






import os
import sys
import unittest


SCREAMING_SNAKE_CASE 							=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE 							=						os.path.join(git_repo_path, 'src', 'diffusers')
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				find_backend('''    if not is_torch_available():''')
     self.assertEqual(_A							,  '''torch''')

     # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
     # self.assertEqual(backend_with_underscore, "tensorflow_text")

     _UpperCAmelCase				:      List[str]			  =				find_backend('''    if not (is_torch_available() and is_transformers_available()):''')
     self.assertEqual(_A							,  '''torch_and_transformers''')

     # double_backend_with_underscore = find_backend(
     #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
     # )
     # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

     _UpperCAmelCase				:      List[str]			  =				find_backend(
         '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):''')
     self.assertEqual(_A							,  '''torch_and_transformers_and_onnx''')


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				read_init()
     # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
     self.assertIn('''torch'''							,  _A)
     self.assertIn('''torch_and_transformers'''							,  _A)
     self.assertIn('''flax_and_transformers'''							,  _A)
     self.assertIn('''torch_and_transformers_and_onnx'''							,  _A)

     # Likewise, we can't assert on the exact content of a key
     self.assertIn('''UNet2DModel'''							,  objects['''torch'''])
     self.assertIn('''FlaxUNet2DConditionModel'''							,  objects['''flax'''])
     self.assertIn('''StableDiffusionPipeline'''							,  objects['''torch_and_transformers'''])
     self.assertIn('''FlaxStableDiffusionPipeline'''							,  objects['''flax_and_transformers'''])
     self.assertIn('''LMSDiscreteScheduler'''							,  objects['''torch_and_scipy'''])
     self.assertIn('''OnnxStableDiffusionPipeline'''							,  objects['''torch_and_transformers_and_onnx'''])


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				create_dummy_object('''CONSTANT'''							,  '''\'torch\'''')
     self.assertEqual(_A							,  '''\nCONSTANT = None\n''')

     _UpperCAmelCase				:      Optional[int]			  =				create_dummy_object('''function'''							,  '''\'torch\'''')
     self.assertEqual(
         _A							,  '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n''')

     _UpperCAmelCase				:      str			  =				'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
     _UpperCAmelCase				:      int			  =				create_dummy_object('''FakeClass'''							,  '''\'torch\'''')
     self.assertEqual(_A							,  _A)


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
     _UpperCAmelCase				:      Any			  =				create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']})
     self.assertEqual(dummy_files['''torch''']							,  _A)












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.''' )
    )




