import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case			      =    50_00_00

__snake_case      ,  __snake_case			      =    os.path.split(__file__)
__snake_case			      =    os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Optional[int]:






      '''simple docstring'''





      UpperCAmelCase    : Tuple      =dataset.select(range(0		,					len(__lowerCAmelCase						)		,					2						)						)





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =dataset.sort('''numbers'''						)





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Dict:






      '''simple docstring'''





      UpperCAmelCase    : Optional[int]      =dataset.shuffle()





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase						)->  str:






      '''simple docstring'''





      UpperCAmelCase    : Optional[int]      =dataset.train_test_split(0.1						)





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=10						)->  Optional[Any]:






      '''simple docstring'''





      for shard_id in range(__lowerCAmelCase						):
            UpperCAmelCase    : Union[str, Any]      =dataset.shard(__lowerCAmelCase		,					__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  str:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      ={'''num examples''': SPEED_TEST_N_EXAMPLES}
      UpperCAmelCase    : Tuple      =(select, sort, shuffle, train_test_split, shard)
      with tempfile.TemporaryDirectory() as tmp_dir:
            print('''generating dataset'''						)
            UpperCAmelCase    : List[str]      =datasets.Features({'''text''': datasets.Value('''string'''						), '''numbers''': datasets.Value('''float32'''						)}						)
            UpperCAmelCase    : Dict      =generate_example_dataset(
                os.path.join(__lowerCAmelCase		,					'''dataset.arrow'''						)		,					__lowerCAmelCase		,					num_examples=__lowerCAmelCase						)
            print('''Functions'''						)
            for func in functions:
                  print(func.__name__						)
                  UpperCAmelCase    : Dict      =func(__lowerCAmelCase						)

      with open(__lowerCAmelCase		,					'''wb'''						) as f:
            f.write(json.dumps(__lowerCAmelCase						).encode('''utf-8'''						)						)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_indices_mapping()

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
