
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class 				UpperCAmelCase :




	'''simple docstring'''





	@property
	def 							UpperCamelCase_    (			self   :					str					):
					return self.get_dummy_input()






	@property
	def 							UpperCamelCase_    (			self   :					int					):
					if self.block_type == "down":
									return (4, 32, 16, 16)
					elif self.block_type == "mid":
									return (4, 32, 32, 32)
					elif self.block_type == "up":
									return (4, 32, 64, 64)

					raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''					)






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					Optional[Any]=True				,A   :					Optional[Any]=False				,A   :					List[str]=False				,A   :					Optional[Any]=False				,):
					__A       =					4
					__A       =					32
					__A       =					(32, 32)

					__A       =					torch.manual_seed(0					)
					__A       =					torch.device(A					)
					__A       =					(batch_size, num_channels) + sizes
					__A       =					randn_tensor(A				,generator=A				,device=A					)
					__A       =					{"hidden_states": hidden_states}

					if include_temb:
									__A       =					1_28
									__A       =					randn_tensor((batch_size, temb_channels)				,generator=A				,device=A					)

					if include_res_hidden_states_tuple:
									__A       =					torch.manual_seed(1					)
									__A       =					(randn_tensor(A				,generator=A				,device=A					),)

					if include_encoder_hidden_states:
									__A       =					floats_tensor((batch_size, 32, 32)					).to(A					)

					if include_skip_sample:
									__A       =					randn_tensor(((batch_size, 3) + sizes)				,generator=A				,device=A					)

					return dummy_input






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					{
					    "in_channels": 32,
					    "out_channels": 32,
					    "temb_channels": 1_28,
					}
					if self.block_type == "up":
									__A       =					32

					if self.block_type == "mid":
									init_dict.pop("out_channels"					)

					__A       =					self.dummy_input
					return init_dict, inputs_dict






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					Tuple					):
					__A     ,				__A       =					self.prepare_init_args_and_inputs_for_common()
					__A       =					self.block_class(**A					)
					unet_block.to(A					)
					unet_block.eval()

					with torch.no_grad():
									__A       =					unet_block(**A					)

					if isinstance(A				,A					):
									__A       =					output[0]

					self.assertEqual(output.shape				,self.output_shape					)

					__A       =					output[0, -1, -3:, -3:]
					__A       =					torch.tensor(A					).to(A					)
					assert torch_all_close(output_slice.flatten()				,A				,atol=5E-3					)







	@unittest.skipIf(torch_device == "mps"				,"Training is not supported in mps"					)
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A     ,				__A       =					self.prepare_init_args_and_inputs_for_common()
					__A       =					self.block_class(**A					)
					model.to(A					)
					model.train()
					__A       =					model(**A					)

					if isinstance(A				,A					):
									__A       =					output[0]

					__A       =					torch.device(A					)
					__A       =					randn_tensor(output.shape				,device=A					)
					__A       =					torch.nn.functional.mse_loss(A				,A					)
					loss.backward()






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCamelCase__ :   List[Any]						=							pytest.mark.integration





@pytest.mark.parametrize('path'  ,							['paws', 'csv']  )
def        __UpperCAmelCase						(							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  )       ->  Any:







			"""simple docstring"""





			inspect_dataset(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'  )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'  )
@pytest.mark.parametrize('path'  ,							['accuracy']  )
def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  Optional[Any]:







			"""simple docstring"""





			inspect_metric(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@pytest.mark.parametrize(
    'path, config_name, expected_splits'  ,							[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )
			assert info.config_name == config_name
			assert list(info.splits.keys()  ) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'  ,							[
        ('paws', None, ValueError),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     int  )       ->  List[Any]:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )





@pytest.mark.parametrize(
    'path, expected'  ,							[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     List[Any]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_config_names(lowerCamelCase_  )
			assert expected in config_names





@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'  ,							[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Union[str, Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_infos(lowerCamelCase_  )
			assert list(infos.keys()  ) == expected_configs
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      expected_configs[0]
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Any					  =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()  ) == expected_splits_in_first_config





@pytest.mark.parametrize(
    'path, expected_config, expected_splits'  ,							[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      get_dataset_infos(lowerCamelCase_  )
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Tuple					  =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()  ) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'  ,							[
        ('paws', None, ValueError),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_split_names(lowerCamelCase_  ,							config_name=lowerCamelCase_  )



