
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__		:     Optional[int]     			=      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 UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							Union[str, Any]=None			,			**lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''     )
      _A:  Optional[Any]					=       model
      _A:  Optional[int]					=       kwargs.get('''model_save_dir'''			,			lowerCAmelCase_     )
      _A:  int					=       kwargs.get('''latest_model_name'''			,			lowerCAmelCase_     )


   def __call__(					self     :							Optional[Any]			,			**lowerCAmelCase_     :							str     ):

      """simple docstring"""
      _A:  Any					=       {k: np.array(lowerCAmelCase_     ) for k, v in kwargs.items()}
      return self.model.run(lowerCAmelCase_			,			lowerCAmelCase_     )


   @staticmethod
   def  __magic_name__				(					lowerCAmelCase_     :							Union[str, Path]			,			lowerCAmelCase_     :							List[str]=None			,			lowerCAmelCase_     :							List[str]=None     ):

      """simple docstring"""
      if provider is None:
         logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''     )
         _A:  List[str]					=       '''CPUExecutionProvider'''

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


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, Path]			,			lowerCAmelCase_     :							Optional[str] = None			,			**lowerCAmelCase_     :							str     ):

      """simple docstring"""
      _A:  str					=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

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


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Union[str, os.PathLike]			,			**lowerCAmelCase_     :							List[Any]			,			):

      """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  __magic_name__				(					cls     :							List[str]			,			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_     :							Optional[Any]			,			):

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


   @classmethod
   def  __magic_name__				(					cls     :							Tuple			,			lowerCAmelCase_     :							Union[str, Path]			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Optional[str] = None			,			lowerCAmelCase_     :							Optional[str] = None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      _A:  List[str]					=       None
      if len(str(lowerCAmelCase_     ).split('''@'''     )     ) == 2:
         _A			,  _A:  List[str]					=       model_id.split('''@'''     )

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__		:     Dict     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''swin2sr'''

   __UpperCamelCase		:    str 				=    {
       '''hidden_size''': '''embed_dim''',
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							int=6_4			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							Dict=3			,			lowerCAmelCase_     :							Dict=1_8_0			,			lowerCAmelCase_     :							Union[str, Any]=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							Tuple=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							int=8			,			lowerCAmelCase_     :							Any=2.0			,			lowerCAmelCase_     :							Union[str, Any]=True			,			lowerCAmelCase_     :							Dict=0.0			,			lowerCAmelCase_     :							Optional[int]=0.0			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Any="gelu"			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							str=0.02			,			lowerCAmelCase_     :							Optional[Any]=1e-5			,			lowerCAmelCase_     :							int=2			,			lowerCAmelCase_     :							int=1.0			,			lowerCAmelCase_     :							Any="1conv"			,			lowerCAmelCase_     :							List[str]="pixelshuffle"			,			**lowerCAmelCase_     :							str			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[str]					=       image_size
      _A:  Any					=       patch_size
      _A:  Any					=       num_channels
      _A:  Union[str, Any]					=       embed_dim
      _A:  int					=       depths
      _A:  List[Any]					=       len(lowerCAmelCase_     )
      _A:  int					=       num_heads
      _A:  Any					=       window_size
      _A:  Optional[int]					=       mlp_ratio
      _A:  int					=       qkv_bias
      _A:  List[Any]					=       hidden_dropout_prob
      _A:  List[str]					=       attention_probs_dropout_prob
      _A:  List[Any]					=       drop_path_rate
      _A:  Any					=       hidden_act
      _A:  List[str]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  str					=       initializer_range
      _A:  int					=       upscale
      _A:  int					=       img_range
      _A:  Optional[Any]					=       resi_connection
      _A:  int					=       upsampler



