




import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
						import tensorflow as tf
						from tensorflow.python.eager import context
						from tensorflow.python.framework import ops

						from transformers import GradientAccumulator, create_optimizer




@require_tf
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Dict    ,   lowercase__					:	Tuple						):
						self.assertEqual(len(lowercase__						)    ,   len(lowercase__						)						)
						for a, b in zip(lowercase__    ,   lowercase__						):
								self.assertAlmostEqual(lowercase__    ,   lowercase__    ,   delta=lowercase__						)



				def       snake_case    (     self					:	str						):
						__lowercase				:		Optional[int]												=				GradientAccumulator()
						accumulator([tf.constant([1.0, 2.0]						)]						)
						accumulator([tf.constant([-2.0, 1.0]						)]						)
						accumulator([tf.constant([-1.0, 2.0]						)]						)
						with self.assertRaises(lowercase__						):
								accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
						self.assertEqual(accumulator.step    ,   3						)
						self.assertEqual(len(accumulator.gradients						)    ,   1						)
						self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,   [-2.0, 5.0]    ,   tol=1e-2						)
						accumulator.reset()
						self.assertEqual(accumulator.step    ,   0						)
						self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,   [0.0, 0.0]    ,   tol=1e-2						)



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		Dict												=				None
						ops.enable_eager_execution_internal()
						__lowercase				:		Optional[Any]												=				tf.config.list_physical_devices("CPU"						)
						if len(lowercase__						) == 1:
								tf.config.set_logical_device_configuration(
								    physical_devices[0]    ,   [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
						__lowercase				:		Dict												=				tf.config.list_logical_devices(device_type="CPU"						)
						__lowercase				:		Dict												=				tf.distribute.MirroredStrategy(devices=devices[:2]						)

						with strategy.scope():
								__lowercase				:		Optional[int]												=				GradientAccumulator()
								__lowercase				:		Tuple												=				tf.Variable([4.0, 3.0]						)
								__lowercase				:		Any												=				create_optimizer(5e-5    ,   1_0    ,   5						)
								__lowercase				:		int												=				tf.Variable([0.0, 0.0]    ,   trainable=lowercase__						)

						def accumulate_on_replica(lowercase__					:	int						):
								accumulator([gradient]						)

						def apply_on_replica():
								optimizer.apply_gradients(list(zip(accumulator.gradients    ,   [variable]						)						)						)

						@tf.function
						def accumulate(lowercase__					:	Any    ,   lowercase__					:	List[Any]						):
								with strategy.scope():
										__lowercase				:		Dict												=				strategy.experimental_local_results(lowercase__						)
										local_variables[0].assign(lowercase__						)
										local_variables[1].assign(lowercase__						)
										strategy.run(lowercase__    ,   args=(gradient_placeholder,)						)

						@tf.function
						def apply_grad():
								with strategy.scope():
										strategy.run(lowercase__						)

						def _check_local_values(lowercase__					:	Dict    ,   lowercase__					:	Optional[int]						):
								__lowercase				:		str												=				strategy.experimental_local_results(accumulator._gradients[0]						)
								self.assertListAlmostEqual(values[0].value()    ,   lowercase__    ,   tol=1e-2						)
								self.assertListAlmostEqual(values[1].value()    ,   lowercase__    ,   tol=1e-2						)

						accumulate([1.0, 2.0]    ,   [-1.0, 1.0]						)
						accumulate([3.0, -1.0]    ,   [-1.0, -1.0]						)
						accumulate([-2.0, 2.0]    ,   [3.0, -2.0]						)
						self.assertEqual(accumulator.step    ,   3						)
						_check_local_values([2.0, 3.0]    ,   [1.0, -2.0]						)
						apply_grad()
						self.assertListAlmostEqual(variable.value()    ,   [4.0, 3.0]    ,   tol=1e-2						)
						accumulator.reset()
						self.assertEqual(accumulator.step    ,   0						)
						_check_local_values([0.0, 0.0]    ,   [0.0, 0.0]						)






"""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__						)
