

from typing import TYPE_CHECKING

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


A											=     {
    '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:
		A											=     ['WhisperTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'WhisperForConditionalGeneration',
		    'WhisperModel',
		    'WhisperPreTrainedModel',
		    'WhisperForAudioClassification',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFWhisperForConditionalGeneration',
		    'TFWhisperModel',
		    'TFWhisperPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    '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

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



def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if not isinstance(lowercase__			, lowercase__	):
		raise ValueError('iterations must be defined as integers'	)
	if not isinstance(lowercase__			, lowercase__	) or not number >= 1:
		raise ValueError(
		    'starting number must be\n                         and integer and be more than 0'	)
	if not iterations >= 1:
		raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'	)

	snake_case_      		= ''
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(lowercase__	)

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()
