





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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """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__						:    Any=None						,**lowerCamelCase__						:    Tuple       )     ->		int:




           '''simple docstring'''


           logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""       )
           SCREAMING_SNAKE_CASE		=						model
           SCREAMING_SNAKE_CASE		=						kwargs.get("""model_save_dir"""						,lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						kwargs.get("""latest_model_name"""						,lowerCamelCase__       )






     def __call__(							self						:    Union[str, Any]						,**lowerCamelCase__						:    List[Any]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{k: np.array(lowerCamelCase__       ) for k, v in kwargs.items()}
           return self.model.run(lowerCamelCase__						,lowerCamelCase__       )






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    Union[str, Path]						,lowerCamelCase__						:    List[Any]=None						,lowerCamelCase__						:    Union[str, Any]=None       )     ->		int:




           '''simple docstring'''


           if provider is None:
                 logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""       )
                 SCREAMING_SNAKE_CASE		=						"""CPUExecutionProvider"""

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,lowerCamelCase__						:    Union[str, Path]						,lowerCamelCase__						:    Optional[str] = None						,**lowerCamelCase__						:    Dict       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]						,lowerCamelCase__						:    Union[str, os.PathLike]						,**lowerCamelCase__						:    Dict						,)     ->		Optional[int]:




           '''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 				SCREAMING_SNAKE_CASE__  (							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__						:    Dict						,)     ->		Union[str, Any]:




           '''simple docstring'''


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






     @classmethod
     def 				SCREAMING_SNAKE_CASE__  (							cls						:    Optional[int]						,lowerCamelCase__						:    Union[str, Path]						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    Optional[str] = None						,lowerCamelCase__						:    Optional[str] = None						,**lowerCamelCase__						:    str						,)     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						None
           if len(str(lowerCamelCase__       ).split("""@"""       )       ) == 2:
                 SCREAMING_SNAKE_CASE,					SCREAMING_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__						,)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

      SCREAMING_SNAKE_CASE_						   =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


