'''simple docstring'''


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


snake_case__	:     str      =    logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf






@dataclass
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:List[Any]		       =						'''tf'''
  lowerCamelCase_						:Optional[str]		       =						field(
      default=lowerCamelCase_		,      metadata={'''help''': '''Name of TPU'''}		,      )

  lowerCamelCase_						:Optional[str]		       =						field(
      default=lowerCamelCase_		,      metadata={'''help''': '''Zone of TPU'''}		,      )

  lowerCamelCase_						:Optional[str]		       =						field(
      default=lowerCamelCase_		,      metadata={'''help''': '''Name of Cloud TPU-enabled project'''}		,      )

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

  lowerCamelCase_						:bool		       =						field(default=lowerCamelCase_		,      metadata={'''help''': '''Whether to activate the XLA compilation or not'''}			)


  @cached_property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  List[str]  = 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:
            UpperCAmelCase_				:  Tuple  = tf.distribute.OneDeviceStrategy(device='/cpu:0'		)
       else:
            try:
                 if self.tpu_name:
                      UpperCAmelCase_				:  List[Any]  = tf.distribute.cluster_resolver.TPUClusterResolver(
                          self.tpu_name ,						zone=self.tpu_zone ,						project=self.gcp_project		)
                 else:
                      UpperCAmelCase_				:  Any  = tf.distribute.cluster_resolver.TPUClusterResolver()
            except ValueError:
                 if self.tpu_name:
                      raise RuntimeError(F'''Couldn\'t connect to TPU {self.tpu_name}!'''		)
                 else:
                      UpperCAmelCase_				:  str  = 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(snake_case_		)
                 tf.tpu.experimental.initialize_tpu_system(snake_case_		)

                 UpperCAmelCase_				:  str  = tf.distribute.TPUStrategy(snake_case_		)

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

       return strategy


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       requires_backends(self ,						['tf']		)
       return self._setup_strategy


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       requires_backends(self ,						['tf']		)
       return self._setup_strategy.num_replicas_in_sync


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return False  # TF Logging is handled by Keras not the Trainer


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       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.'		)
       UpperCAmelCase_				:  Union[str, Any]  = self.per_gpu_train_batch_size or self.per_device_train_batch_size
       return per_device_batch_size * self.n_replicas


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       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.'		)
       UpperCAmelCase_				:  Union[str, Any]  = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
       return per_device_batch_size * self.n_replicas






  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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







'''simple docstring'''


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  str  = min(re.search('^ +'      ,	lowerCamelCase_  ).end() if t.startswith(' '  ) else 0 for t in string.splitlines()  )
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCamelCase_  )]  )


snake_case__	:     Dict      =    '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

snake_case__	:     Union[str, Any]      =    '''\
---
---
# Begin of markdown

Some cool dataset card
'''


snake_case__	:     int      =    '''\
# Begin of markdown

Some cool dataset card
'''






class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Tuple  = Path(snake_case_		) / 'README.md'
            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Optional[Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Union[str, Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  List[str]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertEqual(snake_case_ ,						{}		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '		)
       with self.assertRaises(snake_case_		):
            DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  str  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)






