
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__          =50000
lowercase__          =5000

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




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ):
 for i in range(lowerCAmelCase__  ):
  __a			:  str		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 for i in range(0  ,       len(lowerCAmelCase__  )  ,       lowerCAmelCase__  ):
  __a			:  Optional[int]		    =  dataset[i : i + batch_size]




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




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(0  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ):
   __a			:  int		    =  dataset[i : i + batch_size]




def        __UpperCamelCase					(    ):
 __a			:  Any		    =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
 __a			:  List[Any]		    =  [
     (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}),
 ]

 __a			:  Union[str, Any]		    =  [
     (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'''  )
  __a			:  Optional[Any]		    =  datasets.Features(
      {'''list''': datasets.Sequence(datasets.Value('''float32'''  )  ), '''numbers''': datasets.Value('''float32'''  )}  )
  __a			:  Optional[int]		    =  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__  )  )
   __a			:  str		    =  func(lowerCAmelCase__  ,       **lowerCAmelCase__  )

  print('''shuffling dataset'''  )
  __a			:  int		    =  dataset.shuffle()
  print('''Second set of iterations (after shuffling'''  )
  for func, kwargs in functions_shuffled:
   print('''shuffled '''  ,       func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  List[Any]		    =  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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase__          ={
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2TokenizerFast']

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2FeatureExtractor']
      lowercase__          =['LayoutLMv2ImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LayoutLMv2ForQuestionAnswering',
          'LayoutLMv2ForSequenceClassification',
          'LayoutLMv2ForTokenClassification',
          'LayoutLMv2Layer',
          'LayoutLMv2Model',
          'LayoutLMv2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

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