




'''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


_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     :						Dict      ,       _lowercase     :						Tuple=None      ,       **_lowercase     :						List[str])    ->							Optional[Any]:
								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     :						List[Any]      ,       **_lowercase     :						Optional[Any])    ->							Optional[Any]:
								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     :						int=None      ,       _lowercase     :						List[Any]=None)    ->							str:
								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     :						List[str]      ,       _lowercase     :						Union[str, Path]      ,       _lowercase     :						Optional[str] = None      ,       **_lowercase     :						List[Any])    ->							List[str]:
								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     :						List[Any]      ,       _lowercase     :						Union[str, os.PathLike]      ,       **_lowercase     :						List[Any]      ,       )    ->							List[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     __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     :						List[str]      ,       )    ->							Any:
								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     :						List[str]      ,       _lowercase     :						Union[str, Path]      ,       _lowercase     :						bool = True      ,       _lowercase     :						Optional[str] = None      ,       _lowercase     :						Optional[str] = None      ,       **_lowercase     :						List[Any]      ,       )    ->							int:
								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      ,       )










'''simple docstring'''
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					@require_torch
					def     __snake_case						(	self     :						str)    ->							Optional[int]:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='hf-internal-testing/tiny-clap-htsat-unfused')
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= audio_classifier(_lowercase      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [{'score': 0.5_01, 'label': 'Sound of a dog'}, {'score': 0.4_99, 'label': 'Sound of vaccum cleaner'}]      ,       )



					@unittest.skip('No models are available in TF')
					def     __snake_case						(	self     :						Tuple)    ->							str:
								pass



					@slow
					@require_torch
					def     __snake_case						(	self     :						Optional[Any])    ->							Tuple:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='laion/clap-htsat-unfused'      ,       )
								# This is an audio of a dog
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= audio_classifier(_lowercase      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])

								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        {'score': 0.9_99, 'label': 'Sound of a dog'},
								        {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								    ]      ,       )

								A_									= audio_classifier([audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )
								A_									= audio_classifier(
								    [audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']      ,       batch_size=5)
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )



					@unittest.skip('No models are available in TF')
					def     __snake_case						(	self     :						List[str])    ->							str:
								pass




