from typing import TYPE_CHECKING

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


_lowerCamelCase   :				Tuple     			=						{
    """configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WhisperConfig""", """WhisperOnnxConfig"""],
    """feature_extraction_whisper""": ["""WhisperFeatureExtractor"""],
    """processing_whisper""": ["""WhisperProcessor"""],
    """tokenization_whisper""": ["""WhisperTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCamelCase   :				int     			=						["""WhisperTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCamelCase   :				List[str]     			=						[
					    """WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """WhisperForConditionalGeneration""",
					    """WhisperModel""",
					    """WhisperPreTrainedModel""",
					    """WhisperForAudioClassification""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCamelCase   :				Union[str, Any]     			=						[
					    """TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFWhisperForConditionalGeneration""",
					    """TFWhisperModel""",
					    """TFWhisperPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCamelCase   :				Dict     			=						[
					    """FlaxWhisperForConditionalGeneration""",
					    """FlaxWhisperModel""",
					    """FlaxWhisperPreTrainedModel""",
					    """FlaxWhisperForAudioClassification""",
					]


if TYPE_CHECKING:
					from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
					from .feature_extraction_whisper import WhisperFeatureExtractor
					from .processing_whisper import WhisperProcessor
					from .tokenization_whisper import WhisperTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_whisper_fast import WhisperTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_whisper import (
										    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WhisperForAudioClassification,
										    WhisperForConditionalGeneration,
										    WhisperModel,
										    WhisperPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_whisper import (
										    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFWhisperForConditionalGeneration,
										    TFWhisperModel,
										    TFWhisperPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_whisper import (
										    FlaxWhisperForAudioClassification,
										    FlaxWhisperForConditionalGeneration,
										    FlaxWhisperModel,
										    FlaxWhisperPreTrainedModel,
										)

else:
					import sys

					_lowerCamelCase   :				Dict     			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




