
import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						List[Any]         =							(VQDiffusionScheduler,)





							def       a__			(			self  :       List[Any]					,			**_UpperCAmelCase  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= {
											    'num_vec_classes': 40_97,
											    'num_train_timesteps': 1_00,
											}

											config.update(**_UpperCAmelCase    )
											return config





							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       Any    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= 4
											__lowercase								= 8
											__lowercase								= 8

											__lowercase								= torch.randint(0					,			_UpperCAmelCase					,			(batch_size, height * width)    )

											return sample





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



											"""simple docstring"""


											assert False





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       Union[str, Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											def model(_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Tuple					,			*_UpperCAmelCase  :       Any    ):
															__lowercase		, __lowercase								= sample.shape
															__lowercase								= torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)    )
															__lowercase								= F.log_softmax(logits.double()					,			dim=1    ).float()
															return return_value

											return model





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											for timesteps in [2, 5, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=_UpperCAmelCase    )





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



											"""simple docstring"""


											for num_vec_classes in [5, 1_00, 10_00, 40_00]:
															self.check_over_configs(num_vec_classes=_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											for t in [0, 50, 99]:
															self.check_over_forward(time_step=_UpperCAmelCase    )





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



											"""simple docstring"""


											pass







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class      A__       (						unittest.TestCase   ,						lowerCAmelCase__    ):





							def       a__			(			self  :       Any    )						->		Dict:



											"""simple docstring"""


											__lowercase								= load_tool('translation'    )
											self.tool.setup()
											__lowercase								= load_tool('translation'					,			remote=_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.remote_tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Union[str, Any]    )						->		Tuple:



											"""simple docstring"""


											__lowercase								= self.tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.remote_tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											# There is a single output
											if len(self.tool.outputs    ) == 1:
															__lowercase								= [outputs]

											self.assertListEqual(output_types(_UpperCAmelCase    )					,			self.tool.outputs    )





							def       a__			(			self  :       List[str]    )						->		Any:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )

											for output, output_type in zip(_UpperCAmelCase					,			self.tool.outputs    ):
															__lowercase								= AGENT_TYPE_MAPPING[output_type]
															self.assertTrue(isinstance(_UpperCAmelCase					,			_UpperCAmelCase    )    )





							def       a__			(			self  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']

											__lowercase								= []

											for _input, input_type in zip(_UpperCAmelCase					,			self.tool.inputs    ):
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
															else:
																			_inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )





