

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 	snake_case_   :







			'''simple docstring'''

			@property
			def 				snake_case__(     self :				List[Any]							)      ->List[Any]:
									return self.get_dummy_input()







			@property
			def 				snake_case__(     self :				Optional[int]							)      ->Optional[Any]:
									if self.block_type == "down":
															return (4, 3_2, 1_6, 1_6)
									elif self.block_type == "mid":
															return (4, 3_2, 3_2, 3_2)
									elif self.block_type == "up":
															return (4, 3_2, 6_4, 6_4)

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







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				int=True							,   _UpperCamelCase :				int=False							,   _UpperCamelCase :				List[str]=False							,   _UpperCamelCase :				List[Any]=False							,   )      ->Optional[int]:
									snake_case_   =	4
									snake_case_   =	3_2
									snake_case_   =	(3_2, 3_2)

									snake_case_   =	torch.manual_seed(0							)
									snake_case_   =	torch.device(_UpperCamelCase							)
									snake_case_   =	(batch_size, num_channels) + sizes
									snake_case_   =	randn_tensor(_UpperCamelCase							,   generator=_UpperCamelCase							,   device=_UpperCamelCase							)
									snake_case_   =	{'''hidden_states''': hidden_states}

									if include_temb:
															snake_case_   =	1_2_8
															snake_case_   =	randn_tensor((batch_size, temb_channels)							,   generator=_UpperCamelCase							,   device=_UpperCamelCase							)

									if include_res_hidden_states_tuple:
															snake_case_   =	torch.manual_seed(1							)
															snake_case_   =	(randn_tensor(_UpperCamelCase							,   generator=_UpperCamelCase							,   device=_UpperCamelCase							),)

									if include_encoder_hidden_states:
															snake_case_   =	floats_tensor((batch_size, 3_2, 3_2)							).to(_UpperCamelCase							)

									if include_skip_sample:
															snake_case_   =	randn_tensor(((batch_size, 3) + sizes)							,   generator=_UpperCamelCase							,   device=_UpperCamelCase							)

									return dummy_input







			def 				snake_case__(     self :				Dict							)      ->Optional[int]:
									snake_case_   =	{
									    '''in_channels''': 3_2,
									    '''out_channels''': 3_2,
									    '''temb_channels''': 1_2_8,
									}
									if self.block_type == "up":
															snake_case_   =	3_2

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

									snake_case_   =	self.dummy_input
									return init_dict, inputs_dict







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Union[str, Any]							)      ->str:
									snake_case_,    snake_case_   =	self.prepare_init_args_and_inputs_for_common()
									snake_case_   =	self.block_class(**_UpperCamelCase							)
									unet_block.to(_UpperCamelCase							)
									unet_block.eval()

									with torch.no_grad():
															snake_case_   =	unet_block(**_UpperCamelCase							)

									if isinstance(_UpperCamelCase							,   _UpperCamelCase							):
															snake_case_   =	output[0]

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

									snake_case_   =	output[0, -1, -3:, -3:]
									snake_case_   =	torch.tensor(_UpperCamelCase							).to(_UpperCamelCase							)
									assert torch_all_close(output_slice.flatten()							,   _UpperCamelCase							,   atol=5e-3							)







			@unittest.skipIf(torch_device == '''mps'''							,   '''Training is not supported in mps'''							)
			def 				snake_case__(     self :				Any							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.prepare_init_args_and_inputs_for_common()
									snake_case_   =	self.block_class(**_UpperCamelCase							)
									model.to(_UpperCamelCase							)
									model.train()
									snake_case_   =	model(**_UpperCamelCase							)

									if isinstance(_UpperCamelCase							,   _UpperCamelCase							):
															snake_case_   =	output[0]

									snake_case_   =	torch.device(_UpperCamelCase							)
									snake_case_   =	randn_tensor(output.shape							,   device=_UpperCamelCase							)
									snake_case_   =	torch.nn.functional.mse_loss(_UpperCamelCase							,   _UpperCamelCase							)
									loss.backward()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)