"""simple docstring"""

from typing import TYPE_CHECKING

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


__lowercase    =				{
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				['''GPT2TokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				[
				    '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''GPT2DoubleHeadsModel''',
				    '''GPT2ForQuestionAnswering''',
				    '''GPT2ForSequenceClassification''',
				    '''GPT2ForTokenClassification''',
				    '''GPT2LMHeadModel''',
				    '''GPT2Model''',
				    '''GPT2PreTrainedModel''',
				    '''load_tf_weights_in_gpt2''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				[
				    '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFGPT2DoubleHeadsModel''',
				    '''TFGPT2ForSequenceClassification''',
				    '''TFGPT2LMHeadModel''',
				    '''TFGPT2MainLayer''',
				    '''TFGPT2Model''',
				    '''TFGPT2PreTrainedModel''',
				]

try:
				if not is_keras_nlp_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				['''TFGPT2Tokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
				from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
				from .tokenization_gpta import GPTaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_fast import GPTaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_gpta import (
								    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GPTaDoubleHeadsModel,
								    GPTaForQuestionAnswering,
								    GPTaForSequenceClassification,
								    GPTaForTokenClassification,
								    GPTaLMHeadModel,
								    GPTaModel,
								    GPTaPreTrainedModel,
								    load_tf_weights_in_gpta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_gpta import (
								    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFGPTaDoubleHeadsModel,
								    TFGPTaForSequenceClassification,
								    TFGPTaLMHeadModel,
								    TFGPTaMainLayer,
								    TFGPTaModel,
								    TFGPTaPreTrainedModel,
								)

				try:
								if not is_keras_nlp_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_tf import TFGPTaTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
				import sys

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




"""simple docstring"""

def    lowerCAmelCase			(__UpperCamelCase							:    list							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							for index, item in enumerate(__UpperCamelCase			):
														if item == target:
																					return index
							return -1


def    lowerCAmelCase			(__UpperCamelCase							:    list							,					__UpperCamelCase							:    int							,					__UpperCamelCase							:    int							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							if not (0 <= high < len(__UpperCamelCase			) and 0 <= low < len(__UpperCamelCase			)):
														raise Exception('''Invalid upper or lower bound!'''			)
							if high < low:
														return -1
							if sequence[low] == target:
														return low
							if sequence[high] == target:
														return high
							return rec_linear_search(__UpperCamelCase							,					low + 1							,					high - 1							,					__UpperCamelCase			)


if __name__ == "__main__":
				__lowercase    =				input('''Enter numbers separated by comma:\n''').strip()
				__lowercase    =				[int(item.strip()) for item in user_input.split(''',''')]

				__lowercase    =				int(input('''Enter a single number to be found in the list:\n''').strip())
				__lowercase    =				linear_search(sequence, target)
				if result != -1:
								print(f'''linear_search({sequence}, {target}) = {result}''')
				else:
								print(f'''{target} was not found in {sequence}''')



