
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,
)


_SCREAMING_SNAKE_CASE     :   Optional[int]							 =      {
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Optional[int]							 =      [
					    '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:
					_SCREAMING_SNAKE_CASE     :   int							 =      [
					    '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:
					_SCREAMING_SNAKE_CASE     :   Union[str, Any]							 =      ['TFGPT2Tokenizer']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      ['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

					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652E26,
}

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199E-27,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    float							)			->							float:

				"""simple docstring"""







				if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
								A__	:						int								=				(
								    F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
								    F"Supported values are: {', '.join(__UpperCamelCase							)}"
								)
								raise ValueError(__UpperCamelCase							)
				return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()