


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   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  UNetaDModel
						UpperCAmelCase									=  "sample"




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =14
										_SCREAMING_SNAKE_CASE	     =16

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

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




						@property
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Dict:


										"""simple docstring"""





										return (4, 14, 16)




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


										"""simple docstring"""





										return (4, 14, 16)




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


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										super().test_determinism()




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


										"""simple docstring"""





										super().test_outputs_equivalence()




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


										"""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]     )							->   Optional[int]:


										"""simple docstring"""





										super().test_model_from_pretrained()




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


										"""simple docstring"""





										super().test_output()




						def 							__UpperCamelCase (				self    :					Any     )							->   List[Any]:


										"""simple docstring"""





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




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


										"""simple docstring"""





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

										model.to(_a     )
										_SCREAMING_SNAKE_CASE	     =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    :					int     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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     )

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

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

										_SCREAMING_SNAKE_CASE	     =output[0, -3:, -3:].flatten()
										# fmt: off
										_SCREAMING_SNAKE_CASE	     =torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]     )
										# fmt: on
										self.assertTrue(torch.allclose(_a ,			_a ,			rtol=1E-3     )     )




						def 							__UpperCamelCase (				self    :					int     )							->   Dict:


										"""simple docstring"""





										pass




						@slow
						def 							__UpperCamelCase (				self    :					Tuple     )							->   int:


										"""simple docstring"""





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

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

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

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

										assert (output_sum - 2_24.08_96).abs() < 0.5
										assert (output_max - 0.06_07).abs() < 4E-4

class   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  UNetaDModel
						UpperCAmelCase									=  "sample"




						@property
						def 							__UpperCamelCase (				self    :					int     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =14
										_SCREAMING_SNAKE_CASE	     =16

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

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




						@property
						def 							__UpperCamelCase (				self    :					str     )							->   str:


										"""simple docstring"""





										return (4, 14, 16)




						@property
						def 							__UpperCamelCase (				self    :					List[str]     )							->   Optional[Any]:


										"""simple docstring"""





										return (4, 14, 1)




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


										"""simple docstring"""





										super().test_determinism()




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


										"""simple docstring"""





										super().test_outputs_equivalence()




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


										"""simple docstring"""





										super().test_from_save_pretrained_variant()




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


										"""simple docstring"""





										super().test_model_from_pretrained()




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.prepare_init_args_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE	     =self.model_class(**_a     )
										model.to(_a     )
										model.eval()

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

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

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




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   str:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					Dict     )							->   Any:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Optional[Any]:


										"""simple docstring"""





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




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


										"""simple docstring"""





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

										value_function.to(_a     )
										_SCREAMING_SNAKE_CASE	     =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    :					Optional[int]     )							->   List[Any]:


										"""simple docstring"""





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

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

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

										# fmt: off
										_SCREAMING_SNAKE_CASE	     =torch.tensor([1_65.25] * seq_len     )
										# fmt: on
										self.assertTrue(torch.allclose(_a ,			_a ,			rtol=1E-3     )     )




						def 							__UpperCamelCase (				self    :					List[str]     )							->   int:


										"""simple docstring"""





										pass





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A )

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

