





'''simple docstring'''



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


__magic_name__   :						List[str]    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """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	:



     '''simple docstring'''






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

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

     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       if provider is None:
         logger.info("No onnxruntime provider specified, using CPUExecutionProvider"	)
         _snake_case     		=				"CPUExecutionProvider"

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase	):
       _snake_case     		=				file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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

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






     @classmethod
     def 	UpperCamelCase(					cls						,   lowerCamelCase						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				None
       if len(str(lowerCamelCase	).split("@"	)	) == 2:
         _snake_case		,		_snake_case     		=				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'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__magic_name__   :						Any    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[str]      =   '''bit'''
     UpperCAmelCase__       :						Union[str, Any]      =   ['''preactivation''', '''bottleneck''']
     UpperCAmelCase__       :						int      =   ['''SAME''', '''VALID''']

     def __init__(					self						,   lowerCamelCase=3						,   lowerCamelCase=64						,   lowerCamelCase=[256, 512, 1_024, 2_048]						,   lowerCamelCase=[3, 4, 6, 3]						,   lowerCamelCase="preactivation"						,   lowerCamelCase="relu"						,   lowerCamelCase=None						,   lowerCamelCase=32						,   lowerCamelCase=0.0						,   lowerCamelCase=False						,   lowerCamelCase=32						,   lowerCamelCase=1						,   lowerCamelCase=None						,   lowerCamelCase=None						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       if layer_type not in self.layer_types:
         raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types	)}'''	)
       if global_padding is not None:
         if global_padding.upper() in self.supported_padding:
           _snake_case     		=				global_padding.upper()
         else:
           raise ValueError(F'''Padding strategy {global_padding} not supported'''	)
       _snake_case     		=				num_channels
       _snake_case     		=				embedding_size
       _snake_case     		=				hidden_sizes
       _snake_case     		=				depths
       _snake_case     		=				layer_type
       _snake_case     		=				hidden_act
       _snake_case     		=				global_padding
       _snake_case     		=				num_groups
       _snake_case     		=				drop_path_rate
       _snake_case     		=				embedding_dynamic_padding
       _snake_case     		=				output_stride
       _snake_case     		=				width_factor

       _snake_case     		=				["stem"] + [F'''stage{idx}''' for idx in range(1						,   len(lowerCamelCase	) + 1	)]
       _snake_case		,		_snake_case     		=				get_aligned_output_features_output_indices(
           out_features=lowerCamelCase						,   out_indices=lowerCamelCase						,   stage_names=self.stage_names	)


