


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


UpperCamelCase					:       Any			    =     logging.get_logger(__name__)

UpperCamelCase					:       Optional[Any]			    =     {
    """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			:  str				,				lowerCamelCase__			:  int=None				,				**lowerCamelCase__			:  Tuple       ):
        logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."       )
        a__	:  Optional[int]     =   model
        a__	:  str     =   kwargs.get("model_save_dir"				,				lowerCamelCase__       )
        a__	:  List[str]     =   kwargs.get("latest_model_name"				,				lowerCamelCase__       )

  def __call__( self			:  Tuple				,				**lowerCamelCase__			:  Tuple       ):
        a__	:  Union[str, Any]     =   {k: np.array(lowerCamelCase__       ) for k, v in kwargs.items()}
        return self.model.run(lowerCamelCase__				,				lowerCamelCase__       )

  @staticmethod
  def    _UpperCamelCase( lowerCamelCase__			:  Union[str, Path]				,				lowerCamelCase__			:  List[Any]=None				,				lowerCamelCase__			:  Optional[Any]=None       ):
        if provider is None:
              logger.info("No onnxruntime provider specified, using CPUExecutionProvider"       )
              a__	:  List[Any]     =   "CPUExecutionProvider"

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

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  Union[str, Path]				,				lowerCamelCase__			:  Optional[str] = None				,				**lowerCamelCase__			:  Tuple       ):
        a__	:  Union[str, Any]     =   file_name if file_name is not None else ONNX_WEIGHTS_NAME

        a__	:  Union[str, Any]     =   self.model_save_dir.joinpath(self.latest_model_name       )
        a__	:  Optional[int]     =   Path(lowerCamelCase__       ).joinpath(lowerCamelCase__       )
        try:
              shutil.copyfile(lowerCamelCase__				,				lowerCamelCase__       )
        except shutil.SameFileError:
              pass

        # copy external weights (for models >2GB)
        a__	:  Optional[int]     =   self.model_save_dir.joinpath(lowerCamelCase__       )
        if src_path.exists():
              a__	:  Union[str, Any]     =   Path(lowerCamelCase__       ).joinpath(lowerCamelCase__       )
              try:
                    shutil.copyfile(lowerCamelCase__				,				lowerCamelCase__       )
              except shutil.SameFileError:
                    pass

  def    _UpperCamelCase( self			:  Optional[Any]				,				lowerCamelCase__			:  Union[str, os.PathLike]				,				**lowerCamelCase__			:  Optional[Any]				,				):
        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			:  List[Any]				,				lowerCamelCase__			:  Union[str, Path]				,				lowerCamelCase__			:  Optional[Union[bool, str, None]] = None				,				lowerCamelCase__			:  Optional[Union[str, None]] = None				,				lowerCamelCase__			:  bool = False				,				lowerCamelCase__			:  Optional[str] = None				,				lowerCamelCase__			:  Optional[str] = None				,				lowerCamelCase__			:  Optional[str] = None				,				lowerCamelCase__			:  Optional["ort.SessionOptions"] = None				,				**lowerCamelCase__			:  Union[str, Any]				,				):
        a__	:  int     =   file_name if file_name is not None else ONNX_WEIGHTS_NAME
        # load model from local directory
        if os.path.isdir(lowerCamelCase__       ):
              a__	:  Optional[Any]     =   OnnxRuntimeModel.load_model(
                  os.path.join(lowerCamelCase__				,				lowerCamelCase__       )				,				provider=lowerCamelCase__				,				sess_options=lowerCamelCase__       )
              a__	:  List[Any]     =   Path(lowerCamelCase__       )
        # load model from hub
        else:
              # download model
              a__	:  Any     =   hf_hub_download(
                  repo_id=lowerCamelCase__				,				filename=lowerCamelCase__				,				use_auth_token=lowerCamelCase__				,				revision=lowerCamelCase__				,				cache_dir=lowerCamelCase__				,				force_download=lowerCamelCase__				,				)
              a__	:  Optional[int]     =   Path(lowerCamelCase__       ).parent
              a__	:  Union[str, Any]     =   Path(lowerCamelCase__       ).name
              a__	:  Union[str, Any]     =   OnnxRuntimeModel.load_model(lowerCamelCase__				,				provider=lowerCamelCase__				,				sess_options=lowerCamelCase__       )
        return cls(model=lowerCamelCase__				,				**lowerCamelCase__       )

  @classmethod
  def    _UpperCamelCase( cls			:  Dict				,				lowerCamelCase__			:  Union[str, Path]				,				lowerCamelCase__			:  bool = True				,				lowerCamelCase__			:  Optional[str] = None				,				lowerCamelCase__			:  Optional[str] = None				,				**lowerCamelCase__			:  List[str]				,				):
        a__	:  List[str]     =   None
        if len(str(lowerCamelCase__       ).split("@"       )       ) == 2:
              a__,	a__	:  Optional[Any]     =   model_id.split("@"       )

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



"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'ViTImageProcessor'
		lowercase__         = ('CLIPTokenizer', 'CLIPTokenizerFast')



		def __init__(   self							,						__a=None							,						__a=None							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,						__a							,						)
												_UpperCamelCase									=    kwargs.pop('''feature_extractor''')

							_UpperCamelCase									=    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.''')
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.''')

							super().__init__(__a							,						__a)



		def __call__(   self							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						**__a)			->			Tuple:






							'''simple docstring'''
							if text is None and visual_prompt is None and images is None:
												raise ValueError('''You have to specify either text, visual prompt or images.''')

							if text is not None and visual_prompt is not None:
												raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')

							if text is not None:
												_UpperCamelCase									=    self.tokenizer(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if images is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None and images is not None:
												_UpperCamelCase									=    {
												    '''pixel_values''': image_features.pixel_values,
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							elif text is not None and images is not None:
												_UpperCamelCase									=    image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							elif visual_prompt is not None:
												_UpperCamelCase									=    {
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							else:
												return BatchEncoding(data=dict(**__a)							,						tensor_type=__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,						__a							,						)
							return self.image_processor_class




		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,						__a							,						)
							return self.image_processor






