






import copy
import os
from typing import Union

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


a						:			str     	=					logging.get_logger(__name__)

a						:			Union[str, Any]     	=					{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Optional[int]							=      'git_vision_model'
      def __init__( self   :     str						, __lowercase   :     List[str]=768						, __lowercase   :     List[str]=3072						, __lowercase   :     List[Any]=12						, __lowercase   :     Dict=12						, __lowercase   :     int=3						, __lowercase   :     Any=224						, __lowercase   :     Optional[int]=16						, __lowercase   :     Dict="quick_gelu"						, __lowercase   :     Any=1e-5						, __lowercase   :     str=0.0						, __lowercase   :     int=0.02						, **__lowercase   :     int						, )				-> List[str]:
        super().__init__(**__lowercase   )

        __UpperCAmelCase				:      int	 =    hidden_size
        __UpperCAmelCase				:      List[Any]	 =    intermediate_size
        __UpperCAmelCase				:      Tuple	 =    num_hidden_layers
        __UpperCAmelCase				:      Dict	 =    num_attention_heads
        __UpperCAmelCase				:      int	 =    num_channels
        __UpperCAmelCase				:      str	 =    patch_size
        __UpperCAmelCase				:      Tuple	 =    image_size
        __UpperCAmelCase				:      int	 =    initializer_range
        __UpperCAmelCase				:      Tuple	 =    attention_dropout
        __UpperCAmelCase				:      Optional[int]	 =    layer_norm_eps
        __UpperCAmelCase				:      List[Any]	 =    hidden_act




      @classmethod
      def       UpperCAmelCase ( cls   :     Dict						, __lowercase   :     Union[str, os.PathLike]						, **__lowercase   :     List[str]   )				-> "PretrainedConfig":
        cls._set_token_in_kwargs(__lowercase   )

        __UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    cls.get_config_dict(__lowercase						, **__lowercase   )

        # get the vision config dict if we are loading from GITConfig
        if config_dict.get("""model_type"""   ) == "git":
          __UpperCAmelCase				:      str	 =    config_dict["""vision_config"""]

        if "model_type" in config_dict and hasattr(cls						, """model_type"""   ) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
              f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

        return cls.from_dict(__lowercase						, **__lowercase   )


class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					List[str]							=      'git'
      def __init__( self   :     Optional[int]						, __lowercase   :     List[Any]=None						, __lowercase   :     Tuple=30522						, __lowercase   :     str=768						, __lowercase   :     Optional[int]=6						, __lowercase   :     Union[str, Any]=12						, __lowercase   :     Optional[int]=3072						, __lowercase   :     List[str]="gelu"						, __lowercase   :     Tuple=0.1						, __lowercase   :     Optional[int]=0.1						, __lowercase   :     Optional[int]=1024						, __lowercase   :     Union[str, Any]=0.02						, __lowercase   :     Optional[Any]=1e-1_2						, __lowercase   :     List[Any]=0						, __lowercase   :     Dict="absolute"						, __lowercase   :     Dict=True						, __lowercase   :     Any=False						, __lowercase   :     Optional[int]=101						, __lowercase   :     str=102						, __lowercase   :     Union[str, Any]=None						, **__lowercase   :     Dict						, )				-> Tuple:
        super().__init__(bos_token_id=__lowercase						, eos_token_id=__lowercase						, pad_token_id=__lowercase						, **__lowercase   )

        if vision_config is None:
          __UpperCAmelCase				:      Optional[int]	 =    {}
          logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""   )

        __UpperCAmelCase				:      Tuple	 =    GitVisionConfig(**__lowercase   )
        __UpperCAmelCase				:      Dict	 =    vocab_size
        __UpperCAmelCase				:      Union[str, Any]	 =    hidden_size
        __UpperCAmelCase				:      Tuple	 =    num_hidden_layers
        __UpperCAmelCase				:      Optional[Any]	 =    num_attention_heads
        __UpperCAmelCase				:      List[str]	 =    hidden_act
        __UpperCAmelCase				:      str	 =    intermediate_size
        __UpperCAmelCase				:      Tuple	 =    hidden_dropout_prob
        __UpperCAmelCase				:      Dict	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      int	 =    max_position_embeddings
        __UpperCAmelCase				:      str	 =    initializer_range
        __UpperCAmelCase				:      str	 =    layer_norm_eps
        __UpperCAmelCase				:      Union[str, Any]	 =    position_embedding_type
        __UpperCAmelCase				:      Dict	 =    use_cache
        __UpperCAmelCase				:      int	 =    tie_word_embeddings
        __UpperCAmelCase				:      Optional[int]	 =    num_image_with_embedding

        __UpperCAmelCase				:      Optional[int]	 =    bos_token_id
        __UpperCAmelCase				:      List[Any]	 =    eos_token_id




      def       UpperCAmelCase ( self   :     str   )				-> int:
        __UpperCAmelCase				:      List[Any]	 =    copy.deepcopy(self.__dict__   )
        __UpperCAmelCase				:      List[str]	 =    self.vision_config.to_dict()
        __UpperCAmelCase				:      Union[str, Any]	 =    self.__class__.model_type
        return output





"""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


__UpperCamelCase    : 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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Optional[Any]  ,lowercase_     :							Union[str, Any]=None  ,**lowercase_     :							List[str]						):
						logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''						)
						lowerCAmelCase__      :		List[str]    							=      model
						lowerCAmelCase__      :		Optional[Any]    							=      kwargs.get('''model_save_dir'''  ,lowercase_						)
						lowerCAmelCase__      :		str    							=      kwargs.get('''latest_model_name'''  ,lowercase_						)







					def __call__(			self     :							Any  ,**lowercase_     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      {k: np.array(lowercase_						) for k, v in kwargs.items()}
						return self.model.run(lowercase_  ,lowercase_						)







					@staticmethod
					def 			__lowerCAmelCase					(			lowercase_     :							Union[str, Path]  ,lowercase_     :							str=None  ,lowercase_     :							str=None						):
						if provider is None:
							logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''						)
							lowerCAmelCase__      :		Optional[Any]    							=      '''CPUExecutionProvider'''

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







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							Optional[str] = None  ,**lowercase_     :							str						):
						lowerCAmelCase__      :		Any    							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							Union[str, os.PathLike]  ,**lowercase_     :							Union[str, Any]  ,):
						if os.path.isfile(lowercase_						):
							logger.error(F'Provided path ({save_directory}) should be a directory, not a file'						)
							return

						os.makedirs(lowercase_  ,exist_ok=lowercase_						)

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







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







					@classmethod
					def 			__lowerCAmelCase					(			cls     :							Any  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							bool = True  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional[str] = None  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Union[str, Any]    							=      None
						if len(str(lowercase_						).split('''@'''						)						) == 2:
							lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      model_id.split('''@'''						)

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




