


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE   ,		_SCREAMING_SNAKE_CASE   ,		unittest.TestCase    ):
							_lowerCamelCase				: Union[str, Any]       =					UNetaDModel
							_lowerCamelCase				: Any       =					"""sample"""





							@property
							def   __magic_name__      (							self    ):
								a_     			=   4
								a_     			=   14
								a_     			=   16

								a_     			=   floats_tensor((batch_size, num_features, seq_len)    ).to(_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.tensor([10] * batch_size    ).to(_SCREAMING_SNAKE_CASE    )

								return {"sample": noise, "timestep": time_step}





							@property
							def   __magic_name__      (							self    ):
								return (4, 14, 16)





							@property
							def   __magic_name__      (							self    ):
								return (4, 14, 16)





							def   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self    ):
								pass





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_determinism()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_outputs_equivalence()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_from_save_pretrained()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_from_save_pretrained_variant()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_model_from_pretrained()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_output()





							def   __magic_name__      (							self    ):
								a_     			=   {
								    """block_out_channels""": (32, 64, 128, 256),
								    """in_channels""": 14,
								    """out_channels""": 14,
								    """time_embedding_type""": """positional""",
								    """use_timestep_embedding""": True,
								    """flip_sin_to_cos""": False,
								    """freq_shift""": 1.0,
								    """out_block_type""": """OutConv1DBlock""",
								    """mid_block_type""": """MidResTemporalBlock1D""",
								    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
								    """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
								    """act_fn""": """mish""",
								}
								a_     			=   self.dummy_input
								return init_dict, inputs_dict





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								a_			, a_     			=   UNetaDModel.from_pretrained(
								    """bglick13/hopper-medium-v2-value-function-hor32"""			,       output_loading_info=_SCREAMING_SNAKE_CASE			,       subfolder="""unet"""    )
								self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )
								self.assertEqual(len(loading_info["""missing_keys"""]    )			,       0    )

								model.to(_SCREAMING_SNAKE_CASE    )
								a_     			=   model(**self.dummy_input    )

								assert image is not None, "Make sure output is not None"





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								a_     			=   UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""			,       subfolder="""unet"""    )
								torch.manual_seed(0    )
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0    )

								a_     			=   model.config.in_channels
								a_     			=   16
								a_     			=   torch.randn((1, seq_len, num_features)    ).permute(
								    0			,       2			,       1    )  # match original, we can update values and remove
								a_     			=   torch.full((num_features,)			,       0    )

								with torch.no_grad():
									a_     			=   model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ).sample.permute(0			,       2			,       1    )

								a_     			=   output[0, -3:, -3:].flatten()
								# fmt: off
								a_     			=   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]    )
								# fmt: on
								self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       rtol=1E-3    )    )





							def   __magic_name__      (							self    ):
								# Not implemented yet for this UNet
								pass





							@slow
							def   __magic_name__      (							self    ):
								a_     			=   """harmonai/maestro-150k"""
								a_     			=   UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE			,       subfolder="""unet"""    )
								model.to(_SCREAMING_SNAKE_CASE    )

								a_     			=   6_5536
								a_     			=   torch.sin(torch.arange(_SCREAMING_SNAKE_CASE    )[None, None, :].repeat(1			,       2			,       1    )    ).to(_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.tensor([1]    ).to(_SCREAMING_SNAKE_CASE    )

								with torch.no_grad():
									a_     			=   model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ).sample

								a_     			=   output.abs().sum()
								a_     			=   output.abs().max()

								assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
								assert (output_max - 0.0_6_0_7).abs() < 4E-4







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE   ,		_SCREAMING_SNAKE_CASE   ,		unittest.TestCase    ):
							_lowerCamelCase				: Optional[Any]       =					UNetaDModel
							_lowerCamelCase				: Dict       =					"""sample"""





							@property
							def   __magic_name__      (							self    ):
								a_     			=   4
								a_     			=   14
								a_     			=   16

								a_     			=   floats_tensor((batch_size, num_features, seq_len)    ).to(_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.tensor([10] * batch_size    ).to(_SCREAMING_SNAKE_CASE    )

								return {"sample": noise, "timestep": time_step}





							@property
							def   __magic_name__      (							self    ):
								return (4, 14, 16)





							@property
							def   __magic_name__      (							self    ):
								return (4, 14, 1)





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_determinism()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_outputs_equivalence()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_from_save_pretrained()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_from_save_pretrained_variant()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								super().test_model_from_pretrained()





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								# UNetRL is a value-function is different output shape
								a_			, a_     			=   self.prepare_init_args_and_inputs_for_common()
								a_     			=   self.model_class(**_SCREAMING_SNAKE_CASE    )
								model.to(_SCREAMING_SNAKE_CASE    )
								model.eval()

								with torch.no_grad():
									a_     			=   model(**_SCREAMING_SNAKE_CASE    )

									if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
										a_     			=   output.sample

								self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.Size((inputs_dict["""sample"""].shape[0], 1)    )
								self.assertEqual(output.shape			,       _SCREAMING_SNAKE_CASE			,       """Input and output shapes do not match"""    )





							def   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self    ):
								a_     			=   {
								    """in_channels""": 14,
								    """out_channels""": 14,
								    """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
								    """up_block_types""": [],
								    """out_block_type""": """ValueFunction""",
								    """mid_block_type""": """ValueFunctionMidBlock1D""",
								    """block_out_channels""": [32, 64, 128, 256],
								    """layers_per_block""": 1,
								    """downsample_each_block""": True,
								    """use_timestep_embedding""": True,
								    """freq_shift""": 1.0,
								    """flip_sin_to_cos""": False,
								    """time_embedding_type""": """positional""",
								    """act_fn""": """mish""",
								}
								a_     			=   self.dummy_input
								return init_dict, inputs_dict





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								a_			, a_     			=   UNetaDModel.from_pretrained(
								    """bglick13/hopper-medium-v2-value-function-hor32"""			,       output_loading_info=_SCREAMING_SNAKE_CASE			,       subfolder="""value_function"""    )
								self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )
								self.assertEqual(len(vf_loading_info["""missing_keys"""]    )			,       0    )

								value_function.to(_SCREAMING_SNAKE_CASE    )
								a_     			=   value_function(**self.dummy_input    )

								assert image is not None, "Make sure output is not None"





							@unittest.skipIf(torch_device == """mps"""			,       """mish op not supported in MPS"""    )
							def   __magic_name__      (							self    ):
								a_			, a_     			=   UNetaDModel.from_pretrained(
								    """bglick13/hopper-medium-v2-value-function-hor32"""			,       output_loading_info=_SCREAMING_SNAKE_CASE			,       subfolder="""value_function"""    )
								torch.manual_seed(0    )
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0    )

								a_     			=   value_function.config.in_channels
								a_     			=   14
								a_     			=   torch.randn((1, seq_len, num_features)    ).permute(
								    0			,       2			,       1    )  # match original, we can update values and remove
								a_     			=   torch.full((num_features,)			,       0    )

								with torch.no_grad():
									a_     			=   value_function(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ).sample

								# fmt: off
								a_     			=   torch.tensor([1_6_5.2_5] * seq_len    )
								# fmt: on
								self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       rtol=1E-3    )    )





							def   __magic_name__      (							self    ):
								# Not implemented yet for this UNet
								pass



from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(UpperCamelCase     ,					-1	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	str:



	"""simple docstring"""






	if not balanced_parentheses(UpperCamelCase	):
		raise ValueError("""Mismatched parentheses"""	)
	a_     			=   Stack()
	a_     			=   []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(UpperCamelCase	)
		elif char == "(":
			stack.push(UpperCamelCase	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(UpperCamelCase	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(UpperCamelCase	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(UpperCamelCase	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			_A							     =       'a+b*(c^d-e)^(f+g*h)-i'

			print('Infix to Postfix Notation demonstration:\n')
			print('Infix notation: ' + expression)
			print('Postfix notation: ' + infix_to_postfix(expression))