





from __future__ import annotations

from queue import Queue

class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    str      )					->   None:

			'''simple docstring'''




			__lowercase					=    {}




	def       UpperCAmelCase (	self       :    Any      )					->   None:

			'''simple docstring'''




			for i in self.vertices:
					print(__lowerCamelCase				,			' : '				,			' -> '.join([str(__lowerCamelCase      ) for j in self.vertices[i]]      )      )




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    int      )					->   None:

			'''simple docstring'''




			if from_vertex in self.vertices:
					self.vertices[from_vertex].append(__lowerCamelCase      )
			else:
					__lowercase					=    [to_vertex]






	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    int      )					->   set[int]:

			'''simple docstring'''




			__lowercase					=    set()

			# create a first in first out queue to store all the vertices for BFS
			__lowercase					=    Queue()

			# mark the source node as visited and enqueue it
			visited.add(__lowerCamelCase      )
			queue.put(__lowerCamelCase      )

			while not queue.empty():
					__lowercase					=    queue.get()

					# loop through all adjacent vertex and enqueue it if not yet visited
					for adjacent_vertex in self.vertices[vertex]:
							if adjacent_vertex not in visited:
									queue.put(__lowerCamelCase      )
									visited.add(__lowerCamelCase      )
			return visited


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

		testmod(verbose=True)

		SCREAMING_SNAKE_CASE_      :			str		  =      Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		# 0  :  1 -> 2
		# 1  :  2
		# 2  :  0 -> 3
		# 3  :  3

		assert sorted(g.bfs(2)) == [0, 1, 2, 3]











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 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    str      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

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

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




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			return (4, 14, 16)




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




			pass




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

			'''simple docstring'''




			pass




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Any:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




			super().test_output()




	def       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    {
			    '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',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Any:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='unet'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(loading_info['missing_keys']      )				,			0      )

			model.to(__lowerCamelCase      )
			__lowercase					=    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       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    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      )

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

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample.permute(0				,			2				,			1      )

			__lowercase					=    output[0, -3:, -3:].flatten()
			# fmt: off
			__lowercase					=    torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )




	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			pass






	@slow
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    'harmonai/maestro-150k'
			__lowercase					=    UNetaDModel.from_pretrained(__lowerCamelCase				,			subfolder='unet'      )
			model.to(__lowerCamelCase      )

			__lowercase					=    65_536
			__lowercase					=    torch.sin(torch.arange(__lowerCamelCase      )[None, None, :].repeat(1				,			2				,			1      )      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([1]      ).to(__lowerCamelCase      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample

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

			assert (output_sum - 224.0896).abs() < 0.5
			assert (output_max - 0.0607).abs() < 4E-4





class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    int      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

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

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




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Tuple      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 1)




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   int:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_init_args_and_inputs_for_common()
			__lowercase					=    self.model_class(**__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()

			with torch.no_grad():
					__lowercase					=    model(**__lowerCamelCase      )

					if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
							__lowercase					=    output.sample

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




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Optional[int]:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    {
			    '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',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(vf_loading_info['missing_keys']      )				,			0      )

			value_function.to(__lowerCamelCase      )
			__lowercase					=    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       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

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

			with torch.no_grad():
					__lowercase					=    value_function(__lowerCamelCase				,			__lowerCamelCase      ).sample

			# fmt: off
			__lowercase					=    torch.tensor([165.25] * seq_len      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )






	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			pass




