



'''simple docstring'''






import timeit

import numpy as np

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


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   Tuple:


							def wrapper(*_lowerCAmelCase  :						Any   ,      **_lowerCAmelCase  :						Union[str, Any]  ):
														UpperCAmelCase :       Any 							=		timeit.default_timer()
														UpperCAmelCase :       int 							=		func(*__A   ,      **__A  )
														UpperCAmelCase :       Union[str, Any] 							=		timeit.default_timer() - starttime
														return delta

							UpperCAmelCase :       Optional[Any] 							=		func.__name__

							return wrapper


def  snake_case_     (					_lowerCAmelCase  :						dict   ,      _lowerCAmelCase  :						str=100   ,      _lowerCAmelCase  :						Dict=None  )      ->   str:
							UpperCAmelCase :       List[Any] 							=		[]
							UpperCAmelCase :       Tuple 							=		seq_shapes or {}
							for i in range(__A  ):
														UpperCAmelCase :       List[Any] 							=		{}
														for col_id, (k, v) in enumerate(features.items()  ):
																					if isinstance(__A   ,      _ArrayXD  ):
																												UpperCAmelCase :       List[str] 							=		np.random.rand(*v.shape  ).astype(v.dtype  )
																					elif isinstance(__A   ,      datasets.Value  ):
																												if v.dtype == "string":
																																			UpperCAmelCase :       List[Any] 							=		'''The small grey turtle was surprisingly fast when challenged.'''
																												else:
																																			UpperCAmelCase :       Optional[Any] 							=		np.random.randint(10   ,      size=1  ).astype(v.dtype  ).item()
																					elif isinstance(__A   ,      datasets.Sequence  ):
																												while isinstance(__A   ,      datasets.Sequence  ):
																																			UpperCAmelCase :       Optional[Any] 							=		v.feature
																												UpperCAmelCase :       Optional[int] 							=		seq_shapes[k]
																												UpperCAmelCase :       Union[str, Any] 							=		np.random.rand(*__A  ).astype(v.dtype  )
																					UpperCAmelCase :       List[str] 							=		data

														dummy_data.append((i, example)  )

							return dummy_data




def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict=100   ,      _lowerCAmelCase  :						str=None  )      ->   Optional[Any]:
							UpperCAmelCase :       List[Any] 							=		generate_examples(__A   ,      num_examples=__A   ,      seq_shapes=__A  )

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

														UpperCAmelCase							,		UpperCAmelCase :       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}."""  )

							UpperCAmelCase :       Dict 							=		datasets.Dataset.from_file(filename=__A   ,      info=datasets.DatasetInfo(features=__A  )  )

							return dataset











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,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






