


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__a	 =      5_0000
__a	 =      5000

__a ,       __a	 =      os.path.split(__file__)
__a	 =      os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   for i in range(lowerCAmelCase_ ):
      UpperCAmelCase      					=  dataset[i]
@get_duration
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->List[Any]:
   for i in range(0	,    len(lowerCAmelCase_ )	,    lowerCAmelCase_ ):
      UpperCAmelCase      					=  dataset[i : i + batch_size]
@get_duration
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   with dataset.formatted_as(type=lowerCAmelCase_ ):
      for i in range(lowerCAmelCase_ ):
         UpperCAmelCase      					=  dataset[i]
@get_duration
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->str:
   with dataset.formatted_as(type=lowerCAmelCase_ ):
      for i in range(0	,    lowerCAmelCase_	,    lowerCAmelCase_ ):
         UpperCAmelCase      					=  dataset[i : i + batch_size]
def 	_UpperCamelCase					(    )    ->Any:
   UpperCAmelCase      					=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
   UpperCAmelCase      					=  [
       (read, {"""length""": SMALL_TEST}),
       (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0_0}),
       (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
       (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
       (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
       (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
       (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0}),
       (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0_0_0}),
   ]

   UpperCAmelCase      					=  [
       (read, {"""length""": SMALL_TEST}),
       (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0_0}),
       (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
       (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0}),
       (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0_0_0}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("""generating dataset""" )
      UpperCAmelCase      					=  datasets.Features(
          {"""list""": datasets.Sequence(datasets.Value("""float32""" ) ), """numbers""": datasets.Value("""float32""" )} )
      UpperCAmelCase      					=  generate_example_dataset(
          os.path.join(lowerCAmelCase_	,    """dataset.arrow""" )	,    lowerCAmelCase_	,    num_examples=lowerCAmelCase_	,    seq_shapes={"""list""": (1_0_0,)}	,    )
      print("""first set of iterations""" )
      for func, kwargs in functions:
         print(func.__name__	,    str(lowerCAmelCase_ ) )
         UpperCAmelCase      					=  func(lowerCAmelCase_	,    **lowerCAmelCase_ )

      print("""shuffling dataset""" )
      UpperCAmelCase      					=  dataset.shuffle()
      print("""Second set of iterations (after shuffling""" )
      for func, kwargs in functions_shuffled:
         print("""shuffled """	,    func.__name__	,    str(lowerCAmelCase_ ) )
         UpperCAmelCase      					=  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_iterating()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a	 =      {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """PegasusXForConditionalGeneration""",
     """PegasusXModel""",
     """PegasusXPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



