

'''simple docstring'''



import os

import pytest

from transformers.dynamic_module_utils import get_imports


__snake_case :	Optional[Any]      						=			'\nimport os\n'

__snake_case :	Optional[Any]      						=			'\ndef foo():\n    import os\n    return False\n'

__snake_case :	Optional[int]      						=			'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

__snake_case :	List[str]      						=			'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

__snake_case :	Optional[Any]      						=			'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

__snake_case :	Dict      						=			'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

__snake_case :	int      						=			'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

__snake_case :	Optional[int]      						=			'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

__snake_case :	List[Any]      						=			'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

__snake_case :	Optional[Any]      						=			'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

__snake_case :	List[Any]      						=			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("""case""",		lowercase__       )
def 						__lowerCamelCase       (   __snake_case     :		Union[str, Any],		__snake_case     :		List[Any]       )       ->							str:






						"""simple docstring"""





						A__     :   Optional[Any]      =os.path.join(lowercase__,		"""test_file.py"""       )
						with open(lowercase__,		"""w"""       ) as _tmp_file:
												_tmp_file.write(lowercase__       )

						A__     :   Tuple      =get_imports(lowercase__       )
						assert parsed_imports == ["os"]





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class       A    (   A_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)
									__lowercase=			VectorQuantizer(lowerCAmelCase      ,				lowerCAmelCase      ,				beta=0.25      ,				remap=lowerCAmelCase      ,				sane_index_shape=lowerCAmelCase					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				norm_type=lowerCAmelCase      ,				)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






