

import os
import sys
import unittest


a_     :    Dict     		=   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_     :    Dict     		=   os.path.join(git_repo_path, "src", "diffusers")




class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      find_backend('    if not is_torch_available():'     )
     self.assertEqual(snake_case__				,     'torch'     )

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

     SCREAMING_SNAKE_CASE		    =      find_backend('    if not (is_torch_available() and is_transformers_available()):'     )
     self.assertEqual(snake_case__				,     '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")

     SCREAMING_SNAKE_CASE		    =      find_backend(
         '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'     )
     self.assertEqual(snake_case__				,     'torch_and_transformers_and_onnx'     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      read_init()
     # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
     self.assertIn('torch'				,     snake_case__     )
     self.assertIn('torch_and_transformers'				,     snake_case__     )
     self.assertIn('flax_and_transformers'				,     snake_case__     )
     self.assertIn('torch_and_transformers_and_onnx'				,     snake_case__     )

     # 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 UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      create_dummy_object('CONSTANT'				,     '\'torch\''     )
     self.assertEqual(snake_case__				,     '\nCONSTANT = None\n'     )

     SCREAMING_SNAKE_CASE		    =      create_dummy_object('function'				,     '\'torch\''     )
     self.assertEqual(
         snake_case__				,     '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'     )

     SCREAMING_SNAKE_CASE		    =      '\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'
     SCREAMING_SNAKE_CASE		    =      create_dummy_object('FakeClass'				,     '\'torch\''     )
     self.assertEqual(snake_case__				,     snake_case__     )




    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      '# 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'
     SCREAMING_SNAKE_CASE		    =      create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}     )
     self.assertEqual(dummy_files['torch']				,     snake_case__     )




import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 re.sub('<n>'		,				''		,				_UpperCamelCase						)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_UpperCamelCase						)						)

