

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				(    snake_case__   ,				snake_case__   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	UNetaDModel
       snake_case    							=	'sample'

       @property
       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> List[str]:







        '''simple docstring'''

        lowerCamelCase      						=    4
        lowerCamelCase      						=    14
        lowerCamelCase      						=    16

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

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

       @property
       def   lowerCamelCase__						(	self					:  Any     )   -> Dict:







        '''simple docstring'''

        return (4, 14, 16)

       @property
       def   lowerCamelCase__						(	self					:  str     )   -> Any:







        '''simple docstring'''

        return (4, 14, 16)

       def   lowerCamelCase__						(	self					:  Tuple     )   -> List[str]:







        '''simple docstring'''

        pass

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







        '''simple docstring'''

        pass

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







        '''simple docstring'''

        super().test_determinism()

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







        '''simple docstring'''

        super().test_outputs_equivalence()

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







        '''simple docstring'''

        super().test_from_save_pretrained()

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







        '''simple docstring'''

        super().test_from_save_pretrained_variant()

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







        '''simple docstring'''

        super().test_model_from_pretrained()

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







        '''simple docstring'''

        super().test_output()

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







        '''simple docstring'''

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

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







        '''simple docstring'''

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

        model.to(__snake_case     )
        lowerCamelCase      						=    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   lowerCamelCase__						(	self					:  int     )   -> Tuple:







        '''simple docstring'''

        lowerCamelCase      						=    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     )

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

        with torch.no_grad():
         lowerCamelCase      						=    model(__snake_case						,	__snake_case     ).sample.permute(0						,	2						,	1     )

        lowerCamelCase      						=    output[0, -3:, -3:].flatten()
        # fmt: off
        lowerCamelCase      						=    torch.tensor([-2.13_7172, 1.142_6016, 0.368_8687, -0.76_6922, 0.730_3146, 0.1103_8864, -0.476_0633, 0.1327_0172, 0.0259_1348]     )
        # fmt: on
        self.assertTrue(torch.allclose(__snake_case						,	__snake_case						,	rtol=1e-3     )     )

       def   lowerCamelCase__						(	self					:  Dict     )   -> Optional[Any]:







        '''simple docstring'''

        pass





       @slow
       def   lowerCamelCase__						(	self					:  int     )   -> str:







        '''simple docstring'''

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

        lowerCamelCase      						=    65536
        lowerCamelCase      						=    torch.sin(torch.arange(__snake_case     )[None, None, :].repeat(1						,	2						,	1     )     ).to(__snake_case     )
        lowerCamelCase      						=    torch.tensor([1]     ).to(__snake_case     )

        with torch.no_grad():
         lowerCamelCase      						=    model(__snake_case						,	__snake_case     ).sample

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

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


class     lowerCAmelCase				(    snake_case__   ,				snake_case__   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	UNetaDModel
       snake_case    							=	'sample'

       @property
       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Tuple:







        '''simple docstring'''

        lowerCamelCase      						=    4
        lowerCamelCase      						=    14
        lowerCamelCase      						=    16

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

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

       @property
       def   lowerCamelCase__						(	self					:  int     )   -> Optional[int]:







        '''simple docstring'''

        return (4, 14, 16)

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







        '''simple docstring'''

        return (4, 14, 1)

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







        '''simple docstring'''

        super().test_determinism()

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







        '''simple docstring'''

        super().test_outputs_equivalence()

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







        '''simple docstring'''

        super().test_from_save_pretrained()

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







        '''simple docstring'''

        super().test_from_save_pretrained_variant()

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







        '''simple docstring'''

        super().test_model_from_pretrained()

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







        '''simple docstring'''

        lowerCamelCase      						=    self.prepare_init_args_and_inputs_for_common()
        lowerCamelCase      						=    self.model_class(**__snake_case     )
        model.to(__snake_case     )
        model.eval()

        with torch.no_grad():
         lowerCamelCase      						=    model(**__snake_case     )

         if isinstance(__snake_case						,	__snake_case     ):
          lowerCamelCase      						=    output.sample

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

       def   lowerCamelCase__						(	self					:  Tuple     )   -> Optional[int]:







        '''simple docstring'''

        pass

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







        '''simple docstring'''

        pass

       def   lowerCamelCase__						(	self					:  Dict     )   -> List[str]:







        '''simple docstring'''

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

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







        '''simple docstring'''

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

        value_function.to(__snake_case     )
        lowerCamelCase      						=    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   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Union[str, Any]:







        '''simple docstring'''

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

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

        with torch.no_grad():
         lowerCamelCase      						=    value_function(__snake_case						,	__snake_case     ).sample

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





       def   lowerCamelCase__						(	self					:  Dict     )   -> Optional[Any]:







        '''simple docstring'''

        pass







from __future__ import annotations


class     lowerCAmelCase				:


       '''simple docstring'''







       def __init__(	self					:  Dict						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    vertices
        lowerCamelCase      						=    [[0] * vertices for _ in range(__snake_case     )]

       def   lowerCamelCase__						(	self					:  List[str]						,	__snake_case					:  list[int]     )   -> None:







        '''simple docstring'''

        print('Vertex \t Distance from Source'     )
        for vertex in range(self.vertices     ):
         print(__snake_case						,	'\t\t'						,	distances_from_source[vertex]     )

       def   lowerCamelCase__						(	self					:  Optional[int]						,	__snake_case					:  list[int]						,	__snake_case					:  list[bool]     )   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    1e7
        lowerCamelCase      						=    0

        # Search not nearest vertex not in the shortest path tree
        for vertex in range(self.vertices     ):
         if distances_from_source[vertex] < minimum and visited[vertex] is False:
          lowerCamelCase      						=    distances_from_source[vertex]
          lowerCamelCase      						=    vertex
        return min_index





       def   lowerCamelCase__						(	self					:  Optional[Any]						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    [int(1e7     )] * self.vertices  # distances from the source
        lowerCamelCase      						=    0
        lowerCamelCase      						=    [False] * self.vertices

        for _ in range(self.vertices     ):
         lowerCamelCase      						=    self.minimum_distance(__snake_case						,	__snake_case     )
         lowerCamelCase      						=    True

         # Update dist value of the adjacent vertices
         # of the picked vertex only if the current
         # distance is greater than new distance and
         # the vertex in not in the shortest path tree
         for v in range(self.vertices     ):
          if (
              self.graph[u][v] > 0
              and visited[v] is False
              and distances[v] > distances[u] + self.graph[u][v]
          ):
           lowerCamelCase      						=    distances[u] + self.graph[u][v]

        self.print_solution(__snake_case     )


if __name__ == "__main__":
     _lowerCAmelCase :	Dict     				=    Graph(9)
     _lowerCAmelCase :	int     				=    [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
         [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
         [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)




