
"""simple docstring"""




import os
import sys
import unittest


UpperCAmelCase__ :       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
UpperCAmelCase__ :       Optional[int]       				=					os.path.join(git_repo_path, 'src', 'diffusers')



class       lowerCAmelCase_		(unittest.TestCase    ):


				"""simple docstring"""



				def       __magic_name__       (self       )   ->						Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		:			Any	 =  find_backend("""    if not is_torch_available():"""       )
											self.assertEqual(_UpperCamelCase  ,			"""torch"""       )

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

											SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  find_backend("""    if not (is_torch_available() and is_transformers_available()):"""       )
											self.assertEqual(_UpperCamelCase  ,			"""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__		:			List[str]	 =  find_backend(
											    """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"""       )
											self.assertEqual(_UpperCamelCase  ,			"""torch_and_transformers_and_onnx"""       )



				def       __magic_name__       (self       )   ->						Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		:			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"""  ,			_UpperCamelCase       )
											self.assertIn("""torch_and_transformers"""  ,			_UpperCamelCase       )
											self.assertIn("""flax_and_transformers"""  ,			_UpperCamelCase       )
											self.assertIn("""torch_and_transformers_and_onnx"""  ,			_UpperCamelCase       )

											# 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       __magic_name__       (self       )   ->						str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		:			List[Any]	 =  create_dummy_object("""CONSTANT"""  ,			"""'torch'"""       )
											self.assertEqual(_UpperCamelCase  ,			"""\nCONSTANT = None\n"""       )

											SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  create_dummy_object("""function"""  ,			"""'torch'"""       )
											self.assertEqual(
											    _UpperCamelCase  ,			"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""       )

											SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  """\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__		:			Optional[int]	 =  create_dummy_object("""FakeClass"""  ,			"""'torch'"""       )
											self.assertEqual(_UpperCamelCase  ,			_UpperCamelCase       )



				def       __magic_name__       (self       )   ->						Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		:			Any	 =  """# 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__		:			int	 =  create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}       )
											self.assertEqual(dummy_files["""torch"""]  ,			_UpperCamelCase       )






from math import ceil






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(0       ,				__lowerCAmelCase							)							)

			lowerCAmelCase_         =      [item for sublist in list(device_map.values()							) for item in sublist]

			# Duplicate check
			lowerCAmelCase_         =      []
			for i in device_map_blocks:
						if device_map_blocks.count(__lowerCAmelCase							) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(__lowerCAmelCase							)
    # Missing blocks
			lowerCAmelCase_         =      [i for i in blocks if i not in device_map_blocks]
			lowerCAmelCase_         =      [i for i in device_map_blocks if i not in blocks]

			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
						    " These attention blocks were specified more than once: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
						    "blocks to a device on the device_map: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
						    + str(__lowerCAmelCase							)							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(__lowerCAmelCase							)							)
			lowerCAmelCase_         =      int(ceil(n_layers / len(__lowerCAmelCase							)							)							)
			lowerCAmelCase_         =      [layers[i : i + n_blocks] for i in range(0       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)]

			return dict(zip(__lowerCAmelCase       ,				__lowerCAmelCase							)							)



