

import copy
import random

from transformers import CLIPTokenizer



class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	*A__   ,	**A__      ):
						super().__init__(*A__   ,	**A__      )
						A__							:   Union[str, Any]					       =		{}






					def   __A      (			self   ,	A__   ,	*A__   ,	**A__      ):
						A__							:   Optional[Any]					       =		super().add_tokens(A__   ,	*A__   ,	**A__      )
						if num_added_tokens == 0:
							raise ValueError(
							    F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
							    """ `placeholder_token` that is not already in the tokenizer."""      )






					def   __A      (			self   ,	A__   ,	*A__   ,	A__=1   ,	**A__      ):
						A__							:   Optional[Any]					       =		[]
						if num_vec_per_token == 1:
							self.try_adding_tokens(A__   ,	*A__   ,	**A__      )
							output.append(A__      )
						else:
							A__							:   int					       =		[]
							for i in range(A__      ):
								A__							:   Tuple					       =		placeholder_token + F"""_{i}"""
								self.try_adding_tokens(A__   ,	*A__   ,	**A__      )
								output.append(A__      )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
						for token in self.token_map:
							if token in placeholder_token:
								raise ValueError(
								    F"""The tokenizer already has placeholder token {token} that can get confused with"""
								    F""" {placeholder_token}keep placeholder tokens independent"""      )
						A__							:   Optional[Any]					       =		output






					def   __A      (			self   ,	A__   ,	A__=False   ,	A__=1.0      ):
						if isinstance(A__   ,	A__      ):
							A__							:   str					       =		[]
							for i in range(len(A__      )      ):
								output.append(self.replace_placeholder_tokens_in_text(text[i]   ,	vector_shuffle=A__      )      )
							return output
						for placeholder_token in self.token_map:
							if placeholder_token in text:
								A__							:   Union[str, Any]					       =		self.token_map[placeholder_token]
								A__							:   int					       =		tokens[: 1 + int(len(A__      ) * prop_tokens_to_load      )]
								if vector_shuffle:
									A__							:   Optional[Any]					       =		copy.copy(A__      )
									random.shuffle(A__      )
								A__							:   Optional[int]					       =		text.replace(A__   ,	""" """.join(A__      )      )
						return text






					def __call__(			self   ,	A__   ,	*A__   ,	A__=False   ,	A__=1.0   ,	**A__      ):
						return super().__call__(
						    self.replace_placeholder_tokens_in_text(
						        A__   ,	vector_shuffle=A__   ,	prop_tokens_to_load=A__      )   ,	*A__   ,	**A__   ,	)






					def   __A      (			self   ,	A__   ,	*A__   ,	A__=False   ,	A__=1.0   ,	**A__      ):
						return super().encode(
						    self.replace_placeholder_tokens_in_text(
						        A__   ,	vector_shuffle=A__   ,	prop_tokens_to_load=A__      )   ,	*A__   ,	**A__   ,	)






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


A_	:			Union[str, Any]  	=      logging.get_logger(__name__)

A_	:			str  	=      {
    '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						:


					'''simple docstring'''






					def __init__(			self   ,	A__=None   ,	**A__      ):
						logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""      )
						A__							:   Dict					       =		model
						A__							:   Any					       =		kwargs.get("""model_save_dir"""   ,	A__      )
						A__							:   Optional[int]					       =		kwargs.get("""latest_model_name"""   ,	A__      )






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






					@staticmethod
					def   __A      (			A__   ,	A__=None   ,	A__=None      ):
						if provider is None:
							logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""      )
							A__							:   List[Any]					       =		"""CPUExecutionProvider"""

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






					def   __A      (			self   ,	A__   ,	A__ = None   ,	**A__      ):
						A__							:   List[Any]					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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






					def   __A      (			self   ,	A__   ,	**A__   ,	):
						if os.path.isfile(A__      ):
							logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
							return

						os.makedirs(A__   ,	exist_ok=A__      )

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






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






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[Any]					       =		None
						if len(str(A__      ).split("""@"""      )      ) == 2:
							A__       ,       A__							:   Union[str, Any]					       =		model_id.split("""@"""      )

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



