



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__     :




    '''simple docstring'''






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






    @property
    def 			_lowerCAmelCase					(       self		):
        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,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=False,		):
        A :       int          =   4
        A :       Tuple          =   32
        A :       str          =   (32, 32)

        A :       str          =   torch.manual_seed(0		)
        A :       List[Any]          =   torch.device(lowerCamelCase__		)
        A :       List[str]          =   (batch_size, num_channels) + sizes
        A :       Union[str, Any]          =   randn_tensor(lowerCamelCase__,		generator=lowerCamelCase__,		device=lowerCamelCase__		)
        A :       Optional[Any]          =   {"""hidden_states""": hidden_states}

        if include_temb:
            A :       Tuple          =   128
            A :       List[Any]          =   randn_tensor((batch_size, temb_channels),		generator=lowerCamelCase__,		device=lowerCamelCase__		)

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

        if include_encoder_hidden_states:
            A :       Union[str, Any]          =   floats_tensor((batch_size, 32, 32)		).to(lowerCamelCase__		)

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

        return dummy_input






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   {
            """in_channels""": 32,
            """out_channels""": 32,
            """temb_channels""": 128,
        }
        if self.block_type == "up":
            A :       List[str]          =   32

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

        A :       Optional[Any]          =   self.dummy_input
        return init_dict, inputs_dict






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A     ,							A :       str          =   self.prepare_init_args_and_inputs_for_common()
        A :       Optional[int]          =   self.block_class(**lowerCamelCase__		)
        unet_block.to(lowerCamelCase__		)
        unet_block.eval()

        with torch.no_grad():
            A :       str          =   unet_block(**lowerCamelCase__		)

        if isinstance(lowerCamelCase__,		lowerCamelCase__		):
            A :       List[str]          =   output[0]

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

        A :       int          =   output[0, -1, -3:, -3:]
        A :       Dict          =   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 			_lowerCAmelCase					(       self		):
        A     ,							A :       List[Any]          =   self.prepare_init_args_and_inputs_for_common()
        A :       List[str]          =   self.block_class(**lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.train()
        A :       List[str]          =   model(**lowerCamelCase__		)

        if isinstance(lowerCamelCase__,		lowerCamelCase__		):
            A :       Union[str, Any]          =   output[0]

        A :       Dict          =   torch.device(lowerCamelCase__		)
        A :       Dict          =   randn_tensor(output.shape,		device=lowerCamelCase__		)
        A :       Optional[Any]          =   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__)






