


'''simple docstring'''



import timeit

import numpy as np

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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						List[Any]:
     def wrapper(*UpperCAmelCase_       :							Optional[int] ,       **UpperCAmelCase_       :							int				):
          __lowerCamelCase  :      Tuple														=						timeit.default_timer()
          __lowerCamelCase  :      List[str]														=						func(*UpperCAmelCase_ ,       **UpperCAmelCase_				)
          __lowerCamelCase  :      List[str]														=						timeit.default_timer() - starttime
          return delta

     __lowerCamelCase  :      int														=						func.__name__

     return wrapper



def      UpperCAmelCase__   (      UpperCAmelCase_       :							dict ,       UpperCAmelCase_       :							Optional[Any]=1_00 ,       UpperCAmelCase_       :							Union[str, Any]=None				)	->						List[str]:
     __lowerCamelCase  :      Union[str, Any]														=						[]
     __lowerCamelCase  :      Any														=						seq_shapes or {}
     for i in range(UpperCAmelCase_				):
          __lowerCamelCase  :      Dict														=						{}
          for col_id, (k, v) in enumerate(features.items()				):
               if isinstance(UpperCAmelCase_ ,       _ArrayXD				):
                    __lowerCamelCase  :      Tuple														=						np.random.rand(*v.shape				).astype(v.dtype				)
               elif isinstance(UpperCAmelCase_ ,       datasets.Value				):
                    if v.dtype == "string":
                         __lowerCamelCase  :      Optional[Any]														=						'The small grey turtle was surprisingly fast when challenged.'
                    else:
                         __lowerCamelCase  :      Optional[Any]														=						np.random.randint(10 ,       size=1				).astype(v.dtype				).item()
               elif isinstance(UpperCAmelCase_ ,       datasets.Sequence				):
                    while isinstance(UpperCAmelCase_ ,       datasets.Sequence				):
                         __lowerCamelCase  :      Optional[int]														=						v.feature
                    __lowerCamelCase  :      Union[str, Any]														=						seq_shapes[k]
                    __lowerCamelCase  :      int														=						np.random.rand(*UpperCAmelCase_				).astype(v.dtype				)
               __lowerCamelCase  :      Tuple														=						data

          dummy_data.append((i, example)				)

     return dummy_data



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Dict=1_00 ,       UpperCAmelCase_       :							Tuple=None				)	->						Any:
     __lowerCamelCase  :      Optional[int]														=						generate_examples(UpperCAmelCase_ ,       num_examples=UpperCAmelCase_ ,       seq_shapes=UpperCAmelCase_				)

     with ArrowWriter(features=UpperCAmelCase_ ,       path=UpperCAmelCase_				) as writer:
          for key, record in dummy_data:
               __lowerCamelCase  :      Union[str, Any]														=						features.encode_example(UpperCAmelCase_				)
               writer.write(UpperCAmelCase_				)

          __lowerCamelCase							,			__lowerCamelCase  :      str														=						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  :      Dict														=						datasets.Dataset.from_file(filename=UpperCAmelCase_ ,       info=datasets.DatasetInfo(features=UpperCAmelCase_				)				)

     return dataset










'''simple docstring'''



from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

A__						:   int				=    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A__						:   Dict				=    TaTokenizerFast

A__						:   Dict				=    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """MT5EncoderModel""",
          """MT5ForConditionalGeneration""",
          """MT5ForQuestionAnswering""",
          """MT5Model""",
          """MT5PreTrainedModel""",
          """MT5Stack""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      A__						:   Union[str, Any]				=    _LazyModule(
          __name__,
          globals()["""__file__"""],
          _import_structure,
          extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
          module_spec=__spec__,
      )






