'''simple docstring'''
import os
import sys
import unittest


a      =  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
a      =  os.path.join(git_repo_path, '''src''', '''diffusers''')






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  find_backend('    if not is_torch_available():'    )
         self.assertEqual(_lowerCamelCase     ,			'torch'    )

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

         __UpperCamelCase	:	Optional[Any]		    =  find_backend('    if not (is_torch_available() and is_transformers_available()):'    )
         self.assertEqual(_lowerCamelCase     ,			'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(_lowerCamelCase     ,			'torch_and_transformers_and_onnx'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	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'     ,			_lowerCamelCase    )
         self.assertIn('torch_and_transformers'     ,			_lowerCamelCase    )
         self.assertIn('flax_and_transformers'     ,			_lowerCamelCase    )
         self.assertIn('torch_and_transformers_and_onnx'     ,			_lowerCamelCase    )

         # 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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  create_dummy_object('CONSTANT'     ,			'\'torch\''    )
         self.assertEqual(_lowerCamelCase     ,			'\nCONSTANT = None\n'    )

         __UpperCamelCase	:	str		    =  create_dummy_object('function'     ,			'\'torch\''    )
         self.assertEqual(
             _lowerCamelCase     ,			'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'    )

         __UpperCamelCase	:	Dict		    =  '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
         __UpperCamelCase	:	Union[str, Any]		    =  create_dummy_object('FakeClass'     ,			'\'torch\''    )
         self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n'
         __UpperCamelCase	:	Union[str, Any]		    =  create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}    )
         self.assertEqual(dummy_files['torch']     ,			_lowerCamelCase    )




'''simple docstring'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url







@pytest.mark.parametrize('repo_id'				,     ['canonical_dataset_name', 'org-name/dataset-name']			)
@pytest.mark.parametrize('path'				,     ['filename.csv', 'filename with blanks.csv']			)
@pytest.mark.parametrize('revision'				,     [None, 'v2']			)
def  _UpperCamelCase (     _a	:	Any				,     _a	:	int				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  hf_hub_url(repo_id=_a				,     path=_a				,     revision=_a			)
    assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_a			)}"""



