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  (      _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       UNetaDModel
					__lowerCamelCase	 =       """sample"""




					@property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				4
									snake_case__ :     Optional[Any] 							=				14
									snake_case__ :     Dict 							=				16

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

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




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



									'''simple docstring'''







									return (4, 14, 16)




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



									'''simple docstring'''







									return (4, 14, 16)




					def       __a      (       self		)					->  str:



									'''simple docstring'''







									pass




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									pass




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



									'''simple docstring'''







									super().test_determinism()




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



									'''simple docstring'''







									super().test_outputs_equivalence()




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



									'''simple docstring'''







									super().test_from_save_pretrained()




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



									'''simple docstring'''







									super().test_from_save_pretrained_variant()




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



									'''simple docstring'''







									super().test_model_from_pretrained()




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



									'''simple docstring'''







									super().test_output()




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Union[str, Any] 							=				{
									    '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__ :     str 							=				self.dummy_input
									return init_dict, inputs_dict




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



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Union[str, Any] 							=				UNetaDModel.from_pretrained(
									    'bglick13/hopper-medium-v2-value-function-hor32'   ,	output_loading_info=__UpperCamelCase   ,	subfolder='unet'		)
									self.assertIsNotNone(__UpperCamelCase		)
									self.assertEqual(len(loading_info['missing_keys']		)   ,	0		)

									model.to(__UpperCamelCase		)
									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       __a      (       self		)					->  Any:



									'''simple docstring'''







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

									with torch.no_grad():
													snake_case__ :     Optional[Any] 							=				model(__UpperCamelCase   ,	__UpperCamelCase		).sample.permute(0   ,	2   ,	1		)

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




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



									'''simple docstring'''







									pass




					@slow
					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__ :     str 							=				'harmonai/maestro-150k'
									snake_case__ :     Any 							=				UNetaDModel.from_pretrained(__UpperCamelCase   ,	subfolder='unet'		)
									model.to(__UpperCamelCase		)

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

									with torch.no_grad():
													snake_case__ :     Union[str, Any] 							=				model(__UpperCamelCase   ,	__UpperCamelCase		).sample

									snake_case__ :     Union[str, Any] 							=				output.abs().sum()
									snake_case__ :     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 			__snake_case  (      _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       UNetaDModel
					__lowerCamelCase	 =       """sample"""




					@property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__ :     Union[str, Any] 							=				4
									snake_case__ :     Optional[int] 							=				14
									snake_case__ :     Optional[int] 							=				16

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

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




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



									'''simple docstring'''







									return (4, 14, 16)




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



									'''simple docstring'''







									return (4, 14, 1)




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



									'''simple docstring'''







									super().test_determinism()




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



									'''simple docstring'''







									super().test_outputs_equivalence()




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



									'''simple docstring'''







									super().test_from_save_pretrained()




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



									'''simple docstring'''







									super().test_from_save_pretrained_variant()




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



									'''simple docstring'''







									super().test_model_from_pretrained()




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



									'''simple docstring'''







									snake_case__							,  snake_case__ :     str 							=				self.prepare_init_args_and_inputs_for_common()
									snake_case__ :     str 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									model.eval()

									with torch.no_grad():
													snake_case__ :     int 							=				model(**__UpperCamelCase		)

													if isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
																	snake_case__ :     List[Any] 							=				output.sample

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




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									pass




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									pass




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				{
									    '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__ :     Optional[int] 							=				self.dummy_input
									return init_dict, inputs_dict




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



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Union[str, Any] 							=				UNetaDModel.from_pretrained(
									    'bglick13/hopper-medium-v2-value-function-hor32'   ,	output_loading_info=__UpperCamelCase   ,	subfolder='value_function'		)
									self.assertIsNotNone(__UpperCamelCase		)
									self.assertEqual(len(vf_loading_info['missing_keys']		)   ,	0		)

									value_function.to(__UpperCamelCase		)
									snake_case__ :     Optional[Any] 							=				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       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Optional[Any] 							=				UNetaDModel.from_pretrained(
									    'bglick13/hopper-medium-v2-value-function-hor32'   ,	output_loading_info=__UpperCamelCase   ,	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__ :     Dict 							=				14
									snake_case__ :     Union[str, Any] 							=				torch.randn((1, seq_len, num_features)		).permute(
									    0   ,	2   ,	1		)  # match original, we can update values and remove
									snake_case__ :     Optional[int] 							=				torch.full((num_features,)   ,	0		)

									with torch.no_grad():
													snake_case__ :     Any 							=				value_function(__UpperCamelCase   ,	__UpperCamelCase		).sample

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




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									pass



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


