import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__snake_case			      =    (30, 4_87)
__snake_case			      =    (36, 10_24)
__snake_case			      =    (1_00, 1_00)
__snake_case			      =    1_00

__snake_case			      =    datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

__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		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  str:






      '''simple docstring'''





      with ArrowWriter(features=__lowerCAmelCase		,					path=os.path.join(__lowerCAmelCase		,					'''beta.arrow'''						)						) as writer:
            for key, record in dummy_data:
                  UpperCAmelCase    : Tuple      =my_features.encode_example(__lowerCAmelCase						)
                  writer.write(__lowerCAmelCase						)
            UpperCAmelCase  ,						UpperCAmelCase    : Optional[int]      =writer.finalize()





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      =datasets.Dataset.from_file(
          filename=os.path.join(__lowerCAmelCase		,					'''beta.arrow'''						)		,					info=datasets.DatasetInfo(features=__lowerCAmelCase						)						)
      for _ in dataset:
            pass





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






      '''simple docstring'''





      UpperCAmelCase    : str      =datasets.Dataset.from_file(
          filename=os.path.join(__lowerCAmelCase		,					'''beta.arrow'''						)		,					info=datasets.DatasetInfo(features=__lowerCAmelCase						)						)
      dataset.set_format('''numpy'''						)
      for _ in dataset:
            pass





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  List[str]:






      '''simple docstring'''





      UpperCAmelCase    : Dict      =10
      UpperCAmelCase    : Any      =datasets.Dataset.from_file(
          filename=os.path.join(__lowerCAmelCase		,					'''beta.arrow'''						)		,					info=datasets.DatasetInfo(features=__lowerCAmelCase						)						)
      for i in range(0		,					len(__lowerCAmelCase						)		,					__lowerCAmelCase						):
            UpperCAmelCase    : List[str]      =dataset[i : i + batch_size]





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






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =10
      UpperCAmelCase    : List[str]      =datasets.Dataset.from_file(
          filename=os.path.join(__lowerCAmelCase		,					'''beta.arrow'''						)		,					info=datasets.DatasetInfo(features=__lowerCAmelCase						)						)
      dataset.set_format('''numpy'''						)
      for i in range(0		,					len(__lowerCAmelCase						)		,					__lowerCAmelCase						):
            UpperCAmelCase    : List[Any]      =dataset[i : i + batch_size]





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  str:






      '''simple docstring'''





      UpperCAmelCase    : int      =datasets.Dataset.from_file(
          filename=os.path.join(__lowerCAmelCase		,					'''beta.arrow'''						)		,					info=datasets.DatasetInfo(features=__lowerCAmelCase						)						)
      for col in feats:
            UpperCAmelCase    : Optional[Any]      =dataset[col]





@get_duration
def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : Optional[int]      =datasets.Dataset.from_file(
          filename=os.path.join(__lowerCAmelCase		,					'''beta.arrow'''						)		,					info=datasets.DatasetInfo(features=__lowerCAmelCase						)						)
      dataset.set_format('''numpy'''						)
      for col in feats:
            UpperCAmelCase    : str      =dataset[col]





def   lowerCAmelCase_ (				)->  str:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      ={}
      UpperCAmelCase    : str      =(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase    : Optional[Any]      =datasets.Features({'''image''': ArrayaD(__lowerCAmelCase		,					dtype='''float32'''						)}						)
            UpperCAmelCase    : Tuple      =generate_examples(features=__lowerCAmelCase		,					num_examples=__lowerCAmelCase						)
            UpperCAmelCase    : Optional[int]      =write(__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)
            for read_func in read_functions:
                  UpperCAmelCase    : List[str]      =read_func(__lowerCAmelCase		,					__lowerCAmelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            UpperCAmelCase    : Any      =datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''						)						)						)}						)
            UpperCAmelCase    : Union[str, Any]      =generate_examples(
                features=__lowerCAmelCase		,					num_examples=__lowerCAmelCase		,					seq_shapes={'''image''': SPEED_TEST_SHAPE}						)
            UpperCAmelCase    : List[str]      =write(__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)
            for read_func in read_functions:
                  UpperCAmelCase    : List[Any]      =read_func(__lowerCAmelCase		,					__lowerCAmelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            UpperCAmelCase    : str      =datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''						)						)}						)
            UpperCAmelCase    : Dict      =generate_examples(
                features=__lowerCAmelCase		,					num_examples=__lowerCAmelCase		,					seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,					)
            UpperCAmelCase    : int      =write(__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)
            for read_func in read_functions:
                  UpperCAmelCase    : Union[str, Any]      =read_func(__lowerCAmelCase		,					__lowerCAmelCase						)

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


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

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)
