




'''simple docstring'''






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 					lowercase_			(	lowerCAmelCase_	,					lowerCAmelCase_	,					unittest.TestCase       ):
     A_       							= UNetaDModel
     A_       							= "sample"





     @property
     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Any  			=						4
           snake_case__					:      Optional[int]  			=						14
           snake_case__					:      Optional[Any]  			=						16

           snake_case__					:      str  			=						floats_tensor((batch_size, num_features, seq_len) ).to(__lowerCamelCase )
           snake_case__					:      Any  			=						torch.tensor([10] * batch_size ).to(__lowerCamelCase )

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





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





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





     def  _lowerCAmelCase			(    self    :				List[str] ):
           pass





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           pass





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





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





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





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





     @unittest.skipIf(torch_device == 'mps'    ,       'mish op not supported in MPS' )
     def  _lowerCAmelCase			(    self    :				List[str] ):
           super().test_model_from_pretrained()





     @unittest.skipIf(torch_device == 'mps'    ,       'mish op not supported in MPS' )
     def  _lowerCAmelCase			(    self    :				List[str] ):
           super().test_output()





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      List[str]  			=						{
               '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',
           }
           snake_case__					:      Dict  			=						self.dummy_input
           return init_dict, inputs_dict





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

           snake_case__					:      List[Any]  			=						model.config.in_channels
           snake_case__					:      Tuple  			=						16
           snake_case__					:      List[str]  			=						torch.randn((1, seq_len, num_features) ).permute(
               0    ,       2    ,       1 )  # match original, we can update values and remove
           snake_case__					:      Any  			=						torch.full((num_features,)    ,       0 )

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

           snake_case__					:      Tuple  			=						output[0, -3:, -3:].flatten()
           # fmt: off
           snake_case__					:      Union[str, Any]  			=						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(__lowerCamelCase    ,       __lowerCamelCase    ,       rtol=1E-3 ) )





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





     @slow
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Tuple  			=						'harmonai/maestro-150k'
           snake_case__					:      Any  			=						UNetaDModel.from_pretrained(__lowerCamelCase    ,       subfolder='unet' )
           model.to(__lowerCamelCase )

           snake_case__					:      str  			=						65536
           snake_case__					:      Any  			=						torch.sin(torch.arange(__lowerCamelCase )[None, None, :].repeat(1    ,       2    ,       1 ) ).to(__lowerCamelCase )
           snake_case__					:      List[str]  			=						torch.tensor([1] ).to(__lowerCamelCase )

           with torch.no_grad():
                 snake_case__					:      Optional[int]  			=						model(__lowerCamelCase    ,       __lowerCamelCase ).sample

           snake_case__					:      int  			=						output.abs().sum()
           snake_case__					:      Optional[Any]  			=						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 					lowercase_			(	lowerCAmelCase_	,					lowerCAmelCase_	,					unittest.TestCase       ):
     A_       							= UNetaDModel
     A_       							= "sample"





     @property
     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__					:      Optional[int]  			=						4
           snake_case__					:      Optional[Any]  			=						14
           snake_case__					:      List[Any]  			=						16

           snake_case__					:      Union[str, Any]  			=						floats_tensor((batch_size, num_features, seq_len) ).to(__lowerCamelCase )
           snake_case__					:      int  			=						torch.tensor([10] * batch_size ).to(__lowerCamelCase )

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





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





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





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





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





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





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





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





     @unittest.skipIf(torch_device == 'mps'    ,       'mish op not supported in MPS' )
     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           # UNetRL is a value-function is different output shape
           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.prepare_init_args_and_inputs_for_common()
           snake_case__					:      Dict  			=						self.model_class(**__lowerCamelCase )
           model.to(__lowerCamelCase )
           model.eval()

           with torch.no_grad():
                 snake_case__					:      List[str]  			=						model(**__lowerCamelCase )

                 if isinstance(__lowerCamelCase    ,       __lowerCamelCase ):
                       snake_case__					:      Union[str, Any]  			=						output.sample

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





     def  _lowerCAmelCase			(    self    :				Dict ):
           pass





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           pass





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__					:      str  			=						{
               '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',
           }
           snake_case__					:      List[Any]  			=						self.dummy_input
           return init_dict, inputs_dict





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

           snake_case__					:      List[str]  			=						value_function.config.in_channels
           snake_case__					:      List[Any]  			=						14
           snake_case__					:      List[Any]  			=						torch.randn((1, seq_len, num_features) ).permute(
               0    ,       2    ,       1 )  # match original, we can update values and remove
           snake_case__					:      Optional[Any]  			=						torch.full((num_features,)    ,       0 )

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

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





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






'''simple docstring'''






class 					lowercase_			:





     def __init__(    self    :				Any ):
           snake_case__					:      List[str]  			=						[0]
           snake_case__					:      Optional[Any]  			=						0





     def  _lowerCAmelCase			(    self    :				List[str]    ,       __lowerCamelCase    :				int ):
           snake_case__					:      Optional[int]  			=						self.__heap[i]
           while i // 2 > 0:
                 if self.__heap[i] > self.__heap[i // 2]:
                       snake_case__					:      Union[str, Any]  			=						self.__heap[i // 2]
                       snake_case__					:      Union[str, Any]  			=						temporary
                 i //= 2





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				int ):
           self.__heap.append(__lowerCamelCase )
           self.__size += 1
           self.__swap_up(self.__size )





     def  _lowerCAmelCase			(    self    :				Dict    ,       __lowerCamelCase    :				int ):
           while self.__size >= 2 * i:
                 if 2 * i + 1 > self.__size:
                       snake_case__					:      List[str]  			=						2 * i
                 else:
                       if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                             snake_case__					:      Any  			=						2 * i
                       else:
                             snake_case__					:      Tuple  			=						2 * i + 1
                 snake_case__					:      int  			=						self.__heap[i]
                 if self.__heap[i] < self.__heap[bigger_child]:
                       snake_case__					:      Tuple  			=						self.__heap[bigger_child]
                       snake_case__					:      int  			=						temporary
                 snake_case__					:      int  			=						bigger_child





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Any  			=						self.__heap[1]
           snake_case__					:      List[str]  			=						self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1 )
           return max_value





     @property
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           return self.__heap[1:]





     def __len__(    self    :				int ):
           return self.__size


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # create an instance of BinaryHeap
      A_ 			=		BinaryHeap()
      binary_heap.insert(6)
      binary_heap.insert(10)
      binary_heap.insert(15)
      binary_heap.insert(12)
      # pop root(max-values because it is max heap)
      print(binary_heap.pop())  # 15
      print(binary_heap.pop())  # 12
      # get the list and size after operations
      print(binary_heap.get_list)
      print(len(binary_heap))
