


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				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Any				):
     for i in range(SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=dataset[i]

@get_duration
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     for i in range(0			,     len(SCREAMING_SNAKE_CASE__				)			,     SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=dataset[i : i + batch_size]

@get_duration
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Any				):
     with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__				):
          for i in range(SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=dataset[i]

@get_duration
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					Dict				):
     with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__				):
          for i in range(0			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=dataset[i : i + batch_size]

def 						_UpperCAmelCase				(			):
     __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': 10}),
         (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
         (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
         (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': 10_00}),
     ]

     __UpperCamelCase    							=[
         (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': 1_00}),
         (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
         (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': 10_00}),
     ]
     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(SCREAMING_SNAKE_CASE__			,     'dataset.arrow'				)			,     SCREAMING_SNAKE_CASE__			,     num_examples=SCREAMING_SNAKE_CASE__			,     seq_shapes={'list': (1_00,)}			,     )
          print('first set of iterations'				)
          for func, kwargs in functions:
               print(func.__name__			,     str(SCREAMING_SNAKE_CASE__				)				)
               __UpperCamelCase    							=func(SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)

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

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


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









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A		       =						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['VivitImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'VivitModel',
       'VivitPreTrainedModel',
       'VivitForVideoClassification',
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

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





