num_replicas_in_sync
