






'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase__						:Optional[Any]          =			{
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__						:List[str]          =			["""RoFormerTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__						:Optional[int]          =			[
			    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RoFormerForCausalLM""",
			    """RoFormerForMaskedLM""",
			    """RoFormerForMultipleChoice""",
			    """RoFormerForQuestionAnswering""",
			    """RoFormerForSequenceClassification""",
			    """RoFormerForTokenClassification""",
			    """RoFormerLayer""",
			    """RoFormerModel""",
			    """RoFormerPreTrainedModel""",
			    """load_tf_weights_in_roformer""",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__						:Dict          =			[
			    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFRoFormerForCausalLM""",
			    """TFRoFormerForMaskedLM""",
			    """TFRoFormerForMultipleChoice""",
			    """TFRoFormerForQuestionAnswering""",
			    """TFRoFormerForSequenceClassification""",
			    """TFRoFormerForTokenClassification""",
			    """TFRoFormerLayer""",
			    """TFRoFormerModel""",
			    """TFRoFormerPreTrainedModel""",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__						:int          =			[
			    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """FlaxRoFormerForMaskedLM""",
			    """FlaxRoFormerForMultipleChoice""",
			    """FlaxRoFormerForQuestionAnswering""",
			    """FlaxRoFormerForSequenceClassification""",
			    """FlaxRoFormerForTokenClassification""",
			    """FlaxRoFormerModel""",
			    """FlaxRoFormerPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
			from .tokenization_roformer import RoFormerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roformer_fast import RoFormerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roformer import (
						    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoFormerForCausalLM,
						    RoFormerForMaskedLM,
						    RoFormerForMultipleChoice,
						    RoFormerForQuestionAnswering,
						    RoFormerForSequenceClassification,
						    RoFormerForTokenClassification,
						    RoFormerLayer,
						    RoFormerModel,
						    RoFormerPreTrainedModel,
						    load_tf_weights_in_roformer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roformer import (
						    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerLayer,
						    TFRoFormerModel,
						    TFRoFormerPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roformer import (
						    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						    FlaxRoFormerPreTrainedModel,
						)


else:
			import sys

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











'''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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     str   		=			field(default='automatic-speech-recognition'    ,   metadata={'include_in_asdict_even_if_is_default': True}  )
       snake_case__    :     ClassVar[Features]   		=			Features({'audio': Audio()}  )
       snake_case__    :     ClassVar[Features]   		=			Features({'transcription': Value('string'  )}  )
       snake_case__    :     str   		=			"audio"
       snake_case__    :     str   		=			"transcription"




       def 					a_					(					self      : Any	,    A__      : Any					):


            """simple docstring"""







            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]	,    A__					):
                 raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
            __lowerCamelCase						:       Dict				   =				copy.deepcopy(self					)
            __lowerCamelCase						:       List[Any]				   =				self.input_schema.copy()
            __lowerCamelCase						:       Optional[int]				   =				features[self.audio_column]
            __lowerCamelCase						:       Any				   =				input_schema
            return task_template




       @property
       def 					a_					(					self      : Any					):


            """simple docstring"""







            return {self.audio_column: "audio", self.transcription_column: "transcription"}



