






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE 							=						{
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE 							=						['LlamaTokenizer']

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE 							=						[
           'LlamaForCausalLM',
           'LlamaModel',
           'LlamaPreTrainedModel',
           'LlamaForSequenceClassification',
       ]


if TYPE_CHECKING:
       from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_llama import LlamaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_llama_fast import LlamaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
       import sys

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












from math import pow, sqrt

def      _lowerCamelCase	(						*__A    :							float )      ->					bool:
    _UpperCAmelCase				:      str			  =				len(__A ) > 0 and all(value > 0.0 for value in values )
    return result

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A )
        else ValueError('''Input Error: Molar mass values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,		2 )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )


def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(pow(effusion_rate_a / effusion_rate_a		,		2 ) / molar_mass		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )




