





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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """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 SCREAMING_SNAKE_CASE_  :






						def __init__( self : Union[str, Any]						,				_A : List[Any]=None						,				**_A : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'		)
							snake_case_						:     List[Any]     =		model
							snake_case_						:     Tuple     =		kwargs.get('model_save_dir'						,				_lowerCAmelCase		)
							snake_case_						:     Optional[Any]     =		kwargs.get('latest_model_name'						,				_lowerCAmelCase		)






						def __call__( self : List[Any]						,				**_A : Optional[int]		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     Any     =		{k: np.array(_lowerCAmelCase		) for k, v in kwargs.items()}
							return self.model.run(_lowerCAmelCase						,				_lowerCAmelCase		)






						@staticmethod
						def UpperCAmelCase_			( _A : Union[str, Path]						,				_A : List[Any]=None						,				_A : Any=None		)							->      Any:







							"""simple docstring"""


							if provider is None:
								logger.info('No onnxruntime provider specified, using CPUExecutionProvider'		)
								snake_case_						:     Union[str, Any]     =		"""CPUExecutionProvider"""

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






						def UpperCAmelCase_			( self : Dict						,				_A : Union[str, Path]						,				_A : Optional[str] = None						,				**_A : Union[str, Any]		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Dict     =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

							snake_case_						:     List[Any]     =		self.model_save_dir.joinpath(self.latest_model_name		)
							snake_case_						:     Any     =		Path(_lowerCAmelCase		).joinpath(_lowerCAmelCase		)
							try:
								shutil.copyfile(_lowerCAmelCase						,				_lowerCAmelCase		)
							except shutil.SameFileError:
								pass

							# copy external weights (for models >2GB)
							snake_case_						:     List[str]     =		self.model_save_dir.joinpath(_lowerCAmelCase		)
							if src_path.exists():
								snake_case_						:     List[str]     =		Path(_lowerCAmelCase		).joinpath(_lowerCAmelCase		)
								try:
									shutil.copyfile(_lowerCAmelCase						,				_lowerCAmelCase		)
								except shutil.SameFileError:
									pass






						def UpperCAmelCase_			( self : Optional[int]						,				_A : Union[str, os.PathLike]						,				**_A : Union[str, Any]						,				)							->      Tuple:







							"""simple docstring"""


							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 UpperCAmelCase_			( cls : Optional[Any]						,				_A : Union[str, Path]						,				_A : Optional[Union[bool, str, None]] = None						,				_A : Optional[Union[str, None]] = None						,				_A : bool = False						,				_A : Optional[str] = None						,				_A : Optional[str] = None						,				_A : Optional[str] = None						,				_A : Optional["ort.SessionOptions"] = None						,				**_A : int						,				)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Any     =		file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(_lowerCAmelCase		):
								snake_case_						:     str     =		OnnxRuntimeModel.load_model(
								    os.path.join(_lowerCAmelCase						,				_lowerCAmelCase		)						,				provider=_lowerCAmelCase						,				sess_options=_lowerCAmelCase		)
								snake_case_						:     Dict     =		Path(_lowerCAmelCase		)
							# load model from hub
							else:
								# download model
								snake_case_						:     List[Any]     =		hf_hub_download(
								    repo_id=_lowerCAmelCase						,				filename=_lowerCAmelCase						,				use_auth_token=_lowerCAmelCase						,				revision=_lowerCAmelCase						,				cache_dir=_lowerCAmelCase						,				force_download=_lowerCAmelCase						,				)
								snake_case_						:     int     =		Path(_lowerCAmelCase		).parent
								snake_case_						:     Union[str, Any]     =		Path(_lowerCAmelCase		).name
								snake_case_						:     str     =		OnnxRuntimeModel.load_model(_lowerCAmelCase						,				provider=_lowerCAmelCase						,				sess_options=_lowerCAmelCase		)
							return cls(model=_lowerCAmelCase						,				**_lowerCAmelCase		)






						@classmethod
						def UpperCAmelCase_			( cls : Optional[int]						,				_A : Union[str, Path]						,				_A : bool = True						,				_A : Optional[str] = None						,				_A : Optional[str] = None						,				**_A : int						,				)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		None
							if len(str(_lowerCAmelCase		).split('@'		)		) == 2:
								snake_case_						:     List[Any]     =		model_id.split('@'		)

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








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_   ):






						@register_to_config
						def __init__( self : Optional[Any]						,				*,
						    _A : int = 4						,				_A : int = 768						,				_A : int						,				_A : Tuple						,				)							->      Dict:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     int     =		nn.Parameter(torch.zeros(_A		)		)

							# parameters for additional clip time embeddings
							snake_case_						:     Tuple     =		nn.Linear(_A						,				_A		)
							snake_case_						:     List[Any]     =		nn.Linear(_A						,				_A		)

							# parameters for encoder hidden states
							snake_case_						:     Union[str, Any]     =		clip_extra_context_tokens
							snake_case_						:     str     =		nn.Linear(
							    _A						,				self.clip_extra_context_tokens * cross_attention_dim		)
							snake_case_						:     Any     =		nn.Linear(_A						,				_A		)
							snake_case_						:     Tuple     =		nn.LayerNorm(_A		)






						def UpperCAmelCase_			( self : List[str]						,				*, _A : Tuple						,				_A : List[Any]						,				_A : str						,				_A : Optional[Any]		)							->      List[Any]:







							"""simple docstring"""


							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								snake_case_						:     Optional[int]     =		image_embeddings.shape[0]
								snake_case_						:     Optional[Any]     =		self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
								snake_case_						:     Optional[Any]     =		classifier_free_guidance_embeddings.expand(
								    _A						,				-1		)
								snake_case_						:     Any     =		torch.cat([classifier_free_guidance_embeddings, image_embeddings]						,				dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							snake_case_						:     str     =		prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							snake_case_						:     str     =		self.embedding_proj(_A		)
							snake_case_						:     Dict     =		self.clip_image_embeddings_project_to_time_embeddings(_A		)
							snake_case_						:     Tuple     =		time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							snake_case_						:     List[str]     =		self.clip_extra_context_tokens_proj(_A		)
							snake_case_						:     Optional[Any]     =		clip_extra_context_tokens.reshape(_A						,				-1						,				self.clip_extra_context_tokens		)
							snake_case_						:     int     =		clip_extra_context_tokens.permute(0						,				2						,				1		)

							snake_case_						:     Optional[int]     =		self.encoder_hidden_states_proj(_A		)
							snake_case_						:     Any     =		self.text_encoder_hidden_states_norm(_A		)
							snake_case_						:     Dict     =		torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,				dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings

