




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__A        =    5_00_00
__A        =    50_00

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







@get_duration
def 			lowerCamelCase_		(       UpperCamelCase__				:      datasets.Dataset ,     UpperCamelCase__				:      List[str]      )     ->       Any:





				"""simple docstring"""


				for i in range(UpperCamelCase__      ):
								__lowerCamelCase		       =    dataset[i]







@get_duration
def 			lowerCamelCase_		(       UpperCamelCase__				:      datasets.Dataset ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      int      )     ->       Dict:





				"""simple docstring"""


				for i in range(0 ,     len(UpperCamelCase__      ) ,     UpperCamelCase__      ):
								__lowerCamelCase		       =    dataset[i : i + batch_size]







@get_duration
def 			lowerCamelCase_		(       UpperCamelCase__				:      datasets.Dataset ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				with dataset.formatted_as(type=UpperCamelCase__      ):
								for i in range(UpperCamelCase__      ):
												__lowerCamelCase		       =    dataset[i]







@get_duration
def 			lowerCamelCase_		(       UpperCamelCase__				:      datasets.Dataset ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Optional[Any]      )     ->       str:





				"""simple docstring"""


				with dataset.formatted_as(type=UpperCamelCase__      ):
								for i in range(0 ,     UpperCamelCase__ ,     UpperCamelCase__      ):
												__lowerCamelCase		       =    dataset[i : i + batch_size]







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    {'num examples': SPEED_TEST_N_EXAMPLES}
				__lowerCamelCase		       =    [
				    (read, {'length': SMALL_TEST}),
				    (read, {'length': SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
				    (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': 10}),
				    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
				]

				__lowerCamelCase		       =    [
				    (read, {'length': SMALL_TEST}),
				    (read, {'length': SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
				    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
				    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
				    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print('generating dataset'      )
								__lowerCamelCase		       =    datasets.Features(
								    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
								__lowerCamelCase		       =    generate_example_dataset(
								    os.path.join(UpperCamelCase__ ,     'dataset.arrow'      ) ,     UpperCamelCase__ ,     num_examples=UpperCamelCase__ ,     seq_shapes={'list': (100,)} ,     )
								print('first set of iterations'      )
								for func, kwargs in functions:
												print(func.__name__ ,     str(UpperCamelCase__      )      )
												__lowerCamelCase		       =    func(UpperCamelCase__ ,     **UpperCamelCase__      )

								print('shuffling dataset'      )
								__lowerCamelCase		       =    dataset.shuffle()
								print('Second set of iterations (after shuffling'      )
								for func, kwargs in functions_shuffled:
												print('shuffled ' ,     func.__name__ ,     str(UpperCamelCase__      )      )
												__lowerCamelCase		       =    func(
												    UpperCamelCase__ ,     **UpperCamelCase__      )

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


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



from typing import TYPE_CHECKING

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


UpperCamelCase  							=						{
    '''configuration_groupvit''': [
        '''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''GroupViTConfig''',
        '''GroupViTOnnxConfig''',
        '''GroupViTTextConfig''',
        '''GroupViTVisionConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''GroupViTModel''',
          '''GroupViTPreTrainedModel''',
          '''GroupViTTextModel''',
          '''GroupViTVisionModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFGroupViTModel''',
          '''TFGroupViTPreTrainedModel''',
          '''TFGroupViTTextModel''',
          '''TFGroupViTVisionModel''',
      ]

if TYPE_CHECKING:
      from .configuration_groupvit import (
          GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          GroupViTConfig,
          GroupViTOnnxConfig,
          GroupViTTextConfig,
          GroupViTVisionConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_groupvit import (
                GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                GroupViTModel,
                GroupViTPreTrainedModel,
                GroupViTTextModel,
                GroupViTVisionModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_groupvit import (
                TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGroupViTModel,
                TFGroupViTPreTrainedModel,
                TFGroupViTTextModel,
                TFGroupViTVisionModel,
            )

else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



