

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "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 :       Any,lowercase_ :       int=None,**lowercase_ :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'		)
        A__          =   model
        A__          =   kwargs.get('model_save_dir',lowercase_		)
        A__          =   kwargs.get('latest_model_name',lowercase_		)


    def __call__(      self :       str,**lowercase_ :       Dict		)->    Any:





        '''simple docstring'''







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


    @staticmethod
    def 						snake_case__     (      lowercase_ :       Union[str, Path],lowercase_ :       Optional[Any]=None,lowercase_ :       Union[str, Any]=None		)->    str:





        '''simple docstring'''







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

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


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





        '''simple docstring'''







        A__          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, os.PathLike],**lowercase_ :       Any,)->    List[str]:





        '''simple docstring'''







        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 						snake_case__     (      cls :       Union[str, Any],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_ :       int,)->    List[str]:





        '''simple docstring'''







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


    @classmethod
    def 						snake_case__     (      cls :       str,lowercase_ :       Union[str, Path],lowercase_ :       bool = True,lowercase_ :       Optional[str] = None,lowercase_ :       Optional[str] = None,**lowercase_ :       Tuple,)->    Tuple:





        '''simple docstring'''







        A__          =   None
        if len(str(lowercase_		).split('@'		)		) == 2:
            A__      ,						A__          =   model_id.split('@'		)

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




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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'open-llama'


    def __init__(      self :       Any,lowercase_ :       Optional[int]=1_0_0_0_0_0,lowercase_ :       Union[str, Any]=4_0_9_6,lowercase_ :       Dict=1_1_0_0_8,lowercase_ :       Dict=3_2,lowercase_ :       Optional[int]=3_2,lowercase_ :       Dict="silu",lowercase_ :       Union[str, Any]=2_0_4_8,lowercase_ :       Optional[int]=0.02,lowercase_ :       Dict=1E-6,lowercase_ :       Dict=True,lowercase_ :       List[Any]=0,lowercase_ :       Optional[int]=1,lowercase_ :       str=2,lowercase_ :       str=False,lowercase_ :       str=True,lowercase_ :       int=0.1,lowercase_ :       List[Any]=0.1,lowercase_ :       List[Any]=True,lowercase_ :       Union[str, Any]=True,lowercase_ :       Any=None,**lowercase_ :       List[Any],)->    Tuple:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   max_position_embeddings
        A__          =   hidden_size
        A__          =   intermediate_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   hidden_act
        A__          =   initializer_range
        A__          =   rms_norm_eps
        A__          =   use_cache
        A__          =   kwargs.pop(
            'use_memorry_efficient_attention',lowercase_		)
        A__          =   hidden_dropout_prob
        A__          =   attention_dropout_prob
        A__          =   use_stable_embedding
        A__          =   shared_input_output_embedding
        A__          =   rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,tie_word_embeddings=lowercase_,**lowercase_,)


    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling,lowercase_		) or len(self.rope_scaling		) != 2:
            raise ValueError(
                '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                F'got {self.rope_scaling}'		)
        A__          =   self.rope_scaling.get('type',lowercase_		)
        A__          =   self.rope_scaling.get('factor',lowercase_		)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
        if rope_scaling_factor is None or not isinstance(lowercase_,lowercase_		) or rope_scaling_factor <= 1.0:
            raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)

