


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A      :    Dict		=					{
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    Optional[Any]		=					["VivitImageProcessor"]


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    Optional[int]		=					[
        "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "VivitModel",
        "VivitPreTrainedModel",
        "VivitForVideoClassification",
    ]


if TYPE_CHECKING:
    from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_vivit import VivitImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vivit import (
            VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            VivitForVideoClassification,
            VivitModel,
            VivitPreTrainedModel,
        )


else:
    import sys

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






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

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







@dataclass(frozen=lowercase__)
class 			_lowercase (							lowercase__):


       """simple docstring"""




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





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			int       ):


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





       @property
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



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


