



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


a_								=    50_000
a_								=    5_000

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





@get_duration
def 						a__	(						_UpperCamelCase	:       datasets.Dataset       ,_UpperCamelCase	:       Tuple							):







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





@get_duration
def 						a__	(						_UpperCamelCase	:       datasets.Dataset       ,_UpperCamelCase	:       Optional[Any]       ,_UpperCamelCase	:       int							):







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





@get_duration
def 						a__	(						_UpperCamelCase	:       datasets.Dataset       ,_UpperCamelCase	:       List[Any]       ,_UpperCamelCase	:       Any							):







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





@get_duration
def 						a__	(						_UpperCamelCase	:       datasets.Dataset       ,_UpperCamelCase	:       Any       ,_UpperCamelCase	:       Union[str, Any]       ,_UpperCamelCase	:       Optional[int]							):







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





def 						a__	(						):
   __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''': 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}),
   ]

   __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''': 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'''							)
      __lowerCamelCase  							=			datasets.Features(
          {'''list''': datasets.Sequence(datasets.Value('''float32'''							)							), '''numbers''': datasets.Value('''float32'''							)}							)
      __lowerCamelCase  							=			generate_example_dataset(
          os.path.join(snake_case_       ,'''dataset.arrow'''							)       ,snake_case_       ,num_examples=snake_case_       ,seq_shapes={'''list''': (1_00,)}       ,)
      print('''first set of iterations'''							)
      for func, kwargs in functions:
         print(func.__name__       ,str(snake_case_							)							)
         __lowerCamelCase  							=			func(snake_case_       ,**snake_case_							)

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

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


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













"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

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




