


from typing import TYPE_CHECKING

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


lowerCamelCase		   =			{
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		   =			['LlamaTokenizer']

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		   =			[
     'LlamaForCausalLM',
     'LlamaModel',
     'LlamaPreTrainedModel',
     'LlamaForSequenceClassification',
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama_fast import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

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






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


