


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       A    :


							@property
							def 			_A       (self					):
									return self.get_dummy_input()


							@property
							def 			_A       (self					):
									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 			_A       (self      ,				lowerCAmelCase=True      ,				lowerCAmelCase=False      ,				lowerCAmelCase=False      ,				lowerCAmelCase=False      ,				):
									__lowercase=			4
									__lowercase=			3_2
									__lowercase=			(3_2, 3_2)

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

									if include_temb:
											__lowercase=			1_2_8
											__lowercase=			randn_tensor((batch_size, temb_channels)      ,				generator=lowerCAmelCase      ,				device=lowerCAmelCase					)

									if include_res_hidden_states_tuple:
											__lowercase=			torch.manual_seed(1					)
											__lowercase=			(randn_tensor(lowerCAmelCase      ,				generator=lowerCAmelCase      ,				device=lowerCAmelCase					),)

									if include_encoder_hidden_states:
											__lowercase=			floats_tensor((batch_size, 3_2, 3_2)					).to(lowerCAmelCase					)

									if include_skip_sample:
											__lowercase=			randn_tensor(((batch_size, 3) + sizes)      ,				generator=lowerCAmelCase      ,				device=lowerCAmelCase					)

									return dummy_input


							def 			_A       (self					):
									__lowercase=			{
									    'in_channels': 3_2,
									    'out_channels': 3_2,
									    'temb_channels': 1_2_8,
									}
									if self.block_type == "up":
											__lowercase=			3_2

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

									__lowercase=			self.dummy_input
									return init_dict, inputs_dict


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase,							__lowercase=			self.prepare_init_args_and_inputs_for_common()
									__lowercase=			self.block_class(**lowerCAmelCase					)
									unet_block.to(lowerCAmelCase					)
									unet_block.eval()

									with torch.no_grad():
											__lowercase=			unet_block(**lowerCAmelCase					)

									if isinstance(lowerCAmelCase      ,				lowerCAmelCase					):
											__lowercase=			output[0]

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

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







							@unittest.skipIf(torch_device == 'mps'      ,				'Training is not supported in mps'					)
							def 			_A       (self					):
									__lowercase,							__lowercase=			self.prepare_init_args_and_inputs_for_common()
									__lowercase=			self.block_class(**lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.train()
									__lowercase=			model(**lowerCAmelCase					)

									if isinstance(lowerCAmelCase      ,				lowerCAmelCase					):
											__lowercase=			output[0]

									__lowercase=			torch.device(lowerCAmelCase					)
									__lowercase=			randn_tensor(output.shape      ,				device=lowerCAmelCase					)
									__lowercase=			torch.nn.functional.mse_loss(lowerCAmelCase      ,				lowerCAmelCase					)
									loss.backward()










from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

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






