





'''simple docstring'''

from typing import TYPE_CHECKING

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


a        =						{
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a        =						["ConvBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a        =						[
     "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConvBertForMaskedLM",
     "ConvBertForMultipleChoice",
     "ConvBertForQuestionAnswering",
     "ConvBertForSequenceClassification",
     "ConvBertForTokenClassification",
     "ConvBertLayer",
     "ConvBertModel",
     "ConvBertPreTrainedModel",
     "load_tf_weights_in_convbert",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a        =						[
     "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFConvBertForMaskedLM",
     "TFConvBertForMultipleChoice",
     "TFConvBertForQuestionAnswering",
     "TFConvBertForSequenceClassification",
     "TFConvBertForTokenClassification",
     "TFConvBertLayer",
     "TFConvBertModel",
     "TFConvBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

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












'''simple docstring'''

import timeit

import numpy as np

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

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







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

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       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}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





