




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :  Optional[Any]					=      logging.get_logger(__name__)

__A   :  str					=      {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}




class       lowerCAmelCase__       (			_A    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[int]       =      "mra"



				def __init__(     self					:	str    ,   lowercase__					:	Dict=5_0_2_6_5    ,   lowercase__					:	List[str]=7_6_8    ,   lowercase__					:	int=1_2    ,   lowercase__					:	Optional[Any]=1_2    ,   lowercase__					:	Tuple=3_0_7_2    ,   lowercase__					:	Union[str, Any]="gelu"    ,   lowercase__					:	List[Any]=0.1    ,   lowercase__					:	Dict=0.1    ,   lowercase__					:	Union[str, Any]=5_1_2    ,   lowercase__					:	Optional[Any]=1    ,   lowercase__					:	int=0.0_2    ,   lowercase__					:	Tuple=1e-5    ,   lowercase__					:	Optional[Any]="absolute"    ,   lowercase__					:	Tuple=4    ,   lowercase__					:	List[Any]="full"    ,   lowercase__					:	Tuple=0    ,   lowercase__					:	List[str]=0    ,   lowercase__					:	List[str]=1    ,   lowercase__					:	List[Any]=0    ,   lowercase__					:	List[str]=2    ,   **lowercase__					:	List[Any]    ,   ):
						super().__init__(pad_token_id=UpperCamelCase__    ,   bos_token_id=UpperCamelCase__    ,   eos_token_id=UpperCamelCase__    ,   **UpperCamelCase__						)

						__lowercase				:		Union[str, Any]												=				vocab_size
						__lowercase				:		List[Any]												=				max_position_embeddings
						__lowercase				:		int												=				hidden_size
						__lowercase				:		List[Any]												=				num_hidden_layers
						__lowercase				:		str												=				num_attention_heads
						__lowercase				:		Tuple												=				intermediate_size
						__lowercase				:		Dict												=				hidden_act
						__lowercase				:		List[Any]												=				hidden_dropout_prob
						__lowercase				:		List[str]												=				attention_probs_dropout_prob
						__lowercase				:		str												=				initializer_range
						__lowercase				:		List[Any]												=				type_vocab_size
						__lowercase				:		List[str]												=				layer_norm_eps
						__lowercase				:		Dict												=				position_embedding_type
						__lowercase				:		List[Any]												=				block_per_row
						__lowercase				:		Dict												=				approx_mode
						__lowercase				:		Dict												=				initial_prior_first_n_blocks
						__lowercase				:		int												=				initial_prior_diagonal_n_blocks






"""simple docstring"""







import os
import sys
import unittest


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




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	str						):
						__lowercase				:		int												=				find_backend("    if not is_torch_available():"						)
						self.assertEqual(lowercase__    ,   "torch"						)

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

						__lowercase				:		int												=				find_backend("    if not (is_torch_available() and is_transformers_available()):"						)
						self.assertEqual(lowercase__    ,   "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")

						__lowercase				:		str												=				find_backend(
						    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers_and_onnx"						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		List[Any]												=				read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn("torch"    ,   lowercase__						)
						self.assertIn("torch_and_transformers"    ,   lowercase__						)
						self.assertIn("flax_and_transformers"    ,   lowercase__						)
						self.assertIn("torch_and_transformers_and_onnx"    ,   lowercase__						)

						# 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					:	Dict						):
						__lowercase				:		Tuple												=				create_dummy_object("CONSTANT"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   "\nCONSTANT = None\n"						)

						__lowercase				:		Union[str, Any]												=				create_dummy_object("function"    ,   "'torch'"						)
						self.assertEqual(
						    lowercase__    ,   "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"						)

						__lowercase				:		Tuple												=				"\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"
						__lowercase				:		Dict												=				create_dummy_object("FakeClass"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Optional[int]												=				"# 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"
						__lowercase				:		List[str]												=				create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}						)
						self.assertEqual(dummy_files["torch"]    ,   lowercase__						)
