






from typing import TYPE_CHECKING

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


__A          = {
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A          = [
				    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'OpenAIGPTDoubleHeadsModel',
				    'OpenAIGPTForSequenceClassification',
				    'OpenAIGPTLMHeadModel',
				    'OpenAIGPTModel',
				    'OpenAIGPTPreTrainedModel',
				    'load_tf_weights_in_openai_gpt',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A          = [
				    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFOpenAIGPTDoubleHeadsModel',
				    'TFOpenAIGPTForSequenceClassification',
				    'TFOpenAIGPTLMHeadModel',
				    'TFOpenAIGPTMainLayer',
				    'TFOpenAIGPTModel',
				    'TFOpenAIGPTPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
				from .tokenization_openai import OpenAIGPTTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_openai_fast import OpenAIGPTTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_openai import (
								    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    OpenAIGPTDoubleHeadsModel,
								    OpenAIGPTForSequenceClassification,
								    OpenAIGPTLMHeadModel,
								    OpenAIGPTModel,
								    OpenAIGPTPreTrainedModel,
								    load_tf_weights_in_openai_gpt,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_openai import (
								    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFOpenAIGPTDoubleHeadsModel,
								    TFOpenAIGPTForSequenceClassification,
								    TFOpenAIGPTLMHeadModel,
								    TFOpenAIGPTMainLayer,
								    TFOpenAIGPTModel,
								    TFOpenAIGPTPreTrainedModel,
								)

else:
				import sys

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











from __future__ import annotations

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



