

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     def wrapper(*SCREAMING_SNAKE_CASE		,							**SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    timeit.default_timer()
          _lowerCAmelCase   :					Tuple        =    func(*SCREAMING_SNAKE_CASE		,							**SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Any        =    timeit.default_timer() - starttime
          return delta

     _lowerCAmelCase   :					Union[str, Any]        =    func.__name__

     return wrapper







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=100		,							SCREAMING_SNAKE_CASE=None				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    []
     _lowerCAmelCase   :					int        =    seq_shapes or {}
     for i in range(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Any        =    {}
          for col_id, (k, v) in enumerate(features.items()				):
               if isinstance(SCREAMING_SNAKE_CASE		,							_ArrayXD				):
                    _lowerCAmelCase   :					List[str]        =    np.random.rand(*v.shape				).astype(v.dtype				)
               elif isinstance(SCREAMING_SNAKE_CASE		,							datasets.Value				):
                    if v.dtype == "string":
                         _lowerCAmelCase   :					Tuple        =    "The small grey turtle was surprisingly fast when challenged."
                    else:
                         _lowerCAmelCase   :					Dict        =    np.random.randint(10		,							size=1				).astype(v.dtype				).item()
               elif isinstance(SCREAMING_SNAKE_CASE		,							datasets.Sequence				):
                    while isinstance(SCREAMING_SNAKE_CASE		,							datasets.Sequence				):
                         _lowerCAmelCase   :					Tuple        =    v.feature
                    _lowerCAmelCase   :					Any        =    seq_shapes[k]
                    _lowerCAmelCase   :					Tuple        =    np.random.rand(*SCREAMING_SNAKE_CASE				).astype(v.dtype				)
               _lowerCAmelCase   :					str        =    data

          dummy_data.append((i, example)				)

     return dummy_data







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=100		,							SCREAMING_SNAKE_CASE=None				)       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					List[Any]        =    generate_examples(SCREAMING_SNAKE_CASE		,							num_examples=SCREAMING_SNAKE_CASE		,							seq_shapes=SCREAMING_SNAKE_CASE				)

     with ArrowWriter(features=SCREAMING_SNAKE_CASE		,							path=SCREAMING_SNAKE_CASE				) as writer:
          for key, record in dummy_data:
               _lowerCAmelCase   :					Optional[int]        =    features.encode_example(SCREAMING_SNAKE_CASE				)
               writer.write(SCREAMING_SNAKE_CASE				)

          _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    writer.finalize()

     if not num_final_examples == num_examples:
          raise ValueError(
              f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''				)

     _lowerCAmelCase   :					Optional[Any]        =    datasets.Dataset.from_file(filename=SCREAMING_SNAKE_CASE		,							info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE				)				)

     return dataset









from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

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






