'''simple docstring'''







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 _SCREAMING_SNAKE_CASE:




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




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				Any:
													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      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       str=True							,							UpperCamelCase_ :       Union[str, Any]=False							,							UpperCamelCase_ :       Dict=False							,							UpperCamelCase_ :       Tuple=False							,							)     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 4
													SCREAMING_SNAKE_CASE__     :List[Any]						    = 32
													SCREAMING_SNAKE_CASE__     :Dict						    = (32, 32)

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :str						    = torch.device(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = (batch_size, num_channels) + sizes
													SCREAMING_SNAKE_CASE__     :List[str]						    = randn_tensor(UpperCamelCase_							,							generator=UpperCamelCase_							,							device=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = {'hidden_states': hidden_states}

													if include_temb:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 1_28
																				SCREAMING_SNAKE_CASE__     :List[str]						    = randn_tensor((batch_size, temb_channels)							,							generator=UpperCamelCase_							,							device=UpperCamelCase_      )

													if include_res_hidden_states_tuple:
																				SCREAMING_SNAKE_CASE__     :str						    = torch.manual_seed(1      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = (randn_tensor(UpperCamelCase_							,							generator=UpperCamelCase_							,							device=UpperCamelCase_      ),)

													if include_encoder_hidden_states:
																				SCREAMING_SNAKE_CASE__     :str						    = floats_tensor((batch_size, 32, 32)      ).to(UpperCamelCase_      )

													if include_skip_sample:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = randn_tensor(((batch_size, 3) + sizes)							,							generator=UpperCamelCase_							,							device=UpperCamelCase_      )

													return dummy_input




						def      __lowerCamelCase       (    self :       str      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :int						    = {
													    'in_channels': 32,
													    'out_channels': 32,
													    'temb_channels': 1_28,
													}
													if self.block_type == "up":
																				SCREAMING_SNAKE_CASE__     :Any						    = 32

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

													SCREAMING_SNAKE_CASE__     :int						    = self.dummy_input
													return init_dict, inputs_dict




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       List[Any]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.prepare_init_args_and_inputs_for_common()
													SCREAMING_SNAKE_CASE__     :str						    = self.block_class(**UpperCamelCase_      )
													unet_block.to(UpperCamelCase_      )
													unet_block.eval()

													with torch.no_grad():
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = unet_block(**UpperCamelCase_      )

													if isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :Dict						    = output[0]

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

													SCREAMING_SNAKE_CASE__     :Dict						    = output[0, -1, -3:, -3:]
													SCREAMING_SNAKE_CASE__     :List[Any]						    = 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      __lowerCamelCase       (    self :       int      )     ->				str:
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Tuple						    = self.prepare_init_args_and_inputs_for_common()
													SCREAMING_SNAKE_CASE__     :str						    = self.block_class(**UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													model.train()
													SCREAMING_SNAKE_CASE__     :Tuple						    = model(**UpperCamelCase_      )

													if isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :Tuple						    = output[0]

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.device(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = randn_tensor(output.shape							,							device=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = torch.nn.functional.mse_loss(UpperCamelCase_							,							UpperCamelCase_      )
													loss.backward()







'''simple docstring'''







from typing import TYPE_CHECKING

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


UpperCamelCase_      	=	{
    '''configuration_blenderbot_small''': [
        '''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotSmallConfig''',
        '''BlenderbotSmallOnnxConfig''',
    ],
    '''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BlenderbotSmallTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BlenderbotSmallForCausalLM''',
		    '''BlenderbotSmallForConditionalGeneration''',
		    '''BlenderbotSmallModel''',
		    '''BlenderbotSmallPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''TFBlenderbotSmallForConditionalGeneration''',
		    '''TFBlenderbotSmallModel''',
		    '''TFBlenderbotSmallPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''FlaxBlenderbotSmallForConditionalGeneration''',
		    '''FlaxBlenderbotSmallModel''',
		    '''FlaxBlenderbotSmallPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

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






