
"""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    __lowerCamelCase     (   lowerCAmelCase	,  lowerCAmelCase	,  unittest.TestCase ):
				a__:			str									=						UNetaDModel
				a__:			Any									=						'sample'




				@property
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     4
							lowerCamelCase_ 		=     14
							lowerCamelCase_ 		=     16

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

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




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




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




				def 					UpperCAmelCase__	(							self      ):
							pass




				def 					UpperCAmelCase__	(							self      ):
							pass




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




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




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




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




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




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




				def 					UpperCAmelCase__	(							self      ):
							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 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     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      )
							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 					UpperCAmelCase__	(							self      ):
							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(UpperCAmelCase       ,	UpperCAmelCase      ).sample.permute(0       ,	2       ,	1      )

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





				@slow
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     '''harmonai/maestro-150k'''
							lowerCamelCase_ 		=     UNetaDModel.from_pretrained(UpperCAmelCase       ,	subfolder='''unet'''      )
							model.to(UpperCAmelCase      )

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

							with torch.no_grad():
										lowerCamelCase_ 		=     model(UpperCAmelCase       ,	UpperCAmelCase      ).sample

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

							assert (output_sum - 224.0896).abs() < 0.5
							assert (output_max - 0.0_6_0_7).abs() < 4e-4




class    __lowerCamelCase     (   lowerCAmelCase	,  lowerCAmelCase	,  unittest.TestCase ):
				a__:			List[str]									=						UNetaDModel
				a__:			Any									=						'sample'




				@property
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     4
							lowerCamelCase_ 		=     14
							lowerCamelCase_ 		=     16

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

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




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




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




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




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




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




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




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




				@unittest.skipIf(torch_device == '''mps'''       ,	'''mish op not supported in MPS'''      )
				def 					UpperCAmelCase__	(							self      ):
							# UNetRL is a value-function is different output shape
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.prepare_init_args_and_inputs_for_common()
							lowerCamelCase_ 		=     self.model_class(**UpperCAmelCase      )
							model.to(UpperCAmelCase      )
							model.eval()

							with torch.no_grad():
										lowerCamelCase_ 		=     model(**UpperCAmelCase      )

										if isinstance(UpperCAmelCase       ,	UpperCAmelCase      ):
													lowerCamelCase_ 		=     output.sample

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




				def 					UpperCAmelCase__	(							self      ):
							pass




				def 					UpperCAmelCase__	(							self      ):
							pass




				def 					UpperCAmelCase__	(							self      ):
							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 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     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      )
							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 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     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      )

							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(UpperCAmelCase       ,	UpperCAmelCase      ).sample

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





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





"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
						A_						       =				[]

						A_						       =				os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
						A_						       =				glob.glob(pattern)
						# TODO: deal with TF/Flax too
						A_						       =				[
						    x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
						]

						for test_file in test_files:
												A_						       =				get_tester_classes(test_file)
												for tester_class in tester_classes:
																		# A few tester classes don't have `parent` parameter in `__init__`.
																		# TODO: deal this better
																		try:
																								A_						       =				tester_class(parent=None)
																		except Exception:
																								continue
																		if hasattr(tester, """get_config"""):
																								A_						       =				tester.get_config()
																								for k, v in config.to_dict().items():
																														if isinstance(v, int):
																																				A_						       =				None
																																				if k in ["vocab_size"]:
																																										A_						       =				100
																																				elif k in ["max_position_embeddings"]:
																																										A_						       =				128
																																				elif k in ["hidden_size", "d_model"]:
																																										A_						       =				40
																																				elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																										A_						       =				5
																																				if target is not None and v > target:
																																										failures.append(
																																										    f"{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"
																																										    f" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"
																																										    f" than {target}."
																																										)

						if len(failures) > 0:
												raise Exception(f"There were {len(failures)} failures:\n" + """\n""".join(failures))



