



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


snake_case_         = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_         = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

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










"""simple docstring"""


from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class    A_    (  datasets.BuilderConfig       ):



						"""simple docstring"""
						__UpperCamelCase	       =							None
						__UpperCamelCase	       =							None
						__UpperCamelCase	       =							None






						def 			UpperCAmelCase__  (      self				:str      )       ->    Optional[Any]:
												assert self.generator is not None, "generator must be specified"

												if self.gen_kwargs is None:
																		UpperCAmelCase    =				{}







class    A_    (  datasets.GeneratorBasedBuilder       ):



						"""simple docstring"""
						__UpperCamelCase	       =							GeneratorConfig






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    str:
												return datasets.DatasetInfo(features=self.config.features      )






						def 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:List[Any]      )       ->    Union[str, Any]:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,						gen_kwargs=self.config.gen_kwargs      )]






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						**lowercase_				:Dict      )       ->    str:
												for idx, ex in enumerate(self.config.generator(**lowercase_      )      ):
																		yield idx, ex





