



"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=_lowerCamelCase				)
class       A_			(     _lowerCamelCase				):



						"""simple docstring"""
						__UpperCamelCase						=							field(default="""automatic-speech-recognition"""	,   metadata={"""include_in_asdict_even_if_is_default""": True}				)
						__UpperCamelCase						=							Features({"""audio""": Audio()}				)
						__UpperCamelCase						=							Features({"""transcription""": Value("""string"""				)}				)
						__UpperCamelCase						=							"""audio"""
						__UpperCamelCase						=							"""transcription"""






						def 	UpperCAmelCase__     (					self :Any   ,  lowercase_ :Optional[int]					)				->			Optional[Any]:
												if self.audio_column not in features:
																		raise ValueError(f"""Column {self.audio_column} is not present in features."""					)
												if not isinstance(features[self.audio_column]   ,  lowercase_					):
																		raise ValueError(f"""Column {self.audio_column} is not an Audio type."""					)
												UpperCAmelCase             = copy.deepcopy(self					)
												UpperCAmelCase             = self.input_schema.copy()
												UpperCAmelCase             = features[self.audio_column]
												UpperCAmelCase             = input_schema
												return task_template






						@property
						def 	UpperCAmelCase__     (					self :Any					)				->			str:
												return {self.audio_column: "audio", self.transcription_column: "transcription"}











'''simple docstring'''

from typing import TYPE_CHECKING

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


a  :				Tuple									=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

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

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  [
							    '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

							a  :				str									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

