


import torch

from transformers import AutoTokenizer

class      __snake_case				(   _lowercase):
					def __init__(						self       :						str	,      _lowerCAmelCase       :						List[str]  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      AutoTokenizer.from_pretrained(__lowerCAmelCase  )
					def 	a			(						self       :						str	,      _lowerCAmelCase       :						List[Any]  )							->					List[str]:



											'''simple docstring'''





											if isinstance(__lowerCAmelCase	,      __lowerCAmelCase  ) and all(isinstance(_x	,      __lowerCAmelCase  ) for _x in x  ):
																	__magic_name__					=      None
																	for l in x:
																							__magic_name__					=      self.tokenizer(
																							    __lowerCAmelCase	,      padding='max_length'	,      max_length=3_8_4	,      truncation=__lowerCAmelCase	,      return_tensors='pt'	,      )
																							__magic_name__					=      torch.tensor([len(__lowerCAmelCase  )]  )
																							if d is not None:
																													for k in d.keys():
																																			__magic_name__					=      torch.cat((d[k], t[k])	,      0  )
																							else:
																													__magic_name__					=      t

																	__magic_name__					=      torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'  )  )
																	__magic_name__					=      torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'  )  )

											elif isinstance(__lowerCAmelCase	,      __lowerCAmelCase  ) and all(isinstance(_x	,      __lowerCAmelCase  ) for _x in x  ):
																	__magic_name__					=      self.tokenizer(
																	    __lowerCAmelCase	,      padding='max_length'	,      max_length=3_8_4	,      truncation=__lowerCAmelCase	,      return_tensors='pt'	,      )

											else:
																	raise Exception(
																	    'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
																	    ' strings` for supports are supported.'  )

											return d







					def 	a			(						self       :						int	,      _lowerCAmelCase       :						Optional[int]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						Tuple	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Tuple=0.70  )							->					List[Any]:



											'''simple docstring'''





											__magic_name__					=      []
											for idx in range(len(W_query['input_ids']  )  ):
																	__magic_name__					=      __magic_name__					=      range(p_start.shape[1]  )

																	__magic_name__					=      []
																	for start_id in start_indexes:
																							for end_id in end_indexes:
																													if start_id < end_id:
																																			output.append(
																																			    (
																																			        start_id,
																																			        end_id,
																																			        p_start[idx][start_id].item(),
																																			        p_end[idx][end_id].item(),
																																			    )  )

																	output.sort(key=lambda _lowerCAmelCase  : (tup[2] * tup[3])	,      reverse=__lowerCAmelCase  )
																	__magic_name__					=      []
																	for k in range(len(__lowerCAmelCase  )  ):
																							if output[k][2] * output[k][3] >= thresh:
																													__magic_name__		,      __magic_name__					=      output[k][0], output[k][1]
																													__magic_name__					=      self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]  )
																													temp.append((decoded, output[k][2] * output[k][3])  )

																	final_outputs.append(__lowerCAmelCase  )

											return final_outputs








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 	_UpperCAmelCase						( _lowercase			,							_lowercase			,							unittest.TestCase       ):
					__A        = UNetaDModel
					__A        = "sample"




					@property
					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						4
							UpperCamelCase__								=						14
							UpperCamelCase__								=						16

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

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




					@property
					def 						snake_case_		(    self							)    ->	Optional[int]:
							return (4, 14, 16)




					@property
					def 						snake_case_		(    self							)    ->	Any:
							return (4, 14, 16)




					def 						snake_case_		(    self							)    ->	Any:
							pass




					def 						snake_case_		(    self							)    ->	Tuple:
							pass




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




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




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							super().test_from_save_pretrained()




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




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Optional[int]:
							super().test_model_from_pretrained()




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




					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__								=						{
							    """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""",
							}
							UpperCamelCase__								=						self.dummy_input
							return init_dict, inputs_dict




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

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

							with torch.no_grad():
									UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase							).sample.permute(0     ,   2     ,   1							)

							UpperCamelCase__								=						output[0, -3:, -3:].flatten()
							# fmt: off
							UpperCamelCase__								=						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 						snake_case_		(    self							)    ->	int:
							# Not implemented yet for this UNet
							pass




					@slow
					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						"""harmonai/maestro-150k"""
							UpperCamelCase__								=						UNetaDModel.from_pretrained(__lowerCAmelCase     ,   subfolder="""unet"""							)
							model.to(__lowerCAmelCase							)

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

							with torch.no_grad():
									UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase							).sample

							UpperCamelCase__								=						output.abs().sum()
							UpperCamelCase__								=						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 	_UpperCAmelCase						( _lowercase			,							_lowercase			,							unittest.TestCase       ):
					__A        = UNetaDModel
					__A        = "sample"




					@property
					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						4
							UpperCamelCase__								=						14
							UpperCamelCase__								=						16

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

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




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return (4, 14, 16)




					@property
					def 						snake_case_		(    self							)    ->	List[Any]:
							return (4, 14, 1)




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




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Optional[int]:
							super().test_outputs_equivalence()




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Optional[int]:
							super().test_from_save_pretrained()




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							super().test_from_save_pretrained_variant()




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							super().test_model_from_pretrained()




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	List[str]:
							# UNetRL is a value-function is different output shape
							UpperCamelCase__	,      UpperCamelCase__								=						self.prepare_init_args_and_inputs_for_common()
							UpperCamelCase__								=						self.model_class(**__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()

							with torch.no_grad():
									UpperCamelCase__								=						model(**__lowerCAmelCase							)

									if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							):
											UpperCamelCase__								=						output.sample

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




					def 						snake_case_		(    self							)    ->	Optional[int]:
							pass




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							pass




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						{
							    """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""",
							}
							UpperCamelCase__								=						self.dummy_input
							return init_dict, inputs_dict




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

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

							with torch.no_grad():
									UpperCamelCase__								=						value_function(__lowerCAmelCase     ,   __lowerCAmelCase							).sample

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




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							# Not implemented yet for this UNet
							pass





