


import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}





class       A    :


							def __init__(self      ,				lowerCAmelCase=None      ,				**lowerCAmelCase					):
									logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'					)
									__lowercase=			model
									__lowercase=			kwargs.get('model_save_dir'      ,				lowerCAmelCase					)
									__lowercase=			kwargs.get('latest_model_name'      ,				lowerCAmelCase					)


							def __call__(self      ,				**lowerCAmelCase					):
									__lowercase=			{k: np.array(lowerCAmelCase					) for k, v in kwargs.items()}
									return self.model.run(lowerCAmelCase      ,				lowerCAmelCase					)


							@staticmethod
							def 			_A       (lowerCAmelCase      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None					):
									if provider is None:
											logger.info('No onnxruntime provider specified, using CPUExecutionProvider'					)
											__lowercase=			'CPUExecutionProvider'

									return ort.InferenceSession(lowerCAmelCase      ,				providers=[provider]      ,				sess_options=lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				**lowerCAmelCase					):
									__lowercase=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

									__lowercase=			self.model_save_dir.joinpath(self.latest_model_name					)
									__lowercase=			Path(lowerCAmelCase					).joinpath(lowerCAmelCase					)
									try:
											shutil.copyfile(lowerCAmelCase      ,				lowerCAmelCase					)
									except shutil.SameFileError:
											pass

									# copy external weights (for models >2GB)
									__lowercase=			self.model_save_dir.joinpath(lowerCAmelCase					)
									if src_path.exists():
											__lowercase=			Path(lowerCAmelCase					).joinpath(lowerCAmelCase					)
											try:
													shutil.copyfile(lowerCAmelCase      ,				lowerCAmelCase					)
											except shutil.SameFileError:
													pass


							def 			_A       (self      ,				lowerCAmelCase      ,				**lowerCAmelCase      ,				):
									if os.path.isfile(lowerCAmelCase					):
											logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
											return

									os.makedirs(lowerCAmelCase      ,				exist_ok=lowerCAmelCase					)

									# saving model weights/files
									self._save_pretrained(lowerCAmelCase      ,				**lowerCAmelCase					)


							@classmethod
							def 			_A       (cls      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = False      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									__lowercase=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
									# load model from local directory
									if os.path.isdir(lowerCAmelCase					):
											__lowercase=			OnnxRuntimeModel.load_model(
											    os.path.join(lowerCAmelCase      ,				lowerCAmelCase					)      ,				provider=lowerCAmelCase      ,				sess_options=lowerCAmelCase					)
											__lowercase=			Path(lowerCAmelCase					)
									# load model from hub
									else:
											# download model
											__lowercase=			hf_hub_download(
											    repo_id=lowerCAmelCase      ,				filename=lowerCAmelCase      ,				use_auth_token=lowerCAmelCase      ,				revision=lowerCAmelCase      ,				cache_dir=lowerCAmelCase      ,				force_download=lowerCAmelCase      ,				)
											__lowercase=			Path(lowerCAmelCase					).parent
											__lowercase=			Path(lowerCAmelCase					).name
											__lowercase=			OnnxRuntimeModel.load_model(lowerCAmelCase      ,				provider=lowerCAmelCase      ,				sess_options=lowerCAmelCase					)
									return cls(model=lowerCAmelCase      ,				**lowerCAmelCase					)







							@classmethod
							def 			_A       (cls      ,				lowerCAmelCase      ,				lowerCAmelCase = True      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									__lowercase=			None
									if len(str(lowerCAmelCase					).split('@'					)					) == 2:
											__lowercase,							__lowercase=			model_id.split('@'					)

									return cls._from_pretrained(
									    model_id=lowerCAmelCase      ,				revision=lowerCAmelCase      ,				cache_dir=lowerCAmelCase      ,				force_download=lowerCAmelCase      ,				use_auth_token=lowerCAmelCase      ,				**lowerCAmelCase      ,				)










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					)






